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2302.05355
Fabio Massacci
Francesco Ciclosi, Silvia Vidor, and Fabio Massacci
Building cross-language corpora for human understanding of privacy policies
null
null
null
null
cs.CR cs.CY
http://creativecommons.org/licenses/by-sa/4.0/
Making sure that users understand privacy policies that impact them is a key challenge for a real GDPR deployment. Research studies are mostly carried in English, but in Europe and elsewhere, users speak a language that is not English. Replicating studies in different languages requires the availability of comparable cross-language privacy policies corpora. This work provides a methodology for building comparable cross-language in a national language and a reference study language. We provide an application example of our methodology comparing English and Italian extending the corpus of one of the first studies about users understanding of technical terms in privacy policies. We also investigate other open issues that can make replication harder.
[ { "version": "v1", "created": "Fri, 10 Feb 2023 16:16:55 GMT" } ]
2023-02-13T00:00:00
[ [ "Ciclosi", "Francesco", "" ], [ "Vidor", "Silvia", "" ], [ "Massacci", "Fabio", "" ] ]
new_dataset
0.960544
2302.05393
Pedro Sarmento
Pedro Sarmento, Adarsh Kumar, Yu-Hua Chen, CJ Carr, Zack Zukowski, Mathieu Barthet
GTR-CTRL: Instrument and Genre Conditioning for Guitar-Focused Music Generation with Transformers
This preprint is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). The Version of Record of this contribution is published in Proceedings of EvoMUSART: International Conference on Computational Intelligence in Music, Sound, Art and Design (Part of EvoStar) 2023
EvoMUSART: International Conference on Computational Intelligence in Music, Sound, Art and Design (Part of EvoStar) 2023
null
null
cs.SD cs.AI cs.MM eess.AS
http://creativecommons.org/licenses/by-nc-nd/4.0/
Recently, symbolic music generation with deep learning techniques has witnessed steady improvements. Most works on this topic focus on MIDI representations, but less attention has been paid to symbolic music generation using guitar tablatures (tabs) which can be used to encode multiple instruments. Tabs include information on expressive techniques and fingerings for fretted string instruments in addition to rhythm and pitch. In this work, we use the DadaGP dataset for guitar tab music generation, a corpus of over 26k songs in GuitarPro and token formats. We introduce methods to condition a Transformer-XL deep learning model to generate guitar tabs (GTR-CTRL) based on desired instrumentation (inst-CTRL) and genre (genre-CTRL). Special control tokens are appended at the beginning of each song in the training corpus. We assess the performance of the model with and without conditioning. We propose instrument presence metrics to assess the inst-CTRL model's response to a given instrumentation prompt. We trained a BERT model for downstream genre classification and used it to assess the results obtained with the genre-CTRL model. Statistical analyses evidence significant differences between the conditioned and unconditioned models. Overall, results indicate that the GTR-CTRL methods provide more flexibility and control for guitar-focused symbolic music generation than an unconditioned model.
[ { "version": "v1", "created": "Fri, 10 Feb 2023 17:43:03 GMT" } ]
2023-02-13T00:00:00
[ [ "Sarmento", "Pedro", "" ], [ "Kumar", "Adarsh", "" ], [ "Chen", "Yu-Hua", "" ], [ "Carr", "CJ", "" ], [ "Zukowski", "Zack", "" ], [ "Barthet", "Mathieu", "" ] ]
new_dataset
0.999809
2302.05406
Pedro Colon-Hernandez
Pedro Colon-Hernandez, Henry Lieberman, Yida Xin, Claire Yin, Cynthia Breazeal, Peter Chin
Adversarial Transformer Language Models for Contextual Commonsense Inference
Submitted to Semantic Web Journal special edition. https://semantic-web-journal.org/content/adversarial-transformer-language-models-contextual-commonsense-inference-1
null
null
null
cs.CL
http://creativecommons.org/licenses/by/4.0/
Contextualized or discourse aware commonsense inference is the task of generating coherent commonsense assertions (i.e., facts) from a given story, and a particular sentence from that story. Some problems with the task are: lack of controllability for topics of the inferred facts; lack of commonsense knowledge during training; and, possibly, hallucinated or false facts. In this work, we utilize a transformer model for this task and develop techniques to address the aforementioned problems in the task. We control the inference by introducing a new technique we call "hinting". Hinting is a kind of language model prompting, that utilizes both hard prompts (specific words) and soft prompts (virtual learnable templates). This serves as a control signal to advise the language model "what to talk about". Next, we establish a methodology for performing joint inference with multiple commonsense knowledge bases. Joint inference of commonsense requires care, because it is imprecise and the level of generality is more flexible. You want to be sure that the results "still make sense" for the context. To this end, we align the textual version of assertions from three knowledge graphs (ConceptNet, ATOMIC2020, and GLUCOSE) with a story and a target sentence. This combination allows us to train a single model to perform joint inference with multiple knowledge graphs. We show experimental results for the three knowledge graphs on joint inference. Our final contribution is exploring a GAN architecture that generates the contextualized commonsense assertions and scores them as to their plausibility through a discriminator. The result is an integrated system for contextual commonsense inference in stories, that can controllably generate plausible commonsense assertions, and takes advantage of joint inference between multiple commonsense knowledge bases.
[ { "version": "v1", "created": "Fri, 10 Feb 2023 18:21:13 GMT" } ]
2023-02-13T00:00:00
[ [ "Colon-Hernandez", "Pedro", "" ], [ "Lieberman", "Henry", "" ], [ "Xin", "Yida", "" ], [ "Yin", "Claire", "" ], [ "Breazeal", "Cynthia", "" ], [ "Chin", "Peter", "" ] ]
new_dataset
0.96923
2302.05442
Mostafa Dehghani
Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, Rodolphe Jenatton, Lucas Beyer, Michael Tschannen, Anurag Arnab, Xiao Wang, Carlos Riquelme, Matthias Minderer, Joan Puigcerver, Utku Evci, Manoj Kumar, Sjoerd van Steenkiste, Gamaleldin F. Elsayed, Aravindh Mahendran, Fisher Yu, Avital Oliver, Fantine Huot, Jasmijn Bastings, Mark Patrick Collier, Alexey Gritsenko, Vighnesh Birodkar, Cristina Vasconcelos, Yi Tay, Thomas Mensink, Alexander Kolesnikov, Filip Paveti\'c, Dustin Tran, Thomas Kipf, Mario Lu\v{c}i\'c, Xiaohua Zhai, Daniel Keysers, Jeremiah Harmsen, Neil Houlsby
Scaling Vision Transformers to 22 Billion Parameters
null
null
null
null
cs.CV cs.AI cs.LG
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The scaling of Transformers has driven breakthrough capabilities for language models. At present, the largest large language models (LLMs) contain upwards of 100B parameters. Vision Transformers (ViT) have introduced the same architecture to image and video modelling, but these have not yet been successfully scaled to nearly the same degree; the largest dense ViT contains 4B parameters (Chen et al., 2022). We present a recipe for highly efficient and stable training of a 22B-parameter ViT (ViT-22B) and perform a wide variety of experiments on the resulting model. When evaluated on downstream tasks (often with a lightweight linear model on frozen features), ViT-22B demonstrates increasing performance with scale. We further observe other interesting benefits of scale, including an improved tradeoff between fairness and performance, state-of-the-art alignment to human visual perception in terms of shape/texture bias, and improved robustness. ViT-22B demonstrates the potential for "LLM-like" scaling in vision, and provides key steps towards getting there.
[ { "version": "v1", "created": "Fri, 10 Feb 2023 18:58:21 GMT" } ]
2023-02-13T00:00:00
[ [ "Dehghani", "Mostafa", "" ], [ "Djolonga", "Josip", "" ], [ "Mustafa", "Basil", "" ], [ "Padlewski", "Piotr", "" ], [ "Heek", "Jonathan", "" ], [ "Gilmer", "Justin", "" ], [ "Steiner", "Andreas", "" ], [ "Caron", "Mathilde", "" ], [ "Geirhos", "Robert", "" ], [ "Alabdulmohsin", "Ibrahim", "" ], [ "Jenatton", "Rodolphe", "" ], [ "Beyer", "Lucas", "" ], [ "Tschannen", "Michael", "" ], [ "Arnab", "Anurag", "" ], [ "Wang", "Xiao", "" ], [ "Riquelme", "Carlos", "" ], [ "Minderer", "Matthias", "" ], [ "Puigcerver", "Joan", "" ], [ "Evci", "Utku", "" ], [ "Kumar", "Manoj", "" ], [ "van Steenkiste", "Sjoerd", "" ], [ "Elsayed", "Gamaleldin F.", "" ], [ "Mahendran", "Aravindh", "" ], [ "Yu", "Fisher", "" ], [ "Oliver", "Avital", "" ], [ "Huot", "Fantine", "" ], [ "Bastings", "Jasmijn", "" ], [ "Collier", "Mark Patrick", "" ], [ "Gritsenko", "Alexey", "" ], [ "Birodkar", "Vighnesh", "" ], [ "Vasconcelos", "Cristina", "" ], [ "Tay", "Yi", "" ], [ "Mensink", "Thomas", "" ], [ "Kolesnikov", "Alexander", "" ], [ "Pavetić", "Filip", "" ], [ "Tran", "Dustin", "" ], [ "Kipf", "Thomas", "" ], [ "Lučić", "Mario", "" ], [ "Zhai", "Xiaohua", "" ], [ "Keysers", "Daniel", "" ], [ "Harmsen", "Jeremiah", "" ], [ "Houlsby", "Neil", "" ] ]
new_dataset
0.97029
2207.06590
Jun Yang
Jun Yang, Yuehan Wang, Yiling Lou, Ming Wen and Lingming Zhang
Attention: Not Just Another Dataset for Patch-Correctness Checking
null
null
null
null
cs.SE
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Automated Program Repair (APR) techniques have drawn wide attention from both academia and industry. Meanwhile, one main limitation with the current state-of-the-art APR tools is that patches passing all the original tests are not necessarily the correct ones wanted by developers, i.e., the plausible patch problem. To date, various Patch-Correctness Checking (PCC) techniques have been proposed to address this important issue. However, they are only evaluated on very limited datasets as the APR tools used for generating such patches can only explore a small subset of the search space of possible patches, posing serious threats to external validity to existing PCC studies. In this paper, we construct an extensive PCC dataset (the largest manually labeled PCC dataset to our knowledge) to revisit all state-of-the-art PCC techniques. More specifically, our PCC dataset includes 1,988 patches generated from the recent PraPR APR tool, which leverages highly-optimized bytecode-level patch executions and can exhaustively explore all possible plausible patches within its large predefined search space (including well-known fixing patterns from various prior APR tools). Our extensive study of representative PCC techniques on the new dataset has revealed various surprising findings and provided guidelines for future PCC research.
[ { "version": "v1", "created": "Thu, 14 Jul 2022 01:07:17 GMT" }, { "version": "v2", "created": "Wed, 8 Feb 2023 23:10:09 GMT" } ]
2023-02-10T00:00:00
[ [ "Yang", "Jun", "" ], [ "Wang", "Yuehan", "" ], [ "Lou", "Yiling", "" ], [ "Wen", "Ming", "" ], [ "Zhang", "Lingming", "" ] ]
new_dataset
0.999723
2210.01963
Kanishka Misra
Kanishka Misra, Julia Taylor Rayz, Allyson Ettinger
COMPS: Conceptual Minimal Pair Sentences for testing Robust Property Knowledge and its Inheritance in Pre-trained Language Models
EACL 2023 Camera Ready version. Code can be found at https://github.com/kanishkamisra/comps
null
null
null
cs.CL cs.AI
http://creativecommons.org/licenses/by-sa/4.0/
A characteristic feature of human semantic cognition is its ability to not only store and retrieve the properties of concepts observed through experience, but to also facilitate the inheritance of properties (can breathe) from superordinate concepts (animal) to their subordinates (dog) -- i.e. demonstrate property inheritance. In this paper, we present COMPS, a collection of minimal pair sentences that jointly tests pre-trained language models (PLMs) on their ability to attribute properties to concepts and their ability to demonstrate property inheritance behavior. Analyses of 22 different PLMs on COMPS reveal that they can easily distinguish between concepts on the basis of a property when they are trivially different, but find it relatively difficult when concepts are related on the basis of nuanced knowledge representations. Furthermore, we find that PLMs can demonstrate behavior consistent with property inheritance to a great extent, but fail in the presence of distracting information, which decreases the performance of many models, sometimes even below chance. This lack of robustness in demonstrating simple reasoning raises important questions about PLMs' capacity to make correct inferences even when they appear to possess the prerequisite knowledge.
[ { "version": "v1", "created": "Wed, 5 Oct 2022 00:04:18 GMT" }, { "version": "v2", "created": "Thu, 6 Oct 2022 14:10:29 GMT" }, { "version": "v3", "created": "Fri, 14 Oct 2022 01:57:57 GMT" }, { "version": "v4", "created": "Thu, 9 Feb 2023 02:31:06 GMT" } ]
2023-02-10T00:00:00
[ [ "Misra", "Kanishka", "" ], [ "Rayz", "Julia Taylor", "" ], [ "Ettinger", "Allyson", "" ] ]
new_dataset
0.985695
2210.03170
Rafael Ferreira Da Silva
Tain\~a Coleman, Henri Casanova, Ketan Maheshwari, Lo\"ic Pottier, Sean R. Wilkinson, Justin Wozniak, Fr\'ed\'eric Suter, Mallikarjun Shankar, Rafael Ferreira da Silva
WfBench: Automated Generation of Scientific Workflow Benchmarks
null
null
10.1109/PMBS56514.2022.00014
null
cs.DC
http://creativecommons.org/licenses/by/4.0/
The prevalence of scientific workflows with high computational demands calls for their execution on various distributed computing platforms, including large-scale leadership-class high-performance computing (HPC) clusters. To handle the deployment, monitoring, and optimization of workflow executions, many workflow systems have been developed over the past decade. There is a need for workflow benchmarks that can be used to evaluate the performance of workflow systems on current and future software stacks and hardware platforms. We present a generator of realistic workflow benchmark specifications that can be translated into benchmark code to be executed with current workflow systems. Our approach generates workflow tasks with arbitrary performance characteristics (CPU, memory, and I/O usage) and with realistic task dependency structures based on those seen in production workflows. We present experimental results that show that our approach generates benchmarks that are representative of production workflows, and conduct a case study to demonstrate the use and usefulness of our generated benchmarks to evaluate the performance of workflow systems under different configuration scenarios.
[ { "version": "v1", "created": "Thu, 6 Oct 2022 19:22:06 GMT" } ]
2023-02-10T00:00:00
[ [ "Coleman", "Tainã", "" ], [ "Casanova", "Henri", "" ], [ "Maheshwari", "Ketan", "" ], [ "Pottier", "Loïc", "" ], [ "Wilkinson", "Sean R.", "" ], [ "Wozniak", "Justin", "" ], [ "Suter", "Frédéric", "" ], [ "Shankar", "Mallikarjun", "" ], [ "da Silva", "Rafael Ferreira", "" ] ]
new_dataset
0.975555
2210.15016
Pengchao Hu
Pengchao Hu, Man Lu, Lei Wang, Guoyue Jiang
TPU-MLIR: A Compiler For TPU Using MLIR
A way to design AI Compiler for ASIC chips by MLIR
null
null
null
cs.PL cs.CL cs.LG
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Multi-level intermediate representations (MLIR) show great promise for reducing the cost of building domain-specific compilers by providing a reusable and extensible compiler infrastructure. This work presents TPU-MLIR, an end-to-end compiler based on MLIR that deploys pre-trained neural network (NN) models to a custom ASIC called a Tensor Processing Unit (TPU). TPU-MLIR defines two new dialects to implement its functionality: 1. a Tensor operation (TOP) dialect that encodes the deep learning graph semantics and independent of the deep learning framework and 2. a TPU kernel dialect to provide a standard kernel computation on TPU. A NN model is translated to the TOP dialect and then lowered to the TPU dialect for different TPUs according to the chip's configuration. We demonstrate how to use the MLIR pass pipeline to organize and perform optimization on TPU to generate machine code. The paper also presents a verification procedure to ensure the correctness of each transform stage.
[ { "version": "v1", "created": "Sun, 23 Oct 2022 10:45:54 GMT" }, { "version": "v2", "created": "Thu, 9 Feb 2023 08:01:21 GMT" } ]
2023-02-10T00:00:00
[ [ "Hu", "Pengchao", "" ], [ "Lu", "Man", "" ], [ "Wang", "Lei", "" ], [ "Jiang", "Guoyue", "" ] ]
new_dataset
0.978871
2301.02065
Patrick Ebel
Patrick Ebel, Christoph Lingenfelder, Andreas Vogelsang
On the Forces of Driver Distraction: Explainable Predictions for the Visual Demand of In-Vehicle Touchscreen Interactions
Accepted for publication in Accident Analysis and Prevention
Accident Analysis & Prevention Volume 183, April 2023
10.1016/j.aap.2023.106956
null
cs.HC cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
With modern infotainment systems, drivers are increasingly tempted to engage in secondary tasks while driving. Since distracted driving is already one of the main causes of fatal accidents, in-vehicle touchscreen Human-Machine Interfaces (HMIs) must be as little distracting as possible. To ensure that these systems are safe to use, they undergo elaborate and expensive empirical testing, requiring fully functional prototypes. Thus, early-stage methods informing designers about the implication their design may have on driver distraction are of great value. This paper presents a machine learning method that, based on anticipated usage scenarios, predicts the visual demand of in-vehicle touchscreen interactions and provides local and global explanations of the factors influencing drivers' visual attention allocation. The approach is based on large-scale natural driving data continuously collected from production line vehicles and employs the SHapley Additive exPlanation (SHAP) method to provide explanations leveraging informed design decisions. Our approach is more accurate than related work and identifies interactions during which long glances occur with 68 % accuracy and predicts the total glance duration with a mean error of 2.4 s. Our explanations replicate the results of various recent studies and provide fast and easily accessible insights into the effect of UI elements, driving automation, and vehicle speed on driver distraction. The system can not only help designers to evaluate current designs but also help them to better anticipate and understand the implications their design decisions might have on future designs.
[ { "version": "v1", "created": "Thu, 5 Jan 2023 13:50:26 GMT" } ]
2023-02-10T00:00:00
[ [ "Ebel", "Patrick", "" ], [ "Lingenfelder", "Christoph", "" ], [ "Vogelsang", "Andreas", "" ] ]
new_dataset
0.978436
2302.01707
Thomas Durieux
Thomas Durieux
Parfum: Detection and Automatic Repair of Dockerfile Smells
null
null
null
null
cs.SE
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Docker is a popular tool for developers and organizations to package, deploy, and run applications in a lightweight, portable container. One key component of Docker is the Dockerfile, a simple text file that specifies the steps needed to build a Docker image. While Dockerfiles are easy to create and use, creating an optimal image is complex in particular since it is easy to not follow the best practices, when it happens we call it Docker smell. To improve the quality of Dockerfiles, previous works have focused on detecting Docker smells, but they do not offer suggestions or repair the smells. In this paper, we propose, Parfum, a tool that detects and automatically repairs Docker smells while producing minimal patches. Parfum is based on a new Dockerfile AST parser called Dinghy. We evaluate the effectiveness of Parfum by analyzing and repairing a large set of Dockerfiles and comparing it against existing tools. We also measure the impact of the repair on the Docker image in terms of build failure and image size. Finally, we opened 35 pull requests to collect developers' feedback and ensure that the repairs and the smells are meaningful. Our results show that Parfum is able to repair 806 245 Docker smells and have a significant impact on the Docker image size, and finally, developers are welcoming the patches generated by Parfum while merging 20 pull requests.
[ { "version": "v1", "created": "Fri, 3 Feb 2023 13:04:47 GMT" }, { "version": "v2", "created": "Thu, 9 Feb 2023 15:05:28 GMT" } ]
2023-02-10T00:00:00
[ [ "Durieux", "Thomas", "" ] ]
new_dataset
0.999487
2302.03130
Hyunjik Kim
Matthias Bauer, Emilien Dupont, Andy Brock, Dan Rosenbaum, Jonathan Richard Schwarz, Hyunjik Kim
Spatial Functa: Scaling Functa to ImageNet Classification and Generation
null
null
null
null
cs.LG cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Neural fields, also known as implicit neural representations, have emerged as a powerful means to represent complex signals of various modalities. Based on this Dupont et al. (2022) introduce a framework that views neural fields as data, termed *functa*, and proposes to do deep learning directly on this dataset of neural fields. In this work, we show that the proposed framework faces limitations when scaling up to even moderately complex datasets such as CIFAR-10. We then propose *spatial functa*, which overcome these limitations by using spatially arranged latent representations of neural fields, thereby allowing us to scale up the approach to ImageNet-1k at 256x256 resolution. We demonstrate competitive performance to Vision Transformers (Steiner et al., 2022) on classification and Latent Diffusion (Rombach et al., 2022) on image generation respectively.
[ { "version": "v1", "created": "Mon, 6 Feb 2023 21:35:44 GMT" }, { "version": "v2", "created": "Thu, 9 Feb 2023 12:43:24 GMT" } ]
2023-02-10T00:00:00
[ [ "Bauer", "Matthias", "" ], [ "Dupont", "Emilien", "" ], [ "Brock", "Andy", "" ], [ "Rosenbaum", "Dan", "" ], [ "Schwarz", "Jonathan Richard", "" ], [ "Kim", "Hyunjik", "" ] ]
new_dataset
0.988651
2302.03731
Yifan Sun
Yifan Sun, Jingyan Shen, Yunfan Jiang, Zhaohui Huang, Minsheng Hao, Xuegong Zhang
MMA-RNN: A Multi-level Multi-task Attention-based Recurrent Neural Network for Discrimination and Localization of Atrial Fibrillation
9 pages, 5 figures
null
null
null
cs.LG q-bio.QM
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The automatic detection of atrial fibrillation based on electrocardiograph (ECG) signals has received wide attention both clinically and practically. It is challenging to process ECG signals with cyclical pattern, varying length and unstable quality due to noise and distortion. Besides, there has been insufficient research on separating persistent atrial fibrillation from paroxysmal atrial fibrillation, and little discussion on locating the onsets and end points of AF episodes. It is even more arduous to perform well on these two distinct but interrelated tasks, while avoiding the mistakes inherent from stage-by-stage approaches. This paper proposes the Multi-level Multi-task Attention-based Recurrent Neural Network for three-class discrimination on patients and localization of the exact timing of AF episodes. Our model captures three-level sequential features based on a hierarchical architecture utilizing Bidirectional Long and Short-Term Memory Network (Bi-LSTM) and attention layers, and accomplishes the two tasks simultaneously with a multi-head classifier. The model is designed as an end-to-end framework to enhance information interaction and reduce error accumulation. Finally, we conduct experiments on CPSC 2021 dataset and the result demonstrates the superior performance of our method, indicating the potential application of MMA-RNN to wearable mobile devices for routine AF monitoring and early diagnosis.
[ { "version": "v1", "created": "Tue, 7 Feb 2023 19:59:55 GMT" }, { "version": "v2", "created": "Thu, 9 Feb 2023 01:29:04 GMT" } ]
2023-02-10T00:00:00
[ [ "Sun", "Yifan", "" ], [ "Shen", "Jingyan", "" ], [ "Jiang", "Yunfan", "" ], [ "Huang", "Zhaohui", "" ], [ "Hao", "Minsheng", "" ], [ "Zhang", "Xuegong", "" ] ]
new_dataset
0.9938
2302.04343
Amir Namavar Jahromi
Amir Namavar Jahromi and Ebrahim Pourjafari and Hadis Karimipour and Amit Satpathy and Lovell Hodge
CRL+: A Novel Semi-Supervised Deep Active Contrastive Representation Learning-Based Text Classification Model for Insurance Data
null
null
null
null
cs.CL cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Financial sector and especially the insurance industry collect vast volumes of text on a daily basis and through multiple channels (their agents, customer care centers, emails, social networks, and web in general). The information collected includes policies, expert and health reports, claims and complaints, results of surveys, and relevant social media posts. It is difficult to effectively extract label, classify, and interpret the essential information from such varied and unstructured material. Therefore, the Insurance Industry is among the ones that can benefit from applying technologies for the intelligent analysis of free text through Natural Language Processing (NLP). In this paper, CRL+, a novel text classification model combining Contrastive Representation Learning (CRL) and Active Learning is proposed to handle the challenge of using semi-supervised learning for text classification. In this method, supervised (CRL) is used to train a RoBERTa transformer model to encode the textual data into a contrastive representation space and then classify using a classification layer. This (CRL)-based transformer model is used as the base model in the proposed Active Learning mechanism to classify all the data in an iterative manner. The proposed model is evaluated using unstructured obituary data with objective to determine the cause of the death from the data. This model is compared with the CRL model and an Active Learning model with the RoBERTa base model. The experiment shows that the proposed method can outperform both methods for this specific task.
[ { "version": "v1", "created": "Wed, 8 Feb 2023 21:23:52 GMT" } ]
2023-02-10T00:00:00
[ [ "Jahromi", "Amir Namavar", "" ], [ "Pourjafari", "Ebrahim", "" ], [ "Karimipour", "Hadis", "" ], [ "Satpathy", "Amit", "" ], [ "Hodge", "Lovell", "" ] ]
new_dataset
0.999288
2302.04384
Zhiqiang Zhao
Ying Zhang, Zhiqiang Zhao, Zhuo Feng
SF-SGL: Solver-Free Spectral Graph Learning from Linear Measurements
arXiv admin note: text overlap with arXiv:2104.07867
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 2022 Aug 15
10.1109/TCAD.2022.3198513
null
cs.LG cs.DS
http://creativecommons.org/licenses/by/4.0/
This work introduces a highly-scalable spectral graph densification framework (SGL) for learning resistor networks with linear measurements, such as node voltages and currents. We show that the proposed graph learning approach is equivalent to solving the classical graphical Lasso problems with Laplacian-like precision matrices. We prove that given $O(\log N)$ pairs of voltage and current measurements, it is possible to recover sparse $N$-node resistor networks that can well preserve the effective resistance distances on the original graph. In addition, the learned graphs also preserve the structural (spectral) properties of the original graph, which can potentially be leveraged in many circuit design and optimization tasks. To achieve more scalable performance, we also introduce a solver-free method (SF-SGL) that exploits multilevel spectral approximation of the graphs and allows for a scalable and flexible decomposition of the entire graph spectrum (to be learned) into multiple different eigenvalue clusters (frequency bands). Such a solver-free approach allows us to more efficiently identify the most spectrally-critical edges for reducing various ranges of spectral embedding distortions. Through extensive experiments for a variety of real-world test cases, we show that the proposed approach is highly scalable for learning sparse resistor networks without sacrificing solution quality. We also introduce a data-driven EDA algorithm for vectorless power/thermal integrity verifications to allow estimating worst-case voltage/temperature (gradient) distributions across the entire chip by leveraging a few voltage/temperature measurements.
[ { "version": "v1", "created": "Thu, 9 Feb 2023 00:33:19 GMT" } ]
2023-02-10T00:00:00
[ [ "Zhang", "Ying", "" ], [ "Zhao", "Zhiqiang", "" ], [ "Feng", "Zhuo", "" ] ]
new_dataset
0.991426
2302.04398
Deokhwan Han
Deokhwan Han, Jeonghun Park and Namyoon Lee
FDD Massive MIMO Without CSI Feedback
null
null
null
null
cs.IT eess.SP math.IT
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Transmitter channel state information (CSIT) is indispensable for the spectral efficiency gains offered by massive multiple-input multiple-output (MIMO) systems. In a frequency-division-duplexing (FDD) massive MIMO system, CSIT is typically acquired through downlink channel estimation and user feedback, but as the number of antennas increases, the overhead for CSI training and feedback per user grows, leading to a decrease in spectral efficiency. In this paper, we show that, using uplink pilots in FDD, the downlink sum spectral efficiency gain with perfect downlink CSIT is achievable when the number of antennas at a base station is infinite under some mild channel conditions. The key idea showing our result is the mean squared error-optimal downlink channel reconstruction method using uplink pilots, which exploits the geometry reciprocity of uplink and downlink channels. We also present a robust downlink precoding method harnessing the reconstructed channel with the error covariance matrix. Our system-level simulations show that our proposed precoding method can attain comparable sum spectral efficiency to zero-forcing precoding with perfect downlink CSIT, without CSI training and feedback.
[ { "version": "v1", "created": "Thu, 9 Feb 2023 01:44:49 GMT" } ]
2023-02-10T00:00:00
[ [ "Han", "Deokhwan", "" ], [ "Park", "Jeonghun", "" ], [ "Lee", "Namyoon", "" ] ]
new_dataset
0.996289
2302.04434
Anjana Arunkumar
Anjana Arunkumar, Swaroop Mishra, Bhavdeep Sachdeva, Chitta Baral, Chris Bryan
Real-Time Visual Feedback to Guide Benchmark Creation: A Human-and-Metric-in-the-Loop Workflow
EACL 2023
null
null
null
cs.CL cs.AI cs.HC cs.LG
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Recent research has shown that language models exploit `artifacts' in benchmarks to solve tasks, rather than truly learning them, leading to inflated model performance. In pursuit of creating better benchmarks, we propose VAIDA, a novel benchmark creation paradigm for NLP, that focuses on guiding crowdworkers, an under-explored facet of addressing benchmark idiosyncrasies. VAIDA facilitates sample correction by providing realtime visual feedback and recommendations to improve sample quality. Our approach is domain, model, task, and metric agnostic, and constitutes a paradigm shift for robust, validated, and dynamic benchmark creation via human-and-metric-in-the-loop workflows. We evaluate via expert review and a user study with NASA TLX. We find that VAIDA decreases effort, frustration, mental, and temporal demands of crowdworkers and analysts, simultaneously increasing the performance of both user groups with a 45.8% decrease in the level of artifacts in created samples. As a by product of our user study, we observe that created samples are adversarial across models, leading to decreases of 31.3% (BERT), 22.5% (RoBERTa), 14.98% (GPT-3 fewshot) in performance.
[ { "version": "v1", "created": "Thu, 9 Feb 2023 04:43:10 GMT" } ]
2023-02-10T00:00:00
[ [ "Arunkumar", "Anjana", "" ], [ "Mishra", "Swaroop", "" ], [ "Sachdeva", "Bhavdeep", "" ], [ "Baral", "Chitta", "" ], [ "Bryan", "Chris", "" ] ]
new_dataset
0.986741
2302.04461
Satoshi Takabe
Satoshi Takabe and Takashi Abe
Hubbard-Stratonovich Detector for Simple Trainable MIMO Signal Detection
6 pages, 5 figures
null
null
null
cs.IT cs.LG eess.SP math.IT
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Massive multiple-input multiple-output (MIMO) is a key technology used in fifth-generation wireless communication networks and beyond. Recently, various MIMO signal detectors based on deep learning have been proposed. Especially, deep unfolding (DU), which involves unrolling of an existing iterative algorithm and embedding of trainable parameters, has been applied with remarkable detection performance. Although DU has a lesser number of trainable parameters than conventional deep neural networks, the computational complexities related to training and execution have been problematic because DU-based MIMO detectors usually utilize matrix inversion to improve their detection performance. In this study, we attempted to construct a DU-based trainable MIMO detector with the simplest structure. The proposed detector based on the Hubbard--Stratonovich (HS) transformation and DU is called the trainable HS (THS) detector. It requires only $O(1)$ trainable parameters and its training and execution cost is $O(n^2)$ per iteration, where $n$ is the number of transmitting antennas. Numerical results show that the detection performance of the THS detector is better than that of existing algorithms of the same complexity and close to that of a DU-based detector, which has higher training and execution costs than the THS detector.
[ { "version": "v1", "created": "Thu, 9 Feb 2023 06:51:25 GMT" } ]
2023-02-10T00:00:00
[ [ "Takabe", "Satoshi", "" ], [ "Abe", "Takashi", "" ] ]
new_dataset
0.990388
2302.04486
Can Pu
Can Pu, Chuanyu Yang, Jinnian Pu and Robert B. Fisher
A General Mobile Manipulator Automation Framework for Flexible Manufacturing in Hostile Industrial Environments
25 pages
null
null
null
cs.RO cs.AI cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
To enable a mobile manipulator to perform human tasks from a single teaching demonstration is vital to flexible manufacturing. We call our proposed method MMPA (Mobile Manipulator Process Automation with One-shot Teaching). Currently, there is no effective and robust MMPA framework which is not influenced by harsh industrial environments and the mobile base's parking precision. The proposed MMPA framework consists of two stages: collecting data (mobile base's location, environment information, end-effector's path) in the teaching stage for robot learning; letting the end-effector repeat the nearly same path as the reference path in the world frame to reproduce the work in the automation stage. More specifically, in the automation stage, the robot navigates to the specified location without the need of a precise parking. Then, based on colored point cloud registration, the proposed IPE (Iterative Pose Estimation by Eye & Hand) algorithm could estimate the accurate 6D relative parking pose of the robot arm base without the need of any marker. Finally, the robot could learn the error compensation from the parking pose's bias to modify the end-effector's path to make it repeat a nearly same path in the world coordinate system as recorded in the teaching stage. Hundreds of trials have been conducted with a real mobile manipulator to show the superior robustness of the system and the accuracy of the process automation regardless of the harsh industrial conditions and parking precision. For the released code, please contact marketing@amigaga.com
[ { "version": "v1", "created": "Thu, 9 Feb 2023 08:17:38 GMT" } ]
2023-02-10T00:00:00
[ [ "Pu", "Can", "" ], [ "Yang", "Chuanyu", "" ], [ "Pu", "Jinnian", "" ], [ "Fisher", "Robert B.", "" ] ]
new_dataset
0.996723
2302.04521
Shuang Gao
Xiaoibin Wang, Shuang Gao, Yuntao Zou, Jianlan Guo and Chu Wang
IH-ViT: Vision Transformer-based Integrated Circuit Appear-ance Defect Detection
null
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
For the problems of low recognition rate and slow recognition speed of traditional detection methods in IC appearance defect detection, we propose an IC appearance defect detection algo-rithm IH-ViT. Our proposed model takes advantage of the respective strengths of CNN and ViT to acquire image features from both local and global aspects, and finally fuses the two features for decision making to determine the class of defects, thus obtaining better accuracy of IC defect recognition. To address the problem that IC appearance defects are mainly reflected in the dif-ferences in details, which are difficult to identify by traditional algorithms, we improved the tra-ditional ViT by performing an additional convolution operation inside the batch. For the problem of information imbalance of samples due to diverse sources of data sets, we adopt a dual-channel image segmentation technique to further improve the accuracy of IC appearance defects. Finally, after testing, our proposed hybrid IH-ViT model achieved 72.51% accuracy, which is 2.8% and 6.06% higher than ResNet50 and ViT models alone. The proposed algorithm can quickly and accurately detect the defect status of IC appearance and effectively improve the productivity of IC packaging and testing companies.
[ { "version": "v1", "created": "Thu, 9 Feb 2023 09:27:40 GMT" } ]
2023-02-10T00:00:00
[ [ "Wang", "Xiaoibin", "" ], [ "Gao", "Shuang", "" ], [ "Zou", "Yuntao", "" ], [ "Guo", "Jianlan", "" ], [ "Wang", "Chu", "" ] ]
new_dataset
0.983759
2302.04541
Georgios Karanztas
George Karantzas
Forensic Log Based Detection For Keystroke Injection "BadUsb" Attacks
15 pages, 3 figures, code examples included
null
null
null
cs.CR
http://creativecommons.org/licenses/by/4.0/
This document describes an experiment with main purpose to detect BadUSB attacks that utilize external Human Interaction Device hardware gadgets to inject keystrokes and acquire remote code execution. One of the main goals, is to detect such activity based on behavioral factors and allow everyone with a basic set of cognitive capabilities ,regardless of the user being a human or a computer, to identify anomalous speed related indicators but also correlate such speed changes with other elements such as commonly malicious processes like powershell processes being called in close proximity timing-wise, PnP device events occurring correlated with driver images loaded.
[ { "version": "v1", "created": "Thu, 9 Feb 2023 10:12:54 GMT" } ]
2023-02-10T00:00:00
[ [ "Karantzas", "George", "" ] ]
new_dataset
0.994862
2302.04603
Kars Alfrink
Kars Alfrink, Ianus Keller, Neelke Doorn, Gerd Kortuem
Contestable Camera Cars: A Speculative Design Exploration of Public AI That Is Open and Responsive to Dispute
Conditionally accepted to CHI 2023
null
10.1145/3544548.3580984
null
cs.HC cs.AI cs.CY cs.LG
http://creativecommons.org/licenses/by/4.0/
Local governments increasingly use artificial intelligence (AI) for automated decision-making. Contestability, making systems responsive to dispute, is a way to ensure they respect human rights to autonomy and dignity. We investigate the design of public urban AI systems for contestability through the example of camera cars: human-driven vehicles equipped with image sensors. Applying a provisional framework for contestable AI, we use speculative design to create a concept video of a contestable camera car. Using this concept video, we then conduct semi-structured interviews with 17 civil servants who work with AI employed by a large northwestern European city. The resulting data is analyzed using reflexive thematic analysis to identify the main challenges facing the implementation of contestability in public AI. We describe how civic participation faces issues of representation, public AI systems should integrate with existing democratic practices, and cities must expand capacities for responsible AI development and operation.
[ { "version": "v1", "created": "Thu, 9 Feb 2023 12:38:51 GMT" } ]
2023-02-10T00:00:00
[ [ "Alfrink", "Kars", "" ], [ "Keller", "Ianus", "" ], [ "Doorn", "Neelke", "" ], [ "Kortuem", "Gerd", "" ] ]
new_dataset
0.969046
2302.04611
Shengchao Liu
Shengchao Liu, Yutao Zhu, Jiarui Lu, Zhao Xu, Weili Nie, Anthony Gitter, Chaowei Xiao, Jian Tang, Hongyu Guo, Anima Anandkumar
A Text-guided Protein Design Framework
null
null
null
null
cs.LG cs.AI q-bio.QM stat.ML
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Current AI-assisted protein design mainly utilizes protein sequential and structural information. Meanwhile, there exists tremendous knowledge curated by humans in the text format describing proteins' high-level properties. Yet, whether the incorporation of such text data can help protein design tasks has not been explored. To bridge this gap, we propose ProteinDT, a multi-modal framework that leverages textual descriptions for protein design. ProteinDT consists of three subsequent steps: ProteinCLAP that aligns the representation of two modalities, a facilitator that generates the protein representation from the text modality, and a decoder that generates the protein sequences from the representation. To train ProteinDT, we construct a large dataset, SwissProtCLAP, with 441K text and protein pairs. We empirically verify the effectiveness of ProteinDT from three aspects: (1) consistently superior performance on four out of six protein property prediction benchmarks; (2) over 90% accuracy for text-guided protein generation; and (3) promising results for zero-shot text-guided protein editing.
[ { "version": "v1", "created": "Thu, 9 Feb 2023 12:59:16 GMT" } ]
2023-02-10T00:00:00
[ [ "Liu", "Shengchao", "" ], [ "Zhu", "Yutao", "" ], [ "Lu", "Jiarui", "" ], [ "Xu", "Zhao", "" ], [ "Nie", "Weili", "" ], [ "Gitter", "Anthony", "" ], [ "Xiao", "Chaowei", "" ], [ "Tang", "Jian", "" ], [ "Guo", "Hongyu", "" ], [ "Anandkumar", "Anima", "" ] ]
new_dataset
0.995308
2302.04659
Jiayuan Gu
Jiayuan Gu, Fanbo Xiang, Xuanlin Li, Zhan Ling, Xiqiang Liu, Tongzhou Mu, Yihe Tang, Stone Tao, Xinyue Wei, Yunchao Yao, Xiaodi Yuan, Pengwei Xie, Zhiao Huang, Rui Chen, Hao Su
ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skills
Published as a conference paper at ICLR 2023. Project website: https://maniskill2.github.io/
null
null
null
cs.RO cs.AI
http://creativecommons.org/licenses/by/4.0/
Generalizable manipulation skills, which can be composed to tackle long-horizon and complex daily chores, are one of the cornerstones of Embodied AI. However, existing benchmarks, mostly composed of a suite of simulatable environments, are insufficient to push cutting-edge research works because they lack object-level topological and geometric variations, are not based on fully dynamic simulation, or are short of native support for multiple types of manipulation tasks. To this end, we present ManiSkill2, the next generation of the SAPIEN ManiSkill benchmark, to address critical pain points often encountered by researchers when using benchmarks for generalizable manipulation skills. ManiSkill2 includes 20 manipulation task families with 2000+ object models and 4M+ demonstration frames, which cover stationary/mobile-base, single/dual-arm, and rigid/soft-body manipulation tasks with 2D/3D-input data simulated by fully dynamic engines. It defines a unified interface and evaluation protocol to support a wide range of algorithms (e.g., classic sense-plan-act, RL, IL), visual observations (point cloud, RGBD), and controllers (e.g., action type and parameterization). Moreover, it empowers fast visual input learning algorithms so that a CNN-based policy can collect samples at about 2000 FPS with 1 GPU and 16 processes on a regular workstation. It implements a render server infrastructure to allow sharing rendering resources across all environments, thereby significantly reducing memory usage. We open-source all codes of our benchmark (simulator, environments, and baselines) and host an online challenge open to interdisciplinary researchers.
[ { "version": "v1", "created": "Thu, 9 Feb 2023 14:24:01 GMT" } ]
2023-02-10T00:00:00
[ [ "Gu", "Jiayuan", "" ], [ "Xiang", "Fanbo", "" ], [ "Li", "Xuanlin", "" ], [ "Ling", "Zhan", "" ], [ "Liu", "Xiqiang", "" ], [ "Mu", "Tongzhou", "" ], [ "Tang", "Yihe", "" ], [ "Tao", "Stone", "" ], [ "Wei", "Xinyue", "" ], [ "Yao", "Yunchao", "" ], [ "Yuan", "Xiaodi", "" ], [ "Xie", "Pengwei", "" ], [ "Huang", "Zhiao", "" ], [ "Chen", "Rui", "" ], [ "Su", "Hao", "" ] ]
new_dataset
0.99956
2302.04691
Giuseppe Silano
Giuseppe Silano, Tomas Baca, Robert Penicka, Davide Liuzza, and Martin Saska
Power Line Inspection Tasks with Multi-Aerial Robot Systems via Signal Temporal Logic Specifications
8 pages, 12 figures, journal paper
IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 4169-4176, April, 2021
10.1109/LRA.2021.3068114
null
cs.RO cs.MA
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
A framework for computing feasible and constrained trajectories for a fleet of quad-rotors leveraging on Signal Temporal Logic (STL) specifications for power line inspection tasks is proposed in this paper. The planner allows the formulation of complex missions that avoid obstacles and maintain a safe distance between drones while performing the planned mission. An optimization problem is set to generate optimal strategies that satisfy these specifications and also take vehicle constraints into account. Further, an event-triggered replanner is proposed to reply to unforeseen events and external disturbances. An energy minimization term is also considered to implicitly save quad-rotors battery life while carrying out the mission. Numerical simulations in MATLAB and experimental results show the validity and the effectiveness of the proposed approach, and demonstrate its applicability in real-world scenarios.
[ { "version": "v1", "created": "Thu, 9 Feb 2023 15:18:14 GMT" } ]
2023-02-10T00:00:00
[ [ "Silano", "Giuseppe", "" ], [ "Baca", "Tomas", "" ], [ "Penicka", "Robert", "" ], [ "Liuzza", "Davide", "" ], [ "Saska", "Martin", "" ] ]
new_dataset
0.980772
2302.04708
Giuseppe Silano
Andriy Dmytruk, Giuseppe Silano, Davide Bicego, Daniel Bonilla Licea, and Martin Saska
A Perception-Aware NMPC for Vision-Based Target Tracking and Collision Avoidance with a Multi-Rotor UAV
6 pages, 6 figures, conference
2022 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1668-1673, June, 2022, Dubrovnik, Croatia
10.1109/ICUAS54217.2022.9836071
null
cs.RO
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
A perception-aware Nonlinear Model Predictive Control (NMPC) strategy aimed at performing vision-based target tracking and collision avoidance with a multi-rotor aerial vehicle is presented in this paper. The proposed control strategy considers both realistic actuation limits at the torque level and visual perception constraints to enforce the visibility coverage of a target while complying with the mission objectives. Furthermore, the approach allows to safely navigate in a workspace area populated by dynamic obstacles with a ballistic motion. The formulation is meant to be generic and set upon a large class of multi-rotor vehicles that covers both coplanar designs like quadrotors as well as fully-actuated platforms with tilted propellers. The feasibility and effectiveness of the control strategy are demonstrated via closed-loop simulations achieved in MATLAB.
[ { "version": "v1", "created": "Thu, 9 Feb 2023 15:46:29 GMT" } ]
2023-02-10T00:00:00
[ [ "Dmytruk", "Andriy", "" ], [ "Silano", "Giuseppe", "" ], [ "Bicego", "Davide", "" ], [ "Licea", "Daniel Bonilla", "" ], [ "Saska", "Martin", "" ] ]
new_dataset
0.979924
2302.04778
Giuseppe Silano
Daniel Hert, Tomas Baca, Pavel Petracek, Vit Kratky, Vojtech Spurny, Matej Petrlik, Matous Vrba, David Zaitlik, Pavel Stoudek, Viktor Walter, Petr Stepan, Jiri Horyna, Vaclav Pritzl, Giuseppe Silano, Daniel Bonilla Licea, Petr Stibinger, Robert Penicka, Tiago Nascimento, Martin Saska
MRS Modular UAV Hardware Platforms for Supporting Research in Real-World Outdoor and Indoor Environments
10 pages, 17 figures, conference
2022 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1264-1273, June, 2022, Dubrovnik, Croatia
10.1109/ICUAS54217.2022.9836083
null
cs.RO
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
This paper presents a family of autonomous Unmanned Aerial Vehicles (UAVs) platforms designed for a diverse range of indoor and outdoor applications. The proposed UAV design is highly modular in terms of used actuators, sensor configurations, and even UAV frames. This allows to achieve, with minimal effort, a proper experimental setup for single, as well as, multi robot scenarios. Presented platforms are intended to facilitate the transition from simulations, and simplified laboratory experiments, into the deployment of aerial robots into uncertain and hard-to-model real-world conditions. We present mechanical designs, electric configurations, and dynamic models of the UAVs, followed by numerous recommendations and technical details required for building such a fully autonomous UAV system for experimental verification of scientific achievements. To show strength and high variability of the proposed system, we present results of tens of completely different real-robot experiments in various environments using distinct actuator and sensory configurations.
[ { "version": "v1", "created": "Thu, 9 Feb 2023 17:11:43 GMT" } ]
2023-02-10T00:00:00
[ [ "Hert", "Daniel", "" ], [ "Baca", "Tomas", "" ], [ "Petracek", "Pavel", "" ], [ "Kratky", "Vit", "" ], [ "Spurny", "Vojtech", "" ], [ "Petrlik", "Matej", "" ], [ "Vrba", "Matous", "" ], [ "Zaitlik", "David", "" ], [ "Stoudek", "Pavel", "" ], [ "Walter", "Viktor", "" ], [ "Stepan", "Petr", "" ], [ "Horyna", "Jiri", "" ], [ "Pritzl", "Vaclav", "" ], [ "Silano", "Giuseppe", "" ], [ "Licea", "Daniel Bonilla", "" ], [ "Stibinger", "Petr", "" ], [ "Penicka", "Robert", "" ], [ "Nascimento", "Tiago", "" ], [ "Saska", "Martin", "" ] ]
new_dataset
0.998762
2302.04800
Salwa Al Khatib
Salwa Al Khatib, Mohamed El Amine Boudjoghra, Jameel Hassan
Drawing Attention to Detail: Pose Alignment through Self-Attention for Fine-Grained Object Classification
Course Assignment
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Intra-class variations in the open world lead to various challenges in classification tasks. To overcome these challenges, fine-grained classification was introduced, and many approaches were proposed. Some rely on locating and using distinguishable local parts within images to achieve invariance to viewpoint changes, intra-class differences, and local part deformations. Our approach, which is inspired by P2P-Net, offers an end-to-end trainable attention-based parts alignment module, where we replace the graph-matching component used in it with a self-attention mechanism. The attention module is able to learn the optimal arrangement of parts while attending to each other, before contributing to the global loss.
[ { "version": "v1", "created": "Thu, 9 Feb 2023 17:47:47 GMT" } ]
2023-02-10T00:00:00
[ [ "Khatib", "Salwa Al", "" ], [ "Boudjoghra", "Mohamed El Amine", "" ], [ "Hassan", "Jameel", "" ] ]
new_dataset
0.958482
2302.04815
Abdul Samadh Jameel Hassan
Jameel Hassan Abdul Samadh, Salwa K. Al Khatib
To Perceive or Not to Perceive: Lightweight Stacked Hourglass Network
Course project
null
null
null
cs.CV eess.IV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Human pose estimation (HPE) is a classical task in computer vision that focuses on representing the orientation of a person by identifying the positions of their joints. We design a lighterversion of the stacked hourglass network with minimal loss in performance of the model. The lightweight 2-stacked hourglass has a reduced number of channels with depthwise separable convolutions, residual connections with concatenation, and residual connections between the necks of the hourglasses. The final model has a marginal drop in performance with 79% reduction in the number of parameters and a similar drop in MAdds
[ { "version": "v1", "created": "Thu, 9 Feb 2023 18:04:43 GMT" } ]
2023-02-10T00:00:00
[ [ "Samadh", "Jameel Hassan Abdul", "" ], [ "Khatib", "Salwa K. Al", "" ] ]
new_dataset
0.959737
2302.04824
Daniela Ushizima
Jerome Quenum, David Perlmutter, Ying Huang, Iryna Zenyuk, and Daniela Ushizima
Lithium Metal Battery Quality Control via Transformer-CNN Segmentation
17 pages, 5 figures
null
null
null
cs.CV cs.LG
http://creativecommons.org/licenses/by/4.0/
Lithium metal battery (LMB) has the potential to be the next-generation battery system because of their high theoretical energy density. However, defects known as dendrites are formed by heterogeneous lithium (Li) plating, which hinder the development and utilization of LMBs. Non-destructive techniques to observe the dendrite morphology often use computerized X-ray tomography (XCT) imaging to provide cross-sectional views. To retrieve three-dimensional structures inside a battery, image segmentation becomes essential to quantitatively analyze XCT images. This work proposes a new binary semantic segmentation approach using a transformer-based neural network (T-Net) model capable of segmenting out dendrites from XCT data. In addition, we compare the performance of the proposed T-Net with three other algorithms, such as U-Net, Y-Net, and E-Net, consisting of an Ensemble Network model for XCT analysis. Our results show the advantages of using T-Net in terms of object metrics, such as mean Intersection over Union (mIoU) and mean Dice Similarity Coefficient (mDSC) as well as qualitatively through several comparative visualizations.
[ { "version": "v1", "created": "Thu, 9 Feb 2023 18:25:24 GMT" } ]
2023-02-10T00:00:00
[ [ "Quenum", "Jerome", "" ], [ "Perlmutter", "David", "" ], [ "Huang", "Ying", "" ], [ "Zenyuk", "Iryna", "" ], [ "Ushizima", "Daniela", "" ] ]
new_dataset
0.995592
2302.04868
Junxuan Li
Junxuan Li, Shunsuke Saito, Tomas Simon, Stephen Lombardi, Hongdong Li, Jason Saragih
MEGANE: Morphable Eyeglass and Avatar Network
Project page: https://junxuan-li.github.io/megane/
null
null
null
cs.CV cs.GR
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Eyeglasses play an important role in the perception of identity. Authentic virtual representations of faces can benefit greatly from their inclusion. However, modeling the geometric and appearance interactions of glasses and the face of virtual representations of humans is challenging. Glasses and faces affect each other's geometry at their contact points, and also induce appearance changes due to light transport. Most existing approaches do not capture these physical interactions since they model eyeglasses and faces independently. Others attempt to resolve interactions as a 2D image synthesis problem and suffer from view and temporal inconsistencies. In this work, we propose a 3D compositional morphable model of eyeglasses that accurately incorporates high-fidelity geometric and photometric interaction effects. To support the large variation in eyeglass topology efficiently, we employ a hybrid representation that combines surface geometry and a volumetric representation. Unlike volumetric approaches, our model naturally retains correspondences across glasses, and hence explicit modification of geometry, such as lens insertion and frame deformation, is greatly simplified. In addition, our model is relightable under point lights and natural illumination, supporting high-fidelity rendering of various frame materials, including translucent plastic and metal within a single morphable model. Importantly, our approach models global light transport effects, such as casting shadows between faces and glasses. Our morphable model for eyeglasses can also be fit to novel glasses via inverse rendering. We compare our approach to state-of-the-art methods and demonstrate significant quality improvements.
[ { "version": "v1", "created": "Thu, 9 Feb 2023 18:59:49 GMT" } ]
2023-02-10T00:00:00
[ [ "Li", "Junxuan", "" ], [ "Saito", "Shunsuke", "" ], [ "Simon", "Tomas", "" ], [ "Lombardi", "Stephen", "" ], [ "Li", "Hongdong", "" ], [ "Saragih", "Jason", "" ] ]
new_dataset
0.998967
2112.05555
Mathieu Bernard
Mathieu Bernard and Maxime Poli and Julien Karadayi and Emmanuel Dupoux
Shennong: a Python toolbox for audio speech features extraction
null
Behavior Research Methods, 2023
10.3758/s13428-022-02029-6
null
cs.CL cs.SD eess.AS
http://creativecommons.org/licenses/by-nc-sa/4.0/
We introduce Shennong, a Python toolbox and command-line utility for speech features extraction. It implements a wide range of well-established state of art algorithms including spectro-temporal filters such as Mel-Frequency Cepstral Filterbanks or Predictive Linear Filters, pre-trained neural networks, pitch estimators as well as speaker normalization methods and post-processing algorithms. Shennong is an open source, easy-to-use, reliable and extensible framework. The use of Python makes the integration to others speech modeling and machine learning tools easy. It aims to replace or complement several heterogeneous software, such as Kaldi or Praat. After describing the Shennong software architecture, its core components and implemented algorithms, this paper illustrates its use on three applications: a comparison of speech features performances on a phones discrimination task, an analysis of a Vocal Tract Length Normalization model as a function of the speech duration used for training and a comparison of pitch estimation algorithms under various noise conditions.
[ { "version": "v1", "created": "Fri, 10 Dec 2021 14:08:52 GMT" } ]
2023-02-09T00:00:00
[ [ "Bernard", "Mathieu", "" ], [ "Poli", "Maxime", "" ], [ "Karadayi", "Julien", "" ], [ "Dupoux", "Emmanuel", "" ] ]
new_dataset
0.9882
2201.04806
Shaoxiong Zhang
Shaoxiong Zhang, Yunhong Wang, Tianrui Chai, Annan Li, Anil K. Jain
RealGait: Gait Recognition for Person Re-Identification
null
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Human gait is considered a unique biometric identifier which can be acquired in a covert manner at a distance. However, models trained on existing public domain gait datasets which are captured in controlled scenarios lead to drastic performance decline when applied to real-world unconstrained gait data. On the other hand, video person re-identification techniques have achieved promising performance on large-scale publicly available datasets. Given the diversity of clothing characteristics, clothing cue is not reliable for person recognition in general. So, it is actually not clear why the state-of-the-art person re-identification methods work as well as they do. In this paper, we construct a new gait dataset by extracting silhouettes from an existing video person re-identification challenge which consists of 1,404 persons walking in an unconstrained manner. Based on this dataset, a consistent and comparative study between gait recognition and person re-identification can be carried out. Given that our experimental results show that current gait recognition approaches designed under data collected in controlled scenarios are inappropriate for real surveillance scenarios, we propose a novel gait recognition method, called RealGait. Our results suggest that recognizing people by their gait in real surveillance scenarios is feasible and the underlying gait pattern is probably the true reason why video person re-idenfification works in practice.
[ { "version": "v1", "created": "Thu, 13 Jan 2022 06:30:56 GMT" }, { "version": "v2", "created": "Wed, 8 Feb 2023 08:47:52 GMT" } ]
2023-02-09T00:00:00
[ [ "Zhang", "Shaoxiong", "" ], [ "Wang", "Yunhong", "" ], [ "Chai", "Tianrui", "" ], [ "Li", "Annan", "" ], [ "Jain", "Anil K.", "" ] ]
new_dataset
0.98867
2203.16597
Israel Leyva-Mayorga
Israel Leyva-Mayorga, Beatriz Soret, Bho Matthiesen, Maik R\"oper, Dirk W\"ubben, Armin Dekorsy, and Petar Popovski
NGSO Constellation Design for Global Connectivity
Book chapter submitted to IET Non-Geostationary Satellite Communications Systems
null
10.1049/PBTE105E
null
cs.NI eess.SP
http://creativecommons.org/publicdomain/zero/1.0/
Non-geostationary orbit (NGSO) satellite constellations represent a cornerstone in the NewSpace paradigm and thus have become one of the hottest topics for the industry, academia, but also for national space agencies and regulators. For instance, numerous companies worldwide, including Starlink, OneWeb, Kepler, SPUTNIX, and Amazon have started or will soon start to deploy their own NGSO constellations, which aim to provide either broadband or IoT services. One of the major drivers for such a high interest on NGSO constellations is that, with an appropriate design, they are capable of providing global coverage and connectivity.
[ { "version": "v1", "created": "Wed, 30 Mar 2022 18:26:43 GMT" }, { "version": "v2", "created": "Sat, 9 Apr 2022 15:34:13 GMT" } ]
2023-02-09T00:00:00
[ [ "Leyva-Mayorga", "Israel", "" ], [ "Soret", "Beatriz", "" ], [ "Matthiesen", "Bho", "" ], [ "Röper", "Maik", "" ], [ "Wübben", "Dirk", "" ], [ "Dekorsy", "Armin", "" ], [ "Popovski", "Petar", "" ] ]
new_dataset
0.999613
2205.00738
Corentin Dumery
Corentin Dumery, Fran\c{c}ois Protais, S\'ebastien Mestrallet, Christophe Bourcier, Franck Ledoux
Evocube: a Genetic Labeling Framework for Polycube-Maps
null
Computer Graphics Forum, Volume 41, Issue 6, September 2022, Pages 467-479
10.1111/cgf.14649
null
cs.GR
http://creativecommons.org/licenses/by-nc-nd/4.0/
Polycube-maps are used as base-complexes in various fields of computational geometry, including the generation of regular all-hexahedral meshes free of internal singularities. However, the strict alignment constraints behind polycube-based methods make their computation challenging for CAD models used in numerical simulation via Finite Element Method (FEM). We propose a novel approach based on an evolutionary algorithm to robustly compute polycube-maps in this context. We address the labeling problem, which aims to precompute polycube alignment by assigning one of the base axes to each boundary face on the input. Previous research has described ways to initialize and improve a labeling via greedy local fixes. However, such algorithms lack robustness and often converge to inaccurate solutions for complex geometries. Our proposed framework alleviates this issue by embedding labeling operations in an evolutionary heuristic, defining fitness, crossover, and mutations in the context of labeling optimization. We evaluate our method on a thousand smooth and CAD meshes, showing Evocube converges to valid labelings on a wide range of shapes. The limitations of our method are also discussed thoroughly.
[ { "version": "v1", "created": "Mon, 2 May 2022 08:43:27 GMT" }, { "version": "v2", "created": "Sat, 26 Nov 2022 10:02:59 GMT" }, { "version": "v3", "created": "Wed, 8 Feb 2023 10:30:55 GMT" } ]
2023-02-09T00:00:00
[ [ "Dumery", "Corentin", "" ], [ "Protais", "François", "" ], [ "Mestrallet", "Sébastien", "" ], [ "Bourcier", "Christophe", "" ], [ "Ledoux", "Franck", "" ] ]
new_dataset
0.994502
2207.01206
Shunyu Yao
Shunyu Yao, Howard Chen, John Yang, Karthik Narasimhan
WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents
Project page with code, data, demos: https://webshop-pnlp.github.io. v3 is NeurIPS camera ready version. v4 fixes the choice oracle result as per https://github.com/princeton-nlp/WebShop/issues/15
null
null
null
cs.CL cs.AI cs.LG
http://creativecommons.org/licenses/by/4.0/
Existing benchmarks for grounding language in interactive environments either lack real-world linguistic elements, or prove difficult to scale up due to substantial human involvement in the collection of data or feedback signals. To bridge this gap, we develop WebShop -- a simulated e-commerce website environment with $1.18$ million real-world products and $12,087$ crowd-sourced text instructions. Given a text instruction specifying a product requirement, an agent needs to navigate multiple types of webpages and issue diverse actions to find, customize, and purchase an item. WebShop provides several challenges for language grounding including understanding compositional instructions, query (re-)formulation, comprehending and acting on noisy text in webpages, and performing strategic exploration. We collect over $1,600$ human demonstrations for the task, and train and evaluate a diverse range of agents using reinforcement learning, imitation learning, and pre-trained image and language models. Our best model achieves a task success rate of $29\%$, which outperforms rule-based heuristics ($9.6\%$) but is far lower than human expert performance ($59\%$). We also analyze agent and human trajectories and ablate various model components to provide insights for developing future agents with stronger language understanding and decision making abilities. Finally, we show that agents trained on WebShop exhibit non-trivial sim-to-real transfer when evaluated on amazon.com and ebay.com, indicating the potential value of WebShop in developing practical web-based agents that can operate in the wild.
[ { "version": "v1", "created": "Mon, 4 Jul 2022 05:30:22 GMT" }, { "version": "v2", "created": "Sun, 17 Jul 2022 01:47:06 GMT" }, { "version": "v3", "created": "Thu, 24 Nov 2022 16:44:26 GMT" }, { "version": "v4", "created": "Wed, 8 Feb 2023 01:39:30 GMT" } ]
2023-02-09T00:00:00
[ [ "Yao", "Shunyu", "" ], [ "Chen", "Howard", "" ], [ "Yang", "John", "" ], [ "Narasimhan", "Karthik", "" ] ]
new_dataset
0.980952
2207.04236
Min H. Kim
Inseung Hwang, Daniel S. Jeon, Adolfo Mu\~noz, Diego Gutierrez, Xin Tong, Min H. Kim
Sparse Ellipsometry: Portable Acquisition of Polarimetric SVBRDF and Shape with Unstructured Flash Photography
null
ACM Transactions on Graphics 41, 4, Article 133 (July 2022)
10.1145/3528223.3530075
null
cs.GR cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Ellipsometry techniques allow to measure polarization information of materials, requiring precise rotations of optical components with different configurations of lights and sensors. This results in cumbersome capture devices, carefully calibrated in lab conditions, and in very long acquisition times, usually in the order of a few days per object. Recent techniques allow to capture polarimetric spatially-varying reflectance information, but limited to a single view, or to cover all view directions, but limited to spherical objects made of a single homogeneous material. We present sparse ellipsometry, a portable polarimetric acquisition method that captures both polarimetric SVBRDF and 3D shape simultaneously. Our handheld device consists of off-the-shelf, fixed optical components. Instead of days, the total acquisition time varies between twenty and thirty minutes per object. We develop a complete polarimetric SVBRDF model that includes diffuse and specular components, as well as single scattering, and devise a novel polarimetric inverse rendering algorithm with data augmentation of specular reflection samples via generative modeling. Our results show a strong agreement with a recent ground-truth dataset of captured polarimetric BRDFs of real-world objects.
[ { "version": "v1", "created": "Sat, 9 Jul 2022 09:42:59 GMT" }, { "version": "v2", "created": "Wed, 8 Feb 2023 12:27:13 GMT" } ]
2023-02-09T00:00:00
[ [ "Hwang", "Inseung", "" ], [ "Jeon", "Daniel S.", "" ], [ "Muñoz", "Adolfo", "" ], [ "Gutierrez", "Diego", "" ], [ "Tong", "Xin", "" ], [ "Kim", "Min H.", "" ] ]
new_dataset
0.981512
2207.10985
Yunlong Ran
Yunlong Ran, Jing Zeng, Shibo He, Lincheng Li, Yingfeng Chen, Gimhee Lee, Jiming Chen, Qi Ye
NeurAR: Neural Uncertainty for Autonomous 3D Reconstruction with Implicit Neural Representations
8 pages, 6 figures, 3 tables
IEEE Robotics and Automation Letters(RAL) Volume: 8, Issue: 2, February 2023 Page(s): 1125 - 1132
10.1109/LRA.2023.3235686
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Implicit neural representations have shown compelling results in offline 3D reconstruction and also recently demonstrated the potential for online SLAM systems. However, applying them to autonomous 3D reconstruction, where a robot is required to explore a scene and plan a view path for the reconstruction, has not been studied. In this paper, we explore for the first time the possibility of using implicit neural representations for autonomous 3D scene reconstruction by addressing two key challenges: 1) seeking a criterion to measure the quality of the candidate viewpoints for the view planning based on the new representations, and 2) learning the criterion from data that can generalize to different scenes instead of a hand-crafting one. To solve the challenges, firstly, a proxy of Peak Signal-to-Noise Ratio (PSNR) is proposed to quantify a viewpoint quality; secondly, the proxy is optimized jointly with the parameters of an implicit neural network for the scene. With the proposed view quality criterion from neural networks (termed as Neural Uncertainty), we can then apply implicit representations to autonomous 3D reconstruction. Our method demonstrates significant improvements on various metrics for the rendered image quality and the geometry quality of the reconstructed 3D models when compared with variants using TSDF or reconstruction without view planning. Project webpage https://kingteeloki-ran.github.io/NeurAR/
[ { "version": "v1", "created": "Fri, 22 Jul 2022 10:05:36 GMT" }, { "version": "v2", "created": "Wed, 8 Feb 2023 06:24:39 GMT" } ]
2023-02-09T00:00:00
[ [ "Ran", "Yunlong", "" ], [ "Zeng", "Jing", "" ], [ "He", "Shibo", "" ], [ "Li", "Lincheng", "" ], [ "Chen", "Yingfeng", "" ], [ "Lee", "Gimhee", "" ], [ "Chen", "Jiming", "" ], [ "Ye", "Qi", "" ] ]
new_dataset
0.987024
2209.04355
Hanlei Zhang
Hanlei Zhang, Hua Xu, Xin Wang, Qianrui Zhou, Shaojie Zhao, Jiayan Teng
MIntRec: A New Dataset for Multimodal Intent Recognition
Accepted by ACM MM 2022 (Main Track, Long Paper)
null
10.1145/3503161.3547906
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Multimodal intent recognition is a significant task for understanding human language in real-world multimodal scenes. Most existing intent recognition methods have limitations in leveraging the multimodal information due to the restrictions of the benchmark datasets with only text information. This paper introduces a novel dataset for multimodal intent recognition (MIntRec) to address this issue. It formulates coarse-grained and fine-grained intent taxonomies based on the data collected from the TV series Superstore. The dataset consists of 2,224 high-quality samples with text, video, and audio modalities and has multimodal annotations among twenty intent categories. Furthermore, we provide annotated bounding boxes of speakers in each video segment and achieve an automatic process for speaker annotation. MIntRec is helpful for researchers to mine relationships between different modalities to enhance the capability of intent recognition. We extract features from each modality and model cross-modal interactions by adapting three powerful multimodal fusion methods to build baselines. Extensive experiments show that employing the non-verbal modalities achieves substantial improvements compared with the text-only modality, demonstrating the effectiveness of using multimodal information for intent recognition. The gap between the best-performing methods and humans indicates the challenge and importance of this task for the community. The full dataset and codes are available for use at https://github.com/thuiar/MIntRec.
[ { "version": "v1", "created": "Fri, 9 Sep 2022 15:37:39 GMT" } ]
2023-02-09T00:00:00
[ [ "Zhang", "Hanlei", "" ], [ "Xu", "Hua", "" ], [ "Wang", "Xin", "" ], [ "Zhou", "Qianrui", "" ], [ "Zhao", "Shaojie", "" ], [ "Teng", "Jiayan", "" ] ]
new_dataset
0.999877
2209.05136
Jo\~ao Mota
Jo\~ao Mota, Marco Giunti, Ant\'onio Ravara
On using VeriFast, VerCors, Plural, and KeY to check object usage
null
null
null
null
cs.LO cs.PL
http://creativecommons.org/licenses/by/4.0/
Typestates are a notion of behavioral types that describe protocols for stateful objects, specifying the available methods for each state, in terms of a state machine. Usually, objects with protocol are either forced to be used in a linear way, which restricts what a programmer can do, or deductive verification is required to verify programs where these objects may be aliased. To evaluate the strengths and limitations of static verification tools for object-oriented languages in checking the correct use of shared objects with protocol, we present a survey on four tools for Java: VeriFast, VerCors, Plural, and KeY. We describe the implementation of a file reader and of a linked-list, check for each tool its ability to statically guarantee protocol compliance as well as protocol completion, even when objects are shared in collections, and evaluate the programmer's effort in making the code acceptable to these tools.
[ { "version": "v1", "created": "Mon, 12 Sep 2022 10:44:23 GMT" }, { "version": "v2", "created": "Wed, 8 Feb 2023 13:43:41 GMT" } ]
2023-02-09T00:00:00
[ [ "Mota", "João", "" ], [ "Giunti", "Marco", "" ], [ "Ravara", "António", "" ] ]
new_dataset
0.993494
2209.08499
Alexander Badri-Spr\"owitz
Abhishek Chatterjee, An Mo, Bernadett Kiss, Emre Cemal G\"onen, Alexander Badri-Spr\"owitz
Multi-segmented Adaptive Feet for Versatile Legged Locomotion in Natural Terrain
null
null
null
null
cs.RO
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Most legged robots are built with leg structures from serially mounted links and actuators and are controlled through complex controllers and sensor feedback. In comparison, animals developed multi-segment legs, mechanical coupling between joints, and multi-segmented feet. They run agile over all terrains, arguably with simpler locomotion control. Here we focus on developing foot mechanisms that resist slipping and sinking also in natural terrain. We present first results of multi-segment feet mounted to a bird-inspired robot leg with multi-joint mechanical tendon coupling. Our one- and two-segment, mechanically adaptive feet show increased viable horizontal forces on multiple soft and hard substrates before starting to slip. We also observe that segmented feet reduce sinking on soft substrates compared to ball-feet and cylinder-feet. We report how multi-segmented feet provide a large range of viable centre of pressure points well suited for bipedal robots, but also for quadruped robots on slopes and natural terrain. Our results also offer a functional understanding of segmented feet in animals like ratite birds.
[ { "version": "v1", "created": "Sun, 18 Sep 2022 08:00:02 GMT" }, { "version": "v2", "created": "Wed, 8 Feb 2023 15:38:16 GMT" } ]
2023-02-09T00:00:00
[ [ "Chatterjee", "Abhishek", "" ], [ "Mo", "An", "" ], [ "Kiss", "Bernadett", "" ], [ "Gönen", "Emre Cemal", "" ], [ "Badri-Spröwitz", "Alexander", "" ] ]
new_dataset
0.986356
2212.08021
Francesco Pierri
Francesco Pierri
Political advertisement on Facebook and Instagram in the run up to 2022 Italian general election
10 pages, 12 figures. To be published in the proceedings of the ACM Web Science Conference (2023)
null
10.1145/3578503.3583598
null
cs.CY cs.SI
http://creativecommons.org/licenses/by-nc-sa/4.0/
Targeted advertising on online social platforms has become increasingly relevant in the political marketing toolkit. Monitoring political advertising is crucial to ensure accountability and transparency of democratic processes. Leveraging Meta public library of sponsored content, we study the extent to which political ads were delivered on Facebook and Instagram in the run up to 2022 Italian general election. Analyzing over 23 k unique ads paid by 2.7 k unique sponsors, with an associated amount spent of 4 M EUR and over 1 billion views generated, we investigate temporal, geographical, and demographic patterns of the political campaigning activity of main coalitions. We find results that are in accordance with their political agenda and the electoral outcome, highlighting how the most active coalitions also obtained most of the votes and showing regional differences that are coherent with the (targeted) political base of each group. Our work raises attention to the need for further studies of digital advertising and its implications for individuals' opinions and choices.
[ { "version": "v1", "created": "Mon, 12 Dec 2022 13:37:18 GMT" }, { "version": "v2", "created": "Wed, 8 Feb 2023 12:06:31 GMT" } ]
2023-02-09T00:00:00
[ [ "Pierri", "Francesco", "" ] ]
new_dataset
0.999167
2301.05206
Lin Jiarong
Jiarong Lin, Chongjiang Yuan, Yixi Cai, Haotian Li, Yuying Zou, Xiaoping Hong and Fu Zhang
ImMesh: An Immediate LiDAR Localization and Meshing Framework
null
null
null
null
cs.RO cs.CV
http://creativecommons.org/licenses/by/4.0/
In this paper, we propose a novel LiDAR(-inertial) odometry and mapping framework to achieve the goal of simultaneous localization and meshing in real-time. This proposed framework termed ImMesh comprises four tightly-coupled modules: receiver, localization, meshing, and broadcaster. The localization module utilizes the prepossessed sensor data from the receiver, estimates the sensor pose online by registering LiDAR scans to maps, and dynamically grows the map. Then, our meshing module takes the registered LiDAR scan for incrementally reconstructing the triangle mesh on the fly. Finally, the real-time odometry, map, and mesh are published via our broadcaster. The key contribution of this work is the meshing module, which represents a scene by an efficient hierarchical voxels structure, performs fast finding of voxels observed by new scans, and reconstructs triangle facets in each voxel in an incremental manner. This voxel-wise meshing operation is delicately designed for the purpose of efficiency; it first performs a dimension reduction by projecting 3D points to a 2D local plane contained in the voxel, and then executes the meshing operation with pull, commit and push steps for incremental reconstruction of triangle facets. To the best of our knowledge, this is the first work in literature that can reconstruct online the triangle mesh of large-scale scenes, just relying on a standard CPU without GPU acceleration. To share our findings and make contributions to the community, we make our code publicly available on our GitHub: https://github.com/hku-mars/ImMesh.
[ { "version": "v1", "created": "Thu, 12 Jan 2023 18:43:16 GMT" }, { "version": "v2", "created": "Wed, 8 Feb 2023 02:26:59 GMT" } ]
2023-02-09T00:00:00
[ [ "Lin", "Jiarong", "" ], [ "Yuan", "Chongjiang", "" ], [ "Cai", "Yixi", "" ], [ "Li", "Haotian", "" ], [ "Zou", "Yuying", "" ], [ "Hong", "Xiaoping", "" ], [ "Zhang", "Fu", "" ] ]
new_dataset
0.99223
2302.01791
Jiayu Jiao Jr
Jiayu Jiao, Yu-Ming Tang, Kun-Yu Lin, Yipeng Gao, Jinhua Ma, Yaowei Wang and Wei-Shi Zheng
DilateFormer: Multi-Scale Dilated Transformer for Visual Recognition
Accepted to IEEE Transaction on Multimedia, 2023 (Submission date: 22-Sep-2022)
IEEE Transaction on Multimedia, 2023
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
As a de facto solution, the vanilla Vision Transformers (ViTs) are encouraged to model long-range dependencies between arbitrary image patches while the global attended receptive field leads to quadratic computational cost. Another branch of Vision Transformers exploits local attention inspired by CNNs, which only models the interactions between patches in small neighborhoods. Although such a solution reduces the computational cost, it naturally suffers from small attended receptive fields, which may limit the performance. In this work, we explore effective Vision Transformers to pursue a preferable trade-off between the computational complexity and size of the attended receptive field. By analyzing the patch interaction of global attention in ViTs, we observe two key properties in the shallow layers, namely locality and sparsity, indicating the redundancy of global dependency modeling in shallow layers of ViTs. Accordingly, we propose Multi-Scale Dilated Attention (MSDA) to model local and sparse patch interaction within the sliding window. With a pyramid architecture, we construct a Multi-Scale Dilated Transformer (DilateFormer) by stacking MSDA blocks at low-level stages and global multi-head self-attention blocks at high-level stages. Our experiment results show that our DilateFormer achieves state-of-the-art performance on various vision tasks. On ImageNet-1K classification task, DilateFormer achieves comparable performance with 70% fewer FLOPs compared with existing state-of-the-art models. Our DilateFormer-Base achieves 85.6% top-1 accuracy on ImageNet-1K classification task, 53.5% box mAP/46.1% mask mAP on COCO object detection/instance segmentation task and 51.1% MS mIoU on ADE20K semantic segmentation task.
[ { "version": "v1", "created": "Fri, 3 Feb 2023 14:59:31 GMT" } ]
2023-02-09T00:00:00
[ [ "Jiao", "Jiayu", "" ], [ "Tang", "Yu-Ming", "" ], [ "Lin", "Kun-Yu", "" ], [ "Gao", "Yipeng", "" ], [ "Ma", "Jinhua", "" ], [ "Wang", "Yaowei", "" ], [ "Zheng", "Wei-Shi", "" ] ]
new_dataset
0.997254
2302.02232
Mustafa Jarrar
Sana Ghanem, Mustafa Jarrar, Radi Jarrar, Ibrahim Bounhas
A Benchmark and Scoring Algorithm for Enriching Arabic Synonyms
null
The 12th International Global Wordnet Conference (GWC2023), Global Wordnet Association. (pp. ). San Sebastian, Spain, 2023
null
null
cs.CL
http://creativecommons.org/licenses/by/4.0/
This paper addresses the task of extending a given synset with additional synonyms taking into account synonymy strength as a fuzzy value. Given a mono/multilingual synset and a threshold (a fuzzy value [0-1]), our goal is to extract new synonyms above this threshold from existing lexicons. We present twofold contributions: an algorithm and a benchmark dataset. The dataset consists of 3K candidate synonyms for 500 synsets. Each candidate synonym is annotated with a fuzzy value by four linguists. The dataset is important for (i) understanding how much linguists (dis/)agree on synonymy, in addition to (ii) using the dataset as a baseline to evaluate our algorithm. Our proposed algorithm extracts synonyms from existing lexicons and computes a fuzzy value for each candidate. Our evaluations show that the algorithm behaves like a linguist and its fuzzy values are close to those proposed by linguists (using RMSE and MAE). The dataset and a demo page are publicly available at https://portal.sina.birzeit.edu/synonyms.
[ { "version": "v1", "created": "Sat, 4 Feb 2023 20:30:32 GMT" } ]
2023-02-09T00:00:00
[ [ "Ghanem", "Sana", "" ], [ "Jarrar", "Mustafa", "" ], [ "Jarrar", "Radi", "" ], [ "Bounhas", "Ibrahim", "" ] ]
new_dataset
0.999696
2302.02956
Dmytro Pavlichenko
Dmytro Pavlichenko, Grzegorz Ficht, Arash Amini, Mojtaba Hosseini, Raphael Memmesheimer, Angel Villar-Corrales, Stefan M. Schulz, Marcell Missura, Maren Bennewitz, Sven Behnke
RoboCup 2022 AdultSize Winner NimbRo: Upgraded Perception, Capture Steps Gait and Phase-based In-walk Kicks
null
In: RoboCup 2022: Robot World Cup XXV. LNCS 13561, Springer, May 2023
null
null
cs.RO
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Beating the human world champions by 2050 is an ambitious goal of the Humanoid League that provides a strong incentive for RoboCup teams to further improve and develop their systems. In this paper, we present upgrades of our system which enabled our team NimbRo to win the Soccer Tournament, the Drop-in Games, and the Technical Challenges in the Humanoid AdultSize League of RoboCup 2022. Strong performance in these competitions resulted in the Best Humanoid award in the Humanoid League. The mentioned upgrades include: hardware upgrade of the vision module, balanced walking with Capture Steps, and the introduction of phase-based in-walk kicks.
[ { "version": "v1", "created": "Mon, 6 Feb 2023 17:38:46 GMT" }, { "version": "v2", "created": "Tue, 7 Feb 2023 23:22:20 GMT" } ]
2023-02-09T00:00:00
[ [ "Pavlichenko", "Dmytro", "" ], [ "Ficht", "Grzegorz", "" ], [ "Amini", "Arash", "" ], [ "Hosseini", "Mojtaba", "" ], [ "Memmesheimer", "Raphael", "" ], [ "Villar-Corrales", "Angel", "" ], [ "Schulz", "Stefan M.", "" ], [ "Missura", "Marcell", "" ], [ "Bennewitz", "Maren", "" ], [ "Behnke", "Sven", "" ] ]
new_dataset
0.997856
2302.03126
Sanad Malaysha
Sanad Malaysha, Mustafa Jarrar, Mohammed Khalilia
Context-Gloss Augmentation for Improving Arabic Target Sense Verification
null
The 12th International Global Wordnet Conference (GWC2023), Global Wordnet Association. (pp. ). San Sebastian, Spain, 2023
null
null
cs.CL
http://creativecommons.org/licenses/by-sa/4.0/
Arabic language lacks semantic datasets and sense inventories. The most common semantically-labeled dataset for Arabic is the ArabGlossBERT, a relatively small dataset that consists of 167K context-gloss pairs (about 60K positive and 107K negative pairs), collected from Arabic dictionaries. This paper presents an enrichment to the ArabGlossBERT dataset, by augmenting it using (Arabic-English-Arabic) machine back-translation. Augmentation increased the dataset size to 352K pairs (149K positive and 203K negative pairs). We measure the impact of augmentation using different data configurations to fine-tune BERT on target sense verification (TSV) task. Overall, the accuracy ranges between 78% to 84% for different data configurations. Although our approach performed at par with the baseline, we did observe some improvements for some POS tags in some experiments. Furthermore, our fine-tuned models are trained on a larger dataset covering larger vocabulary and contexts. We provide an in-depth analysis of the accuracy for each part-of-speech (POS).
[ { "version": "v1", "created": "Mon, 6 Feb 2023 21:24:02 GMT" } ]
2023-02-09T00:00:00
[ [ "Malaysha", "Sanad", "" ], [ "Jarrar", "Mustafa", "" ], [ "Khalilia", "Mohammed", "" ] ]
new_dataset
0.999684
2302.03778
Rachel Horne Ms
Rachel Horne, Tom Putland, Mark Brady
Regulating trusted autonomous systems in Australia
12 pages
null
null
null
cs.CY cs.ET
http://creativecommons.org/licenses/by-nc-nd/4.0/
Australia is a leader in autonomous systems technology, particularly in the mining industry, borne from necessity in a geographically dispersed and complex natural environment. Increasingly advanced autonomous systems are becoming more prevalent in Australia, particularly as the safety, environmental and efficiency benefits become better understood, and the increasing sophistication of technology improves capability and availability. Increasing use of these systems, including in the maritime domain and air domain, is placing pressure on the national safety regulators, who must either continue to apply their traditional regulatory approach requiring exemptions to enable operation of emerging technology, or seize the opportunity to put in place an agile and adaptive approach better suited to the rapid developments of the twenty first century. In Australia the key national safety regulators have demonstrated an appetite for working with industry to facilitate innovation, but their limited resources mean progress is slow. There is a critical role to be played by third parties from industry, government, and academia who can work together to develop, test and publish new assurance and accreditation frameworks for trusted autonomous systems, and assist in the transition to an adaptive and agile regulatory philosophy. This is necessary to ensure the benefits of autonomous systems can be realised, without compromising safety. This paper will identify the growing use cases for autonomous systems in Australia, in the maritime, air and land domains, assess the current regulatory framework, argue that Australia's regulatory approach needs to become more agile and anticipatory, and investigate how third party projects could positively impact the assurance and accreditation process for autonomous systems in the future.
[ { "version": "v1", "created": "Tue, 7 Feb 2023 22:26:17 GMT" } ]
2023-02-09T00:00:00
[ [ "Horne", "Rachel", "" ], [ "Putland", "Tom", "" ], [ "Brady", "Mark", "" ] ]
new_dataset
0.992978
2302.03815
Shuaiqi Liu
Shuaiqi Liu, Jiannong Cao, Ruosong Yang, Zhiyuan Wen
Long Text and Multi-Table Summarization: Dataset and Method
EMNLP 2022 Findings
null
null
null
cs.CL cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Automatic document summarization aims to produce a concise summary covering the input document's salient information. Within a report document, the salient information can be scattered in the textual and non-textual content. However, existing document summarization datasets and methods usually focus on the text and filter out the non-textual content. Missing tabular data can limit produced summaries' informativeness, especially when summaries require covering quantitative descriptions of critical metrics in tables. Existing datasets and methods cannot meet the requirements of summarizing long text and multiple tables in each report. To deal with the scarcity of available data, we propose FINDSum, the first large-scale dataset for long text and multi-table summarization. Built on 21,125 annual reports from 3,794 companies, it has two subsets for summarizing each company's results of operations and liquidity. To summarize the long text and dozens of tables in each report, we present three types of summarization methods. Besides, we propose a set of evaluation metrics to assess the usage of numerical information in produced summaries. Dataset analyses and experimental results indicate the importance of jointly considering input textual and tabular data when summarizing report documents.
[ { "version": "v1", "created": "Wed, 8 Feb 2023 00:46:55 GMT" } ]
2023-02-09T00:00:00
[ [ "Liu", "Shuaiqi", "" ], [ "Cao", "Jiannong", "" ], [ "Yang", "Ruosong", "" ], [ "Wen", "Zhiyuan", "" ] ]
new_dataset
0.999249
2302.03820
Fan Yang
Fan Yang, Shigeyuki Odashima, Sosuke Yamao, Hiroaki Fujimoto, Shoichi Masui, and Shan Jiang
A Unified Multi-view Multi-person Tracking Framework
Accepted to Computational Visual Media
Computational Visual Media, 2023
null
null
cs.CV cs.AI
http://creativecommons.org/licenses/by/4.0/
Although there is a significant development in 3D Multi-view Multi-person Tracking (3D MM-Tracking), current 3D MM-Tracking frameworks are designed separately for footprint and pose tracking. Specifically, frameworks designed for footprint tracking cannot be utilized in 3D pose tracking, because they directly obtain 3D positions on the ground plane with a homography projection, which is inapplicable to 3D poses above the ground. In contrast, frameworks designed for pose tracking generally isolate multi-view and multi-frame associations and may not be robust to footprint tracking, since footprint tracking utilizes fewer key points than pose tracking, which weakens multi-view association cues in a single frame. This study presents a Unified Multi-view Multi-person Tracking framework to bridge the gap between footprint tracking and pose tracking. Without additional modifications, the framework can adopt monocular 2D bounding boxes and 2D poses as the input to produce robust 3D trajectories for multiple persons. Importantly, multi-frame and multi-view information are jointly employed to improve the performance of association and triangulation. The effectiveness of our framework is verified by accomplishing state-of-the-art performance on the Campus and Shelf datasets for 3D pose tracking, and by comparable results on the WILDTRACK and MMPTRACK datasets for 3D footprint tracking.
[ { "version": "v1", "created": "Wed, 8 Feb 2023 01:08:02 GMT" } ]
2023-02-09T00:00:00
[ [ "Yang", "Fan", "" ], [ "Odashima", "Shigeyuki", "" ], [ "Yamao", "Sosuke", "" ], [ "Fujimoto", "Hiroaki", "" ], [ "Masui", "Shoichi", "" ], [ "Jiang", "Shan", "" ] ]
new_dataset
0.993802
2302.03877
Mijanur Rahman
Md. Mijanur Rahman, Md Tanzinul Kabir Tonmoy, Saifur Rahman Shihab, Riya Farhana
Blockchain-based certificate authentication system with enabling correction
Under peer-review
null
null
null
cs.CR cs.NI
http://creativecommons.org/licenses/by/4.0/
Blockchain has proven to be an emerging technology in the digital world, changing the way everyone thinks about data security and bringing efficiency to several industries. It has already been applied to a wide range of applications, from financial services and supply chain management to voting systems and identity verification. An organization must verify its candidates before selecting them. Choosing an unqualified candidate can ruin an organization's reputation. In this digital era, many key fraudulent schemes are rampant in many companies and one of them is certificate fraud. It is possible to validate a candidate's qualifications using traditional methods, but there are drawbacks such as security issues and time consumption. In this paper, a blockchain-based academic certificate authentication system will be used to ensure authenticity and make the assertion of the decentralized system secure. However, the system will generate, authenticate and make corrections on academic certificates. Ultimately, some blockchain-based authentication systems already exist, they can't correct any errors that occur during generation. The proposed system will help in many ways, such as providing a user-friendly university admission, and smooth job hiring process, etc. In conclusion, our proposed system can permanently eradicate certificate forgeries and create and promote trust in society.
[ { "version": "v1", "created": "Wed, 8 Feb 2023 04:42:48 GMT" } ]
2023-02-09T00:00:00
[ [ "Rahman", "Md. Mijanur", "" ], [ "Tonmoy", "Md Tanzinul Kabir", "" ], [ "Shihab", "Saifur Rahman", "" ], [ "Farhana", "Riya", "" ] ]
new_dataset
0.99713
2302.03893
Shiyuan Sun
Shiyuan Sun, Weidong Mei, Fang Yang, Nan An, Jian Song, and Rui Zhang
Optical Intelligent Reflecting Surface Assisted MIMO VLC: Channel Modeling and Capacity Characterization
30 pages, 7 figures, 3 tables
null
null
null
cs.IT math.IT
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Although the multi-antenna or so-called multiple-input multiple-output (MIMO) transmission has been the enabling technology for the past generations of radio-frequency (RF)-based wireless communication systems, its application to the visible light communication (VLC) still faces a critical challenge as the MIMO spatial multiplexing gain can be hardly attained in VLC channels due to their strong spatial correlation. In this paper, we tackle this problem by deploying the optical intelligent reflecting surface (OIRS) in the environment to boost the capacity of MIMO VLC. Firstly, based on the extremely near-field channel condition in VLC, we propose a new channel model for OIRS-assisted MIMO VLC and reveal its peculiar ``no crosstalk'' property, where the OIRS reflecting elements can be respectively configured to align with one pair of transmitter and receiver antennas without causing crosstalk to each other. Next, we characterize the OIRS-assisted MIMO VLC capacities under different practical power constraints and then proceed to maximize them by jointly optimizing the OIRS element alignment and transmitter emission power. In particular, for optimizing the OIRS element alignment, we propose two algorithms, namely, location-aided interior-point algorithm and log-det-based alternating optimization algorithm, to balance the performance versus complexity trade-off; while the optimal transmitter emission power is derived in closed form. Numerical results are provided, which validate the capacity improvement of OIRS-assisted MIMO VLC against the VLC without OIRS and demonstrate the superior performance of the proposed algorithms compared to baseline schemes.
[ { "version": "v1", "created": "Wed, 8 Feb 2023 06:00:58 GMT" } ]
2023-02-09T00:00:00
[ [ "Sun", "Shiyuan", "" ], [ "Mei", "Weidong", "" ], [ "Yang", "Fang", "" ], [ "An", "Nan", "" ], [ "Song", "Jian", "" ], [ "Zhang", "Rui", "" ] ]
new_dataset
0.992415
2302.03905
Chengyue Jiang
Chengyue Jiang, Yong Jiang, Weiqi Wu, Yuting Zheng, Pengjun Xie, Kewei Tu
COMBO: A Complete Benchmark for Open KG Canonicalization
18 pages
null
null
null
cs.CL cs.AI
http://creativecommons.org/licenses/by/4.0/
Open knowledge graph (KG) consists of (subject, relation, object) triples extracted from millions of raw text. The subject and object noun phrases and the relation in open KG have severe redundancy and ambiguity and need to be canonicalized. Existing datasets for open KG canonicalization only provide gold entity-level canonicalization for noun phrases. In this paper, we present COMBO, a Complete Benchmark for Open KG canonicalization. Compared with existing datasets, we additionally provide gold canonicalization for relation phrases, gold ontology-level canonicalization for noun phrases, as well as source sentences from which triples are extracted. We also propose metrics for evaluating each type of canonicalization. On the COMBO dataset, we empirically compare previously proposed canonicalization methods as well as a few simple baseline methods based on pretrained language models. We find that properly encoding the phrases in a triple using pretrained language models results in better relation canonicalization and ontology-level canonicalization of the noun phrase. We release our dataset, baselines, and evaluation scripts at https://github.com/jeffchy/COMBO/tree/main.
[ { "version": "v1", "created": "Wed, 8 Feb 2023 06:46:01 GMT" } ]
2023-02-09T00:00:00
[ [ "Jiang", "Chengyue", "" ], [ "Jiang", "Yong", "" ], [ "Wu", "Weiqi", "" ], [ "Zheng", "Yuting", "" ], [ "Xie", "Pengjun", "" ], [ "Tu", "Kewei", "" ] ]
new_dataset
0.999353
2302.03914
Jiawei Liu
Jiawei Liu and Xingping Dong and Sanyuan Zhao and Jianbing Shen
Generalized Few-Shot 3D Object Detection of LiDAR Point Cloud for Autonomous Driving
null
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Recent years have witnessed huge successes in 3D object detection to recognize common objects for autonomous driving (e.g., vehicles and pedestrians). However, most methods rely heavily on a large amount of well-labeled training data. This limits their capability of detecting rare fine-grained objects (e.g., police cars and ambulances), which is important for special cases, such as emergency rescue, and so on. To achieve simultaneous detection for both common and rare objects, we propose a novel task, called generalized few-shot 3D object detection, where we have a large amount of training data for common (base) objects, but only a few data for rare (novel) classes. Specifically, we analyze in-depth differences between images and point clouds, and then present a practical principle for the few-shot setting in the 3D LiDAR dataset. To solve this task, we propose a simple and effective detection framework, including (1) an incremental fine-tuning method to extend existing 3D detection models to recognize both common and rare objects, and (2) a sample adaptive balance loss to alleviate the issue of long-tailed data distribution in autonomous driving scenarios. On the nuScenes dataset, we conduct sufficient experiments to demonstrate that our approach can successfully detect the rare (novel) classes that contain only a few training data, while also maintaining the detection accuracy of common objects.
[ { "version": "v1", "created": "Wed, 8 Feb 2023 07:11:36 GMT" } ]
2023-02-09T00:00:00
[ [ "Liu", "Jiawei", "" ], [ "Dong", "Xingping", "" ], [ "Zhao", "Sanyuan", "" ], [ "Shen", "Jianbing", "" ] ]
new_dataset
0.994821
2302.03924
Zhongxin Liu
Zhongxin Liu, Zhijie Tang, Xin Xia, Xiaohu Yang
CCRep: Learning Code Change Representations via Pre-Trained Code Model and Query Back
Accepted by ICSE 2023
null
null
null
cs.SE cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Representing code changes as numeric feature vectors, i.e., code change representations, is usually an essential step to automate many software engineering tasks related to code changes, e.g., commit message generation and just-in-time defect prediction. Intuitively, the quality of code change representations is crucial for the effectiveness of automated approaches. Prior work on code changes usually designs and evaluates code change representation approaches for a specific task, and little work has investigated code change encoders that can be used and jointly trained on various tasks. To fill this gap, this work proposes a novel Code Change Representation learning approach named CCRep, which can learn to encode code changes as feature vectors for diverse downstream tasks. Specifically, CCRep regards a code change as the combination of its before-change and after-change code, leverages a pre-trained code model to obtain high-quality contextual embeddings of code, and uses a novel mechanism named query back to extract and encode the changed code fragments and make them explicitly interact with the whole code change. To evaluate CCRep and demonstrate its applicability to diverse code-change-related tasks, we apply it to three tasks: commit message generation, patch correctness assessment, and just-in-time defect prediction. Experimental results show that CCRep outperforms the state-of-the-art techniques on each task.
[ { "version": "v1", "created": "Wed, 8 Feb 2023 07:43:55 GMT" } ]
2023-02-09T00:00:00
[ [ "Liu", "Zhongxin", "" ], [ "Tang", "Zhijie", "" ], [ "Xia", "Xin", "" ], [ "Yang", "Xiaohu", "" ] ]
new_dataset
0.972443
2302.03941
Sankarshan Damle
Sankarshan Damle, Vlasis Koutsos, Dimitrios Papadopoulos, Dimitris Chatzopoulos, Sujit Gujar
AVeCQ: Anonymous Verifiable Crowdsourcing with Worker Qualities
null
null
null
null
cs.CR
http://creativecommons.org/licenses/by/4.0/
In crowdsourcing systems, requesters publish tasks, and interested workers provide answers to get rewards. Worker anonymity motivates participation since it protects their privacy. Anonymity with unlinkability is an enhanced version of anonymity because it makes it impossible to ``link'' workers across the tasks they participate in. Another core feature of crowdsourcing systems is worker quality which expresses a worker's trustworthiness and quantifies their historical performance. Notably, worker quality depends on the participation history, revealing information about it, while unlinkability aims to disassociate the workers' identities from their past activity. In this work, we present AVeCQ, the first crowdsourcing system that reconciles these properties, achieving enhanced anonymity and verifiable worker quality updates. AVeCQ relies on a suite of cryptographic tools, such as zero-knowledge proofs, to (i) guarantee workers' privacy, (ii) prove the correctness of worker quality scores and task answers, and (iii) commensurate payments. AVeCQ is developed modularly, where the requesters and workers communicate over a platform that supports pseudonymity, information logging, and payments. In order to compare AVeCQ with the state-of-the-art, we prototype it over Ethereum. AVeCQ outperforms the state-of-the-art in three popular crowdsourcing tasks (image annotation, average review, and Gallup polls). For instance, for an Average Review task with $5$ choices and $128$ participating workers AVeCQ is 40\% faster (including overhead to compute and verify the necessary proofs and blockchain transaction processing time) with the task's requester consuming 87\% fewer gas units.
[ { "version": "v1", "created": "Wed, 8 Feb 2023 08:34:28 GMT" } ]
2023-02-09T00:00:00
[ [ "Damle", "Sankarshan", "" ], [ "Koutsos", "Vlasis", "" ], [ "Papadopoulos", "Dimitrios", "" ], [ "Chatzopoulos", "Dimitris", "" ], [ "Gujar", "Sujit", "" ] ]
new_dataset
0.95819
2302.03984
Andrew Adamatzky
Andrew Adamatzky, Giuseppe Tarabella, Neil Phillips, Alessandro Chiolerio, Passquale D'Angelo, Anna Nicolaidou, Georgios Ch. Sirakoulis
Kombucha electronics
null
null
null
null
cs.ET
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
A kombucha is a tea and sugar fermented by over sixty kinds of yeasts and bacteria. This symbiotic community produces kombucha mats, which are cellulose-based hydrogels. The kombucha mats can be used as an alternative to animal leather in industry and fashion once they have been dried and cured. Prior to this study, we demonstrated that living kombucha mats display dynamic electrical activity and distinct stimulating responses. For use in organic textiles, cured mats of kombucha are inert. To make kombucha wearables functional, it is necessary to incorporate electrical circuits. We demonstrate that creating electrical conductors on kombucha mats is possible. After repeated bending and stretching, the circuits maintain their functionality. In addition, the abilities and electronic properties of the proposed kombucha, such as being lighter, less expensive, and more flexible than conventional electronic systems, pave the way for their use in a diverse range of applications.
[ { "version": "v1", "created": "Wed, 8 Feb 2023 10:48:42 GMT" } ]
2023-02-09T00:00:00
[ [ "Adamatzky", "Andrew", "" ], [ "Tarabella", "Giuseppe", "" ], [ "Phillips", "Neil", "" ], [ "Chiolerio", "Alessandro", "" ], [ "D'Angelo", "Passquale", "" ], [ "Nicolaidou", "Anna", "" ], [ "Sirakoulis", "Georgios Ch.", "" ] ]
new_dataset
0.999564
2302.03997
Yuan Cao
Yuan Cao, Xudong Zhang, Fan Zhang, Feifei Kou, Josiah Poon, Xiongnan Jin, Yongheng Wang and Jinpeng Chen
SimCGNN: Simple Contrastive Graph Neural Network for Session-based Recommendation
null
null
null
null
cs.IR cs.AI
http://creativecommons.org/licenses/by/4.0/
Session-based recommendation (SBR) problem, which focuses on next-item prediction for anonymous users, has received increasingly more attention from researchers. Existing graph-based SBR methods all lack the ability to differentiate between sessions with the same last item, and suffer from severe popularity bias. Inspired by nowadays emerging contrastive learning methods, this paper presents a Simple Contrastive Graph Neural Network for Session-based Recommendation (SimCGNN). In SimCGNN, we first obtain normalized session embeddings on constructed session graphs. We next construct positive and negative samples of the sessions by two forward propagation and a novel negative sample selection strategy, and then calculate the constructive loss. Finally, session embeddings are used to give prediction. Extensive experiments conducted on two real-word datasets show our SimCGNN achieves a significant improvement over state-of-the-art methods.
[ { "version": "v1", "created": "Wed, 8 Feb 2023 11:13:22 GMT" } ]
2023-02-09T00:00:00
[ [ "Cao", "Yuan", "" ], [ "Zhang", "Xudong", "" ], [ "Zhang", "Fan", "" ], [ "Kou", "Feifei", "" ], [ "Poon", "Josiah", "" ], [ "Jin", "Xiongnan", "" ], [ "Wang", "Yongheng", "" ], [ "Chen", "Jinpeng", "" ] ]
new_dataset
0.996042
2302.04038
Andr\'e Panisson
Arthur Capozzi, Gianmarco De Francisci Morales, Yelena Mejova, Corrado Monti, Andr\'e Panisson
The Thin Ideology of Populist Advertising on Facebook during the 2019 EU Elections
null
In Proceedings of the ACM Web Conference 2023 (WWW '23), May 1-5, 2023, Austin, TX, USA. ACM, New York, NY, USA, 11 pages
10.1145/3543507.3583267
null
cs.SI cs.CY
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Social media has been an important tool in the expansion of the populist message, and it is thought to have contributed to the electoral success of populist parties in the past decade. This study compares how populist parties advertised on Facebook during the 2019 European Parliamentary election. In particular, we examine commonalities and differences in which audiences they reach and on which issues they focus. By using data from Meta (previously Facebook) Ad Library, we analyze 45k ad campaigns by 39 parties, both populist and mainstream, in Germany, United Kingdom, Italy, Spain, and Poland. While populist parties represent just over 20% of the total expenditure on political ads, they account for 40% of the total impressions$\unicode{x2013}$most of which from Eurosceptic and far-right parties$\unicode{x2013}$thus hinting at a competitive advantage for populist parties on Facebook. We further find that ads posted by populist parties are more likely to reach male audiences, and sometimes much older ones. In terms of issues, populist politicians focus on monetary policy, state bureaucracy and reforms, and security, while the focus on EU and Brexit is on par with non-populist, mainstream parties. However, issue preferences are largely country-specific, thus supporting the view in political science that populism is a "thin ideology", that does not have a universal, coherent policy agenda. This study illustrates the usefulness of publicly available advertising data for monitoring the populist outreach to, and engagement with, millions of potential voters, while outlining the limitations of currently available data.
[ { "version": "v1", "created": "Wed, 8 Feb 2023 13:22:11 GMT" } ]
2023-02-09T00:00:00
[ [ "Capozzi", "Arthur", "" ], [ "Morales", "Gianmarco De Francisci", "" ], [ "Mejova", "Yelena", "" ], [ "Monti", "Corrado", "" ], [ "Panisson", "André", "" ] ]
new_dataset
0.99659
2302.04156
Rui Cao
Rui Cao, Roy Ka-Wei Lee, Wen-Haw Chong, Jing Jiang
Prompting for Multimodal Hateful Meme Classification
Accepted in EMNLP, 2022
null
null
null
cs.CL cs.IR cs.MM
http://creativecommons.org/licenses/by/4.0/
Hateful meme classification is a challenging multimodal task that requires complex reasoning and contextual background knowledge. Ideally, we could leverage an explicit external knowledge base to supplement contextual and cultural information in hateful memes. However, there is no known explicit external knowledge base that could provide such hate speech contextual information. To address this gap, we propose PromptHate, a simple yet effective prompt-based model that prompts pre-trained language models (PLMs) for hateful meme classification. Specifically, we construct simple prompts and provide a few in-context examples to exploit the implicit knowledge in the pre-trained RoBERTa language model for hateful meme classification. We conduct extensive experiments on two publicly available hateful and offensive meme datasets. Our experimental results show that PromptHate is able to achieve a high AUC of 90.96, outperforming state-of-the-art baselines on the hateful meme classification task. We also perform fine-grained analyses and case studies on various prompt settings and demonstrate the effectiveness of the prompts on hateful meme classification.
[ { "version": "v1", "created": "Wed, 8 Feb 2023 16:04:08 GMT" } ]
2023-02-09T00:00:00
[ [ "Cao", "Rui", "" ], [ "Lee", "Roy Ka-Wei", "" ], [ "Chong", "Wen-Haw", "" ], [ "Jiang", "Jing", "" ] ]
new_dataset
0.995637
2011.08340
Manish Motwani
Manish Motwani and Yuriy Brun
Better Automatic Program Repair by Using Bug Reports and Tests Together
accepted in ICSE'23 technical track
null
null
null
cs.SE
http://creativecommons.org/licenses/by/4.0/
Automated program repair is already deployed in industry, but concerns remain about repair quality. Recent research has shown that one of the main reasons repair tools produce incorrect (but seemingly correct) patches is imperfect fault localization (FL). This paper demonstrates that combining information from natural-language bug reports and test executions when localizing faults can have a significant positive impact on repair quality. For example, existing repair tools with such FL are able to correctly repair 7 defects in the Defects4J benchmark that no prior tools have repaired correctly. We develop, Blues, the first information-retrieval-based, statement-level FL technique that requires no training data. We further develop RAFL, the first unsupervised method for combining multiple FL techniques, which outperforms a supervised method. Using RAFL, we create SBIR by combining Blues with a spectrum-based (SBFL) technique. Evaluated on 815 real-world defects, SBIR consistently ranks buggy statements higher than its underlying techniques. We then modify three state-of-the-art repair tools, Arja, SequenceR, and SimFix, to use SBIR, SBFL, and Blues as their internal FL. We evaluate the quality of the produced patches on 689 real-world defects. Arja and SequenceR significantly benefit from SBIR: Arja using SBIR correctly repairs 28 defects, but only 21 using SBFL, and only 15 using Blues; SequenceR using SBIR correctly repairs 12 defects, but only 10 using SBFL, and only 4 using Blues. SimFix, (which has internal mechanisms to overcome poor FL), correctly repairs 30 defects using SBIR and SBFL, but only 13 using Blues. Our work is the first investigation of simultaneously using multiple software artifacts for automated program repair, and our promising findings suggest future research in this directions is likely to be fruitful.
[ { "version": "v1", "created": "Mon, 16 Nov 2020 23:51:42 GMT" }, { "version": "v2", "created": "Wed, 18 Nov 2020 02:19:30 GMT" }, { "version": "v3", "created": "Tue, 15 Mar 2022 14:58:47 GMT" }, { "version": "v4", "created": "Mon, 6 Feb 2023 20:42:43 GMT" } ]
2023-02-08T00:00:00
[ [ "Motwani", "Manish", "" ], [ "Brun", "Yuriy", "" ] ]
new_dataset
0.952139
2111.12924
Shichao Li
Shichao Li and Kwang-Ting Cheng
Joint stereo 3D object detection and implicit surface reconstruction
null
null
null
null
cs.CV cs.GR cs.RO
http://creativecommons.org/licenses/by/4.0/
We present a new learning-based framework S-3D-RCNN that can recover accurate object orientation in SO(3) and simultaneously predict implicit shapes for outdoor rigid objects from stereo RGB images. In contrast to previous studies that map local appearance to observation angles, we explore a progressive approach by extracting meaningful Intermediate Geometrical Representations (IGRs) to estimate egocentric object orientation. This approach features a deep model that transforms perceived intensities to object part coordinates, which are mapped to a 3D representation encoding object orientation in the camera coordinate system. To enable implicit shape estimation, the IGRs are further extended to model visible object surface with a point-based representation and explicitly addresses the unseen surface hallucination problem. Extensive experiments validate the effectiveness of the proposed IGRs and S-3D-RCNN achieves superior 3D scene understanding performance using existing and proposed new metrics on the KITTI benchmark. Code and pre-trained models will be available at this https URL.
[ { "version": "v1", "created": "Thu, 25 Nov 2021 05:52:30 GMT" }, { "version": "v2", "created": "Mon, 18 Jul 2022 11:58:27 GMT" }, { "version": "v3", "created": "Tue, 7 Feb 2023 05:53:41 GMT" } ]
2023-02-08T00:00:00
[ [ "Li", "Shichao", "" ], [ "Cheng", "Kwang-Ting", "" ] ]
new_dataset
0.951757
2201.09463
Zhengwei Bai
Zhengwei Bai, Guoyuan Wu, Xuewei Qi, Yongkang Liu, Kentaro Oguchi, Matthew J. Barth
Cyber Mobility Mirror for Enabling Cooperative Driving Automation in Mixed Traffic: A Co-Simulation Platform
Accepted by the IEEE Intelligent Transportation Systems Magazine
IEEE Intelligent Transportation Systems Magazine 2022
10.1109/MITS.2022.3203662
null
cs.SE cs.CV
http://creativecommons.org/licenses/by-nc-nd/4.0/
Endowed with automation and connectivity, Connected and Automated Vehicles are meant to be a revolutionary promoter for Cooperative Driving Automation. Nevertheless, CAVs need high-fidelity perception information on their surroundings, which is available but costly to collect from various onboard sensors as well as vehicle-to-everything (V2X) communications. Therefore, authentic perception information based on high-fidelity sensors via a cost-effective platform is crucial for enabling CDA-related research, e.g., cooperative decision-making or control. Most state-of-the-art traffic simulation studies for CAVs rely on situation-awareness information by directly calling on intrinsic attributes of the objects, which impedes the reliability and fidelity of the assessment of CDA algorithms. In this study, a \textit{Cyber Mobility Mirror (CMM)} Co-Simulation Platform is designed for enabling CDA by providing authentic perception information. The \textit{CMM} Co-Simulation Platform can emulate the real world with a high-fidelity sensor perception system and a cyber world with a real-time rebuilding system acting as a "\textit{Mirror}" of the real-world environment. Concretely, the real-world simulator is mainly in charge of simulating the traffic environment, sensors, as well as the authentic perception process. The mirror-world simulator is responsible for rebuilding objects and providing their information as intrinsic attributes of the simulator to support the development and evaluation of CDA algorithms. To illustrate the functionality of the proposed co-simulation platform, a roadside LiDAR-based vehicle perception system for enabling CDA is prototyped as a study case. Specific traffic environments and CDA tasks are designed for experiments whose results are demonstrated and analyzed to show the performance of the platform.
[ { "version": "v1", "created": "Mon, 24 Jan 2022 05:27:20 GMT" }, { "version": "v2", "created": "Tue, 30 Aug 2022 22:52:25 GMT" } ]
2023-02-08T00:00:00
[ [ "Bai", "Zhengwei", "" ], [ "Wu", "Guoyuan", "" ], [ "Qi", "Xuewei", "" ], [ "Liu", "Yongkang", "" ], [ "Oguchi", "Kentaro", "" ], [ "Barth", "Matthew J.", "" ] ]
new_dataset
0.980608
2201.10881
Pablo Ortiz
Per Erik Solberg and Pablo Ortiz
The Norwegian Parliamentary Speech Corpus
6 pages, submitted to LREC 2022
LREC 2022
null
null
cs.CL cs.SD eess.AS
http://creativecommons.org/licenses/by/4.0/
The Norwegian Parliamentary Speech Corpus (NPSC) is a speech dataset with recordings of meetings from Stortinget, the Norwegian parliament. It is the first, publicly available dataset containing unscripted, Norwegian speech designed for training of automatic speech recognition (ASR) systems. The recordings are manually transcribed and annotated with language codes and speakers, and there are detailed metadata about the speakers. The transcriptions exist in both normalized and non-normalized form, and non-standardized words are explicitly marked and annotated with standardized equivalents. To test the usefulness of this dataset, we have compared an ASR system trained on the NPSC with a baseline system trained on only manuscript-read speech. These systems were tested on an independent dataset containing spontaneous, dialectal speech. The NPSC-trained system performed significantly better, with a 22.9% relative improvement in word error rate (WER). Moreover, training on the NPSC is shown to have a "democratizing" effect in terms of dialects, as improvements are generally larger for dialects with higher WER from the baseline system.
[ { "version": "v1", "created": "Wed, 26 Jan 2022 11:41:55 GMT" } ]
2023-02-08T00:00:00
[ [ "Solberg", "Per Erik", "" ], [ "Ortiz", "Pablo", "" ] ]
new_dataset
0.999766
2203.06319
Zhengwei Bai
Zhengwei Bai, Guoyuan Wu, Matthew J. Barth, Yongkang Liu, Emrah Akin Sisbot, Kentaro Oguchi
PillarGrid: Deep Learning-based Cooperative Perception for 3D Object Detection from Onboard-Roadside LiDAR
Submitted to The 25th IEEE International Conference on Intelligent Transportation Systems (IEEE ITSC 2022)
null
10.1109/ITSC55140.2022.9921947
null
cs.CV cs.AI
http://creativecommons.org/licenses/by-nc-nd/4.0/
3D object detection plays a fundamental role in enabling autonomous driving, which is regarded as the significant key to unlocking the bottleneck of contemporary transportation systems from the perspectives of safety, mobility, and sustainability. Most of the state-of-the-art (SOTA) object detection methods from point clouds are developed based on a single onboard LiDAR, whose performance will be inevitably limited by the range and occlusion, especially in dense traffic scenarios. In this paper, we propose \textit{PillarGrid}, a novel cooperative perception method fusing information from multiple 3D LiDARs (both on-board and roadside), to enhance the situation awareness for connected and automated vehicles (CAVs). PillarGrid consists of four main phases: 1) cooperative preprocessing of point clouds, 2) pillar-wise voxelization and feature extraction, 3) grid-wise deep fusion of features from multiple sensors, and 4) convolutional neural network (CNN)-based augmented 3D object detection. A novel cooperative perception platform is developed for model training and testing. Extensive experimentation shows that PillarGrid outperforms the SOTA single-LiDAR-based 3D object detection methods with respect to both accuracy and range by a large margin.
[ { "version": "v1", "created": "Sat, 12 Mar 2022 02:28:41 GMT" }, { "version": "v2", "created": "Tue, 15 Mar 2022 03:30:02 GMT" }, { "version": "v3", "created": "Sat, 19 Mar 2022 22:58:55 GMT" } ]
2023-02-08T00:00:00
[ [ "Bai", "Zhengwei", "" ], [ "Wu", "Guoyuan", "" ], [ "Barth", "Matthew J.", "" ], [ "Liu", "Yongkang", "" ], [ "Sisbot", "Emrah Akin", "" ], [ "Oguchi", "Kentaro", "" ] ]
new_dataset
0.996481
2203.08388
Zhiruo Wang
Zhiruo Wang, Grace Cuenca, Shuyan Zhou, Frank F. Xu, Graham Neubig
MCoNaLa: A Benchmark for Code Generation from Multiple Natural Languages
null
null
null
null
cs.CL
http://creativecommons.org/licenses/by-sa/4.0/
While there has been a recent burgeoning of applications at the intersection of natural and programming languages, such as code generation and code summarization, these applications are usually English-centric. This creates a barrier for program developers who are not proficient in English. To mitigate this gap in technology development across languages, we propose a multilingual dataset, MCoNaLa, to benchmark code generation from natural language commands extending beyond English. Modeled off of the methodology from the English Code/Natural Language Challenge (CoNaLa) dataset, we annotated a total of 896 NL-code pairs in three languages: Spanish, Japanese, and Russian. We present a quantitative evaluation of performance on the MCoNaLa dataset by testing with state-of-the-art code generation systems. While the difficulties vary across these three languages, all systems lag significantly behind their English counterparts, revealing the challenges in adapting code generation to new languages.
[ { "version": "v1", "created": "Wed, 16 Mar 2022 04:21:50 GMT" }, { "version": "v2", "created": "Tue, 7 Feb 2023 03:12:50 GMT" } ]
2023-02-08T00:00:00
[ [ "Wang", "Zhiruo", "" ], [ "Cuenca", "Grace", "" ], [ "Zhou", "Shuyan", "" ], [ "Xu", "Frank F.", "" ], [ "Neubig", "Graham", "" ] ]
new_dataset
0.999364
2205.07030
Koray Kavakl{\i}
Koray Kavakl{\i}, Yuta Itoh, Hakan Urey, Kaan Ak\c{s}it
Realistic Defocus Blur for Multiplane Computer-Generated Holography
16 pages in total, first 9 pages are for the manuscript, remaining pages are for supplementary. For more visit: https://complightlab.com/publications/realistic_defocus_cgh For our codebase visit https://github.com/complight/realistic_defocus
null
null
null
cs.CV cs.GR
http://creativecommons.org/licenses/by-nc-nd/4.0/
This paper introduces a new multiplane CGH computation method to reconstruct artefact-free high-quality holograms with natural-looking defocus blur. Our method introduces a new targeting scheme and a new loss function. While the targeting scheme accounts for defocused parts of the scene at each depth plane, the new loss function analyzes focused and defocused parts separately in reconstructed images. Our method support phase-only CGH calculations using various iterative (e.g., Gerchberg-Saxton, Gradient Descent) and non-iterative (e.g., Double Phase) CGH techniques. We achieve our best image quality using a modified gradient descent-based optimization recipe where we introduce a constraint inspired by the double phase method. We validate our method experimentally using our proof-of-concept holographic display, comparing various algorithms, including multi-depth scenes with sparse and dense contents.
[ { "version": "v1", "created": "Sat, 14 May 2022 10:17:34 GMT" }, { "version": "v2", "created": "Mon, 6 Feb 2023 21:06:15 GMT" } ]
2023-02-08T00:00:00
[ [ "Kavaklı", "Koray", "" ], [ "Itoh", "Yuta", "" ], [ "Urey", "Hakan", "" ], [ "Akşit", "Kaan", "" ] ]
new_dataset
0.966711
2207.07253
Jingjing Wu
Jingjing Wu, Pengyuan Lyu, Guangming Lu, Chengquan Zhang, and Wenjie Pei
Single Shot Self-Reliant Scene Text Spotter by Decoupled yet Collaborative Detection and Recognition
null
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Typical text spotters follow the two-stage spotting paradigm which detects the boundary for a text instance first and then performs text recognition within the detected regions. Despite the remarkable progress of such spotting paradigm, an important limitation is that the performance of text recognition depends heavily on the precision of text detection, resulting in the potential error propagation from detection to recognition. In this work, we propose the single shot Self-Reliant Scene Text Spotter v2 (SRSTS v2), which circumvents this limitation by decoupling recognition from detection while optimizing two tasks collaboratively. Specifically, our SRSTS v2 samples representative feature points around each potential text instance, and conducts both text detection and recognition in parallel guided by these sampled points. Thus, the text recognition is no longer dependent on detection, thereby alleviating the error propagation from detection to recognition. Moreover, the sampling module is learned under the supervision from both detection and recognition, which allows for the collaborative optimization and mutual enhancement between two tasks. Benefiting from such sampling-driven concurrent spotting framework, our approach is able to recognize the text instances correctly even if the precise text boundaries are challenging to detect. Extensive experiments on four benchmarks demonstrate that our method compares favorably to state-of-the-art spotters.
[ { "version": "v1", "created": "Fri, 15 Jul 2022 01:59:14 GMT" }, { "version": "v2", "created": "Mon, 18 Jul 2022 11:38:34 GMT" }, { "version": "v3", "created": "Mon, 6 Feb 2023 04:06:44 GMT" }, { "version": "v4", "created": "Tue, 7 Feb 2023 08:41:17 GMT" } ]
2023-02-08T00:00:00
[ [ "Wu", "Jingjing", "" ], [ "Lyu", "Pengyuan", "" ], [ "Lu", "Guangming", "" ], [ "Zhang", "Chengquan", "" ], [ "Pei", "Wenjie", "" ] ]
new_dataset
0.965557
2209.08803
Jeongeun Park
Jeongeun Park, Taerim Yoon, Jejoon Hong, Youngjae Yu, Matthew Pan, and Sungjoon Choi
Zero-shot Active Visual Search (ZAVIS): Intelligent Object Search for Robotic Assistants
To be appear on ICRA 2023
null
null
null
cs.RO cs.CV
http://creativecommons.org/licenses/by/4.0/
In this paper, we focus on the problem of efficiently locating a target object described with free-form language using a mobile robot equipped with vision sensors (e.g., an RGBD camera). Conventional active visual search predefines a set of objects to search for, rendering these techniques restrictive in practice. To provide added flexibility in active visual searching, we propose a system where a user can enter target commands using free-form language; we call this system Active Visual Search in the Wild (AVSW). AVSW detects and plans to search for a target object inputted by a user through a semantic grid map represented by static landmarks (e.g., desk or bed). For efficient planning of object search patterns, AVSW considers commonsense knowledge-based co-occurrence and predictive uncertainty while deciding which landmarks to visit first. We validate the proposed method with respect to SR (success rate) and SPL (success weighted by path length) in both simulated and real-world environments. The proposed method outperforms previous methods in terms of SPL in simulated scenarios with an average gap of 0.283. We further demonstrate AVSW with a Pioneer-3AT robot in real-world studies.
[ { "version": "v1", "created": "Mon, 19 Sep 2022 07:18:46 GMT" }, { "version": "v2", "created": "Tue, 20 Sep 2022 09:55:31 GMT" }, { "version": "v3", "created": "Tue, 7 Feb 2023 15:46:11 GMT" } ]
2023-02-08T00:00:00
[ [ "Park", "Jeongeun", "" ], [ "Yoon", "Taerim", "" ], [ "Hong", "Jejoon", "" ], [ "Yu", "Youngjae", "" ], [ "Pan", "Matthew", "" ], [ "Choi", "Sungjoon", "" ] ]
new_dataset
0.996214
2209.13756
Tianhao Wu
Tianhao Wu, Boyang Li, Yihang Luo, Yingqian Wang, Chao Xiao, Ting Liu, Jungang Yang, Wei An, Yulan Guo
MTU-Net: Multi-level TransUNet for Space-based Infrared Tiny Ship Detection
null
null
10.1109/TGRS.2023.3235002
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Space-based infrared tiny ship detection aims at separating tiny ships from the images captured by earth orbiting satellites. Due to the extremely large image coverage area (e.g., thousands square kilometers), candidate targets in these images are much smaller, dimer, more changeable than those targets observed by aerial-based and land-based imaging devices. Existing short imaging distance-based infrared datasets and target detection methods cannot be well adopted to the space-based surveillance task. To address these problems, we develop a space-based infrared tiny ship detection dataset (namely, NUDT-SIRST-Sea) with 48 space-based infrared images and 17598 pixel-level tiny ship annotations. Each image covers about 10000 square kilometers of area with 10000X10000 pixels. Considering the extreme characteristics (e.g., small, dim, changeable) of those tiny ships in such challenging scenes, we propose a multi-level TransUNet (MTU-Net) in this paper. Specifically, we design a Vision Transformer (ViT) Convolutional Neural Network (CNN) hybrid encoder to extract multi-level features. Local feature maps are first extracted by several convolution layers and then fed into the multi-level feature extraction module (MVTM) to capture long-distance dependency. We further propose a copy-rotate-resize-paste (CRRP) data augmentation approach to accelerate the training phase, which effectively alleviates the issue of sample imbalance between targets and background. Besides, we design a FocalIoU loss to achieve both target localization and shape description. Experimental results on the NUDT-SIRST-Sea dataset show that our MTU-Net outperforms traditional and existing deep learning based SIRST methods in terms of probability of detection, false alarm rate and intersection over union.
[ { "version": "v1", "created": "Wed, 28 Sep 2022 00:48:14 GMT" } ]
2023-02-08T00:00:00
[ [ "Wu", "Tianhao", "" ], [ "Li", "Boyang", "" ], [ "Luo", "Yihang", "" ], [ "Wang", "Yingqian", "" ], [ "Xiao", "Chao", "" ], [ "Liu", "Ting", "" ], [ "Yang", "Jungang", "" ], [ "An", "Wei", "" ], [ "Guo", "Yulan", "" ] ]
new_dataset
0.999759
2210.02517
Zulfiqar Zaidi
Zulfiqar Zaidi, Daniel Martin, Nathaniel Belles, Viacheslav Zakharov, Arjun Krishna, Kin Man Lee, Peter Wagstaff, Sumedh Naik, Matthew Sklar, Sugju Choi, Yoshiki Kakehi, Ruturaj Patil, Divya Mallemadugula, Florian Pesce, Peter Wilson, Wendell Hom, Matan Diamond, Bryan Zhao, Nina Moorman, Rohan Paleja, Letian Chen, Esmaeil Seraj, Matthew Gombolay
Athletic Mobile Manipulator System for Robotic Wheelchair Tennis
8 pages, accepted at RA-L, will also be presented at IROS 2023
null
null
null
cs.RO
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Athletics are a quintessential and universal expression of humanity. From French monks who in the 12th century invented jeu de paume, the precursor to modern lawn tennis, back to the K'iche' people who played the Maya Ballgame as a form of religious expression over three thousand years ago, humans have sought to train their minds and bodies to excel in sporting contests. Advances in robotics are opening up the possibility of robots in sports. Yet, key challenges remain, as most prior works in robotics for sports are limited to pristine sensing environments, do not require significant force generation, or are on miniaturized scales unsuited for joint human-robot play. In this paper, we propose the first open-source, autonomous robot for playing regulation wheelchair tennis. We demonstrate the performance of our full-stack system in executing ground strokes and evaluate each of the system's hardware and software components. The goal of this paper is to (1) inspire more research in human-scale robot athletics and (2) establish the first baseline for a reproducible wheelchair tennis robot for regulation singles play. Our paper contributes to the science of systems design and poses a set of key challenges for the robotics community to address in striving towards robots that can match human capabilities in sports.
[ { "version": "v1", "created": "Wed, 5 Oct 2022 19:25:41 GMT" }, { "version": "v2", "created": "Tue, 7 Feb 2023 17:41:53 GMT" } ]
2023-02-08T00:00:00
[ [ "Zaidi", "Zulfiqar", "" ], [ "Martin", "Daniel", "" ], [ "Belles", "Nathaniel", "" ], [ "Zakharov", "Viacheslav", "" ], [ "Krishna", "Arjun", "" ], [ "Lee", "Kin Man", "" ], [ "Wagstaff", "Peter", "" ], [ "Naik", "Sumedh", "" ], [ "Sklar", "Matthew", "" ], [ "Choi", "Sugju", "" ], [ "Kakehi", "Yoshiki", "" ], [ "Patil", "Ruturaj", "" ], [ "Mallemadugula", "Divya", "" ], [ "Pesce", "Florian", "" ], [ "Wilson", "Peter", "" ], [ "Hom", "Wendell", "" ], [ "Diamond", "Matan", "" ], [ "Zhao", "Bryan", "" ], [ "Moorman", "Nina", "" ], [ "Paleja", "Rohan", "" ], [ "Chen", "Letian", "" ], [ "Seraj", "Esmaeil", "" ], [ "Gombolay", "Matthew", "" ] ]
new_dataset
0.998876
2210.05406
Sean Moran
Lili Tao, Alexandru-Petre Cazan, Senad Ibraimoski, Sean Moran
Code Librarian: A Software Package Recommendation System
null
null
null
null
cs.SE cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The use of packaged libraries can significantly shorten the software development cycle by improving the quality and readability of code. In this paper, we present a recommendation engine called Librarian for open source libraries. A candidate library package is recommended for a given context if: 1) it has been frequently used with the imported libraries in the program; 2) it has similar functionality to the imported libraries in the program; 3) it has similar functionality to the developer's implementation, and 4) it can be used efficiently in the context of the provided code. We apply the state-of-the-art CodeBERT-based model for analysing the context of the source code to deliver relevant library recommendations to users.
[ { "version": "v1", "created": "Tue, 11 Oct 2022 12:30:05 GMT" }, { "version": "v2", "created": "Tue, 7 Feb 2023 17:51:12 GMT" } ]
2023-02-08T00:00:00
[ [ "Tao", "Lili", "" ], [ "Cazan", "Alexandru-Petre", "" ], [ "Ibraimoski", "Senad", "" ], [ "Moran", "Sean", "" ] ]
new_dataset
0.999563
2210.07598
Hanlin Wu
Hanlin Wu, Ning Ni, Libao Zhang
Lightweight Stepless Super-Resolution of Remote Sensing Images via Saliency-Aware Dynamic Routing Strategy
null
null
10.1109/TGRS.2023.3236624
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Deep learning-based algorithms have greatly improved the performance of remote sensing image (RSI) super-resolution (SR). However, increasing network depth and parameters cause a huge burden of computing and storage. Directly reducing the depth or width of existing models results in a large performance drop. We observe that the SR difficulty of different regions in an RSI varies greatly, and existing methods use the same deep network to process all regions in an image, resulting in a waste of computing resources. In addition, existing SR methods generally predefine integer scale factors and cannot perform stepless SR, i.e., a single model can deal with any potential scale factor. Retraining the model on each scale factor wastes considerable computing resources and model storage space. To address the above problems, we propose a saliency-aware dynamic routing network (SalDRN) for lightweight and stepless SR of RSIs. First, we introduce visual saliency as an indicator of region-level SR difficulty and integrate a lightweight saliency detector into the SalDRN to capture pixel-level visual characteristics. Then, we devise a saliency-aware dynamic routing strategy that employs path selection switches to adaptively select feature extraction paths of appropriate depth according to the SR difficulty of sub-image patches. Finally, we propose a novel lightweight stepless upsampling module whose core is an implicit feature function for realizing mapping from low-resolution feature space to high-resolution feature space. Comprehensive experiments verify that the SalDRN can achieve a good trade-off between performance and complexity. The code is available at \url{https://github.com/hanlinwu/SalDRN}.
[ { "version": "v1", "created": "Fri, 14 Oct 2022 07:49:03 GMT" } ]
2023-02-08T00:00:00
[ [ "Wu", "Hanlin", "" ], [ "Ni", "Ning", "" ], [ "Zhang", "Libao", "" ] ]
new_dataset
0.975437
2210.08616
Marco Di Renzo
Marco Di Renzo, Davide Dardari, and Nicolo' Decarli
LoS MIMO-Arrays vs. LoS MIMO-Surfaces
IEEE EuCAP 2023
null
null
null
cs.IT eess.SP math.IT
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The wireless research community has expressed major interest in the sub-terahertz band for enabling mobile communications in future wireless networks. The sub-terahertz band offers a large amount of available bandwidth and, therefore, the promise to realize wireless communications at optical speeds. At such high frequency bands, the transceivers need to have larger apertures and need to be deployed more densely than at lower frequency bands. These factors proportionally increase the far-field limit and the spherical curvature of the electromagnetic waves cannot be ignored anymore. This offers the opportunity to realize spatial multiplexing even in line-of-sight channels. In this paper, we overview and compare existing design options to realize high-rank transmissions in line-of-sight channels.
[ { "version": "v1", "created": "Sun, 16 Oct 2022 19:19:57 GMT" }, { "version": "v2", "created": "Tue, 7 Feb 2023 18:44:09 GMT" } ]
2023-02-08T00:00:00
[ [ "Di Renzo", "Marco", "" ], [ "Dardari", "Davide", "" ], [ "Decarli", "Nicolo'", "" ] ]
new_dataset
0.999058
2210.16765
Jiawei Lian
Jiawei Lian, Shaohui Mei, Shun Zhang and Mingyang Ma
Benchmarking Adversarial Patch Against Aerial Detection
14 pages, 14 figures
null
10.1109/TGRS.2022.3225306
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
DNNs are vulnerable to adversarial examples, which poses great security concerns for security-critical systems. In this paper, a novel adaptive-patch-based physical attack (AP-PA) framework is proposed, which aims to generate adversarial patches that are adaptive in both physical dynamics and varying scales, and by which the particular targets can be hidden from being detected. Furthermore, the adversarial patch is also gifted with attack effectiveness against all targets of the same class with a patch outside the target (No need to smear targeted objects) and robust enough in the physical world. In addition, a new loss is devised to consider more available information of detected objects to optimize the adversarial patch, which can significantly improve the patch's attack efficacy (Average precision drop up to 87.86% and 85.48% in white-box and black-box settings, respectively) and optimizing efficiency. We also establish one of the first comprehensive, coherent, and rigorous benchmarks to evaluate the attack efficacy of adversarial patches on aerial detection tasks. Finally, several proportionally scaled experiments are performed physically to demonstrate that the elaborated adversarial patches can successfully deceive aerial detection algorithms in dynamic physical circumstances. The code is available at https://github.com/JiaweiLian/AP-PA.
[ { "version": "v1", "created": "Sun, 30 Oct 2022 07:55:59 GMT" } ]
2023-02-08T00:00:00
[ [ "Lian", "Jiawei", "" ], [ "Mei", "Shaohui", "" ], [ "Zhang", "Shun", "" ], [ "Ma", "Mingyang", "" ] ]
new_dataset
0.998947
2211.12109
Anastasia Antsiferova
Anastasia Antsiferova, Sergey Lavrushkin, Maksim Smirnov, Alexander Gushchin, Dmitriy Vatolin, Dmitriy Kulikov
Video compression dataset and benchmark of learning-based video-quality metrics
10 pages, 4 figures, 6 tables, 1 supplementary material
null
null
null
cs.CV cs.MM
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Video-quality measurement is a critical task in video processing. Nowadays, many implementations of new encoding standards - such as AV1, VVC, and LCEVC - use deep-learning-based decoding algorithms with perceptual metrics that serve as optimization objectives. But investigations of the performance of modern video- and image-quality metrics commonly employ videos compressed using older standards, such as AVC. In this paper, we present a new benchmark for video-quality metrics that evaluates video compression. It is based on a new dataset consisting of about 2,500 streams encoded using different standards, including AVC, HEVC, AV1, VP9, and VVC. Subjective scores were collected using crowdsourced pairwise comparisons. The list of evaluated metrics includes recent ones based on machine learning and neural networks. The results demonstrate that new no-reference metrics exhibit a high correlation with subjective quality and approach the capability of top full-reference metrics.
[ { "version": "v1", "created": "Tue, 22 Nov 2022 09:22:28 GMT" }, { "version": "v2", "created": "Tue, 7 Feb 2023 09:28:48 GMT" } ]
2023-02-08T00:00:00
[ [ "Antsiferova", "Anastasia", "" ], [ "Lavrushkin", "Sergey", "" ], [ "Smirnov", "Maksim", "" ], [ "Gushchin", "Alexander", "" ], [ "Vatolin", "Dmitriy", "" ], [ "Kulikov", "Dmitriy", "" ] ]
new_dataset
0.999612
2212.02277
Yuanxin Ye
Bai Zhu, Chao Yang, Jinkun Dai, Jianwei Fan, Yuanxin Ye
R2FD2: Fast and Robust Matching of Multimodal Remote Sensing Image via Repeatable Feature Detector and Rotation-invariant Feature Descriptor
33 pages, 15 figures
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
Automatically identifying feature correspondences between multimodal images is facing enormous challenges because of the significant differences both in radiation and geometry. To address these problems, we propose a novel feature matching method (named R2FD2) that is robust to radiation and rotation differences. Our R2FD2 is conducted in two critical contributions, consisting of a repeatable feature detector and a rotation-invariant feature descriptor. In the first stage, a repeatable feature detector called the Multi-channel Auto-correlation of the Log-Gabor (MALG) is presented for feature detection, which combines the multi-channel auto-correlation strategy with the Log-Gabor wavelets to detect interest points (IPs) with high repeatability and uniform distribution. In the second stage, a rotation-invariant feature descriptor is constructed, named the Rotation-invariant Maximum index map of the Log-Gabor (RMLG), which consists of two components: fast assignment of dominant orientation and construction of feature representation. In the process of fast assignment of dominant orientation, a Rotation-invariant Maximum Index Map (RMIM) is built to address rotation deformations. Then, the proposed RMLG incorporates the rotation-invariant RMIM with the spatial configuration of DAISY to depict a more discriminative feature representation, which improves RMLG's resistance to radiation and rotation variances.Experimental results show that the proposed R2FD2 outperforms five state-of-the-art feature matching methods, and has superior advantages in adaptability and universality. Moreover, our R2FD2 achieves the accuracy of matching within two pixels and has a great advantage in matching efficiency over other state-of-the-art methods.
[ { "version": "v1", "created": "Mon, 5 Dec 2022 13:55:02 GMT" }, { "version": "v2", "created": "Tue, 6 Dec 2022 07:05:04 GMT" }, { "version": "v3", "created": "Thu, 2 Feb 2023 06:46:04 GMT" }, { "version": "v4", "created": "Tue, 7 Feb 2023 08:03:40 GMT" } ]
2023-02-08T00:00:00
[ [ "Zhu", "Bai", "" ], [ "Yang", "Chao", "" ], [ "Dai", "Jinkun", "" ], [ "Fan", "Jianwei", "" ], [ "Ye", "Yuanxin", "" ] ]
new_dataset
0.984351
2301.12028
Melissa Antonelli
Melissa Antonelli and Ugo Dal Lago and Davide Davoli and Isabel Oitavem and Paolo Pistone
An Arithmetic Theory for the Poly-Time Random Functions
37 pages, pre-print
null
null
null
cs.CC cs.LO
http://creativecommons.org/licenses/by/4.0/
We introduce a new bounded theory RS^1_2 and show that the functions which are Sigma^b_1-representable in it are precisely random functions which can be computed in polynomial time. Concretely, we pass through a class of oracle functions over string, called POR, together with the theory of arithmetic RS^1_2. Then, we show that functions computed by poly-time PTMs are arithmetically characterized by a class of probabilistic bounded formulas.
[ { "version": "v1", "created": "Fri, 27 Jan 2023 23:45:18 GMT" }, { "version": "v2", "created": "Mon, 6 Feb 2023 20:40:21 GMT" } ]
2023-02-08T00:00:00
[ [ "Antonelli", "Melissa", "" ], [ "Lago", "Ugo Dal", "" ], [ "Davoli", "Davide", "" ], [ "Oitavem", "Isabel", "" ], [ "Pistone", "Paolo", "" ] ]
new_dataset
0.987625
2301.12344
Xuchen Liu
Xuchen Liu (1 and 2), Minghao Dou (1 and 2), Dongyue Huang (1 and 2), Biao Wang (3 and 4), Jinqiang Cui (4), Qinyuan Ren (5 and 4), Lihua Dou (6), Zhi Gao (7), Jie Chen (1) and Ben M. Chen (2) ((1) Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai, China, (2) Department of Mechanical and Automation Engineering, the Chinese University of Hong Kong, Hong Kong, China, (3) College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China, (4) Peng Cheng Laboratory, Shenzhen, China, (5) College of Control Science and Engineering, Zhejiang University, Hangzhou, China, (6) School of Automation, Beijing Institute of Technology, Beijing, China, (7) School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China)
TJ-FlyingFish: Design and Implementation of an Aerial-Aquatic Quadrotor with Tiltable Propulsion Units
6 pages, 9 figures, accepted to 2023 IEEE International Conference on Robotics and Automation (ICRA)
null
null
null
cs.RO cs.SY eess.SY
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Aerial-aquatic vehicles are capable to move in the two most dominant fluids, making them more promising for a wide range of applications. We propose a prototype with special designs for propulsion and thruster configuration to cope with the vast differences in the fluid properties of water and air. For propulsion, the operating range is switched for the different mediums by the dual-speed propulsion unit, providing sufficient thrust and also ensuring output efficiency. For thruster configuration, thrust vectoring is realized by the rotation of the propulsion unit around the mount arm, thus enhancing the underwater maneuverability. This paper presents a quadrotor prototype of this concept and the design details and realization in practice.
[ { "version": "v1", "created": "Sun, 29 Jan 2023 03:54:05 GMT" }, { "version": "v2", "created": "Tue, 7 Feb 2023 02:49:27 GMT" } ]
2023-02-08T00:00:00
[ [ "Liu", "Xuchen", "", "1 and 2" ], [ "Dou", "Minghao", "", "1 and 2" ], [ "Huang", "Dongyue", "", "1 and 2" ], [ "Wang", "Biao", "", "3 and 4" ], [ "Cui", "Jinqiang", "", "5 and 4" ], [ "Ren", "Qinyuan", "", "5 and 4" ], [ "Dou", "Lihua", "" ], [ "Gao", "Zhi", "" ], [ "Chen", "Jie", "" ], [ "Chen", "Ben M.", "" ] ]
new_dataset
0.999861
2301.12458
Xiang Li
Xiang Li, Tiandi Ye, Caihua Shan, Dongsheng Li, Ming Gao
SeeGera: Self-supervised Semi-implicit Graph Variational Auto-encoders with Masking
Accepted by WebConf 2023
null
null
null
cs.LG
http://creativecommons.org/licenses/by-nc-nd/4.0/
Generative graph self-supervised learning (SSL) aims to learn node representations by reconstructing the input graph data. However, most existing methods focus on unsupervised learning tasks only and very few work has shown its superiority over the state-of-the-art graph contrastive learning (GCL) models, especially on the classification task. While a very recent model has been proposed to bridge the gap, its performance on unsupervised learning tasks is still unknown. In this paper, to comprehensively enhance the performance of generative graph SSL against other GCL models on both unsupervised and supervised learning tasks, we propose the SeeGera model, which is based on the family of self-supervised variational graph auto-encoder (VGAE). Specifically, SeeGera adopts the semi-implicit variational inference framework, a hierarchical variational framework, and mainly focuses on feature reconstruction and structure/feature masking. On the one hand, SeeGera co-embeds both nodes and features in the encoder and reconstructs both links and features in the decoder. Since feature embeddings contain rich semantic information on features, they can be combined with node embeddings to provide fine-grained knowledge for feature reconstruction. On the other hand, SeeGera adds an additional layer for structure/feature masking to the hierarchical variational framework, which boosts the model generalizability. We conduct extensive experiments comparing SeeGera with 9 other state-of-the-art competitors. Our results show that SeeGera can compare favorably against other state-of-the-art GCL methods in a variety of unsupervised and supervised learning tasks.
[ { "version": "v1", "created": "Sun, 29 Jan 2023 15:00:43 GMT" }, { "version": "v2", "created": "Mon, 6 Feb 2023 08:49:21 GMT" }, { "version": "v3", "created": "Tue, 7 Feb 2023 08:45:35 GMT" } ]
2023-02-08T00:00:00
[ [ "Li", "Xiang", "" ], [ "Ye", "Tiandi", "" ], [ "Shan", "Caihua", "" ], [ "Li", "Dongsheng", "" ], [ "Gao", "Ming", "" ] ]
new_dataset
0.986039
2302.00627
Juan Ignacio Iba\~nez
Juan Ignacio Iba\~nez, Francisco Rua
The energy consumption of Proof-of-Stake systems: Replication and expansion
27 pages, 3 figures, working paper
null
null
null
cs.DC
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Blockchain technology and, more generally, distributed ledger technology (DLT) systems, face public scrutiny for their energy consumption levels. However, many point out that high energy consumption is a feature of (small block size) proof-of-work (PoW) DLTs, but not of proof-of-stake (PoS) DLTs. With the energy consumption of PoS systems being an under-researched area, we replicate, expand and update embryonary work modelling it and comparing different PoS-based DLTs with each other and with other non-PoS systems. In doing so, we suggest and implement a number of improvements to an existing PoS energy consumption model. We find that there may be significant differences in the energy consumption of PoS systems analysed and confirm that, regardless of these differences, their energy consumption is several orders of magnitude below that of Bitcoin Core.
[ { "version": "v1", "created": "Sat, 14 Jan 2023 00:10:23 GMT" }, { "version": "v2", "created": "Thu, 2 Feb 2023 19:39:15 GMT" }, { "version": "v3", "created": "Tue, 7 Feb 2023 02:08:43 GMT" } ]
2023-02-08T00:00:00
[ [ "Ibañez", "Juan Ignacio", "" ], [ "Rua", "Francisco", "" ] ]
new_dataset
0.996657
2302.02088
Susan Liang
Susan Liang, Chao Huang, Yapeng Tian, Anurag Kumar, Chenliang Xu
AV-NeRF: Learning Neural Fields for Real-World Audio-Visual Scene Synthesis
null
null
null
null
cs.CV cs.GR cs.SD eess.AS
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Human perception of the complex world relies on a comprehensive analysis of multi-modal signals, and the co-occurrences of audio and video signals provide humans with rich cues. This paper focuses on novel audio-visual scene synthesis in the real world. Given a video recording of an audio-visual scene, the task is to synthesize new videos with spatial audios along arbitrary novel camera trajectories in that audio-visual scene. Directly using a NeRF-based model for audio synthesis is insufficient due to its lack of prior knowledge and acoustic supervision. To tackle the challenges, we first propose an acoustic-aware audio generation module that integrates our prior knowledge of audio propagation into NeRF, in which we associate audio generation with the 3D geometry of the visual environment. In addition, we propose a coordinate transformation module that expresses a viewing direction relative to the sound source. Such a direction transformation helps the model learn sound source-centric acoustic fields. Moreover, we utilize a head-related impulse response function to synthesize pseudo binaural audio for data augmentation that strengthens training. We qualitatively and quantitatively demonstrate the advantage of our model on real-world audio-visual scenes. We refer interested readers to view our video results for convincing comparisons.
[ { "version": "v1", "created": "Sat, 4 Feb 2023 04:17:19 GMT" }, { "version": "v2", "created": "Tue, 7 Feb 2023 17:38:18 GMT" } ]
2023-02-08T00:00:00
[ [ "Liang", "Susan", "" ], [ "Huang", "Chao", "" ], [ "Tian", "Yapeng", "" ], [ "Kumar", "Anurag", "" ], [ "Xu", "Chenliang", "" ] ]
new_dataset
0.977254
2302.02150
Dimitris Iakovidis
Dimitrios E. Diamantis, Panagiota Gatoula, Anastasios Koulaouzidis, and Dimitris K. Iakovidis
This Intestine Does Not Exist: Multiscale Residual Variational Autoencoder for Realistic Wireless Capsule Endoscopy Image Generation
10 pages
null
null
null
cs.CV cs.LG eess.IV
http://creativecommons.org/licenses/by-nc-nd/4.0/
Medical image synthesis has emerged as a promising solution to address the limited availability of annotated medical data needed for training machine learning algorithms in the context of image-based Clinical Decision Support (CDS) systems. To this end, Generative Adversarial Networks (GANs) have been mainly applied to support the algorithm training process by generating synthetic images for data augmentation. However, in the field of Wireless Capsule Endoscopy (WCE), the limited content diversity and size of existing publicly available annotated datasets, adversely affect both the training stability and synthesis performance of GANs. Aiming to a viable solution for WCE image synthesis, a novel Variational Autoencoder architecture is proposed, namely "This Intestine Does not Exist" (TIDE). The proposed architecture comprises multiscale feature extraction convolutional blocks and residual connections, which enable the generation of high-quality and diverse datasets even with a limited number of training images. Contrary to the current approaches, which are oriented towards the augmentation of the available datasets, this study demonstrates that using TIDE, real WCE datasets can be fully substituted by artificially generated ones, without compromising classification performance. Furthermore, qualitative and user evaluation studies by experienced WCE specialists, validate from a medical viewpoint that both the normal and abnormal WCE images synthesized by TIDE are sufficiently realistic.
[ { "version": "v1", "created": "Sat, 4 Feb 2023 11:49:38 GMT" }, { "version": "v2", "created": "Tue, 7 Feb 2023 03:50:25 GMT" } ]
2023-02-08T00:00:00
[ [ "Diamantis", "Dimitrios E.", "" ], [ "Gatoula", "Panagiota", "" ], [ "Koulaouzidis", "Anastasios", "" ], [ "Iakovidis", "Dimitris K.", "" ] ]
new_dataset
0.999269
2302.02693
Xue Yang
Qinrou Wen, Jirui Yang, Xue Yang, Kewei Liang
PatchDCT: Patch Refinement for High Quality Instance Segmentation
15 pages, 7 figures, 13 tables, accepted by ICLR 2023, the source code is available at https://github.com/olivia-w12/PatchDCT
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
High-quality instance segmentation has shown emerging importance in computer vision. Without any refinement, DCT-Mask directly generates high-resolution masks by compressed vectors. To further refine masks obtained by compressed vectors, we propose for the first time a compressed vector based multi-stage refinement framework. However, the vanilla combination does not bring significant gains, because changes in some elements of the DCT vector will affect the prediction of the entire mask. Thus, we propose a simple and novel method named PatchDCT, which separates the mask decoded from a DCT vector into several patches and refines each patch by the designed classifier and regressor. Specifically, the classifier is used to distinguish mixed patches from all patches, and to correct previously mispredicted foreground and background patches. In contrast, the regressor is used for DCT vector prediction of mixed patches, further refining the segmentation quality at boundary locations. Experiments on COCO show that our method achieves 2.0%, 3.2%, 4.5% AP and 3.4%, 5.3%, 7.0% Boundary AP improvements over Mask-RCNN on COCO, LVIS, and Cityscapes, respectively. It also surpasses DCT-Mask by 0.7%, 1.1%, 1.3% AP and 0.9%, 1.7%, 4.2% Boundary AP on COCO, LVIS and Cityscapes. Besides, the performance of PatchDCT is also competitive with other state-of-the-art methods.
[ { "version": "v1", "created": "Mon, 6 Feb 2023 10:51:21 GMT" }, { "version": "v2", "created": "Tue, 7 Feb 2023 13:14:04 GMT" } ]
2023-02-08T00:00:00
[ [ "Wen", "Qinrou", "" ], [ "Yang", "Jirui", "" ], [ "Yang", "Xue", "" ], [ "Liang", "Kewei", "" ] ]
new_dataset
0.997626
2302.03036
Joe Toplyn
Joe Toplyn
Witscript 2: A System for Generating Improvised Jokes Without Wordplay
5 pages. Published in the Proceedings of the 13th International Conference on Computational Creativity (ICCC 2022), pages 54-58. arXiv admin note: text overlap with arXiv:2301.02695. substantial text overlap with arXiv:2302.02008
null
null
null
cs.CL cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
A previous paper presented Witscript, a system for generating conversational jokes that rely on wordplay. This paper extends that work by presenting Witscript 2, which uses a large language model to generate conversational jokes that rely on common sense instead of wordplay. Like Witscript, Witscript 2 is based on joke-writing algorithms created by an expert comedy writer. Human evaluators judged Witscript 2's responses to input sentences to be jokes 46% of the time, compared to 70% of the time for human-written responses. This is evidence that Witscript 2 represents another step toward giving a chatbot a humanlike sense of humor.
[ { "version": "v1", "created": "Fri, 3 Feb 2023 21:51:55 GMT" } ]
2023-02-08T00:00:00
[ [ "Toplyn", "Joe", "" ] ]
new_dataset
0.999153
2302.03037
Khaza Anuarul Hoque
Ripan Kumar Kundu, Rifatul Islam, John Quarles, Khaza Anuarul Hoque
LiteVR: Interpretable and Lightweight Cybersickness Detection using Explainable AI
Accepted for publication in IEEE VR 2023 conference. arXiv admin note: substantial text overlap with arXiv:2302.01985
null
null
null
cs.HC cs.AI cs.LG
http://creativecommons.org/licenses/by-nc-nd/4.0/
Cybersickness is a common ailment associated with virtual reality (VR) user experiences. Several automated methods exist based on machine learning (ML) and deep learning (DL) to detect cybersickness. However, most of these cybersickness detection methods are perceived as computationally intensive and black-box methods. Thus, those techniques are neither trustworthy nor practical for deploying on standalone energy-constrained VR head-mounted devices (HMDs). In this work, we present an explainable artificial intelligence (XAI)-based framework, LiteVR, for cybersickness detection, explaining the model's outcome and reducing the feature dimensions and overall computational costs. First, we develop three cybersickness DL models based on long-term short-term memory (LSTM), gated recurrent unit (GRU), and multilayer perceptron (MLP). Then, we employed a post-hoc explanation, such as SHapley Additive Explanations (SHAP), to explain the results and extract the most dominant features of cybersickness. Finally, we retrain the DL models with the reduced number of features. Our results show that eye-tracking features are the most dominant for cybersickness detection. Furthermore, based on the XAI-based feature ranking and dimensionality reduction, we significantly reduce the model's size by up to 4.3x, training time by up to 5.6x, and its inference time by up to 3.8x, with higher cybersickness detection accuracy and low regression error (i.e., on Fast Motion Scale (FMS)). Our proposed lite LSTM model obtained an accuracy of 94% in classifying cybersickness and regressing (i.e., FMS 1-10) with a Root Mean Square Error (RMSE) of 0.30, which outperforms the state-of-the-art. Our proposed LiteVR framework can help researchers and practitioners analyze, detect, and deploy their DL-based cybersickness detection models in standalone VR HMDs.
[ { "version": "v1", "created": "Sun, 5 Feb 2023 21:51:12 GMT" } ]
2023-02-08T00:00:00
[ [ "Kundu", "Ripan Kumar", "" ], [ "Islam", "Rifatul", "" ], [ "Quarles", "John", "" ], [ "Hoque", "Khaza Anuarul", "" ] ]
new_dataset
0.990389
2302.03074
Siddharth Bhaskar
Siddharth Bhaskar, Jane Chandlee, Adam Jardine
Rational functions via recursive schemes
null
null
null
null
cs.LO cs.FL
http://creativecommons.org/licenses/by/4.0/
We give a new characterization of the class of rational string functions from formal language theory using order-preserving interpretations with respect to a very weak monadic programming language. This refines the known characterization of rational functions by order-preserving MSO interpretations.
[ { "version": "v1", "created": "Mon, 6 Feb 2023 19:22:18 GMT" } ]
2023-02-08T00:00:00
[ [ "Bhaskar", "Siddharth", "" ], [ "Chandlee", "Jane", "" ], [ "Jardine", "Adam", "" ] ]
new_dataset
0.992429
2302.03088
Laura Stegner
David Porfirio, Laura Stegner, Maya Cakmak, Allison Saupp\'e, Aws Albarghouthi, Bilge Mutlu
Sketching Robot Programs On the Fly
Accepted at HRI '23, March 13-16, 2023, Stockholm, Sweden
null
10.1145/3568162.3576991
null
cs.RO cs.HC
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Service robots for personal use in the home and the workplace require end-user development solutions for swiftly scripting robot tasks as the need arises. Many existing solutions preserve ease, efficiency, and convenience through simple programming interfaces or by restricting task complexity. Others facilitate meticulous task design but often do so at the expense of simplicity and efficiency. There is a need for robot programming solutions that reconcile the complexity of robotics with the on-the-fly goals of end-user development. In response to this need, we present a novel, multimodal, and on-the-fly development system, Tabula. Inspired by a formative design study with a prototype, Tabula leverages a combination of spoken language for specifying the core of a robot task and sketching for contextualizing the core. The result is that developers can script partial, sloppy versions of robot programs to be completed and refined by a program synthesizer. Lastly, we demonstrate our anticipated use cases of Tabula via a set of application scenarios.
[ { "version": "v1", "created": "Mon, 6 Feb 2023 19:44:05 GMT" } ]
2023-02-08T00:00:00
[ [ "Porfirio", "David", "" ], [ "Stegner", "Laura", "" ], [ "Cakmak", "Maya", "" ], [ "Sauppé", "Allison", "" ], [ "Albarghouthi", "Aws", "" ], [ "Mutlu", "Bilge", "" ] ]
new_dataset
0.998605
2302.03099
Alex Qiu
Alex Qiu, Claire Young, Anthony Gunderman, Milad Azizkhani, Yue Chen, Ai-Ping Hu
Tendon-Driven Soft Robotic Gripper with Integrated Ripeness Sensing for Blackberry Harvesting
7 Pages, 9 figures, submitted to and accepted by ICRA 2023
null
null
null
cs.RO
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Growing global demand for food, coupled with continuing labor shortages, motivates the need for automated agricultural harvesting. While some specialty crops (e.g., apples, peaches, blueberries) can be harvested via existing harvesting modalities, fruits such as blackberries and raspberries require delicate handling to mitigate fruit damage that could significantly impact marketability. This motivates the development of soft robotic solutions that enable efficient, delicate harvesting. This paper presents the design, fabrication, and feasibility testing of a tendon-driven soft gripping system focused on blackberries, which are a fragile fruit susceptible to post-harvest damage. The gripper is both low-cost and small form factor, allowing for the integration of a micro-servo for tendon retraction, a near-infrared (NIR) based blackberry ripeness sensor utilizing the reflectance modality for identifying fully ripe blackberries, and an endoscopic camera for visual servoing with a UR-5. The gripper was used to harvest 139 berries with manual positioning in two separate field tests. Field testing found an average retention force of 2.06 N and 6.08 N for ripe and unripe blackberries, respectively. Sensor tests identified an average reflectance of 16.78 and 21.70 for ripe and unripe blackberries, respectively, indicating a clear distinction between the two ripeness levels. Finally, the soft robotic gripper was integrated onto a UR5 robot arm and successfully harvested fifteen artificial blackberries in a lab setting using visual servoing.
[ { "version": "v1", "created": "Mon, 6 Feb 2023 19:59:33 GMT" } ]
2023-02-08T00:00:00
[ [ "Qiu", "Alex", "" ], [ "Young", "Claire", "" ], [ "Gunderman", "Anthony", "" ], [ "Azizkhani", "Milad", "" ], [ "Chen", "Yue", "" ], [ "Hu", "Ai-Ping", "" ] ]
new_dataset
0.998175
2302.03128
Zhengwei Bai
Zhengwei Bai, Guoyuan Wu, Matthew J. Barth, Yongkang Liu, Emrah Akin Sisbot, Kentaro Oguchi
Cooperverse: A Mobile-Edge-Cloud Framework for Universal Cooperative Perception with Mixed Connectivity and Automation
6 pages, 7 figures
null
null
null
cs.CV cs.MA
http://creativecommons.org/licenses/by-nc-nd/4.0/
Cooperative perception (CP) is attracting increasing attention and is regarded as the core foundation to support cooperative driving automation, a potential key solution to addressing the safety, mobility, and sustainability issues of contemporary transportation systems. However, current research on CP is still at the beginning stages where a systematic problem formulation of CP is still missing, acting as the essential guideline of the system design of a CP system under real-world situations. In this paper, we formulate a universal CP system into an optimization problem and a mobile-edge-cloud framework called Cooperverse. This system addresses CP in a mixed connectivity and automation environment. A Dynamic Feature Sharing (DFS) methodology is introduced to support this CP system under certain constraints and a Random Priority Filtering (RPF) method is proposed to conduct DFS with high performance. Experiments have been conducted based on a high-fidelity CP platform, and the results show that the Cooperverse framework is effective for dynamic node engagement and the proposed DFS methodology can improve system CP performance by 14.5% and the RPF method can reduce the communication cost for mobile nodes by 90% with only 1.7% drop for average precision.
[ { "version": "v1", "created": "Mon, 6 Feb 2023 21:30:08 GMT" } ]
2023-02-08T00:00:00
[ [ "Bai", "Zhengwei", "" ], [ "Wu", "Guoyuan", "" ], [ "Barth", "Matthew J.", "" ], [ "Liu", "Yongkang", "" ], [ "Sisbot", "Emrah Akin", "" ], [ "Oguchi", "Kentaro", "" ] ]
new_dataset
0.999021
2302.03288
Toon Van de Maele
Toon Van de Maele, Tim Verbelen, Pietro Mazzaglia, Stefano Ferraro, Bart Dhoedt
Object-Centric Scene Representations using Active Inference
null
null
null
null
cs.RO cs.AI
http://creativecommons.org/licenses/by/4.0/
Representing a scene and its constituent objects from raw sensory data is a core ability for enabling robots to interact with their environment. In this paper, we propose a novel approach for scene understanding, leveraging a hierarchical object-centric generative model that enables an agent to infer object category and pose in an allocentric reference frame using active inference, a neuro-inspired framework for action and perception. For evaluating the behavior of an active vision agent, we also propose a new benchmark where, given a target viewpoint of a particular object, the agent needs to find the best matching viewpoint given a workspace with randomly positioned objects in 3D. We demonstrate that our active inference agent is able to balance epistemic foraging and goal-driven behavior, and outperforms both supervised and reinforcement learning baselines by a large margin.
[ { "version": "v1", "created": "Tue, 7 Feb 2023 06:45:19 GMT" } ]
2023-02-08T00:00:00
[ [ "Van de Maele", "Toon", "" ], [ "Verbelen", "Tim", "" ], [ "Mazzaglia", "Pietro", "" ], [ "Ferraro", "Stefano", "" ], [ "Dhoedt", "Bart", "" ] ]
new_dataset
0.99431
2302.03385
Haoran Li
Li Haoran and Liu Shasha and Ma Mingjun and Hu Guangzheng and Chen Yaran and Zhao Dongbin
NeuronsGym: A Hybrid Framework and Benchmark for Robot Tasks with Sim2Real Policy Learning
16 pages, 10 figures
null
null
null
cs.RO
http://creativecommons.org/licenses/by/4.0/
The rise of embodied AI has greatly improved the possibility of general mobile agent systems. At present, many evaluation platforms with rich scenes, high visual fidelity and various application scenarios have been developed. In this paper, we present a hybrid framework named NeuronsGym that can be used for policy learning of robot tasks, covering a simulation platform for training policy, and a physical system for studying sim2real problems. Unlike most current single-task, slow-moving robotic platforms, our framework provides agile physical robots with a wider range of speeds, and can be employed to train robotic navigation and confrontation policies. At the same time, in order to evaluate the safety of robot navigation, we propose a safety-weighted path length (SFPL) to improve the safety evaluation in the current mobile robot navigation. Based on this platform, we build a new benchmark for navigation and confrontation tasks under this platform by comparing the current mainstream sim2real methods, and hold the 2022 IEEE Conference on Games (CoG) RoboMaster sim2real challenge. We release the codes of this framework\footnote{\url{https://github.com/DRL-CASIA/NeuronsGym}} and hope that this platform can promote the development of more flexible and agile general mobile agent algorithms.
[ { "version": "v1", "created": "Tue, 7 Feb 2023 10:45:20 GMT" } ]
2023-02-08T00:00:00
[ [ "Haoran", "Li", "" ], [ "Shasha", "Liu", "" ], [ "Mingjun", "Ma", "" ], [ "Guangzheng", "Hu", "" ], [ "Yaran", "Chen", "" ], [ "Dongbin", "Zhao", "" ] ]
new_dataset
0.999162
2302.03474
Mathias Bos
Mathias Bos, Bastiaan Vandewal, Wilm Decr\'e, Jan Swevers
MPC-based Motion Planning for Autonomous Truck-Trailer Maneuvering
This work has been submitted to IFAC for possible publication. (IFAC World Congress 2023)
null
null
null
cs.RO
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Time-optimal motion planning of autonomous vehicles in complex environments is a highly researched topic. This paper describes a novel approach to optimize and execute locally feasible trajectories for the maneuvering of a truck-trailer Autonomous Mobile Robot (AMR), by dividing the environment in a sequence or route of freely accessible overlapping corridors. Multi-stage optimal control generates local trajectories through advancing subsets of this route. To cope with the advancing subsets and changing environments, the optimal control problem is solved online with a receding horizon in a Model Predictive Control (MPC) fashion with an improved update strategy. This strategy seamlessly integrates the computationally expensive MPC updates with a low-cost feedback controller for trajectory tracking, for disturbance rejection, and for stabilization of the unstable kinematics of the reversing truck-trailer AMR. This methodology is implemented in a flexible software framework for an effortless transition from offline simulations to deployment of experiments. An experimental setup showcasing the truck-trailer AMR performing two reverse parking maneuvers validates the presented method.
[ { "version": "v1", "created": "Tue, 7 Feb 2023 14:00:22 GMT" } ]
2023-02-08T00:00:00
[ [ "Bos", "Mathias", "" ], [ "Vandewal", "Bastiaan", "" ], [ "Decré", "Wilm", "" ], [ "Swevers", "Jan", "" ] ]
new_dataset
0.989936
2302.03540
Eugene Kharitonov
Eugene Kharitonov and Damien Vincent and Zal\'an Borsos and Rapha\"el Marinier and Sertan Girgin and Olivier Pietquin and Matt Sharifi and Marco Tagliasacchi and Neil Zeghidour
Speak, Read and Prompt: High-Fidelity Text-to-Speech with Minimal Supervision
null
null
null
null
cs.SD eess.AS
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We introduce SPEAR-TTS, a multi-speaker text-to-speech (TTS) system that can be trained with minimal supervision. By combining two types of discrete speech representations, we cast TTS as a composition of two sequence-to-sequence tasks: from text to high-level semantic tokens (akin to "reading") and from semantic tokens to low-level acoustic tokens ("speaking"). Decoupling these two tasks enables training of the "speaking" module using abundant audio-only data, and unlocks the highly efficient combination of pretraining and backtranslation to reduce the need for parallel data when training the "reading" component. To control the speaker identity, we adopt example prompting, which allows SPEAR-TTS to generalize to unseen speakers using only a short sample of 3 seconds, without any explicit speaker representation or speaker-id labels. Our experiments demonstrate that SPEAR-TTS achieves a character error rate that is competitive with state-of-the-art methods using only 15 minutes of parallel data, while matching ground-truth speech in terms of naturalness and acoustic quality, as measured in subjective tests.
[ { "version": "v1", "created": "Tue, 7 Feb 2023 15:48:31 GMT" } ]
2023-02-08T00:00:00
[ [ "Kharitonov", "Eugene", "" ], [ "Vincent", "Damien", "" ], [ "Borsos", "Zalán", "" ], [ "Marinier", "Raphaël", "" ], [ "Girgin", "Sertan", "" ], [ "Pietquin", "Olivier", "" ], [ "Sharifi", "Matt", "" ], [ "Tagliasacchi", "Marco", "" ], [ "Zeghidour", "Neil", "" ] ]
new_dataset
0.98093
2302.03580
T. Konstantin Rusch
L\'eonard Equer, T. Konstantin Rusch, Siddhartha Mishra
Multi-Scale Message Passing Neural PDE Solvers
null
null
null
null
cs.LG cs.NA math.NA stat.ML
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We propose a novel multi-scale message passing neural network algorithm for learning the solutions of time-dependent PDEs. Our algorithm possesses both temporal and spatial multi-scale resolution features by incorporating multi-scale sequence models and graph gating modules in the encoder and processor, respectively. Benchmark numerical experiments are presented to demonstrate that the proposed algorithm outperforms baselines, particularly on a PDE with a range of spatial and temporal scales.
[ { "version": "v1", "created": "Tue, 7 Feb 2023 16:45:52 GMT" } ]
2023-02-08T00:00:00
[ [ "Equer", "Léonard", "" ], [ "Rusch", "T. Konstantin", "" ], [ "Mishra", "Siddhartha", "" ] ]
new_dataset
0.998246
2302.03589
Ludovica Pannitto
Ludovica Pannitto and Aur\'elie Herbelot
CALaMo: a Constructionist Assessment of Language Models
null
null
null
null
cs.CL
http://creativecommons.org/licenses/by-sa/4.0/
This paper presents a novel framework for evaluating Neural Language Models' linguistic abilities using a constructionist approach. Not only is the usage-based model in line with the underlying stochastic philosophy of neural architectures, but it also allows the linguist to keep meaning as a determinant factor in the analysis. We outline the framework and present two possible scenarios for its application.
[ { "version": "v1", "created": "Tue, 7 Feb 2023 16:56:48 GMT" } ]
2023-02-08T00:00:00
[ [ "Pannitto", "Ludovica", "" ], [ "Herbelot", "Aurélie", "" ] ]
new_dataset
0.999126
2302.03594
Zihan Zhu
Zihan Zhu, Songyou Peng, Viktor Larsson, Zhaopeng Cui, Martin R. Oswald, Andreas Geiger, Marc Pollefeys
NICER-SLAM: Neural Implicit Scene Encoding for RGB SLAM
Video: https://youtu.be/tUXzqEZWg2w
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Neural implicit representations have recently become popular in simultaneous localization and mapping (SLAM), especially in dense visual SLAM. However, previous works in this direction either rely on RGB-D sensors, or require a separate monocular SLAM approach for camera tracking and do not produce high-fidelity dense 3D scene reconstruction. In this paper, we present NICER-SLAM, a dense RGB SLAM system that simultaneously optimizes for camera poses and a hierarchical neural implicit map representation, which also allows for high-quality novel view synthesis. To facilitate the optimization process for mapping, we integrate additional supervision signals including easy-to-obtain monocular geometric cues and optical flow, and also introduce a simple warping loss to further enforce geometry consistency. Moreover, to further boost performance in complicated indoor scenes, we also propose a local adaptive transformation from signed distance functions (SDFs) to density in the volume rendering equation. On both synthetic and real-world datasets we demonstrate strong performance in dense mapping, tracking, and novel view synthesis, even competitive with recent RGB-D SLAM systems.
[ { "version": "v1", "created": "Tue, 7 Feb 2023 17:06:34 GMT" } ]
2023-02-08T00:00:00
[ [ "Zhu", "Zihan", "" ], [ "Peng", "Songyou", "" ], [ "Larsson", "Viktor", "" ], [ "Cui", "Zhaopeng", "" ], [ "Oswald", "Martin R.", "" ], [ "Geiger", "Andreas", "" ], [ "Pollefeys", "Marc", "" ] ]
new_dataset
0.987494
2302.03644
Henning Zwirnmann
Henning Zwirnmann, Dennis Knobbe, Utku Culha and Sami Haddadin
Dual-Material 3D-Printed PaCoMe-Like Fingers for Flexible Biolaboratory Automation
null
null
null
null
cs.RO
http://creativecommons.org/licenses/by-nc-sa/4.0/
Robotic automation in life science research is a paradigm that has gained increasing relevance in recent years. Current solutions in this area often have limited scope, such as pick-and-place tasks for a specific object. Thus, each new process requires a separate toolset, which prevents the realization of more complex workflows and reduces the acceptance of robotic automation tools. Here, we present a novel finger system for a parallel gripper for biolaboratory automation that can handle a wide range of liquid containers. This flexibility is enabled by developing the fingers as a dual-extrusion 3D print. The coating with a soft material from the second extruder in one seamless print and the fingertip design are key features to enhance grasping capabilities. By employing a passive compliant mechanism that was previously presented in a finger called ``PaCoMe'', a simple actuation system and a low weight are maintained. The ability to resist chemicals and high temperatures and the integration with a tool exchange system make the fingers usable for daily laboratory use and complex workflows. We present their task suitability in several experiments showing the wide range of vessels that can be handled as well as their tolerance against displacements and grasp stability.
[ { "version": "v1", "created": "Tue, 7 Feb 2023 17:52:19 GMT" } ]
2023-02-08T00:00:00
[ [ "Zwirnmann", "Henning", "" ], [ "Knobbe", "Dennis", "" ], [ "Culha", "Utku", "" ], [ "Haddadin", "Sami", "" ] ]
new_dataset
0.998731
2005.12806
Nikolaos Misirlis
Nikolaos Misirlis, Miriam H. Zwaan, David Weber
International students' loneliness, depression and stress levels in COVID-19 crisis. The role of social media and the host university
14 pages
Journal of Contemporary Education Theory & Research 4 (2), 20-25 (2020)
10.5281/zenodo.4256624
null
cs.SI cs.CY
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The move to university life is characterized by strong emotions, some of them negative, such as loneliness, anxiety, and depression. These negative emotions are strengthened due to the obligatory lockdown due to the COVID-19 pandemic. Previous research indicates association among the use of social media, university satisfaction, and the aforementioned emotions. We report findings from 248 international undergraduates in The Netherlands, all students at the International School of Business. Our results indicate strong correlations between anxiety, loneliness, and COVID-19-related stress with university satisfaction together with social capital. Keywords: COVID-19; Pandemic; lockdown; loneliness; depression; anxiety; international students
[ { "version": "v1", "created": "Tue, 26 May 2020 15:39:27 GMT" } ]
2023-02-07T00:00:00
[ [ "Misirlis", "Nikolaos", "" ], [ "Zwaan", "Miriam H.", "" ], [ "Weber", "David", "" ] ]
new_dataset
0.99826
2101.10463
An Zou
An Zou, Jing Li, Christopher D. Gill, and Xuan Zhang
RTGPU: Real-Time GPU Scheduling of Hard Deadline Parallel Tasks with Fine-Grain Utilization
null
null
null
null
cs.DC cs.AI cs.AR cs.GR
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Many emerging cyber-physical systems, such as autonomous vehicles and robots, rely heavily on artificial intelligence and machine learning algorithms to perform important system operations. Since these highly parallel applications are computationally intensive, they need to be accelerated by graphics processing units (GPUs) to meet stringent timing constraints. However, despite the wide adoption of GPUs, efficiently scheduling multiple GPU applications while providing rigorous real-time guarantees remains a challenge. In this paper, we propose RTGPU, which can schedule the execution of multiple GPU applications in real-time to meet hard deadlines. Each GPU application can have multiple CPU execution and memory copy segments, as well as GPU kernels. We start with a model to explicitly account for the CPU and memory copy segments of these applications. We then consider the GPU architecture in the development of a precise timing model for the GPU kernels and leverage a technique known as persistent threads to implement fine-grained kernel scheduling with improved performance through interleaved execution. Next, we propose a general method for scheduling parallel GPU applications in real time. Finally, to schedule multiple parallel GPU applications, we propose a practical real-time scheduling algorithm based on federated scheduling and grid search (for GPU kernel segments) with uniprocessor fixed priority scheduling (for multiple CPU and memory copy segments). Our approach provides superior schedulability compared with previous work, and gives real-time guarantees to meet hard deadlines for multiple GPU applications according to comprehensive validation and evaluation on a real NVIDIA GTX1080Ti GPU system.
[ { "version": "v1", "created": "Mon, 25 Jan 2021 22:34:06 GMT" }, { "version": "v2", "created": "Wed, 27 Jan 2021 02:22:33 GMT" }, { "version": "v3", "created": "Mon, 6 Feb 2023 11:39:58 GMT" } ]
2023-02-07T00:00:00
[ [ "Zou", "An", "" ], [ "Li", "Jing", "" ], [ "Gill", "Christopher D.", "" ], [ "Zhang", "Xuan", "" ] ]
new_dataset
0.998231
2109.04243
Flavio Bertini
Taron Davtian, Flavio Bertini and Rajesh Sharma
Understanding Cycling Mobility: Bologna Case Study
null
null
10.1007/s43762-022-00073-8
null
cs.CY cs.SI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Understanding human mobility in urban environments is of the utmost importance to manage traffic and for deploying new resources and services. In recent years, the problem is exacerbated due to rapid urbanization and climate changes. In an urban context, human mobility has many facets, and cycling represents one of the most eco-friendly and efficient/effective ways to move in touristic and historical cities. The main objective of this work is to study the cycling mobility within the city of Bologna, Italy. We used six months dataset that consists of 320,118 self-reported bike trips. In particular, we performed several descriptive analysis to understand spatial and temporal patterns of bike users for understanding popular roads, and most favorite points within the city. This analysis involved several other public datasets in order to explore variables that can possibly affect the cycling activity, such as weather, pollution, and events. The main results of this study indicate that bike usage is more correlated to temperature, and precipitation and has no correlation to wind speed and pollution. In addition, we also exploited various machine learning and deep learning approaches for predicting short-term trips in the near future (that is for the following 30, and 60 minutes), that could help local governmental agencies for urban planning. Our best model achieved an R square of 0.91, a Mean Absolute Error of 5.38 and a Root Mean Squared Error of 8.12 for the 30-minutes time interval.
[ { "version": "v1", "created": "Thu, 9 Sep 2021 13:11:35 GMT" } ]
2023-02-07T00:00:00
[ [ "Davtian", "Taron", "" ], [ "Bertini", "Flavio", "" ], [ "Sharma", "Rajesh", "" ] ]
new_dataset
0.999571