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2304.10632
Piyush Batra
Piyush Batra, Gagan Raj Singh, Ritik Gandhi
NFT Marketplace
Report for MULTIMEDIA COMMUNICATIONS course project
null
null
null
cs.MM cs.CR
http://creativecommons.org/licenses/by/4.0/
In an increasingly digitized world, the secure management and trade of digital assets have become a pressing issue. This project aims to address this challenge by developing a decentralized application (dApp) that leverages blockchain technology and deep learning models to provide secure and efficient digital asset management, with a focus on NFTs. The dApp includes features such as secure wallet connections, NFT image generation, minting, marketplace, and profile management. The back-end of the dApp is implemented using the Goerli testnet with Solidity-based smart contracts, while IPFS and ReactJS/EtherJS are used for decentralized storage and front-end development, respectively. Additionally, the OpenAI API is integrated to generate unique NFT images based on user input. The project demonstrates the practical application of blockchain technology and deep learning models in developing dApps for secure and decentralized digital asset management. Overall, the project contributes to the ongoing research on blockchain-based solutions for secure digital asset management, while highlighting the potential of blockchain and deep learning technologies to transform the way we manage and trade digital assets.
[ { "version": "v1", "created": "Thu, 20 Apr 2023 20:24:20 GMT" } ]
2023-04-24T00:00:00
[ [ "Batra", "Piyush", "" ], [ "Singh", "Gagan Raj", "" ], [ "Gandhi", "Ritik", "" ] ]
new_dataset
0.998527
2304.10639
Yasir Alanazi
Yasir Alanazi, Malachi Schram, Kishansingh Rajput, Steven Goldenberg, Lasitha Vidyaratne, Chris Pappas, Majdi I. Radaideh, Dan Lu, Pradeep Ramuhalli, Sarah Cousineau
Multi-module based CVAE to predict HVCM faults in the SNS accelerator
null
null
null
null
cs.LG
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We present a multi-module framework based on Conditional Variational Autoencoder (CVAE) to detect anomalies in the power signals coming from multiple High Voltage Converter Modulators (HVCMs). We condition the model with the specific modulator type to capture different representations of the normal waveforms and to improve the sensitivity of the model to identify a specific type of fault when we have limited samples for a given module type. We studied several neural network (NN) architectures for our CVAE model and evaluated the model performance by looking at their loss landscape for stability and generalization. Our results for the Spallation Neutron Source (SNS) experimental data show that the trained model generalizes well to detecting multiple fault types for several HVCM module types. The results of this study can be used to improve the HVCM reliability and overall SNS uptime
[ { "version": "v1", "created": "Thu, 20 Apr 2023 20:41:38 GMT" } ]
2023-04-24T00:00:00
[ [ "Alanazi", "Yasir", "" ], [ "Schram", "Malachi", "" ], [ "Rajput", "Kishansingh", "" ], [ "Goldenberg", "Steven", "" ], [ "Vidyaratne", "Lasitha", "" ], [ "Pappas", "Chris", "" ], [ "Radaideh", "Majdi I.", "" ], [ "Lu", "Dan", "" ], [ "Ramuhalli", "Pradeep", "" ], [ "Cousineau", "Sarah", "" ] ]
new_dataset
0.997697
2304.10646
Rachit Nigam
Rachit Nigam, Pedro Henrique Azevedo De Amorim, Adrian Sampson
Modular Hardware Design with Timeline Types
Extended version of PLDI '23 paper
null
10.1145/3591234
null
cs.AR cs.PL
http://creativecommons.org/licenses/by/4.0/
Modular design is a key challenge for enabling large-scale reuse of hardware modules. Unlike software, however, hardware designs correspond to physical circuits and inherit constraints from them. Timing constraints -- which cycle a signal arrives, when an input is read -- and structural constraints -- how often a multiplier accepts new inputs -- are fundamental to hardware interfaces. Existing hardware design languages do not provide a way to encode these constraints; a user must read documentation, build scripts, or in the worst case, a module's implementation to understand how to use it. We present Filament, a language for modular hardware design that supports the specification and enforcement of timing and structural constraints for statically scheduled pipelines. Filament uses timeline types, which describe the intervals of clock-cycle time when a given signal is available or required. Filament enables safe composition of hardware modules, ensures that the resulting designs are correctly pipelined, and predictably lowers them to efficient hardware.
[ { "version": "v1", "created": "Thu, 20 Apr 2023 21:12:09 GMT" } ]
2023-04-24T00:00:00
[ [ "Nigam", "Rachit", "" ], [ "De Amorim", "Pedro Henrique Azevedo", "" ], [ "Sampson", "Adrian", "" ] ]
new_dataset
0.990752
2304.10840
Dhinakaran D
L. Srinivasan, D. Selvaraj, D. Dhinakaran, T. P. Anish
IoT-Based Solution for Paraplegic Sufferer to Send Signals to Physician via Internet
null
null
10.14445/23488379/IJEEE-V10I1P104
null
cs.CY
http://creativecommons.org/licenses/by/4.0/
We come across hospitals and non-profit organizations that care for people with paralysis who have experienced all or portion of their physique being incapacitated by the paralyzing attack. Due to a lack of motor coordination by their mind, these persons are typically unable to communicate their requirements because they can speak clearly or use sign language. In such a case, we suggest a system that enables a disabled person to move any area of his body capable of moving to broadcast a text on the LCD. This method also addresses the circumstance in which the patient cannot be attended to in person and instead sends an SMS message using GSM. By detecting the user part's tilt direction, our suggested system operates. As a result, patients can communicate with physicians, therapists, or their loved ones at home or work over the web. Case-specific data, such as heart rate, must be continuously reported in health centers. The suggested method tracks the body of the case's pulse rate and other comparable data. For instance, photoplethysmography is used to assess heart rate. The decoded periodic data is transmitted continually via a Microcontroller coupled to a transmitting module. The croaker's cabin contains a receiver device that obtains and deciphers data as well as constantly exhibits it on Graphical interfaces viewable on the laptop. As a result, the croaker can monitor and handle multiple situations at once.
[ { "version": "v1", "created": "Fri, 21 Apr 2023 09:32:50 GMT" } ]
2023-04-24T00:00:00
[ [ "Srinivasan", "L.", "" ], [ "Selvaraj", "D.", "" ], [ "Dhinakaran", "D.", "" ], [ "Anish", "T. P.", "" ] ]
new_dataset
0.994248
2304.10854
Zhengcheng Shen
Zhengcheng Shen, Yi Gao, Linh K\"astner, Jens Lambrecht
HabitatDyn Dataset: Dynamic Object Detection to Kinematics Estimation
The paper is under review
null
null
null
cs.CV
http://creativecommons.org/licenses/by-nc-nd/4.0/
The advancement of computer vision and machine learning has made datasets a crucial element for further research and applications. However, the creation and development of robots with advanced recognition capabilities are hindered by the lack of appropriate datasets. Existing image or video processing datasets are unable to accurately depict observations from a moving robot, and they do not contain the kinematics information necessary for robotic tasks. Synthetic data, on the other hand, are cost-effective to create and offer greater flexibility for adapting to various applications. Hence, they are widely utilized in both research and industry. In this paper, we propose the dataset HabitatDyn, which contains both synthetic RGB videos, semantic labels, and depth information, as well as kinetics information. HabitatDyn was created from the perspective of a mobile robot with a moving camera, and contains 30 scenes featuring six different types of moving objects with varying velocities. To demonstrate the usability of our dataset, two existing algorithms are used for evaluation and an approach to estimate the distance between the object and camera is implemented based on these segmentation methods and evaluated through the dataset. With the availability of this dataset, we aspire to foster further advancements in the field of mobile robotics, leading to more capable and intelligent robots that can navigate and interact with their environments more effectively. The code is publicly available at https://github.com/ignc-research/HabitatDyn.
[ { "version": "v1", "created": "Fri, 21 Apr 2023 09:57:35 GMT" } ]
2023-04-24T00:00:00
[ [ "Shen", "Zhengcheng", "" ], [ "Gao", "Yi", "" ], [ "Kästner", "Linh", "" ], [ "Lambrecht", "Jens", "" ] ]
new_dataset
0.999852
2304.10877
Pengfei Qiu
Yu Jin, Pengfei Qiu, Chunlu Wang, Yihao Yang, Dongsheng Wang, Gang Qu
Timing the Transient Execution: A New Side-Channel Attack on Intel CPUs
null
null
null
null
cs.CR
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The transient execution attack is a type of attack leveraging the vulnerability of modern CPU optimization technologies. New attacks surface rapidly. The side-channel is a key part of transient execution attacks to leak data. In this work, we discover a vulnerability that the change of the EFLAGS register in transient execution may have a side effect on the Jcc (jump on condition code) instruction after it in Intel CPUs. Based on our discovery, we propose a new side-channel attack that leverages the timing of both transient execution and Jcc instructions to deliver data. This attack encodes secret data to the change of register which makes the execution time of context slightly slower, which can be measured by the attacker to decode data. This attack doesn't rely on the cache system and doesn't need to reset the EFLAGS register manually to its initial state before the attack, which may make it more difficult to detect or mitigate. We implemented this side-channel on machines with Intel Core i7-6700, i7-7700, and i9-10980XE CPUs. In the first two processors, we combined it as the side-channel of the Meltdown attack, which could achieve 100\% success leaking rate. We evaluate and discuss potential defenses against the attack. Our contributions include discovering security vulnerabilities in the implementation of Jcc instructions and EFLAGS register and proposing a new side-channel attack that does not rely on the cache system.
[ { "version": "v1", "created": "Fri, 21 Apr 2023 10:40:20 GMT" } ]
2023-04-24T00:00:00
[ [ "Jin", "Yu", "" ], [ "Qiu", "Pengfei", "" ], [ "Wang", "Chunlu", "" ], [ "Yang", "Yihao", "" ], [ "Wang", "Dongsheng", "" ], [ "Qu", "Gang", "" ] ]
new_dataset
0.998425
2304.10878
Rebekah Rousi Dr
Rebekah Rousi
AI Design, Design AI, Human-Centred AI and the Theatre of the Absurd the language, life and times of a UX designer
14 pages, 6 figures, Nordic network for research on communicative product design (Nordcode) seminar 2019
null
null
null
cs.HC
http://creativecommons.org/licenses/by-nc-sa/4.0/
This article connects the concepts and phenomena of Design AI, AI in creative industries and AIs capacity for creativity. It links Design AI to UX design and UX designer discourse. Its vagueness and the prominence of UX designers as speakers and writers in the spectacle of cultural AI discourse. The article then, draws comparisons between the Theatre of the Absurd and the UX designer performances of design AI. It additionally sheds light on ToA and the human condition in terms of existentialism, present within the practice of engaging in design that intends to link human experience to technological system logic. This is a theoretical article that utilises examples from UX events published on Youtube, as well as UX designer blogs, in order to illustrate the mechanics of the ToA present within contemporary AI and UX designer discourse.
[ { "version": "v1", "created": "Fri, 21 Apr 2023 10:40:58 GMT" } ]
2023-04-24T00:00:00
[ [ "Rousi", "Rebekah", "" ] ]
new_dataset
0.998379
2304.10893
Runwei Guan
Runwei Guan, Ka Lok Man, Feifan Chen, Shanliang Yao, Rongsheng Hu, Xiaohui Zhu, Jeremy Smith, Eng Gee Lim and Yutao Yue
FindVehicle and VehicleFinder: A NER dataset for natural language-based vehicle retrieval and a keyword-based cross-modal vehicle retrieval system
null
null
null
null
cs.CV cs.MM
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Natural language (NL) based vehicle retrieval is a task aiming to retrieve a vehicle that is most consistent with a given NL query from among all candidate vehicles. Because NL query can be easily obtained, such a task has a promising prospect in building an interactive intelligent traffic system (ITS). Current solutions mainly focus on extracting both text and image features and mapping them to the same latent space to compare the similarity. However, existing methods usually use dependency analysis or semantic role-labelling techniques to find keywords related to vehicle attributes. These techniques may require a lot of pre-processing and post-processing work, and also suffer from extracting the wrong keyword when the NL query is complex. To tackle these problems and simplify, we borrow the idea from named entity recognition (NER) and construct FindVehicle, a NER dataset in the traffic domain. It has 42.3k labelled NL descriptions of vehicle tracks, containing information such as the location, orientation, type and colour of the vehicle. FindVehicle also adopts both overlapping entities and fine-grained entities to meet further requirements. To verify its effectiveness, we propose a baseline NL-based vehicle retrieval model called VehicleFinder. Our experiment shows that by using text encoders pre-trained by FindVehicle, VehicleFinder achieves 87.7\% precision and 89.4\% recall when retrieving a target vehicle by text command on our homemade dataset based on UA-DETRAC. The time cost of VehicleFinder is 279.35 ms on one ARM v8.2 CPU and 93.72 ms on one RTX A4000 GPU, which is much faster than the Transformer-based system. The dataset is open-source via the link https://github.com/GuanRunwei/FindVehicle, and the implementation can be found via the link https://github.com/GuanRunwei/VehicleFinder-CTIM.
[ { "version": "v1", "created": "Fri, 21 Apr 2023 11:20:23 GMT" } ]
2023-04-24T00:00:00
[ [ "Guan", "Runwei", "" ], [ "Man", "Ka Lok", "" ], [ "Chen", "Feifan", "" ], [ "Yao", "Shanliang", "" ], [ "Hu", "Rongsheng", "" ], [ "Zhu", "Xiaohui", "" ], [ "Smith", "Jeremy", "" ], [ "Lim", "Eng Gee", "" ], [ "Yue", "Yutao", "" ] ]
new_dataset
0.999621
2304.10899
Yuriy Pershin
Zixi Zhang, Yuriy V. Pershin, and Ivar Martin
Electromechanical memcapacitive neurons for energy-efficient spiking neural networks
null
null
null
null
cs.ET cond-mat.mes-hall
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
In this article, we introduce a new nanoscale electromechanical device -- a leaky memcapacitor -- and show that it may be useful for the hardware implementation of spiking neurons. The leaky memcapacitor is a movable-plate capacitor that becomes quite conductive when the plates come close to each other. The equivalent circuit of the leaky memcapacitor involves a memcapacitive and memristive system connected in parallel. In the leaky memcapacitor, the resistance and capacitance depend on the same internal state variable, which is the displacement of the movable plate. We have performed a comprehensive analysis showing that several spiking types observed in biological neurons can be implemented with the leaky memcapacitor. Significant attention is paid to the dynamic properties of the model. As in leaky memcapacitors the capacitive and leaking resistive functionalities are implemented naturally within the same device structure, their use will simplify the creation of spiking neural networks.
[ { "version": "v1", "created": "Fri, 21 Apr 2023 11:34:58 GMT" } ]
2023-04-24T00:00:00
[ [ "Zhang", "Zixi", "" ], [ "Pershin", "Yuriy V.", "" ], [ "Martin", "Ivar", "" ] ]
new_dataset
0.999007
2304.10973
David Garcia
Segun Taofeek Aroyehun, Lukas Malik, Hannah Metzler, Nikolas Haimerl, Anna Di Natale, David Garcia
LEIA: Linguistic Embeddings for the Identification of Affect
null
null
null
null
cs.CL cs.AI cs.CY cs.LG
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The wealth of text data generated by social media has enabled new kinds of analysis of emotions with language models. These models are often trained on small and costly datasets of text annotations produced by readers who guess the emotions expressed by others in social media posts. This affects the quality of emotion identification methods due to training data size limitations and noise in the production of labels used in model development. We present LEIA, a model for emotion identification in text that has been trained on a dataset of more than 6 million posts with self-annotated emotion labels for happiness, affection, sadness, anger, and fear. LEIA is based on a word masking method that enhances the learning of emotion words during model pre-training. LEIA achieves macro-F1 values of approximately 73 on three in-domain test datasets, outperforming other supervised and unsupervised methods in a strong benchmark that shows that LEIA generalizes across posts, users, and time periods. We further perform an out-of-domain evaluation on five different datasets of social media and other sources, showing LEIA's robust performance across media, data collection methods, and annotation schemes. Our results show that LEIA generalizes its classification of anger, happiness, and sadness beyond the domain it was trained on. LEIA can be applied in future research to provide better identification of emotions in text from the perspective of the writer. The models produced for this article are publicly available at https://huggingface.co/LEIA
[ { "version": "v1", "created": "Fri, 21 Apr 2023 14:17:10 GMT" } ]
2023-04-24T00:00:00
[ [ "Aroyehun", "Segun Taofeek", "" ], [ "Malik", "Lukas", "" ], [ "Metzler", "Hannah", "" ], [ "Haimerl", "Nikolas", "" ], [ "Di Natale", "Anna", "" ], [ "Garcia", "David", "" ] ]
new_dataset
0.998789
2304.10983
Sergey Titov
Alexander Agroskin, Elena Lyulina, Sergey Titov, Vladimir Kovalenko
Constructing Temporal Networks of OSS Programming Language Ecosystems
Accepted to SANER 2023
null
null
null
cs.SE cs.SI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
One of the primary factors that encourage developers to contribute to open source software (OSS) projects is the collaborative nature of OSS development. However, the collaborative structure of these communities largely remains unclear, partly due to the enormous scale of data to be gathered, processed, and analyzed. In this work, we utilize the World Of Code dataset, which contains commit activity data for millions of OSS projects, to build collaboration networks for ten popular programming language ecosystems, containing in total over 290M commits across over 18M projects. We build a collaboration graph representation for each language ecosystem, having authors and projects as nodes, which enables various forms of social network analysis on the scale of language ecosystems. Moreover, we capture the information on the ecosystems' evolution by slicing each network into 30 historical snapshots. Additionally, we calculate multiple collaboration metrics that characterize the ecosystems' states. We make the resulting dataset publicly available, including the constructed graphs and the pipeline enabling the analysis of more ecosystems.
[ { "version": "v1", "created": "Fri, 21 Apr 2023 14:30:30 GMT" } ]
2023-04-24T00:00:00
[ [ "Agroskin", "Alexander", "" ], [ "Lyulina", "Elena", "" ], [ "Titov", "Sergey", "" ], [ "Kovalenko", "Vladimir", "" ] ]
new_dataset
0.962362
2304.10987
Jianheng Liu
Jianheng Liu, Xuanfu Li, Yueqian Liu, Haoyao Chen
RGB-D Inertial Odometry for a Resource-Restricted Robot in Dynamic Environments
null
IEEE Robotics and Automation Letters ( Volume: 7, Issue: 4, October 2022)
10.1109/LRA.2022.3191193
null
cs.RO
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Current simultaneous localization and mapping (SLAM) algorithms perform well in static environments but easily fail in dynamic environments. Recent works introduce deep learning-based semantic information to SLAM systems to reduce the influence of dynamic objects. However, it is still challenging to apply a robust localization in dynamic environments for resource-restricted robots. This paper proposes a real-time RGB-D inertial odometry system for resource-restricted robots in dynamic environments named Dynamic-VINS. Three main threads run in parallel: object detection, feature tracking, and state optimization. The proposed Dynamic-VINS combines object detection and depth information for dynamic feature recognition and achieves performance comparable to semantic segmentation. Dynamic-VINS adopts grid-based feature detection and proposes a fast and efficient method to extract high-quality FAST feature points. IMU is applied to predict motion for feature tracking and moving consistency check. The proposed method is evaluated on both public datasets and real-world applications and shows competitive localization accuracy and robustness in dynamic environments. Yet, to the best of our knowledge, it is the best-performance real-time RGB-D inertial odometry for resource-restricted platforms in dynamic environments for now. The proposed system is open source at: https://github.com/HITSZ-NRSL/Dynamic-VINS.git
[ { "version": "v1", "created": "Fri, 21 Apr 2023 14:37:11 GMT" } ]
2023-04-24T00:00:00
[ [ "Liu", "Jianheng", "" ], [ "Li", "Xuanfu", "" ], [ "Liu", "Yueqian", "" ], [ "Chen", "Haoyao", "" ] ]
new_dataset
0.978142
2304.10990
Iris Andrussow
Iris Andrussow, Huanbo Sun, Katherine J. Kuchenbecker, and Georg Martius
Minsight: A Fingertip-Sized Vision-Based Tactile Sensor for Robotic Manipulation
null
null
null
null
cs.RO
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Intelligent interaction with the physical world requires perceptual abilities beyond vision and hearing; vibrant tactile sensing is essential for autonomous robots to dexterously manipulate unfamiliar objects or safely contact humans. Therefore, robotic manipulators need high-resolution touch sensors that are compact, robust, inexpensive, and efficient. The soft vision-based haptic sensor presented herein is a miniaturized and optimized version of the previously published sensor Insight. Minsight has the size and shape of a human fingertip and uses machine learning methods to output high-resolution maps of 3D contact force vectors at 60 Hz. Experiments confirm its excellent sensing performance, with a mean absolute force error of 0.07 N and contact location error of 0.6 mm across its surface area. Minsight's utility is shown in two robotic tasks on a 3-DoF manipulator. First, closed-loop force control enables the robot to track the movements of a human finger based only on tactile data. Second, the informative value of the sensor output is shown by detecting whether a hard lump is embedded within a soft elastomer with an accuracy of 98%. These findings indicate that Minsight can give robots the detailed fingertip touch sensing needed for dexterous manipulation and physical human-robot interaction.
[ { "version": "v1", "created": "Fri, 21 Apr 2023 14:39:47 GMT" } ]
2023-04-24T00:00:00
[ [ "Andrussow", "Iris", "" ], [ "Sun", "Huanbo", "" ], [ "Kuchenbecker", "Katherine J.", "" ], [ "Martius", "Georg", "" ] ]
new_dataset
0.999156
2304.11014
Philip Saville
Hugo Paquet, Philip Saville
Strong pseudomonads and premonoidal bicategories
Comments and feedback welcome!
null
null
null
cs.LO math.CT
http://creativecommons.org/licenses/by/4.0/
Strong monads and premonoidal categories play a central role in clarifying the denotational semantics of effectful programming languages. Unfortunately, this theory excludes many modern semantic models in which the associativity and unit laws only hold up to coherent isomorphism: for instance, because composition is defined using a universal property. This paper remedies the situation. We define premonoidal bicategories and a notion of strength for pseudomonads, and show that the Kleisli bicategory of a strong pseudomonad is premonoidal. As often in 2-dimensional category theory, the main difficulty is to find the correct coherence axioms on 2-cells. We therefore justify our definitions with numerous examples and by proving a correspondence theorem between actions and strengths, generalizing a well-known category-theoretic result.
[ { "version": "v1", "created": "Fri, 21 Apr 2023 15:01:25 GMT" } ]
2023-04-24T00:00:00
[ [ "Paquet", "Hugo", "" ], [ "Saville", "Philip", "" ] ]
new_dataset
0.955001
2304.11030
Jiaao Yu
Jiaao Yu, Paul-Philipp Manea, Sara Ameli, Mohammad Hizzani, Amro Eldebiky, John Paul Strachan
Analog Feedback-Controlled Memristor programming Circuit for analog Content Addressable Memory
null
null
null
null
cs.ET cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Recent breakthroughs in associative memories suggest that silicon memories are coming closer to human memories, especially for memristive Content Addressable Memories (CAMs) which are capable to read and write in analog values. However, the Program-Verify algorithm, the state-of-the-art memristor programming algorithm, requires frequent switching between verifying and programming memristor conductance, which brings many defects such as high dynamic power and long programming time. Here, we propose an analog feedback-controlled memristor programming circuit that makes use of a novel look-up table-based (LUT-based) programming algorithm. With the proposed algorithm, the programming and the verification of a memristor can be performed in a single-direction sequential process. Besides, we also integrated a single proposed programming circuit with eight analog CAM (aCAM) cells to build an aCAM array. We present SPICE simulations on TSMC 28nm process. The theoretical analysis shows that 1. A memristor conductance within an aCAM cell can be converted to an output boundary voltage in aCAM searching operations and 2. An output boundary voltage in aCAM searching operations can be converted to a programming data line voltage in aCAM programming operations. The simulation results of the proposed programming circuit prove the theoretical analysis and thus verify the feasibility to program memristors without frequently switching between verifying and programming the conductance. Besides, the simulation results of the proposed aCAM array show that the proposed programming circuit can be integrated into a large array architecture.
[ { "version": "v1", "created": "Fri, 21 Apr 2023 15:23:50 GMT" } ]
2023-04-24T00:00:00
[ [ "Yu", "Jiaao", "" ], [ "Manea", "Paul-Philipp", "" ], [ "Ameli", "Sara", "" ], [ "Hizzani", "Mohammad", "" ], [ "Eldebiky", "Amro", "" ], [ "Strachan", "John Paul", "" ] ]
new_dataset
0.997723
2304.11052
Thomas Kunz
Thomas Kunz, Christian Fisher, James La Novara-Gsell, Christopher Nguyen, Li Li
A Multiagent CyberBattleSim for RL Cyber Operation Agents
To appear in Proceedings of the 2022 International Conference on Computational Science and Computational Intelligence
null
null
null
cs.CR cs.AI cs.LG
http://creativecommons.org/licenses/by/4.0/
Hardening cyber physical assets is both crucial and labor-intensive. Recently, Machine Learning (ML) in general and Reinforcement Learning RL) more specifically has shown great promise to automate tasks that otherwise would require significant human insight/intelligence. The development of autonomous RL agents requires a suitable training environment that allows us to quickly evaluate various alternatives, in particular how to arrange training scenarios that pit attackers and defenders against each other. CyberBattleSim is a training environment that supports the training of red agents, i.e., attackers. We added the capability to train blue agents, i.e., defenders. The paper describes our changes and reports on the results we obtained when training blue agents, either in isolation or jointly with red agents. Our results show that training a blue agent does lead to stronger defenses against attacks. In particular, training a blue agent jointly with a red agent increases the blue agent's capability to thwart sophisticated red agents.
[ { "version": "v1", "created": "Mon, 3 Apr 2023 20:43:19 GMT" } ]
2023-04-24T00:00:00
[ [ "Kunz", "Thomas", "" ], [ "Fisher", "Christian", "" ], [ "La Novara-Gsell", "James", "" ], [ "Nguyen", "Christopher", "" ], [ "Li", "Li", "" ] ]
new_dataset
0.995703
2304.11060
Nan Li
Nan Li, Bo Kang, Tijl De Bie
SkillGPT: a RESTful API service for skill extraction and standardization using a Large Language Model
null
null
null
null
cs.CL cs.AI
http://creativecommons.org/licenses/by-nc-sa/4.0/
We present SkillGPT, a tool for skill extraction and standardization (SES) from free-style job descriptions and user profiles with an open-source Large Language Model (LLM) as backbone. Most previous methods for similar tasks either need supervision or rely on heavy data-preprocessing and feature engineering. Directly prompting the latest conversational LLM for standard skills, however, is slow, costly and inaccurate. In contrast, SkillGPT utilizes a LLM to perform its tasks in steps via summarization and vector similarity search, to balance speed with precision. The backbone LLM of SkillGPT is based on Llama, free for academic use and thus useful for exploratory research and prototype development. Hence, our cost-free SkillGPT gives users the convenience of conversational SES, efficiently and reliably.
[ { "version": "v1", "created": "Mon, 17 Apr 2023 08:43:20 GMT" } ]
2023-04-24T00:00:00
[ [ "Li", "Nan", "" ], [ "Kang", "Bo", "" ], [ "De Bie", "Tijl", "" ] ]
new_dataset
0.960301
2304.11077
Vitaly Shalumov
Vitaly Shalumov and Harel Haskey
HeRo: RoBERTa and Longformer Hebrew Language Models
null
null
null
null
cs.CL cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
In this paper, we fill in an existing gap in resources available to the Hebrew NLP community by providing it with the largest so far pre-train dataset HeDC4, a state-of-the-art pre-trained language model HeRo for standard length inputs and an efficient transformer LongHeRo for long input sequences. The HeRo model was evaluated on the sentiment analysis, the named entity recognition, and the question answering tasks while the LongHeRo model was evaluated on the document classification task with a dataset composed of long documents. Both HeRo and LongHeRo presented state-of-the-art performance. The dataset and model checkpoints used in this work are publicly available.
[ { "version": "v1", "created": "Tue, 18 Apr 2023 05:56:32 GMT" } ]
2023-04-24T00:00:00
[ [ "Shalumov", "Vitaly", "" ], [ "Haskey", "Harel", "" ] ]
new_dataset
0.999727
2304.11081
Shashank Gupta
Avval Amil and Shashank Gupta
Cryptanalysis of quantum permutation pad
7 pages, 1 figures, comments are welcome
null
null
null
cs.CR math.CO
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Cryptanalysis increases the level of confidence in cryptographic algorithms. We analyze the security of a symmetric cryptographic algorithm - quantum permutation pad (QPP) [8]. We found the instances of ciphertext the same as plaintext even after the action of QPP with the probability 1/N when the entire set of permutation matrices of dimension N is used and with the probability 1/N^m when an incomplete set of m permutation matrices of dimension N are used. We visually show such instances in a cipher image created by QPP of 256 permutation matrices of different dimensions. For any practical usage of QPP, we recommend a set of 256 permutation matrices of a dimension more or equal to 2048.
[ { "version": "v1", "created": "Wed, 5 Apr 2023 17:22:31 GMT" } ]
2023-04-24T00:00:00
[ [ "Amil", "Avval", "" ], [ "Gupta", "Shashank", "" ] ]
new_dataset
0.995503
2304.11087
Ebenezer Isaac
Ebenezer R. H. P. Isaac and Jim Reno
AI Product Security: A Primer for Developers
10 pages, 1 figure
null
null
null
cs.CR cs.AI
http://creativecommons.org/licenses/by/4.0/
Not too long ago, AI security used to mean the research and practice of how AI can empower cybersecurity, that is, AI for security. Ever since Ian Goodfellow and his team popularized adversarial attacks on machine learning, security for AI became an important concern and also part of AI security. It is imperative to understand the threats to machine learning products and avoid common pitfalls in AI product development. This article is addressed to developers, designers, managers and researchers of AI software products.
[ { "version": "v1", "created": "Tue, 18 Apr 2023 05:22:34 GMT" } ]
2023-04-24T00:00:00
[ [ "Isaac", "Ebenezer R. H. P.", "" ], [ "Reno", "Jim", "" ] ]
new_dataset
0.981333
2304.11093
Meidai Xuanyuan
Meidai Xuanyuan, Yuwang Wang, Honglei Guo, Xiao Ma, Yuchen Guo, Tao Yu, Qionghai Dai
Hi Sheldon! Creating Deep Personalized Characters from TV Shows
null
null
null
null
cs.CL cs.AI cs.CV cs.LG
http://creativecommons.org/licenses/by/4.0/
Imagine an interesting multimodal interactive scenario that you can see, hear, and chat with an AI-generated digital character, who is capable of behaving like Sheldon from The Big Bang Theory, as a DEEP copy from appearance to personality. Towards this fantastic multimodal chatting scenario, we propose a novel task, named Deep Personalized Character Creation (DPCC): creating multimodal chat personalized characters from multimodal data such as TV shows. Specifically, given a single- or multi-modality input (text, audio, video), the goal of DPCC is to generate a multi-modality (text, audio, video) response, which should be well-matched the personality of a specific character such as Sheldon, and of high quality as well. To support this novel task, we further collect a character centric multimodal dialogue dataset, named Deep Personalized Character Dataset (DPCD), from TV shows. DPCD contains character-specific multimodal dialogue data of ~10k utterances and ~6 hours of audio/video per character, which is around 10 times larger compared to existing related datasets.On DPCD, we present a baseline method for the DPCC task and create 5 Deep personalized digital Characters (DeepCharacters) from Big Bang TV Shows. We conduct both subjective and objective experiments to evaluate the multimodal response from DeepCharacters in terms of characterization and quality. The results demonstrates that, on our collected DPCD dataset, the proposed baseline can create personalized digital characters for generating multimodal response.Our collected DPCD dataset, the code of data collection and our baseline will be published soon.
[ { "version": "v1", "created": "Sun, 9 Apr 2023 00:39:43 GMT" } ]
2023-04-24T00:00:00
[ [ "Xuanyuan", "Meidai", "" ], [ "Wang", "Yuwang", "" ], [ "Guo", "Honglei", "" ], [ "Ma", "Xiao", "" ], [ "Guo", "Yuchen", "" ], [ "Yu", "Tao", "" ], [ "Dai", "Qionghai", "" ] ]
new_dataset
0.995532
1312.3604
Michael May
Michael P. May
A closed-form solution for the flat-state geometry of cylindrical surface intersections bounded on all sides by orthogonal planes
null
null
null
null
cs.CG
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
A closed-form solution for the boundary of the flat state of an orthogonal cross section of contiguous surface geometry formed by the intersection of two cylinders of equal radii oriented in dual directions of rotation about their intersecting axes.
[ { "version": "v1", "created": "Thu, 12 Dec 2013 19:51:48 GMT" }, { "version": "v2", "created": "Thu, 20 Apr 2023 00:16:15 GMT" } ]
2023-04-21T00:00:00
[ [ "May", "Michael P.", "" ] ]
new_dataset
0.999725
2103.03032
Hans van Ditmarsch
Hans van Ditmarsch, Roman Kuznets
Wanted Dead or Alive : Epistemic logic for impure simplicial complexes
null
null
null
null
cs.DC cs.LO
http://creativecommons.org/licenses/by/4.0/
We propose a logic of knowledge for impure simplicial complexes. Impure simplicial complexes represent synchronous distributed systems under uncertainty over which processes are still active (are alive) and which processes have failed or crashed (are dead). Our work generalizes the logic of knowledge for pure simplicial complexes, where all processes are alive, by Goubault et al. In our semantics, given a designated face in a complex, a formula can only be true or false there if it is defined. The following are undefined: dead processes cannot know or be ignorant of any proposition, and live processes cannot know or be ignorant of factual propositions involving processes they know to be dead. The semantics are therefore three-valued, with undefined as the third value. We propose an axiomatization that is a version of the modal logic S5. We also show that impure simplicial complexes correspond to certain Kripke models where agents' accessibility relations are equivalence relations on a subset of the domain only. This work extends a WoLLIC 21 conference publication with the same title.
[ { "version": "v1", "created": "Thu, 4 Mar 2021 13:47:35 GMT" }, { "version": "v2", "created": "Wed, 16 Feb 2022 12:42:11 GMT" }, { "version": "v3", "created": "Thu, 20 Apr 2023 07:23:24 GMT" } ]
2023-04-21T00:00:00
[ [ "van Ditmarsch", "Hans", "" ], [ "Kuznets", "Roman", "" ] ]
new_dataset
0.979116
2204.03245
Jiashun Suo
Jiashun Suo, Tianyi Wang, Xingzhou Zhang, Haiyang Chen, Wei Zhou, Weisong Shi
HIT-UAV: A high-altitude infrared thermal dataset for Unmanned Aerial Vehicle-based object detection
null
Sci Data 10, 227 (2023)
10.1038/s41597-023-02066-6
null
cs.CV cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We present the HIT-UAV dataset, a high-altitude infrared thermal dataset for object detection applications on Unmanned Aerial Vehicles (UAVs). The dataset comprises 2,898 infrared thermal images extracted from 43,470 frames in hundreds of videos captured by UAVs in various scenarios including schools, parking lots, roads, and playgrounds. Moreover, the HIT-UAV provides essential flight data for each image, such as flight altitude, camera perspective, date, and daylight intensity. For each image, we have manually annotated object instances with bounding boxes of two types (oriented and standard) to tackle the challenge of significant overlap of object instances in aerial images. To the best of our knowledge, the HIT-UAV is the first publicly available high-altitude UAV-based infrared thermal dataset for detecting persons and vehicles. We have trained and evaluated well-established object detection algorithms on the HIT-UAV. Our results demonstrate that the detection algorithms perform exceptionally well on the HIT-UAV compared to visual light datasets since infrared thermal images do not contain significant irrelevant information about objects. We believe that the HIT-UAV will contribute to various UAV-based applications and researches. The dataset is freely available at https://github.com/suojiashun/HIT-UAV-Infrared-Thermal-Dataset.
[ { "version": "v1", "created": "Thu, 7 Apr 2022 06:23:02 GMT" }, { "version": "v2", "created": "Fri, 31 Mar 2023 11:04:55 GMT" } ]
2023-04-21T00:00:00
[ [ "Suo", "Jiashun", "" ], [ "Wang", "Tianyi", "" ], [ "Zhang", "Xingzhou", "" ], [ "Chen", "Haiyang", "" ], [ "Zhou", "Wei", "" ], [ "Shi", "Weisong", "" ] ]
new_dataset
0.999855
2208.14788
Ondrej Bajgar
Ondrej Bajgar and Jan Horenovsky
Negative Human Rights as a Basis for Long-term AI Safety and Regulation
null
Journal of Artificial Intelligence Research 76 (2023) 1043-1075
10.1613/jair.1.14020
null
cs.CY cs.AI
http://creativecommons.org/licenses/by/4.0/
If autonomous AI systems are to be reliably safe in novel situations, they will need to incorporate general principles guiding them to recognize and avoid harmful behaviours. Such principles may need to be supported by a binding system of regulation, which would need the underlying principles to be widely accepted. They should also be specific enough for technical implementation. Drawing inspiration from law, this article explains how negative human rights could fulfil the role of such principles and serve as a foundation both for an international regulatory system and for building technical safety constraints for future AI systems.
[ { "version": "v1", "created": "Wed, 31 Aug 2022 11:57:13 GMT" }, { "version": "v2", "created": "Thu, 20 Apr 2023 09:27:07 GMT" } ]
2023-04-21T00:00:00
[ [ "Bajgar", "Ondrej", "" ], [ "Horenovsky", "Jan", "" ] ]
new_dataset
0.961295
2210.11234
Guowen Li
Guowen Li, Zhiyao Yang, Yangyang Fu, Lingyu Ren, Zheng O'Neill, Chirag Parikh
Development of a hardware-In-the-Loop (HIL) testbed for cyber-physical security in smart buildings
Presented at the 2023 ASHRAE Winter Conference
null
null
null
cs.CR
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
As smart buildings move towards open communication technologies, providing access to the Building Automation System (BAS) through the intranet, or even remotely through the Internet, has become a common practice. However, BAS was historically developed as a closed environment and designed with limited cyber-security considerations. Thus, smart buildings are vulnerable to cyber-attacks with the increased accessibility. This study introduces the development and capability of a Hardware-in-the-Loop (HIL) testbed for testing and evaluating the cyber-physical security of typical BASs in smart buildings. The testbed consists of three subsystems: (1) a real-time HIL emulator simulating the behavior of a virtual building as well as the Heating, Ventilation, and Air Conditioning (HVAC) equipment via a dynamic simulation in Modelica; (2) a set of real HVAC controllers monitoring the virtual building operation and providing local control signals to control HVAC equipment in the HIL emulator; and (3) a BAS server along with a web-based service for users to fully access the schedule, setpoints, trends, alarms, and other control functions of the HVAC controllers remotely through the BACnet network. The server generates rule-based setpoints to local HVAC controllers. Based on these three subsystems, the HIL testbed supports attack/fault-free and attack/fault-injection experiments at various levels of the building system. The resulting test data can be used to inform the building community and support the cyber-physical security technology transfer to the building industry.
[ { "version": "v1", "created": "Mon, 17 Oct 2022 02:39:07 GMT" }, { "version": "v2", "created": "Fri, 21 Oct 2022 00:48:15 GMT" }, { "version": "v3", "created": "Thu, 20 Apr 2023 17:15:31 GMT" } ]
2023-04-21T00:00:00
[ [ "Li", "Guowen", "" ], [ "Yang", "Zhiyao", "" ], [ "Fu", "Yangyang", "" ], [ "Ren", "Lingyu", "" ], [ "O'Neill", "Zheng", "" ], [ "Parikh", "Chirag", "" ] ]
new_dataset
0.97773
2301.02711
Thomas Thuesen Enevoldsen
Thomas T. Enevoldsen, Mogens Blanke, Roberto Galeazzi
Autonomy for Ferries and Harbour Buses: a Collision Avoidance Perspective
Accepted for presentation at the IFAC World Congress 2023
null
null
null
cs.RO cs.SY eess.SY
http://creativecommons.org/licenses/by-nc-nd/4.0/
This paper provides a collision avoidance perspective to maritime autonomy, in the shift towards Maritime Autonomous Surface Ships (MASS). In particular, the paper presents the developments related to the Greenhopper, Denmark's first autonomous harbour bus. The collision and grounding avoidance scheme, called the Short Horizon Planner (SHP), is described and discussed in detail. Furthermore, the required autonomy stack for facilitating safe and rule-compliant collision avoidance is presented. The inherent difficulties related to adhering to the COLREGs are outlined, highlighting some of the operational constraints and challenges within the space of autonomous ferries and harbour buses. Finally, collision and grounding avoidance is demonstrated using a simulation of the whole Greenhopper autonomy stack.
[ { "version": "v1", "created": "Fri, 6 Jan 2023 20:57:47 GMT" }, { "version": "v2", "created": "Thu, 20 Apr 2023 09:05:48 GMT" } ]
2023-04-21T00:00:00
[ [ "Enevoldsen", "Thomas T.", "" ], [ "Blanke", "Mogens", "" ], [ "Galeazzi", "Roberto", "" ] ]
new_dataset
0.997864
2302.00782
Jacob Schrum
Alejandro Medina and Melanie Richey and Mark Mueller and Jacob Schrum
Evolving Flying Machines in Minecraft Using Quality Diversity
In Genetic and Evolutionary Computation Conference (GECCO '23), July 15-19, 2023, Lisbon, Portugal
null
10.1145/3583131.3590352
null
cs.NE cs.AI
http://creativecommons.org/licenses/by/4.0/
Minecraft is a great testbed for human creativity that has inspired the design of various structures and even functioning machines, including flying machines. EvoCraft is an API for programmatically generating structures in Minecraft, but the initial work in this domain was not capable of evolving flying machines. This paper applies fitness-based evolution and quality diversity search in order to evolve flying machines. Although fitness alone can occasionally produce flying machines, thanks in part to a more sophisticated fitness function than was used previously, the quality diversity algorithm MAP-Elites is capable of discovering flying machines much more reliably, at least when an appropriate behavior characterization is used to guide the search for diverse solutions.
[ { "version": "v1", "created": "Wed, 1 Feb 2023 22:25:28 GMT" }, { "version": "v2", "created": "Wed, 19 Apr 2023 21:35:07 GMT" } ]
2023-04-21T00:00:00
[ [ "Medina", "Alejandro", "" ], [ "Richey", "Melanie", "" ], [ "Mueller", "Mark", "" ], [ "Schrum", "Jacob", "" ] ]
new_dataset
0.995285
2302.07777
Florentin Putz
Leon W\"ursching, Florentin Putz, Steffen Haesler, Matthias Hollick
FIDO2 the Rescue? Platform vs. Roaming Authentication on Smartphones
16 pages, 6 figures, the dataset is available at https://doi.org/10.5281/zenodo.7572697 and the source code is available at https://github.com/seemoo-lab/fido2-the-smartphone
ACM CHI 2023
10.1145/3544548.3580993
null
cs.CR cs.HC cs.NI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Modern smartphones support FIDO2 passwordless authentication using either external security keys or internal biometric authentication, but it is unclear whether users appreciate and accept these new forms of web authentication for their own accounts. We present the first lab study (N=87) comparing platform and roaming authentication on smartphones, determining the practical strengths and weaknesses of FIDO2 as perceived by users in a mobile scenario. Most participants were willing to adopt passwordless authentication during our in-person user study, but closer analysis shows that participants prioritize usability, security, and availability differently depending on the account type. We identify remaining adoption barriers that prevent FIDO2 from succeeding password authentication, such as missing support for contemporary usage patterns, including account delegation and usage on multiple clients.
[ { "version": "v1", "created": "Wed, 15 Feb 2023 16:54:34 GMT" } ]
2023-04-21T00:00:00
[ [ "Würsching", "Leon", "" ], [ "Putz", "Florentin", "" ], [ "Haesler", "Steffen", "" ], [ "Hollick", "Matthias", "" ] ]
new_dataset
0.97079
2303.00304
Obin Kwon
Obin Kwon, Jeongho Park, Songhwai Oh
Renderable Neural Radiance Map for Visual Navigation
Preprint version. CVPR 2023 accepted, highlight paper. Project page: https://rllab-snu.github.io/projects/RNR-Map/
null
null
null
cs.CV cs.RO
http://creativecommons.org/licenses/by-nc-sa/4.0/
We propose a novel type of map for visual navigation, a renderable neural radiance map (RNR-Map), which is designed to contain the overall visual information of a 3D environment. The RNR-Map has a grid form and consists of latent codes at each pixel. These latent codes are embedded from image observations, and can be converted to the neural radiance field which enables image rendering given a camera pose. The recorded latent codes implicitly contain visual information about the environment, which makes the RNR-Map visually descriptive. This visual information in RNR-Map can be a useful guideline for visual localization and navigation. We develop localization and navigation frameworks that can effectively utilize the RNR-Map. We evaluate the proposed frameworks on camera tracking, visual localization, and image-goal navigation. Experimental results show that the RNR-Map-based localization framework can find the target location based on a single query image with fast speed and competitive accuracy compared to other baselines. Also, this localization framework is robust to environmental changes, and even finds the most visually similar places when a query image from a different environment is given. The proposed navigation framework outperforms the existing image-goal navigation methods in difficult scenarios, under odometry and actuation noises. The navigation framework shows 65.7% success rate in curved scenarios of the NRNS dataset, which is an improvement of 18.6% over the current state-of-the-art. Project page: https://rllab-snu.github.io/projects/RNR-Map/
[ { "version": "v1", "created": "Wed, 1 Mar 2023 08:00:46 GMT" }, { "version": "v2", "created": "Fri, 3 Mar 2023 11:12:20 GMT" }, { "version": "v3", "created": "Thu, 23 Mar 2023 05:59:24 GMT" }, { "version": "v4", "created": "Thu, 20 Apr 2023 01:50:55 GMT" } ]
2023-04-21T00:00:00
[ [ "Kwon", "Obin", "" ], [ "Park", "Jeongho", "" ], [ "Oh", "Songhwai", "" ] ]
new_dataset
0.999347
2304.02122
Joe Yue-Hei Ng
Joe Yue-Hei Ng, Kevin McCloskey, Jian Cui, Vincent R. Meijer, Erica Brand, Aaron Sarna, Nita Goyal, Christopher Van Arsdale, Scott Geraedts
OpenContrails: Benchmarking Contrail Detection on GOES-16 ABI
null
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Contrails (condensation trails) are line-shaped ice clouds caused by aircraft and are likely the largest contributor of aviation-induced climate change. Contrail avoidance is potentially an inexpensive way to significantly reduce the climate impact of aviation. An automated contrail detection system is an essential tool to develop and evaluate contrail avoidance systems. In this paper, we present a human-labeled dataset named OpenContrails to train and evaluate contrail detection models based on GOES-16 Advanced Baseline Imager (ABI) data. We propose and evaluate a contrail detection model that incorporates temporal context for improved detection accuracy. The human labeled dataset and the contrail detection outputs are publicly available on Google Cloud Storage at gs://goes_contrails_dataset.
[ { "version": "v1", "created": "Tue, 4 Apr 2023 21:03:46 GMT" }, { "version": "v2", "created": "Thu, 20 Apr 2023 06:00:41 GMT" } ]
2023-04-21T00:00:00
[ [ "Ng", "Joe Yue-Hei", "" ], [ "McCloskey", "Kevin", "" ], [ "Cui", "Jian", "" ], [ "Meijer", "Vincent R.", "" ], [ "Brand", "Erica", "" ], [ "Sarna", "Aaron", "" ], [ "Goyal", "Nita", "" ], [ "Van Arsdale", "Christopher", "" ], [ "Geraedts", "Scott", "" ] ]
new_dataset
0.999789
2304.04087
Tanveer Ahmed Belal
Tanveer Ahmed Belal, G. M. Shahariar, Md. Hasanul Kabir
Interpretable Multi Labeled Bengali Toxic Comments Classification using Deep Learning
null
2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)
10.1109/ECCE57851.2023.10101588
null
cs.CL cs.AI cs.LG
http://creativecommons.org/licenses/by-nc-nd/4.0/
This paper presents a deep learning-based pipeline for categorizing Bengali toxic comments, in which at first a binary classification model is used to determine whether a comment is toxic or not, and then a multi-label classifier is employed to determine which toxicity type the comment belongs to. For this purpose, we have prepared a manually labeled dataset consisting of 16,073 instances among which 8,488 are Toxic and any toxic comment may correspond to one or more of the six toxic categories - vulgar, hate, religious, threat, troll, and insult simultaneously. Long Short Term Memory (LSTM) with BERT Embedding achieved 89.42% accuracy for the binary classification task while as a multi-label classifier, a combination of Convolutional Neural Network and Bi-directional Long Short Term Memory (CNN-BiLSTM) with attention mechanism achieved 78.92% accuracy and 0.86 as weighted F1-score. To explain the predictions and interpret the word feature importance during classification by the proposed models, we utilized Local Interpretable Model-Agnostic Explanations (LIME) framework. We have made our dataset public and can be accessed at - https://github.com/deepu099cse/Multi-Labeled-Bengali-Toxic-Comments-Classification
[ { "version": "v1", "created": "Sat, 8 Apr 2023 19:28:26 GMT" } ]
2023-04-21T00:00:00
[ [ "Belal", "Tanveer Ahmed", "" ], [ "Shahariar", "G. M.", "" ], [ "Kabir", "Md. Hasanul", "" ] ]
new_dataset
0.99894
2304.08756
Yang Liu
Yang Liu, Shen Yan, Yuge Zhang, Kan Ren, Quanlu Zhang, Zebin Ren, Deng Cai, Mi Zhang
AutoTaskFormer: Searching Vision Transformers for Multi-task Learning
15 pages
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Vision Transformers have shown great performance in single tasks such as classification and segmentation. However, real-world problems are not isolated, which calls for vision transformers that can perform multiple tasks concurrently. Existing multi-task vision transformers are handcrafted and heavily rely on human expertise. In this work, we propose a novel one-shot neural architecture search framework, dubbed AutoTaskFormer (Automated Multi-Task Vision TransFormer), to automate this process. AutoTaskFormer not only identifies the weights to share across multiple tasks automatically, but also provides thousands of well-trained vision transformers with a wide range of parameters (e.g., number of heads and network depth) for deployment under various resource constraints. Experiments on both small-scale (2-task Cityscapes and 3-task NYUv2) and large-scale (16-task Taskonomy) datasets show that AutoTaskFormer outperforms state-of-the-art handcrafted vision transformers in multi-task learning. The entire code and models will be open-sourced.
[ { "version": "v1", "created": "Tue, 18 Apr 2023 06:30:20 GMT" }, { "version": "v2", "created": "Thu, 20 Apr 2023 02:27:04 GMT" } ]
2023-04-21T00:00:00
[ [ "Liu", "Yang", "" ], [ "Yan", "Shen", "" ], [ "Zhang", "Yuge", "" ], [ "Ren", "Kan", "" ], [ "Zhang", "Quanlu", "" ], [ "Ren", "Zebin", "" ], [ "Cai", "Deng", "" ], [ "Zhang", "Mi", "" ] ]
new_dataset
0.998874
2304.09859
Daniel G. Krakowczyk
Daniel G. Krakowczyk, David R. Reich, Jakob Chwastek, Deborah N. Jakobi, Paul Prasse, Assunta S\"uss, Oleksii Turuta, Pawe{\l} Kasprowski, Lena A. J\"ager
pymovements: A Python Package for Eye Movement Data Processing
Preprint for ETRA '23: 2023 Symposium on Eye Tracking Research and Applications
null
10.1145/3588015.3590134
null
cs.HC
http://creativecommons.org/licenses/by/4.0/
We introduce pymovements: a Python package for analyzing eye-tracking data that follows best practices in software development, including rigorous testing and adherence to coding standards. The package provides functionality for key processes along the entire preprocessing pipeline. This includes parsing of eye tracker data files, transforming positional data into velocity data, detecting gaze events like saccades and fixations, computing event properties like saccade amplitude and fixational dispersion and visualizing data and results with several types of plotting methods. Moreover, pymovements also provides an easily accessible interface for downloading and processing publicly available datasets. Additionally, we emphasize how rigorous testing in scientific software packages is critical to the reproducibility and transparency of research, enabling other researchers to verify and build upon previous findings.
[ { "version": "v1", "created": "Tue, 11 Apr 2023 18:39:37 GMT" } ]
2023-04-21T00:00:00
[ [ "Krakowczyk", "Daniel G.", "" ], [ "Reich", "David R.", "" ], [ "Chwastek", "Jakob", "" ], [ "Jakobi", "Deborah N.", "" ], [ "Prasse", "Paul", "" ], [ "Süss", "Assunta", "" ], [ "Turuta", "Oleksii", "" ], [ "Kasprowski", "Paweł", "" ], [ "Jäger", "Lena A.", "" ] ]
new_dataset
0.960809
2304.09860
Manuel Striani
Manuel Striani
NRTS: A Client-Server architecture for supporting data recording, transmission and evaluation of multidisciplinary teams during the neonatal resuscitation simulation scenario
8 pages, 13 figures, 6 references
null
null
null
cs.HC cs.AI cs.NI
http://creativecommons.org/licenses/by-sa/4.0/
In this technical report, we describe Neonatal Resuscitation Training Simulator (NRTS), an Android mobile app designed to support medical experts to input, transmit and record data during a High-Fidelity Simulation course for neonatal resuscitation. This mobile app allows one to automatically send all the recorded data from "Neonatal Intensive Care Unit" (NICU) of Casale Monferrato Children's Hospital, (Italy) to a server located at the Department of Science and Technological Innovation (DiSIT), University of Piemonte Orientale (Italy). Finally, the medical instructor can view statistics on a simulation exercise that may be used during the de-briefing phase for the evaluation of multidisciplinary teams involved in the simulation scenarios.
[ { "version": "v1", "created": "Wed, 12 Apr 2023 07:36:40 GMT" } ]
2023-04-21T00:00:00
[ [ "Striani", "Manuel", "" ] ]
new_dataset
0.998444
2304.09873
Mojtaba Eshghie
Mahshid Eshghie, Mojtaba Eshghie
ChatGPT as a Therapist Assistant: A Suitability Study
null
null
null
null
cs.HC cs.AI
http://creativecommons.org/licenses/by/4.0/
This paper proposes using ChatGPT, an innovative technology with various applications, as an assistant for psychotherapy. ChatGPT can serve as a patient information collector, a companion for patients in between therapy sessions, and an organizer of gathered information for therapists to facilitate treatment processes. The research identifies five research questions and discovers useful prompts for fine-tuning the assistant, which shows that ChatGPT can participate in positive conversations, listen attentively, offer validation and potential coping strategies without providing explicit medical advice, and help therapists discover new insights from multiple conversations with the same patient. Using ChatGPT as an assistant for psychotherapy poses several challenges that need to be addressed, including technical as well as human-centric challenges which are discussed.
[ { "version": "v1", "created": "Wed, 19 Apr 2023 13:35:23 GMT" } ]
2023-04-21T00:00:00
[ [ "Eshghie", "Mahshid", "" ], [ "Eshghie", "Mojtaba", "" ] ]
new_dataset
0.955025
2304.09919
Marcus Schwarting
Vesa Akerman and David Baines and Damien Daspit and Ulf Hermjakob and Taeho Jang and Colin Leong and Michael Martin and Joel Mathew and Jonathan Robie and Marcus Schwarting
The eBible Corpus: Data and Model Benchmarks for Bible Translation for Low-Resource Languages
null
null
null
null
cs.CL cs.AI
http://creativecommons.org/licenses/by/4.0/
Efficiently and accurately translating a corpus into a low-resource language remains a challenge, regardless of the strategies employed, whether manual, automated, or a combination of the two. Many Christian organizations are dedicated to the task of translating the Holy Bible into languages that lack a modern translation. Bible translation (BT) work is currently underway for over 3000 extremely low resource languages. We introduce the eBible corpus: a dataset containing 1009 translations of portions of the Bible with data in 833 different languages across 75 language families. In addition to a BT benchmarking dataset, we introduce model performance benchmarks built on the No Language Left Behind (NLLB) neural machine translation (NMT) models. Finally, we describe several problems specific to the domain of BT and consider how the established data and model benchmarks might be used for future translation efforts. For a BT task trained with NLLB, Austronesian and Trans-New Guinea language families achieve 35.1 and 31.6 BLEU scores respectively, which spurs future innovations for NMT for low-resource languages in Papua New Guinea.
[ { "version": "v1", "created": "Wed, 19 Apr 2023 18:52:49 GMT" } ]
2023-04-21T00:00:00
[ [ "Akerman", "Vesa", "" ], [ "Baines", "David", "" ], [ "Daspit", "Damien", "" ], [ "Hermjakob", "Ulf", "" ], [ "Jang", "Taeho", "" ], [ "Leong", "Colin", "" ], [ "Martin", "Michael", "" ], [ "Mathew", "Joel", "" ], [ "Robie", "Jonathan", "" ], [ "Schwarting", "Marcus", "" ] ]
new_dataset
0.999841
2304.09952
Franc Grootjen
Franc Grootjen and Nikolai Schauer
Baugh-Wooley Multiplication for the RISCV Processor
null
null
null
null
cs.AR
http://creativecommons.org/licenses/by-nc-sa/4.0/
This article describes an efficient way to implement the multiplication instructions for a RISCV processor. Instead of using three predefined IP blocks for signed, unsigned and mixed multiplication, this article presents a novel extension to the Baugh-Wooley multiplication algorithm which reduces area and power consumption with roughly a factor three.
[ { "version": "v1", "created": "Wed, 19 Apr 2023 20:06:08 GMT" } ]
2023-04-21T00:00:00
[ [ "Grootjen", "Franc", "" ], [ "Schauer", "Nikolai", "" ] ]
new_dataset
0.972578
2304.09982
Maite Taboada
Valentin-Gabriel Soumah, Prashanth Rao, Philipp Eibl, Maite Taboada
Radar de Parit\'e: An NLP system to measure gender representation in French news stories
Full conference paper plus appendix
The 36th Canadian Conference on Artificial Intelligence. 5-9 June 2023, Montr\'eal
null
null
cs.CL
http://creativecommons.org/licenses/by/4.0/
We present the Radar de Parit\'e, an automated Natural Language Processing (NLP) system that measures the proportion of women and men quoted daily in six Canadian French-language media outlets. We outline the system's architecture and detail the challenges we overcame to address French-specific issues, in particular regarding coreference resolution, a new contribution to the NLP literature on French. We also showcase statistics covering over one year's worth of data (282,512 news articles). Our results highlight the underrepresentation of women in news stories, while also illustrating the application of modern NLP methods to measure gender representation and address societal issues.
[ { "version": "v1", "created": "Wed, 19 Apr 2023 21:33:59 GMT" } ]
2023-04-21T00:00:00
[ [ "Soumah", "Valentin-Gabriel", "" ], [ "Rao", "Prashanth", "" ], [ "Eibl", "Philipp", "" ], [ "Taboada", "Maite", "" ] ]
new_dataset
0.990866
2304.10068
Joel Dabrowski Dr
Joel Janek Dabrowski and Ashfaqur Rahman
Fruit Picker Activity Recognition with Wearable Sensors and Machine Learning
Accepted at IEEE International Joint Conference on Neural Networks (IJCNN) conference, 2023
null
null
null
cs.LG eess.SP
http://creativecommons.org/licenses/by/4.0/
In this paper we present a novel application of detecting fruit picker activities based on time series data generated from wearable sensors. During harvesting, fruit pickers pick fruit into wearable bags and empty these bags into harvesting bins located in the orchard. Once full, these bins are quickly transported to a cooled pack house to improve the shelf life of picked fruits. For farmers and managers, the knowledge of when a picker bag is emptied is important for managing harvesting bins more effectively to minimise the time the picked fruit is left out in the heat (resulting in reduced shelf life). We propose a means to detect these bag-emptying events using human activity recognition with wearable sensors and machine learning methods. We develop a semi-supervised approach to labelling the data. A feature-based machine learning ensemble model and a deep recurrent convolutional neural network are developed and tested on a real-world dataset. When compared, the neural network achieves 86% detection accuracy.
[ { "version": "v1", "created": "Thu, 20 Apr 2023 03:38:08 GMT" } ]
2023-04-21T00:00:00
[ [ "Dabrowski", "Joel Janek", "" ], [ "Rahman", "Ashfaqur", "" ] ]
new_dataset
0.99869
2304.10113
Goirik Chakrabarty
Goirik Chakrabarty, Manogna Sreenivas and Soma Biswas
SATA: Source Anchoring and Target Alignment Network for Continual Test Time Adaptation
null
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Adapting a trained model to perform satisfactorily on continually changing testing domains/environments is an important and challenging task. In this work, we propose a novel framework, SATA, which aims to satisfy the following characteristics required for online adaptation: 1) can work seamlessly with different (preferably small) batch sizes to reduce latency; 2) should continue to work well for the source domain; 3) should have minimal tunable hyper-parameters and storage requirements. Given a pre-trained network trained on source domain data, the proposed SATA framework modifies the batch-norm affine parameters using source anchoring based self-distillation. This ensures that the model incorporates the knowledge of the newly encountered domains, without catastrophically forgetting about the previously seen ones. We also propose a source-prototype driven contrastive alignment to ensure natural grouping of the target samples, while maintaining the already learnt semantic information. Extensive evaluation on three benchmark datasets under challenging settings justify the effectiveness of SATA for real-world applications.
[ { "version": "v1", "created": "Thu, 20 Apr 2023 06:38:33 GMT" } ]
2023-04-21T00:00:00
[ [ "Chakrabarty", "Goirik", "" ], [ "Sreenivas", "Manogna", "" ], [ "Biswas", "Soma", "" ] ]
new_dataset
0.992213
2304.10154
Andrea Fusiello
Abdul Salam Rasmi Asraf Ali and Andrea Fusiello and Claudio Landi and Cristina Sarti and Anneke Annassia Putri Siswadi
Motion Artifacts Detection in Short-scan Dental CBCT Reconstructions
null
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Cone Beam Computed Tomography (CBCT) is widely used in dentistry for diagnostics and treatment planning. CBCT Imaging has a long acquisition time and consequently, the patient is likely to move. This motion causes significant artifacts in the reconstructed data which may lead to misdiagnosis. Existing motion correction algorithms only address this issue partially, struggling with inconsistencies due to truncation, accuracy, and execution speed. On the other hand, a short-scan reconstruction using a subset of motion-free projections with appropriate weighting methods can have a sufficient clinical image quality for most diagnostic purposes. Therefore, a framework is used in this study to extract the motion-free part of the scanned projections with which a clean short-scan volume can be reconstructed without using correction algorithms. Motion artifacts are detected using deep learning with a slice-based prediction scheme followed by volume averaging to get the final result. A realistic motion simulation strategy and data augmentation has been implemented to address data scarcity. The framework has been validated by testing it with real motion-affected data while the model was trained only with simulated motion data. This shows the feasibility to apply the proposed framework to a broad variety of motion cases for further research.
[ { "version": "v1", "created": "Thu, 20 Apr 2023 08:28:44 GMT" } ]
2023-04-21T00:00:00
[ [ "Ali", "Abdul Salam Rasmi Asraf", "" ], [ "Fusiello", "Andrea", "" ], [ "Landi", "Claudio", "" ], [ "Sarti", "Cristina", "" ], [ "Siswadi", "Anneke Annassia Putri", "" ] ]
new_dataset
0.996005
2304.10201
Savvas Papaioannou
Savvas Papaioannou, Panayiotis Kolios, Theocharis Theocharides, Christos G. Panayiotou and Marios M. Polycarpou
UAV-based Receding Horizon Control for 3D Inspection Planning
2022 International Conference on Unmanned Aircraft Systems (ICUAS), 21-24 June 2022, Dubrovnik, Croatia
null
10.1109/ICUAS54217.2022.9836051
null
cs.RO cs.SY eess.SY
http://creativecommons.org/licenses/by/4.0/
Nowadays, unmanned aerial vehicles or UAVs are being used for a wide range of tasks, including infrastructure inspection, automated monitoring and coverage. This paper investigates the problem of 3D inspection planning with an autonomous UAV agent which is subject to dynamical and sensing constraints. We propose a receding horizon 3D inspection planning control approach for generating optimal trajectories which enable an autonomous UAV agent to inspect a finite number of feature-points scattered on the surface of a cuboid-like structure of interest. The inspection planning problem is formulated as a constrained open-loop optimal control problem and is solved using mixed integer programming (MIP) optimization. Quantitative and qualitative evaluation demonstrates the effectiveness of the proposed approach.
[ { "version": "v1", "created": "Thu, 20 Apr 2023 10:42:18 GMT" } ]
2023-04-21T00:00:00
[ [ "Papaioannou", "Savvas", "" ], [ "Kolios", "Panayiotis", "" ], [ "Theocharides", "Theocharis", "" ], [ "Panayiotou", "Christos G.", "" ], [ "Polycarpou", "Marios M.", "" ] ]
new_dataset
0.998315
2304.10211
Sami Barchid
Sami Barchid, Benjamin Allaert, Amel Aissaoui, Jos\'e Mennesson, Chaabane Dj\'eraba
Spiking-Fer: Spiking Neural Network for Facial Expression Recognition With Event Cameras
null
null
null
null
cs.CV cs.AI
http://creativecommons.org/licenses/by/4.0/
Facial Expression Recognition (FER) is an active research domain that has shown great progress recently, notably thanks to the use of large deep learning models. However, such approaches are particularly energy intensive, which makes their deployment difficult for edge devices. To address this issue, Spiking Neural Networks (SNNs) coupled with event cameras are a promising alternative, capable of processing sparse and asynchronous events with lower energy consumption. In this paper, we establish the first use of event cameras for FER, named "Event-based FER", and propose the first related benchmarks by converting popular video FER datasets to event streams. To deal with this new task, we propose "Spiking-FER", a deep convolutional SNN model, and compare it against a similar Artificial Neural Network (ANN). Experiments show that the proposed approach achieves comparable performance to the ANN architecture, while consuming less energy by orders of magnitude (up to 65.39x). In addition, an experimental study of various event-based data augmentation techniques is performed to provide insights into the efficient transformations specific to event-based FER.
[ { "version": "v1", "created": "Thu, 20 Apr 2023 10:59:56 GMT" } ]
2023-04-21T00:00:00
[ [ "Barchid", "Sami", "" ], [ "Allaert", "Benjamin", "" ], [ "Aissaoui", "Amel", "" ], [ "Mennesson", "José", "" ], [ "Djéraba", "Chaabane", "" ] ]
new_dataset
0.989975
2304.10256
Kaushal Goyal
Dr. Velmathi G, Kaushal Goyal
Indian Sign Language Recognition Using Mediapipe Holistic
16 pages, 22 figures
null
null
null
cs.CV cs.CL cs.LG
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Deaf individuals confront significant communication obstacles on a daily basis. Their inability to hear makes it difficult for them to communicate with those who do not understand sign language. Moreover, it presents difficulties in educational, occupational, and social contexts. By providing alternative communication channels, technology can play a crucial role in overcoming these obstacles. One such technology that can facilitate communication between deaf and hearing individuals is sign language recognition. We will create a robust system for sign language recognition in order to convert Indian Sign Language to text or speech. We will evaluate the proposed system and compare CNN and LSTM models. Since there are both static and gesture sign languages, a robust model is required to distinguish between them. In this study, we discovered that a CNN model captures letters and characters for recognition of static sign language better than an LSTM model, but it outperforms CNN by monitoring hands, faces, and pose in gesture sign language phrases and sentences. The creation of a text-to-sign language paradigm is essential since it will enhance the sign language-dependent deaf and hard-of-hearing population's communication skills. Even though the sign-to-text translation is just one side of communication, not all deaf or hard-of-hearing people are proficient in reading or writing text. Some may have difficulty comprehending written language due to educational or literacy issues. Therefore, a text-to-sign language paradigm would allow them to comprehend text-based information and participate in a variety of social, educational, and professional settings. Keywords: deaf and hard-of-hearing, DHH, Indian sign language, CNN, LSTM, static and gesture sign languages, text-to-sign language model, MediaPipe Holistic, sign language recognition, SLR, SLT
[ { "version": "v1", "created": "Thu, 20 Apr 2023 12:25:47 GMT" } ]
2023-04-21T00:00:00
[ [ "G", "Dr. Velmathi", "" ], [ "Goyal", "Kaushal", "" ] ]
new_dataset
0.999048
2304.10282
Omar Hashash
Omar Hashash, Christina Chaccour, Walid Saad, Tao Yu, Kei Sakaguchi, Merouane Debbah
The Seven Worlds and Experiences of the Wireless Metaverse: Challenges and Opportunities
null
null
null
null
cs.IT cs.AI math.IT
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The wireless metaverse will create diverse user experiences at the intersection of the physical, digital, and virtual worlds. These experiences will enable novel interactions between the constituents (e.g., extended reality (XR) users and avatars) of the three worlds. However, remarkably, to date, there is no holistic vision that identifies the full set of metaverse worlds, constituents, and experiences, and the implications of their associated interactions on next-generation communication and computing systems. In this paper, we present a holistic vision of a limitless, wireless metaverse that distills the metaverse into an intersection of seven worlds and experiences that include the: i) physical, digital, and virtual worlds, along with the ii) cyber, extended, live, and parallel experiences. We then articulate how these experiences bring forth interactions between diverse metaverse constituents, namely, a) humans and avatars and b) connected intelligence systems and their digital twins (DTs). Then, we explore the wireless, computing, and artificial intelligence (AI) challenges that must be addressed to establish metaverse-ready networks that support these experiences and interactions. We particularly highlight the need for end-to-end synchronization of DTs, and the role of human-level AI and reasoning abilities for cognitive avatars. Moreover, we articulate a sequel of open questions that should ignite the quest for the future metaverse. We conclude with a set of recommendations to deploy the limitless metaverse over future wireless systems.
[ { "version": "v1", "created": "Thu, 20 Apr 2023 13:04:52 GMT" } ]
2023-04-21T00:00:00
[ [ "Hashash", "Omar", "" ], [ "Chaccour", "Christina", "" ], [ "Saad", "Walid", "" ], [ "Yu", "Tao", "" ], [ "Sakaguchi", "Kei", "" ], [ "Debbah", "Merouane", "" ] ]
new_dataset
0.980967
2304.10313
Georgios Spathoulas
Lydia Negka, Angeliki Katsika, Georgios Spathoulas, Vassilis Plagianakos
ORIGAMI: A flexible state channels design for public blockchain systems
33 pages, 12 figures
null
null
null
cs.CR
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Public blockchain systems offer security guarantees that cannot be matched by any centralised system. This offering has attracted a lot of interest and has exposed a significant limitation of most blockchain designs with regards to scalability. One of the scaling solutions proposed is state channels which enables serving given applications with minimum number of transactions. Existing state channels designs set multiple compatibility requirements for applications to be deployed. Origami is a novel state channels design which removes most of the requirements of existing approaches, while it also offers a number of new features. Origami enables dynamic groups of users to interact in an unordered way completely off-chain after an initial on-boarding on-chain transaction. The proposed design is analysed in detail and compared to existing schemes, while a formal security analysis validates the security properties it offers.
[ { "version": "v1", "created": "Thu, 20 Apr 2023 13:44:15 GMT" } ]
2023-04-21T00:00:00
[ [ "Negka", "Lydia", "" ], [ "Katsika", "Angeliki", "" ], [ "Spathoulas", "Georgios", "" ], [ "Plagianakos", "Vassilis", "" ] ]
new_dataset
0.999227
2304.10348
Bata Vasic Dr
Bata Vasc, Nithin Raveendran and Bane Vasic
Neuro-OSVETA: A Robust Watermarking of 3D Meshes
10 pages, 5 figures
Proceedings of the International Telemetering Conference (ITC 2019), ISSN 1546-2188, vol. 55, pp. 387 - 396, Las Vegas, NV, USA, Octobar 21 - 24, 2019
null
null
cs.MM
http://creativecommons.org/licenses/by-nc-sa/4.0/
Best and practical watermarking schemes for copyright protection of 3D meshes are required to be blind and robust to attacks and errors. In this paper, we present the latest developments in 3D blind watermarking with a special emphasis on our Ordered Statistics Vertex Extraction and Tracing Algorithm (OSVETA) algorithm and its improvements. OSVETA is based on a combination of quantization index modulation (QIM) and error correction coding using novel ways for judicial selection of mesh vertices which are stable under mesh simplification, and the technique we propose in this paper offers a systematic method for vertex selection based on neural networks replacing a heuristic approach in the OSVETA. The Neuro-OSVETA enables a more precise mesh geometry estimation and better curvature and topological feature estimation. These enhancements result in a more accurate identification of stable vertices resulting in significant reduction of deletion probability.
[ { "version": "v1", "created": "Thu, 20 Apr 2023 14:39:24 GMT" } ]
2023-04-21T00:00:00
[ [ "Vasc", "Bata", "" ], [ "Raveendran", "Nithin", "" ], [ "Vasic", "Bane", "" ] ]
new_dataset
0.99836
2304.10381
Diego Figueira
Diego Figueira, Santiago Figueira, Edwin Pin
PDL on Steroids: on Expressive Extensions of PDL with Intersection and Converse
null
null
null
null
cs.LO cs.AI cs.DB
http://creativecommons.org/licenses/by/4.0/
We introduce CPDL+, a family of expressive logics rooted in Propositional Dynamic Logic (PDL). In terms of expressive power, CPDL+ strictly contains PDL extended with intersection and converse (a.k.a. ICPDL) as well as Conjunctive Queries (CQ), Conjunctive Regular Path Queries (CRPQ), or some known extensions thereof (Regular Queries and CQPDL). We investigate the expressive power, characterization of bisimulation, satisfiability, and model checking for CPDL+. We argue that natural subclasses of CPDL+ can be defined in terms of the tree-width of the underlying graphs of the formulas. We show that the class of CPDL+ formulas of tree-width 2 is equivalent to ICPDL, and that it also coincides with CPDL+ formulas of tree-width 1. However, beyond tree-width 2, incrementing the tree-width strictly increases the expressive power. We characterize the expressive power for every class of fixed tree-width formulas in terms of a bisimulation game with pebbles. Based on this characterization, we show that CPDL+ has a tree-like model property. We prove that the satisfiability problem is decidable in 2ExpTime on fixed tree-width formulas, coinciding with the complexity of ICPDL. We also exhibit classes for which satisfiability is reduced to ExpTime. Finally, we establish that the model checking problem for fixed tree-width formulas is in \ptime, contrary to the full class CPDL+.
[ { "version": "v1", "created": "Thu, 20 Apr 2023 15:21:01 GMT" } ]
2023-04-21T00:00:00
[ [ "Figueira", "Diego", "" ], [ "Figueira", "Santiago", "" ], [ "Pin", "Edwin", "" ] ]
new_dataset
0.99124
2304.10391
Avital Boruchovsky
Avital Boruchovsky, Daniella Bar-Lev and Eitan Yaakobi
DNA-Correcting Codes: End-to-end Correction in DNA Storage Systems
null
null
null
null
cs.IT math.IT
http://creativecommons.org/licenses/by-nc-nd/4.0/
This paper introduces a new solution to DNA storage that integrates all three steps of retrieval, namely clustering, reconstruction, and error correction. DNA-correcting codes are presented as a unique solution to the problem of ensuring that the output of the storage system is unique for any valid set of input strands. To this end, we introduce a novel distance metric to capture the unique behavior of the DNA storage system and provide necessary and sufficient conditions for DNA-correcting codes. The paper also includes several upper bounds and constructions of DNA-correcting codes.
[ { "version": "v1", "created": "Thu, 20 Apr 2023 15:27:14 GMT" } ]
2023-04-21T00:00:00
[ [ "Boruchovsky", "Avital", "" ], [ "Bar-Lev", "Daniella", "" ], [ "Yaakobi", "Eitan", "" ] ]
new_dataset
0.999111
2304.10392
Quyet V. Do
Tianqing Fang, Quyet V. Do, Sehyun Choi, Weiqi Wang, Yangqiu Song
CKBP v2: An Expert-Annotated Evaluation Set for Commonsense Knowledge Base Population
null
null
null
null
cs.CL
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Populating Commonsense Knowledge Bases (CSKB) is an important yet hard task in NLP, as it tackles knowledge from external sources with unseen events and entities. Fang et al. (2021a) proposed a CSKB Population benchmark with an evaluation set CKBP v1. However, CKBP v1 adopts crowdsourced annotations that suffer from a substantial fraction of incorrect answers, and the evaluation set is not well-aligned with the external knowledge source as a result of random sampling. In this paper, we introduce CKBP v2, a new high-quality CSKB Population benchmark, which addresses the two mentioned problems by using experts instead of crowd-sourced annotation and by adding diversified adversarial samples to make the evaluation set more representative. We conduct extensive experiments comparing state-of-the-art methods for CSKB Population on the new evaluation set for future research comparisons. Empirical results show that the population task is still challenging, even for large language models (LLM) such as ChatGPT. Codes and data are available at https://github.com/HKUST-KnowComp/CSKB-Population.
[ { "version": "v1", "created": "Thu, 20 Apr 2023 15:27:29 GMT" } ]
2023-04-21T00:00:00
[ [ "Fang", "Tianqing", "" ], [ "Do", "Quyet V.", "" ], [ "Choi", "Sehyun", "" ], [ "Wang", "Weiqi", "" ], [ "Song", "Yangqiu", "" ] ]
new_dataset
0.972316
2304.10415
Zhengyu Liang
Yingqian Wang, Longguang Wang, Zhengyu Liang, Jungang Yang, Radu Timofte, Yulan Guo
NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results
null
null
null
null
cs.CV
http://creativecommons.org/licenses/by-sa/4.0/
In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradation with a magnification factor of 4. This challenge develops a new LF dataset called NTIRE-2023 for validation and test, and provides a toolbox called BasicLFSR to facilitate model development. Compared with single image SR, the major challenge of LF image SR lies in how to exploit complementary angular information from plenty of views with varying disparities. In total, 148 participants have registered the challenge, and 11 teams have successfully submitted results with PSNR scores higher than the baseline method LF-InterNet \cite{LF-InterNet}. These newly developed methods have set new state-of-the-art in LF image SR, e.g., the winning method achieves around 1 dB PSNR improvement over the existing state-of-the-art method DistgSSR \cite{DistgLF}. We report the solutions proposed by the participants, and summarize their common trends and useful tricks. We hope this challenge can stimulate future research and inspire new ideas in LF image SR.
[ { "version": "v1", "created": "Thu, 20 Apr 2023 15:59:31 GMT" } ]
2023-04-21T00:00:00
[ [ "Wang", "Yingqian", "" ], [ "Wang", "Longguang", "" ], [ "Liang", "Zhengyu", "" ], [ "Yang", "Jungang", "" ], [ "Timofte", "Radu", "" ], [ "Guo", "Yulan", "" ] ]
new_dataset
0.99974
2304.10448
Riccardo Spezialetti
Marco Toschi, Riccardo De Matteo, Riccardo Spezialetti, Daniele De Gregorio, Luigi Di Stefano, Samuele Salti
ReLight My NeRF: A Dataset for Novel View Synthesis and Relighting of Real World Objects
Accepted at CVPR 2023 as a highlight
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
In this paper, we focus on the problem of rendering novel views from a Neural Radiance Field (NeRF) under unobserved light conditions. To this end, we introduce a novel dataset, dubbed ReNe (Relighting NeRF), framing real world objects under one-light-at-time (OLAT) conditions, annotated with accurate ground-truth camera and light poses. Our acquisition pipeline leverages two robotic arms holding, respectively, a camera and an omni-directional point-wise light source. We release a total of 20 scenes depicting a variety of objects with complex geometry and challenging materials. Each scene includes 2000 images, acquired from 50 different points of views under 40 different OLAT conditions. By leveraging the dataset, we perform an ablation study on the relighting capability of variants of the vanilla NeRF architecture and identify a lightweight architecture that can render novel views of an object under novel light conditions, which we use to establish a non-trivial baseline for the dataset. Dataset and benchmark are available at https://eyecan-ai.github.io/rene.
[ { "version": "v1", "created": "Thu, 20 Apr 2023 16:43:58 GMT" } ]
2023-04-21T00:00:00
[ [ "Toschi", "Marco", "" ], [ "De Matteo", "Riccardo", "" ], [ "Spezialetti", "Riccardo", "" ], [ "De Gregorio", "Daniele", "" ], [ "Di Stefano", "Luigi", "" ], [ "Salti", "Samuele", "" ] ]
new_dataset
0.999736
2304.10495
Luis Camacho Prof.
Luis Camacho
A primer on getting neologisms from foreign languages to under-resourced languages
13 pages, 3 tables
null
null
null
cs.CL
http://creativecommons.org/licenses/by-nc-nd/4.0/
Mainly due to lack of support, most under-resourced languages have a reduced lexicon in most realms and domains of increasing importance, then their speakers need to significantly augment it. Although neologisms should arise from the languages themselves, external sources are widely accepted. However, we dispute the "common sense" of using the imposed official languages, which are highly probably a legacy of colonialism, as the only source, and we propose to introduce neologisms from any language as long as these neologisms "sound like" native words of the target languages.
[ { "version": "v1", "created": "Tue, 7 Mar 2023 15:10:37 GMT" } ]
2023-04-21T00:00:00
[ [ "Camacho", "Luis", "" ] ]
new_dataset
0.966575
1604.01673
Antti Kuusisto
Antti Kuusisto
On the uniform one-dimensional fragment
null
null
null
null
cs.LO cs.AI math.LO
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The uniform one-dimensional fragment of first-order logic, U1, is a formalism that extends two-variable logic in a natural way to contexts with relations of all arities. We survey properties of U1 and investigate its relationship to description logics designed to accommodate higher arity relations, with particular attention given to DLR_reg. We also define a description logic version of a variant of U1 and prove a range of new results concerning the expressivity of U1 and related logics.
[ { "version": "v1", "created": "Wed, 6 Apr 2016 16:03:42 GMT" }, { "version": "v2", "created": "Thu, 7 Apr 2016 15:09:02 GMT" }, { "version": "v3", "created": "Wed, 19 Apr 2023 17:46:40 GMT" } ]
2023-04-20T00:00:00
[ [ "Kuusisto", "Antti", "" ] ]
new_dataset
0.999211
2202.03936
Madhurananda Pahar
Madhurananda Pahar, Igor Miranda, Andreas Diacon and Thomas Niesler
Accelerometer-based Bed Occupancy Detection for Automatic, Non-invasive Long-term Cough Monitoring
null
IEEE Access, vol. 11, pp. 30739-30752, 2023
10.1109/ACCESS.2023.3261557
null
cs.LG
http://creativecommons.org/licenses/by/4.0/
We present a new machine learning based bed-occupancy detection system that uses the accelerometer signal captured by a bed-attached consumer smartphone. Automatic bed-occupancy detection is necessary for automatic long-term cough monitoring, since the time which the monitored patient occupies the bed is required to accurately calculate a cough rate. Accelerometer measurements are more cost effective and less intrusive than alternatives such as video monitoring or pressure sensors. A 249-hour dataset of manually-labelled acceleration signals gathered from seven patients undergoing treatment for tuberculosis (TB) was compiled for experimentation. These signals are characterised by brief activity bursts interspersed with long periods of little or no activity, even when the bed is occupied. To process them effectively, we propose an architecture consisting of three interconnected components. An occupancy-change detector locates instances at which bed occupancy is likely to have changed, an occupancy-interval detector classifies periods between detected occupancy changes and an occupancy-state detector corrects falsely-identified occupancy changes. Using long short-term memory (LSTM) networks, this architecture was demonstrated to achieve an AUC of 0.94. When integrated into a complete cough monitoring system, the daily cough rate of a patient undergoing TB treatment was determined over a period of 14 days. As the colony forming unit (CFU) counts decreased and the time to positivity (TPP) increased, the measured cough rate decreased, indicating effective TB treatment. This provides a first indication that automatic cough monitoring based on bed-mounted accelerometer measurements may present a non-invasive, non-intrusive and cost-effective means of monitoring long-term recovery of TB patients.
[ { "version": "v1", "created": "Tue, 8 Feb 2022 15:38:34 GMT" }, { "version": "v2", "created": "Sun, 13 Mar 2022 17:56:19 GMT" } ]
2023-04-20T00:00:00
[ [ "Pahar", "Madhurananda", "" ], [ "Miranda", "Igor", "" ], [ "Diacon", "Andreas", "" ], [ "Niesler", "Thomas", "" ] ]
new_dataset
0.999621
2202.13876
Zhengyun Zhao
Zhengyun Zhao, Qiao Jin, Fangyuan Chen, Tuorui Peng, Sheng Yu
PMC-Patients: A Large-scale Dataset of Patient Summaries and Relations for Benchmarking Retrieval-based Clinical Decision Support Systems
35 pages, 3 figures, 5 tables. Dataset and code are available at https://github.com/pmc-patients/pmc-patients
null
null
null
cs.CL cs.IR
http://creativecommons.org/licenses/by/4.0/
Objective: Retrieval-based Clinical Decision Support (ReCDS) can aid clinical workflow by providing relevant literature and similar patients for a given patient. However, the development of ReCDS systems has been severely obstructed by the lack of diverse patient collections and publicly available large-scale patient-level annotation datasets. In this paper, we aim to define and benchmark two ReCDS tasks: Patient-to-Article Retrieval (ReCDS-PAR) and Patient-to-Patient Retrieval (ReCDS-PPR) using a novel dataset called PMC-Patients. Methods: We extract patient summaries from PubMed Central articles using simple heuristics and utilize the PubMed citation graph to define patient-article relevance and patient-patient similarity. We also implement and evaluate several ReCDS systems on the PMC-Patients benchmarks, including sparse retrievers, dense retrievers, and nearest neighbor retrievers. We conduct several case studies to show the clinical utility of PMC-Patients. Results: PMC-Patients contains 167k patient summaries with 3.1M patient-article relevance annotations and 293k patient-patient similarity annotations, which is the largest-scale resource for ReCDS and also one of the largest patient collections. Human evaluation and analysis show that PMC-Patients is a diverse dataset with high-quality annotations. The evaluation of various ReCDS systems shows that the PMC-Patients benchmark is challenging and calls for further research. Conclusion: We present PMC-Patients, a large-scale, diverse, and publicly available patient summary dataset with the largest-scale patient-level relation annotations. Based on PMC-Patients, we formally define two benchmark tasks for ReCDS systems and evaluate various existing retrieval methods. PMC-Patients can largely facilitate methodology research on ReCDS systems and shows real-world clinical utility.
[ { "version": "v1", "created": "Mon, 28 Feb 2022 15:24:33 GMT" }, { "version": "v2", "created": "Wed, 4 Jan 2023 04:14:52 GMT" }, { "version": "v3", "created": "Tue, 18 Apr 2023 07:32:25 GMT" }, { "version": "v4", "created": "Wed, 19 Apr 2023 03:24:56 GMT" } ]
2023-04-20T00:00:00
[ [ "Zhao", "Zhengyun", "" ], [ "Jin", "Qiao", "" ], [ "Chen", "Fangyuan", "" ], [ "Peng", "Tuorui", "" ], [ "Yu", "Sheng", "" ] ]
new_dataset
0.999778
2205.13213
Bohan Zhuang
Zizheng Pan, Jianfei Cai, Bohan Zhuang
Fast Vision Transformers with HiLo Attention
NeurIPS 2022 camera ready
null
null
null
cs.CV cs.AI cs.LG
http://creativecommons.org/licenses/by/4.0/
Vision Transformers (ViTs) have triggered the most recent and significant breakthroughs in computer vision. Their efficient designs are mostly guided by the indirect metric of computational complexity, i.e., FLOPs, which however has a clear gap with the direct metric such as throughput. Thus, we propose to use the direct speed evaluation on the target platform as the design principle for efficient ViTs. Particularly, we introduce LITv2, a simple and effective ViT which performs favourably against the existing state-of-the-art methods across a spectrum of different model sizes with faster speed. At the core of LITv2 is a novel self-attention mechanism, which we dub HiLo. HiLo is inspired by the insight that high frequencies in an image capture local fine details and low frequencies focus on global structures, whereas a multi-head self-attention layer neglects the characteristic of different frequencies. Therefore, we propose to disentangle the high/low frequency patterns in an attention layer by separating the heads into two groups, where one group encodes high frequencies via self-attention within each local window, and another group encodes low frequencies by performing global attention between the average-pooled low-frequency keys and values from each window and each query position in the input feature map. Benefiting from the efficient design for both groups, we show that HiLo is superior to the existing attention mechanisms by comprehensively benchmarking FLOPs, speed and memory consumption on GPUs and CPUs. For example, HiLo is 1.4x faster than spatial reduction attention and 1.6x faster than local window attention on CPUs. Powered by HiLo, LITv2 serves as a strong backbone for mainstream vision tasks including image classification, dense detection and segmentation. Code is available at https://github.com/ziplab/LITv2.
[ { "version": "v1", "created": "Thu, 26 May 2022 08:16:14 GMT" }, { "version": "v2", "created": "Sat, 17 Sep 2022 06:24:35 GMT" }, { "version": "v3", "created": "Sat, 15 Oct 2022 03:37:47 GMT" }, { "version": "v4", "created": "Thu, 19 Jan 2023 01:12:54 GMT" }, { "version": "v5", "created": "Wed, 19 Apr 2023 12:04:13 GMT" } ]
2023-04-20T00:00:00
[ [ "Pan", "Zizheng", "" ], [ "Cai", "Jianfei", "" ], [ "Zhuang", "Bohan", "" ] ]
new_dataset
0.99915
2206.00052
Akshita Jha
Akshita Jha, and Chandan K. Reddy
CodeAttack: Code-Based Adversarial Attacks for Pre-trained Programming Language Models
AAAI Conference on Artificial Intelligence (AAAI) 2023
null
null
null
cs.CL cs.CR
http://creativecommons.org/licenses/by/4.0/
Pre-trained programming language (PL) models (such as CodeT5, CodeBERT, GraphCodeBERT, etc.,) have the potential to automate software engineering tasks involving code understanding and code generation. However, these models operate in the natural channel of code, i.e., they are primarily concerned with the human understanding of the code. They are not robust to changes in the input and thus, are potentially susceptible to adversarial attacks in the natural channel. We propose, CodeAttack, a simple yet effective black-box attack model that uses code structure to generate effective, efficient, and imperceptible adversarial code samples and demonstrates the vulnerabilities of the state-of-the-art PL models to code-specific adversarial attacks. We evaluate the transferability of CodeAttack on several code-code (translation and repair) and code-NL (summarization) tasks across different programming languages. CodeAttack outperforms state-of-the-art adversarial NLP attack models to achieve the best overall drop in performance while being more efficient, imperceptible, consistent, and fluent. The code can be found at https://github.com/reddy-lab-code-research/CodeAttack.
[ { "version": "v1", "created": "Tue, 31 May 2022 18:40:01 GMT" }, { "version": "v2", "created": "Tue, 6 Dec 2022 05:07:45 GMT" }, { "version": "v3", "created": "Tue, 18 Apr 2023 22:12:55 GMT" } ]
2023-04-20T00:00:00
[ [ "Jha", "Akshita", "" ], [ "Reddy", "Chandan K.", "" ] ]
new_dataset
0.999814
2208.05972
Eshwar Jagadeesh Savitha
Eshwar J. Savitha and Roger A. Sauer
A new anisotropic bending model for nonlinear shells: Comparison with existing models and isogeometric finite element implementation
null
null
10.1016/j.ijsolstr.2023.112169
null
cs.CE
http://creativecommons.org/licenses/by-nc-nd/4.0/
A new nonlinear hyperelastic bending model for shells formulated directly in surface form is presented, and compared to four prominently used bending models. Through an essential set of elementary nonlinear bending test cases, the stresses and moments of each model are examined analytically. Only the proposed bending model passes all the test cases while the other bending models either fail or only pass the test cases for small deformations. The proposed new bending model can handle large deformations and initially curved surfaces. It is based on the principal curvatures and their directions in the initial configuration, and it thus can have different bending moduli along those directions. These characteristics make it flexible in modeling a given material, while it does not suffer from the pathologies of existing bending models. Further, the bending models are compared computationally through four classical benchmark examples and one contact example. As the underlying shell theory is based on Kirchhoff-Love kinematics, isogeometric NURBS shape functions are used to discretize the shell surface. The linearization and efficient finite element implementation of the proposed new model are also provided.
[ { "version": "v1", "created": "Thu, 11 Aug 2022 17:33:41 GMT" }, { "version": "v2", "created": "Tue, 4 Apr 2023 17:15:34 GMT" }, { "version": "v3", "created": "Tue, 18 Apr 2023 19:54:13 GMT" } ]
2023-04-20T00:00:00
[ [ "Savitha", "Eshwar J.", "" ], [ "Sauer", "Roger A.", "" ] ]
new_dataset
0.951577
2210.04333
James Motes
James Motes, Tan Chen, Timothy Bretl, Marco Morales, Nancy M. Amato
Hypergraph-based Multi-Robot Task and Motion Planning
This work has been submitted for review
null
null
null
cs.RO cs.AI cs.MA
http://creativecommons.org/licenses/by/4.0/
We present a multi-robot task and motion planning method that, when applied to the rearrangement of objects by manipulators, results in solution times up to three orders of magnitude faster than existing methods and successfully plans for problems with up to twenty objects, more than three times as many objects as comparable methods. We achieve this improvement by decomposing the planning space to consider manipulators alone, objects, and manipulators holding objects. We represent this decomposition with a hypergraph where vertices are decomposed elements of the planning spaces and hyperarcs are transitions between elements. Existing methods use graph-based representations where vertices are full composite spaces and edges are transitions between these. Using the hypergraph reduces the representation size of the planning space-for multi-manipulator object rearrangement, the number of hypergraph vertices scales linearly with the number of either robots or objects, while the number of hyperarcs scales quadratically with the number of robots and linearly with the number of objects. In contrast, the number of vertices and edges in graph-based representations scales exponentially in the number of robots and objects. We show that similar gains can be achieved for other multi-robot task and motion planning problems.
[ { "version": "v1", "created": "Sun, 9 Oct 2022 19:43:21 GMT" }, { "version": "v2", "created": "Wed, 19 Apr 2023 11:04:29 GMT" } ]
2023-04-20T00:00:00
[ [ "Motes", "James", "" ], [ "Chen", "Tan", "" ], [ "Bretl", "Timothy", "" ], [ "Morales", "Marco", "" ], [ "Amato", "Nancy M.", "" ] ]
new_dataset
0.99594
2210.05038
Pedro Rodriguez
Pedro Rodriguez, Mahmoud Azab, Becka Silvert, Renato Sanchez, Linzy Labson, Hardik Shah and Seungwhan Moon
Fighting FIRe with FIRE: Assessing the Validity of Text-to-Video Retrieval Benchmarks
EACL 2023 Camera Ready
null
null
null
cs.CL cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Searching troves of videos with textual descriptions is a core multimodal retrieval task. Owing to the lack of a purpose-built dataset for text-to-video retrieval, video captioning datasets have been re-purposed to evaluate models by (1) treating captions as positive matches to their respective videos and (2) assuming all other videos to be negatives. However, this methodology leads to a fundamental flaw during evaluation: since captions are marked as relevant only to their original video, many alternate videos also match the caption, which introduces false-negative caption-video pairs. We show that when these false negatives are corrected, a recent state-of-the-art model gains 25\% recall points -- a difference that threatens the validity of the benchmark itself. To diagnose and mitigate this issue, we annotate and release 683K additional caption-video pairs. Using these, we recompute effectiveness scores for three models on two standard benchmarks (MSR-VTT and MSVD). We find that (1) the recomputed metrics are up to 25\% recall points higher for the best models, (2) these benchmarks are nearing saturation for Recall@10, (3) caption length (generality) is related to the number of positives, and (4) annotation costs can be mitigated through sampling. We recommend retiring these benchmarks in their current form, and we make recommendations for future text-to-video retrieval benchmarks.
[ { "version": "v1", "created": "Mon, 10 Oct 2022 22:45:06 GMT" }, { "version": "v2", "created": "Wed, 19 Apr 2023 03:50:48 GMT" } ]
2023-04-20T00:00:00
[ [ "Rodriguez", "Pedro", "" ], [ "Azab", "Mahmoud", "" ], [ "Silvert", "Becka", "" ], [ "Sanchez", "Renato", "" ], [ "Labson", "Linzy", "" ], [ "Shah", "Hardik", "" ], [ "Moon", "Seungwhan", "" ] ]
new_dataset
0.999774
2211.05340
Vijaya Yajnanarayana Ph.D
Vijaya Yajnanarayana, Henk Wymeersch
Multistatic Sensing of Passive Targets Using 6G Cellular Infrastructure
To appear in IEEE EuCNC 2023
null
null
null
cs.IT math.IT
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sensing using cellular infrastructure may be one of the defining feature of sixth generation (6G) wireless systems. Wideband 6G communication channels operating at higher frequency bands (upper mmWave bands) are better modeled using clustered geometric channel models. In this paper, we propose methods for detection of passive targets and estimating their position using communication deployment without any assistance from the target. A novel AI architecture called CsiSenseNet is developed for this purpose. We analyze the resolution, coverage and position uncertainty for practical indoor deployments. Using the proposed method, we show that human sized target can be sensed with high accuracy and sub-meter positioning errors in a practical indoor deployment scenario.
[ { "version": "v1", "created": "Thu, 10 Nov 2022 04:45:48 GMT" }, { "version": "v2", "created": "Wed, 19 Apr 2023 05:06:15 GMT" } ]
2023-04-20T00:00:00
[ [ "Yajnanarayana", "Vijaya", "" ], [ "Wymeersch", "Henk", "" ] ]
new_dataset
0.984335
2211.09791
Tiancai Wang
Yuang Zhang, Tiancai Wang, Xiangyu Zhang
MOTRv2: Bootstrapping End-to-End Multi-Object Tracking by Pretrained Object Detectors
Accepted by CVPR 2023
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
In this paper, we propose MOTRv2, a simple yet effective pipeline to bootstrap end-to-end multi-object tracking with a pretrained object detector. Existing end-to-end methods, MOTR and TrackFormer are inferior to their tracking-by-detection counterparts mainly due to their poor detection performance. We aim to improve MOTR by elegantly incorporating an extra object detector. We first adopt the anchor formulation of queries and then use an extra object detector to generate proposals as anchors, providing detection prior to MOTR. The simple modification greatly eases the conflict between joint learning detection and association tasks in MOTR. MOTRv2 keeps the query propogation feature and scales well on large-scale benchmarks. MOTRv2 ranks the 1st place (73.4% HOTA on DanceTrack) in the 1st Multiple People Tracking in Group Dance Challenge. Moreover, MOTRv2 reaches state-of-the-art performance on the BDD100K dataset. We hope this simple and effective pipeline can provide some new insights to the end-to-end MOT community. Code is available at \url{https://github.com/megvii-research/MOTRv2}.
[ { "version": "v1", "created": "Thu, 17 Nov 2022 18:57:12 GMT" }, { "version": "v2", "created": "Wed, 19 Apr 2023 07:28:54 GMT" } ]
2023-04-20T00:00:00
[ [ "Zhang", "Yuang", "" ], [ "Wang", "Tiancai", "" ], [ "Zhang", "Xiangyu", "" ] ]
new_dataset
0.999651
2211.12979
Anatol Garioud
Anatol Garioud, St\'ephane Peillet, Eva Bookjans, S\'ebastien Giordano, Boris Wattrelos
FLAIR #1: semantic segmentation and domain adaptation dataset
Data access update
null
10.13140/RG.2.2.30183.73128/1
null
cs.CV eess.IV
http://creativecommons.org/licenses/by-sa/4.0/
The French National Institute of Geographical and Forest Information (IGN) has the mission to document and measure land-cover on French territory and provides referential geographical datasets, including high-resolution aerial images and topographic maps. The monitoring of land-cover plays a crucial role in land management and planning initiatives, which can have significant socio-economic and environmental impact. Together with remote sensing technologies, artificial intelligence (IA) promises to become a powerful tool in determining land-cover and its evolution. IGN is currently exploring the potential of IA in the production of high-resolution land cover maps. Notably, deep learning methods are employed to obtain a semantic segmentation of aerial images. However, territories as large as France imply heterogeneous contexts: variations in landscapes and image acquisition make it challenging to provide uniform, reliable and accurate results across all of France. The FLAIR-one dataset presented is part of the dataset currently used at IGN to establish the French national reference land cover map "Occupation du sol \`a grande \'echelle" (OCS- GE).
[ { "version": "v1", "created": "Wed, 23 Nov 2022 14:38:59 GMT" }, { "version": "v2", "created": "Fri, 25 Nov 2022 08:45:24 GMT" }, { "version": "v3", "created": "Mon, 28 Nov 2022 18:05:30 GMT" }, { "version": "v4", "created": "Sun, 4 Dec 2022 00:42:14 GMT" }, { "version": "v5", "created": "Wed, 19 Apr 2023 08:42:41 GMT" } ]
2023-04-20T00:00:00
[ [ "Garioud", "Anatol", "" ], [ "Peillet", "Stéphane", "" ], [ "Bookjans", "Eva", "" ], [ "Giordano", "Sébastien", "" ], [ "Wattrelos", "Boris", "" ] ]
new_dataset
0.999668
2212.04638
Aoyang Liu
Yansong Tang, Jinpeng Liu, Aoyang Liu, Bin Yang, Wenxun Dai, Yongming Rao, Jiwen Lu, Jie Zhou, Xiu Li
FLAG3D: A 3D Fitness Activity Dataset with Language Instruction
Accepted to CVPR2023
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
With the continuously thriving popularity around the world, fitness activity analytic has become an emerging research topic in computer vision. While a variety of new tasks and algorithms have been proposed recently, there are growing hunger for data resources involved in high-quality data, fine-grained labels, and diverse environments. In this paper, we present FLAG3D, a large-scale 3D fitness activity dataset with language instruction containing 180K sequences of 60 categories. FLAG3D features the following three aspects: 1) accurate and dense 3D human pose captured from advanced MoCap system to handle the complex activity and large movement, 2) detailed and professional language instruction to describe how to perform a specific activity, 3) versatile video resources from a high-tech MoCap system, rendering software, and cost-effective smartphones in natural environments. Extensive experiments and in-depth analysis show that FLAG3D contributes great research value for various challenges, such as cross-domain human action recognition, dynamic human mesh recovery, and language-guided human action generation. Our dataset and source code are publicly available at https://andytang15.github.io/FLAG3D.
[ { "version": "v1", "created": "Fri, 9 Dec 2022 02:33:33 GMT" }, { "version": "v2", "created": "Wed, 19 Apr 2023 13:31:03 GMT" } ]
2023-04-20T00:00:00
[ [ "Tang", "Yansong", "" ], [ "Liu", "Jinpeng", "" ], [ "Liu", "Aoyang", "" ], [ "Yang", "Bin", "" ], [ "Dai", "Wenxun", "" ], [ "Rao", "Yongming", "" ], [ "Lu", "Jiwen", "" ], [ "Zhou", "Jie", "" ], [ "Li", "Xiu", "" ] ]
new_dataset
0.999885
2301.00337
Young-Ho Kim
Christian DeBuys and Florin C. Ghesu and Jagadeesan Jayender and Reza Langari and Young-Ho Kim
Separable Tendon-Driven Robotic Manipulator with a Long, Flexible, Passive Proximal Section
null
null
null
null
cs.RO
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
This work tackles practical issues which arise when using a tendon-driven robotic manipulator (TDRM) with a long, flexible, passive proximal section in medical applications. Tendon-driven devices are preferred in medicine for their improved outcomes via minimally invasive procedures, but TDRMs come with unique challenges such as sterilization and reuse, simultaneous control of tendons, hysteresis in the tendon-sheath mechanism, and unmodeled effects of the proximal section shape. A separable TDRM which overcomes difficulties in actuation and sterilization is introduced, in which the body containing the electronics is reusable and the remainder is disposable. An open-loop redundant controller which resolves the redundancy in the kinematics is developed. Simple linear hysteresis compensation and re-tension compensation based on the physical properties of the device are proposed. The controller and compensation methods are evaluated on a testbed for a straight proximal section, a curved proximal section at various static angles, and a proximal section which dynamically changes angles; and overall, distal tip error was reduced.
[ { "version": "v1", "created": "Sun, 1 Jan 2023 03:31:15 GMT" }, { "version": "v2", "created": "Tue, 18 Apr 2023 20:33:39 GMT" } ]
2023-04-20T00:00:00
[ [ "DeBuys", "Christian", "" ], [ "Ghesu", "Florin C.", "" ], [ "Jayender", "Jagadeesan", "" ], [ "Langari", "Reza", "" ], [ "Kim", "Young-Ho", "" ] ]
new_dataset
0.997943
2303.03129
Tuomas V\"alim\"aki
Tuomas V\"alim\"aki, Bharath Garigipati and Reza Ghabcheloo
Motion-based extrinsic sensor-to-sensor calibration: Effect of reference frame selection for new and existing methods
null
Sensors 2023, 23(7), 3740
10.3390/s23073740
null
cs.RO
http://creativecommons.org/licenses/by/4.0/
This paper studies the effect of reference frame selection in sensor-to-sensor extrinsic calibration when formulated as a motion-based hand-eye calibration problem. Different reference selection options are tested under varying noise conditions in simulation, and the findings are validated with real data from the KITTI dataset. We propose two nonlinear cost functions for optimization and compare them with four state-of-the-art methods. One of the proposed cost functions incorporates outlier rejection to improve calibration performance and was shown to significantly improve performance in the presence of outliers, and either match or outperform the other algorithms in other noise conditions. However, the performance gain from reference frame selection was deemed larger than that from algorithm selection. In addition, we show that with realistic noise, the reference frame selection method commonly used in literature is inferior to other tested options, and that relative error metrics are not reliable for telling which method achieves best calibration performance.
[ { "version": "v1", "created": "Mon, 6 Mar 2023 13:44:23 GMT" } ]
2023-04-20T00:00:00
[ [ "Välimäki", "Tuomas", "" ], [ "Garigipati", "Bharath", "" ], [ "Ghabcheloo", "Reza", "" ] ]
new_dataset
0.999438
2303.06962
Tao Wang
Tao Wang, Jie Lv, Haonan Tong, Changsheng You, Changchuan Yin
A Novel Two-Layer Codebook Based Near-Field Beam Training for Intelligent Reflecting Surface
6 pages, 4 figures
null
null
null
cs.IT eess.SP math.IT
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
In this paper, we study the codebook-based near-field beam training for intelligent reflecting surfaces (IRSs) aided wireless system. In the considered model, the near-field beam training is critical to focus signals at the location of user equipment (UE) to obtain prominent IRS array gain. However, existing codebook schemes cannot achieve low training overhead and high receiving power simultaneously. To tackle this issue, a novel two-layer codebook based beam training scheme is proposed. The layer-1 codebook is designed based on the omnidirectionality of a random-phase beam pattern, which estimates the UE distance with training overhead equivalent to that of one DFT codeword. Then, based on the estimated UE distance, the layer-2 codebook is generated to scan candidate UE locations and obtain the optimal codeword for IRS beamforming. Numerical results show that compared with benchmarks, the proposed two-layer beam training scheme achieves more accurate UE distance and angle estimation, higher data rate, and smaller training overhead.
[ { "version": "v1", "created": "Mon, 13 Mar 2023 10:04:46 GMT" }, { "version": "v2", "created": "Wed, 19 Apr 2023 01:43:57 GMT" } ]
2023-04-20T00:00:00
[ [ "Wang", "Tao", "" ], [ "Lv", "Jie", "" ], [ "Tong", "Haonan", "" ], [ "You", "Changsheng", "" ], [ "Yin", "Changchuan", "" ] ]
new_dataset
0.98346
2303.14933
Zicheng Zhang
Zicheng Zhang, Wei Wu, Wei Sun, Dangyang Tu, Wei Lu, Xiongkuo Min, Ying Chen, Guangtao Zhai
MD-VQA: Multi-Dimensional Quality Assessment for UGC Live Videos
Accepted to CVPR2023
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
User-generated content (UGC) live videos are often bothered by various distortions during capture procedures and thus exhibit diverse visual qualities. Such source videos are further compressed and transcoded by media server providers before being distributed to end-users. Because of the flourishing of UGC live videos, effective video quality assessment (VQA) tools are needed to monitor and perceptually optimize live streaming videos in the distributing process. In this paper, we address \textbf{UGC Live VQA} problems by constructing a first-of-a-kind subjective UGC Live VQA database and developing an effective evaluation tool. Concretely, 418 source UGC videos are collected in real live streaming scenarios and 3,762 compressed ones at different bit rates are generated for the subsequent subjective VQA experiments. Based on the built database, we develop a \underline{M}ulti-\underline{D}imensional \underline{VQA} (\textbf{MD-VQA}) evaluator to measure the visual quality of UGC live videos from semantic, distortion, and motion aspects respectively. Extensive experimental results show that MD-VQA achieves state-of-the-art performance on both our UGC Live VQA database and existing compressed UGC VQA databases.
[ { "version": "v1", "created": "Mon, 27 Mar 2023 06:17:10 GMT" }, { "version": "v2", "created": "Wed, 19 Apr 2023 07:51:02 GMT" } ]
2023-04-20T00:00:00
[ [ "Zhang", "Zicheng", "" ], [ "Wu", "Wei", "" ], [ "Sun", "Wei", "" ], [ "Tu", "Dangyang", "" ], [ "Lu", "Wei", "" ], [ "Min", "Xiongkuo", "" ], [ "Chen", "Ying", "" ], [ "Zhai", "Guangtao", "" ] ]
new_dataset
0.973251
2304.09181
Shantanu Mandal
Shantanu Mandal, Adhrik Chethan, Vahid Janfaza, S M Farabi Mahmud, Todd A Anderson, Javier Turek, Jesmin Jahan Tithi, Abdullah Muzahid
Large Language Models Based Automatic Synthesis of Software Specifications
null
null
null
null
cs.SE cs.AI
http://creativecommons.org/licenses/by/4.0/
Software configurations play a crucial role in determining the behavior of software systems. In order to ensure safe and error-free operation, it is necessary to identify the correct configuration, along with their valid bounds and rules, which are commonly referred to as software specifications. As software systems grow in complexity and scale, the number of configurations and associated specifications required to ensure the correct operation can become large and prohibitively difficult to manipulate manually. Due to the fast pace of software development, it is often the case that correct software specifications are not thoroughly checked or validated within the software itself. Rather, they are frequently discussed and documented in a variety of external sources, including software manuals, code comments, and online discussion forums. Therefore, it is hard for the system administrator to know the correct specifications of configurations due to the lack of clarity, organization, and a centralized unified source to look at. To address this challenge, we propose SpecSyn a framework that leverages a state-of-the-art large language model to automatically synthesize software specifications from natural language sources. Our approach formulates software specification synthesis as a sequence-to-sequence learning problem and investigates the extraction of specifications from large contextual texts. This is the first work that uses a large language model for end-to-end specification synthesis from natural language texts. Empirical results demonstrate that our system outperforms prior the state-of-the-art specification synthesis tool by 21% in terms of F1 score and can find specifications from single as well as multiple sentences.
[ { "version": "v1", "created": "Tue, 18 Apr 2023 01:22:44 GMT" } ]
2023-04-20T00:00:00
[ [ "Mandal", "Shantanu", "" ], [ "Chethan", "Adhrik", "" ], [ "Janfaza", "Vahid", "" ], [ "Mahmud", "S M Farabi", "" ], [ "Anderson", "Todd A", "" ], [ "Turek", "Javier", "" ], [ "Tithi", "Jesmin Jahan", "" ], [ "Muzahid", "Abdullah", "" ] ]
new_dataset
0.95239
2304.09285
Benjamin Killeen
Benjamin D. Killeen, Han Zhang, Jan Mangulabnan, Mehran Armand, Russel H. Taylor, Greg Osgood, Mathias Unberath
Pelphix: Surgical Phase Recognition from X-ray Images in Percutaneous Pelvic Fixation
null
null
null
null
cs.LG cs.AI cs.CV q-bio.QM
http://creativecommons.org/licenses/by/4.0/
Surgical phase recognition (SPR) is a crucial element in the digital transformation of the modern operating theater. While SPR based on video sources is well-established, incorporation of interventional X-ray sequences has not yet been explored. This paper presents Pelphix, a first approach to SPR for X-ray-guided percutaneous pelvic fracture fixation, which models the procedure at four levels of granularity -- corridor, activity, view, and frame value -- simulating the pelvic fracture fixation workflow as a Markov process to provide fully annotated training data. Using added supervision from detection of bony corridors, tools, and anatomy, we learn image representations that are fed into a transformer model to regress surgical phases at the four granularity levels. Our approach demonstrates the feasibility of X-ray-based SPR, achieving an average accuracy of 93.8% on simulated sequences and 67.57% in cadaver across all granularity levels, with up to 88% accuracy for the target corridor in real data. This work constitutes the first step toward SPR for the X-ray domain, establishing an approach to categorizing phases in X-ray-guided surgery, simulating realistic image sequences to enable machine learning model development, and demonstrating that this approach is feasible for the analysis of real procedures. As X-ray-based SPR continues to mature, it will benefit procedures in orthopedic surgery, angiography, and interventional radiology by equipping intelligent surgical systems with situational awareness in the operating room.
[ { "version": "v1", "created": "Tue, 18 Apr 2023 20:48:14 GMT" } ]
2023-04-20T00:00:00
[ [ "Killeen", "Benjamin D.", "" ], [ "Zhang", "Han", "" ], [ "Mangulabnan", "Jan", "" ], [ "Armand", "Mehran", "" ], [ "Taylor", "Russel H.", "" ], [ "Osgood", "Greg", "" ], [ "Unberath", "Mathias", "" ] ]
new_dataset
0.999217
2304.09286
Young-Ho Kim
Young-Ho Kim and \`Eric Lluch and Gulsun Mehmet and Florin C. Ghesu and Ankur Kapoor
AI-based Agents for Automated Robotic Endovascular Guidewire Manipulation
null
null
null
null
cs.RO
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Endovascular guidewire manipulation is essential for minimally-invasive clinical applications (Percutaneous Coronary Intervention (PCI), Mechanical thrombectomy techniques for acute ischemic stroke (AIS), or Transjugular intrahepatic portosystemic shunt (TIPS)). All procedures commonly require 3D vessel geometries from 3D CTA (Computed Tomography Angiography) images. During these procedures, the clinician generally places a guiding catheter in the ostium of the relevant vessel and then manipulates a wire through the catheter and across the blockage. The clinician only uses X-ray fluoroscopy intermittently to visualize and guide the catheter, guidewire, and other devices. However, clinicians still passively control guidewires/catheters by relying on limited indirect observation (i.e., 2D partial view of devices, and intermittent updates due to radiation limit) from X-ray fluoroscopy. Modeling and controlling the guidewire manipulation in coronary vessels remains challenging because of the complicated interaction between guidewire motions with different physical properties (i.e., loads, coating) and vessel geometries with lumen conditions resulting in a highly non-linear system. This paper introduces a scalable learning pipeline to train AI-based agent models toward automated endovascular predictive device controls. First, we create a scalable environment by pre-processing 3D CTA images, providing patient-specific 3D vessel geometry and the centerline of the coronary. Next, we apply a large quantity of randomly generated motion sequences from the proximal end to generate wire states associated with each environment using a physics-based device simulator. Then, we reformulate the control problem to a sequence-to-sequence learning problem, in which we use a Transformer-based model, trained to handle non-linear sequential forward/inverse transition functions.
[ { "version": "v1", "created": "Tue, 18 Apr 2023 20:53:25 GMT" } ]
2023-04-20T00:00:00
[ [ "Kim", "Young-Ho", "" ], [ "Lluch", "Èric", "" ], [ "Mehmet", "Gulsun", "" ], [ "Ghesu", "Florin C.", "" ], [ "Kapoor", "Ankur", "" ] ]
new_dataset
0.995341
2304.09299
Sam Ross
Sam Ross, Nicole Sullivan, Jina Yoon
Virtual Fidgets: Opportunities and Design Principles for Bringing Fidgeting to Online Learning
6 pages, 3 figures, CHI LBW 2023
null
null
null
cs.HC
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We present design guidelines for incorporating fidgeting into the virtual world as a tool for students in online lectures. Fidgeting is associated with increased attention and self-regulation, and has the potential to help students focus. Currently there are no fidgets, physical or virtual, designed for preserving attention specifically in online learning environments, and no heuristics for designing fidgets within this domain. We identify three virtual fidget proxies to serve as design probes for studying student experiences with virtual fidgeting. Through a study of eight students using our virtual fidget proxies in online lectures, we identify eight emergent themes that encompass student experience with virtual fidgeting in lectures. Based on these themes, we present four principles for designing domain-specific virtual fidgets for online lectures. We identify that virtual fidgets for lectures should be context-aware, visually appealing, easy to adopt, and physically interactive.
[ { "version": "v1", "created": "Tue, 18 Apr 2023 21:03:30 GMT" } ]
2023-04-20T00:00:00
[ [ "Ross", "Sam", "" ], [ "Sullivan", "Nicole", "" ], [ "Yoon", "Jina", "" ] ]
new_dataset
0.993795
2304.09370
Theodore Tyler
Ted Tyler, Vaibhav Malhotra, Adam Montague, Zhigen Zhao, Frank L. Hammond III, and Ye Zhao
Integrating Reconfigurable Foot Design, Multi-modal Contact Sensing, and Terrain Classification for Bipedal Locomotion
7 pages, 6 figures
null
null
null
cs.RO
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The ability of bipedal robots to adapt to diverse and unstructured terrain conditions is crucial for their deployment in real-world environments. To this end, we present a novel, bio-inspired robot foot design with stabilizing tarsal segments and a multifarious sensor suite involving acoustic, capacitive, tactile, temperature, and acceleration sensors. A real-time signal processing and terrain classification system is developed and evaluated. The sensed terrain information is used to control actuated segments of the foot, leading to improved ground contact and stability. The proposed framework highlights the potential of the sensor-integrated adaptive foot for intelligent and adaptive locomotion.
[ { "version": "v1", "created": "Wed, 19 Apr 2023 01:53:30 GMT" } ]
2023-04-20T00:00:00
[ [ "Tyler", "Ted", "" ], [ "Malhotra", "Vaibhav", "" ], [ "Montague", "Adam", "" ], [ "Zhao", "Zhigen", "" ], [ "Hammond", "Frank L.", "III" ], [ "Zhao", "Ye", "" ] ]
new_dataset
0.9991
2304.09384
Chrystian Chrystian
Chrystian, Wahyono
SP-BatikGAN: An Efficient Generative Adversarial Network for Symmetric Pattern Generation
null
null
null
null
cs.CV cs.MM eess.IV
http://creativecommons.org/licenses/by-nc-sa/4.0/
Following the contention of AI arts, our research focuses on bringing AI for all, particularly for artists, to create AI arts with limited data and settings. We are interested in geometrically symmetric pattern generation, which appears on many artworks such as Portuguese, Moroccan tiles, and Batik, a cultural heritage in Southeast Asia. Symmetric pattern generation is a complex problem, with prior research creating too-specific models for certain patterns only. We provide publicly, the first-ever 1,216 high-quality symmetric patterns straight from design files for this task. We then formulate symmetric pattern enforcement (SPE) loss to leverage underlying symmetric-based structures that exist on current image distributions. Our SPE improves and accelerates training on any GAN configuration, and, with efficient attention, SP-BatikGAN compared to FastGAN, the state-of-the-art GAN for limited setting, improves the FID score from 110.11 to 90.76, an 18% decrease, and model diversity recall score from 0.047 to 0.204, a 334% increase.
[ { "version": "v1", "created": "Wed, 19 Apr 2023 02:38:11 GMT" } ]
2023-04-20T00:00:00
[ [ "Chrystian", "", "" ], [ "Wahyono", "", "" ] ]
new_dataset
0.992634
2304.09395
Yan Jin
Xuanhao Pan, Yan Jin, Yuandong Ding, Mingxiao Feng, Li Zhao, Lei Song, Jiang Bian
H-TSP: Hierarchically Solving the Large-Scale Travelling Salesman Problem
Accepted by AAAI 2023, February 2023
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We propose an end-to-end learning framework based on hierarchical reinforcement learning, called H-TSP, for addressing the large-scale Travelling Salesman Problem (TSP). The proposed H-TSP constructs a solution of a TSP instance starting from the scratch relying on two components: the upper-level policy chooses a small subset of nodes (up to 200 in our experiment) from all nodes that are to be traversed, while the lower-level policy takes the chosen nodes as input and outputs a tour connecting them to the existing partial route (initially only containing the depot). After jointly training the upper-level and lower-level policies, our approach can directly generate solutions for the given TSP instances without relying on any time-consuming search procedures. To demonstrate effectiveness of the proposed approach, we have conducted extensive experiments on randomly generated TSP instances with different numbers of nodes. We show that H-TSP can achieve comparable results (gap 3.42% vs. 7.32%) as SOTA search-based approaches, and more importantly, we reduce the time consumption up to two orders of magnitude (3.32s vs. 395.85s). To the best of our knowledge, H-TSP is the first end-to-end deep reinforcement learning approach that can scale to TSP instances of up to 10000 nodes. Although there are still gaps to SOTA results with respect to solution quality, we believe that H-TSP will be useful for practical applications, particularly those that are time-sensitive e.g., on-call routing and ride hailing service.
[ { "version": "v1", "created": "Wed, 19 Apr 2023 03:10:30 GMT" } ]
2023-04-20T00:00:00
[ [ "Pan", "Xuanhao", "" ], [ "Jin", "Yan", "" ], [ "Ding", "Yuandong", "" ], [ "Feng", "Mingxiao", "" ], [ "Zhao", "Li", "" ], [ "Song", "Lei", "" ], [ "Bian", "Jiang", "" ] ]
new_dataset
0.999678
2304.09400
Qianqian Zhang
Qianqian Zhang, Hu Zhou, Ying-Chang Liang, Sumei Sun, Wei Zhang, and H. Vincent Poor
On the Capacity Region of Reconfigurable Intelligent Surface Assisted Symbiotic Radios
null
null
null
null
cs.IT math.IT
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
In this paper, we are interested in reconfigurable intelligent surface (RIS)-assisted symbiotic radio (SR) systems, where an RIS assists a primary transmission by passive beamforming and simultaneously acts as an information transmitter by periodically adjusting its reflecting coefficients. The above modulation scheme innately enables a new multiplicative multiple access channel (M-MAC), where the primary and secondary signals are superposed in a multiplicative and additive manner. To pursue the fundamental performance limits of the M-MAC, we focus on the characterization of the capacity region of such systems. Due to the passive nature of RISs, the transmitted signal of the RIS should satisfy the peak power constraint. Under this constraint at the RIS as well as the average power constraint at the primary transmitter (PTx), we analyze the capacity-achieving distributions of the transmitted signals and characterize the capacity region of the M-MAC. Then, theoretical analysis is performed to reveal insights into the RIS-assisted SR. It is observed that: 1) the capacity region of the M-MAC is strictly convex and larger than that of the conventional TDMA scheme; 2) the secondary transmission can achieve the maximum rate when the PTx transmits the constant envelope signals; 3) and the sum rate can achieve the maximum when the PTx transmits Gaussian signals and the RIS transmits the constant envelope signals. Finally, extensive numerical results are provided to evaluate the performance of the RIS-assisted SR and verify the accuracy of our theoretical analysis.
[ { "version": "v1", "created": "Wed, 19 Apr 2023 03:28:32 GMT" } ]
2023-04-20T00:00:00
[ [ "Zhang", "Qianqian", "" ], [ "Zhou", "Hu", "" ], [ "Liang", "Ying-Chang", "" ], [ "Sun", "Sumei", "" ], [ "Zhang", "Wei", "" ], [ "Poor", "H. Vincent", "" ] ]
new_dataset
0.972346
2304.09411
Yi Zheng
Yi Zheng, Aasheesh Kolli, Shaizeen Aga
Egalitarian ORAM: Wear-Leveling for ORAM
null
null
null
null
cs.AR
http://creativecommons.org/publicdomain/zero/1.0/
While non-volatile memories (NVMs) provide several desirable characteristics like better density and comparable energy efficiency than DRAM, DRAM-like performance, and disk-like durability, the limited endurance NVMs manifest remains a challenge with these memories. Indeed, the endurance constraints of NVMs can prevent solutions that are commonly employed for other mainstream memories like DRAM from being carried over as-is to NVMs. Specifically, in this work we observe that, Oblivious RAM (ORAM) primitive, the state-ofart solution to tackle memory bus side channel vulnerability, while widely studied for DRAMs, is particularly challenging to implement as-is for NVMs as it severely affects endurance of NVMs. This is so, as the inherent nature of ORAM primitive causes an order of magnitude increase in write traffic and furthermore, causes some regions of memory to be written far more often than others. This non-uniform write traffic as manifested by ORAM primitive stands to severely affect the lifetime of non-volatile memories (1% of baseline without ORAM) to even make it impractical to address this security vulnerability
[ { "version": "v1", "created": "Wed, 19 Apr 2023 03:56:45 GMT" } ]
2023-04-20T00:00:00
[ [ "Zheng", "Yi", "" ], [ "Kolli", "Aasheesh", "" ], [ "Aga", "Shaizeen", "" ] ]
new_dataset
0.982122
2304.09412
Snehesh Shrestha
Snehesh Shrestha, Ishan Tamrakar, Cornelia Fermuller, Yiannis Aloimonos
hDesigner: Real-Time Haptic Feedback Pattern Designer
null
null
null
null
cs.HC cs.MM cs.SY eess.SY
http://creativecommons.org/licenses/by/4.0/
Haptic sensing can provide a new dimension to enhance people's musical and cinematic experiences. However, designing a haptic pattern is neither intuitive nor trivial. Imagined haptic patterns tend to be different from experienced ones. As a result, researchers use simple step-curve patterns to create haptic stimuli. To this end, we designed and developed an intuitive haptic pattern designer that lets you rapidly prototype creative patterns. Our simple architecture, wireless connectivity, and easy-to-program communication protocol make it modular and easy to scale. In this demo, workshop participants can select from a library of haptic patterns and design new ones. They can feel the pattern as they make changes in the user interface. With this new workflow, researchers and artists can design and rapidly test haptic patterns for downstream tasks such as research experiments or create new musical and cinematic experiences. More details about the project are available at https://www.snehesh.com/hDesigner
[ { "version": "v1", "created": "Wed, 19 Apr 2023 04:00:48 GMT" } ]
2023-04-20T00:00:00
[ [ "Shrestha", "Snehesh", "" ], [ "Tamrakar", "Ishan", "" ], [ "Fermuller", "Cornelia", "" ], [ "Aloimonos", "Yiannis", "" ] ]
new_dataset
0.999499
2304.09421
Zhao Kang
Quanjiang Guo, Zhao Kang, Ling Tian, Zhouguo Chen
TieFake: Title-Text Similarity and Emotion-Aware Fake News Detection
Appear on IJCNN 2023
null
null
null
cs.CL cs.CV cs.LG cs.SI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Fake news detection aims to detect fake news widely spreading on social media platforms, which can negatively influence the public and the government. Many approaches have been developed to exploit relevant information from news images, text, or videos. However, these methods may suffer from the following limitations: (1) ignore the inherent emotional information of the news, which could be beneficial since it contains the subjective intentions of the authors; (2) pay little attention to the relation (similarity) between the title and textual information in news articles, which often use irrelevant title to attract reader' attention. To this end, we propose a novel Title-Text similarity and emotion-aware Fake news detection (TieFake) method by jointly modeling the multi-modal context information and the author sentiment in a unified framework. Specifically, we respectively employ BERT and ResNeSt to learn the representations for text and images, and utilize publisher emotion extractor to capture the author's subjective emotion in the news content. We also propose a scale-dot product attention mechanism to capture the similarity between title features and textual features. Experiments are conducted on two publicly available multi-modal datasets, and the results demonstrate that our proposed method can significantly improve the performance of fake news detection. Our code is available at https://github.com/UESTC-GQJ/TieFake.
[ { "version": "v1", "created": "Wed, 19 Apr 2023 04:47:36 GMT" } ]
2023-04-20T00:00:00
[ [ "Guo", "Quanjiang", "" ], [ "Kang", "Zhao", "" ], [ "Tian", "Ling", "" ], [ "Chen", "Zhouguo", "" ] ]
new_dataset
0.997762
2304.09448
Yao Mu Mark
Yao Mu, Shunyu Yao, Mingyu Ding, Ping Luo, Chuang Gan
EC^2: Emergent Communication for Embodied Control
Published in CVPR2023
null
null
null
cs.LG cs.CL cs.CV
http://creativecommons.org/licenses/by/4.0/
Embodied control requires agents to leverage multi-modal pre-training to quickly learn how to act in new environments, where video demonstrations contain visual and motion details needed for low-level perception and control, and language instructions support generalization with abstract, symbolic structures. While recent approaches apply contrastive learning to force alignment between the two modalities, we hypothesize better modeling their complementary differences can lead to more holistic representations for downstream adaption. To this end, we propose Emergent Communication for Embodied Control (EC^2), a novel scheme to pre-train video-language representations for few-shot embodied control. The key idea is to learn an unsupervised "language" of videos via emergent communication, which bridges the semantics of video details and structures of natural language. We learn embodied representations of video trajectories, emergent language, and natural language using a language model, which is then used to finetune a lightweight policy network for downstream control. Through extensive experiments in Metaworld and Franka Kitchen embodied benchmarks, EC^2 is shown to consistently outperform previous contrastive learning methods for both videos and texts as task inputs. Further ablations confirm the importance of the emergent language, which is beneficial for both video and language learning, and significantly superior to using pre-trained video captions. We also present a quantitative and qualitative analysis of the emergent language and discuss future directions toward better understanding and leveraging emergent communication in embodied tasks.
[ { "version": "v1", "created": "Wed, 19 Apr 2023 06:36:02 GMT" } ]
2023-04-20T00:00:00
[ [ "Mu", "Yao", "" ], [ "Yao", "Shunyu", "" ], [ "Ding", "Mingyu", "" ], [ "Luo", "Ping", "" ], [ "Gan", "Chuang", "" ] ]
new_dataset
0.96837
2304.09463
Zhuo Chen
Zhuo Chen, Xudong Xu, Yichao Yan, Ye Pan, Wenhan Zhu, Wayne Wu, Bo Dai and Xiaokang Yang
HyperStyle3D: Text-Guided 3D Portrait Stylization via Hypernetworks
null
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Portrait stylization is a long-standing task enabling extensive applications. Although 2D-based methods have made great progress in recent years, real-world applications such as metaverse and games often demand 3D content. On the other hand, the requirement of 3D data, which is costly to acquire, significantly impedes the development of 3D portrait stylization methods. In this paper, inspired by the success of 3D-aware GANs that bridge 2D and 3D domains with 3D fields as the intermediate representation for rendering 2D images, we propose a novel method, dubbed HyperStyle3D, based on 3D-aware GANs for 3D portrait stylization. At the core of our method is a hyper-network learned to manipulate the parameters of the generator in a single forward pass. It not only offers a strong capacity to handle multiple styles with a single model, but also enables flexible fine-grained stylization that affects only texture, shape, or local part of the portrait. While the use of 3D-aware GANs bypasses the requirement of 3D data, we further alleviate the necessity of style images with the CLIP model being the stylization guidance. We conduct an extensive set of experiments across the style, attribute, and shape, and meanwhile, measure the 3D consistency. These experiments demonstrate the superior capability of our HyperStyle3D model in rendering 3D-consistent images in diverse styles, deforming the face shape, and editing various attributes.
[ { "version": "v1", "created": "Wed, 19 Apr 2023 07:22:05 GMT" } ]
2023-04-20T00:00:00
[ [ "Chen", "Zhuo", "" ], [ "Xu", "Xudong", "" ], [ "Yan", "Yichao", "" ], [ "Pan", "Ye", "" ], [ "Zhu", "Wenhan", "" ], [ "Wu", "Wayne", "" ], [ "Dai", "Bo", "" ], [ "Yang", "Xiaokang", "" ] ]
new_dataset
0.963067
2304.09468
Ali AlQahtani
Hosam Alamleh, Ali Abdullah S. AlQahtani, Baker Al Smadi
Secure Mobile Payment Architecture Enabling Multi-factor Authentication
null
null
null
null
cs.CR cs.NI
http://creativecommons.org/licenses/by/4.0/
The rise of smartphones has led to a significant increase in the usage of mobile payments. Mobile payments allow individuals to access financial resources and make transactions through their mobile devices while on the go. However, the current mobile payment systems were designed to align with traditional payment structures, which limits the full potential of smartphones, including their security features. This has become a major concern in the rapidly growing mobile payment market. To address these security concerns,in this paper we propose new mobile payment architecture. This architecture leverages the advanced capabilities of modern smartphones to verify various aspects of a payment, such as funds, biometrics, location, and others. The proposed system aims to guarantee the legitimacy of transactions and protect against identity theft by verifying multiple elements of a payment. The security of mobile payment systems is crucial, given the rapid growth of the market. Evaluating mobile payment systems based on their authentication, encryption, and fraud detection capabilities is of utmost importance. The proposed architecture provides a secure mobile payment solution that enhances the overall payment experience by taking advantage of the advanced capabilities of modern smartphones. This will not only improve the security of mobile payments but also offer a more user-friendly payment experience for consumers.
[ { "version": "v1", "created": "Wed, 19 Apr 2023 07:30:18 GMT" } ]
2023-04-20T00:00:00
[ [ "Alamleh", "Hosam", "" ], [ "AlQahtani", "Ali Abdullah S.", "" ], [ "Smadi", "Baker Al", "" ] ]
new_dataset
0.997263
2304.09469
Adriel Isaiah Amoguis
Adriel Isaiah V. Amoguis, Gian Joseph B. Madrid, Benito Miguel D. Flores IV, Macario O. Cordel II
Baybayin Character Instance Detection
null
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The Philippine Government recently passed the "National Writing System Act," which promotes using Baybayin in Philippine texts. In support of this effort to promote the use of Baybayin, we present a computer vision system which can aid individuals who cannot easily read Baybayin script. In this paper, we survey the existing methods of identifying Baybayin scripts using computer vision and machine learning techniques and discuss their capabilities and limitations. Further, we propose a Baybayin Optical Character Instance Segmentation and Classification model using state-of-the-art Convolutional Neural Networks (CNNs) that detect Baybayin character instances in an image then outputs the Latin alphabet counterparts of each character instance in the image. Most existing systems are limited to character-level image classification and often misclassify or not natively support characters with diacritics. In addition, these existing models often have specific input requirements that limit it to classifying Baybayin text in a controlled setting, such as limitations in clarity and contrast, among others. To our knowledge, our proposed method is the first end-to-end character instance detection model for Baybayin, achieving a mAP50 score of 93.30%, mAP50-95 score of 80.50%, and F1-Score of 84.84%.
[ { "version": "v1", "created": "Wed, 19 Apr 2023 07:35:41 GMT" } ]
2023-04-20T00:00:00
[ [ "Amoguis", "Adriel Isaiah V.", "" ], [ "Madrid", "Gian Joseph B.", "" ], [ "Flores", "Benito Miguel D.", "IV" ], [ "Cordel", "Macario O.", "II" ] ]
new_dataset
0.999568
2304.09574
Sudhakar Singh
Amisha Gangwar, Sudhakar Singh, Richa Mishra, Shiv Prakash
The State-of-the-Art in Air Pollution Monitoring and Forecasting Systems using IoT, Big Data, and Machine Learning
30 pages, 11 figures, Wireless Personal Communications. Wireless Pers Commun (2023)
null
10.1007/s11277-023-10351-1
WIRE-D-22-01442-R1
cs.LG cs.DC
http://creativecommons.org/licenses/by/4.0/
The quality of air is closely linked with the life quality of humans, plantations, and wildlife. It needs to be monitored and preserved continuously. Transportations, industries, construction sites, generators, fireworks, and waste burning have a major percentage in degrading the air quality. These sources are required to be used in a safe and controlled manner. Using traditional laboratory analysis or installing bulk and expensive models every few miles is no longer efficient. Smart devices are needed for collecting and analyzing air data. The quality of air depends on various factors, including location, traffic, and time. Recent researches are using machine learning algorithms, big data technologies, and the Internet of Things to propose a stable and efficient model for the stated purpose. This review paper focuses on studying and compiling recent research in this field and emphasizes the Data sources, Monitoring, and Forecasting models. The main objective of this paper is to provide the astuteness of the researches happening to improve the various aspects of air polluting models. Further, it casts light on the various research issues and challenges also.
[ { "version": "v1", "created": "Wed, 19 Apr 2023 11:24:53 GMT" } ]
2023-04-20T00:00:00
[ [ "Gangwar", "Amisha", "" ], [ "Singh", "Sudhakar", "" ], [ "Mishra", "Richa", "" ], [ "Prakash", "Shiv", "" ] ]
new_dataset
0.953228
2304.09588
Yu Guo
Yu Guo, Ryan Wen Liu, Jiangtian Nie, Lingjuan Lyu, Zehui Xiong, Jiawen Kang, Han Yu, Dusit Niyato
DADFNet: Dual Attention and Dual Frequency-Guided Dehazing Network for Video-Empowered Intelligent Transportation
This paper is accepted by AAAI 2022 Workshop: AI for Transportation
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Visual surveillance technology is an indispensable functional component of advanced traffic management systems. It has been applied to perform traffic supervision tasks, such as object detection, tracking and recognition. However, adverse weather conditions, e.g., fog, haze and mist, pose severe challenges for video-based transportation surveillance. To eliminate the influences of adverse weather conditions, we propose a dual attention and dual frequency-guided dehazing network (termed DADFNet) for real-time visibility enhancement. It consists of a dual attention module (DAM) and a high-low frequency-guided sub-net (HLFN) to jointly consider the attention and frequency mapping to guide haze-free scene reconstruction. Extensive experiments on both synthetic and real-world images demonstrate the superiority of DADFNet over state-of-the-art methods in terms of visibility enhancement and improvement in detection accuracy. Furthermore, DADFNet only takes $6.3$ ms to process a 1,920 * 1,080 image on the 2080 Ti GPU, making it highly efficient for deployment in intelligent transportation systems.
[ { "version": "v1", "created": "Wed, 19 Apr 2023 11:55:30 GMT" } ]
2023-04-20T00:00:00
[ [ "Guo", "Yu", "" ], [ "Liu", "Ryan Wen", "" ], [ "Nie", "Jiangtian", "" ], [ "Lyu", "Lingjuan", "" ], [ "Xiong", "Zehui", "" ], [ "Kang", "Jiawen", "" ], [ "Yu", "Han", "" ], [ "Niyato", "Dusit", "" ] ]
new_dataset
0.999192
2304.09609
Wendong Zhang
Wendong Zhang
MMDR: A Result Feature Fusion Object Detection Approach for Autonomous System
9 pages, 12 figures
null
null
null
cs.CV cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Object detection has been extensively utilized in autonomous systems in recent years, encompassing both 2D and 3D object detection. Recent research in this field has primarily centered around multimodal approaches for addressing this issue.In this paper, a multimodal fusion approach based on result feature-level fusion is proposed. This method utilizes the outcome features generated from single modality sources, and fuses them for downstream tasks.Based on this method, a new post-fusing network is proposed for multimodal object detection, which leverages the single modality outcomes as features. The proposed approach, called Multi-Modal Detector based on Result features (MMDR), is designed to work for both 2D and 3D object detection tasks. Compared to previous multimodal models, the proposed approach in this paper performs feature fusion at a later stage, enabling better representation of the deep-level features of single modality sources. Additionally, the MMDR model incorporates shallow global features during the feature fusion stage, endowing the model with the ability to perceive background information and the overall input, thereby avoiding issues such as missed detections.
[ { "version": "v1", "created": "Wed, 19 Apr 2023 12:28:42 GMT" } ]
2023-04-20T00:00:00
[ [ "Zhang", "Wendong", "" ] ]
new_dataset
0.983558
2304.09639
Alessandro Ronca
Alessandro Ronca
The Krohn-Rhodes Logics
null
null
null
null
cs.LO cs.AI cs.FL
http://creativecommons.org/licenses/by-nc-nd/4.0/
We present a new family of modal temporal logics of the past, obtained by extending Past LTL with a rich set of temporal operators based on the theory by Krohn and Rhodes for automata cascades. The theory says that every automaton can be expressed as a cascade of some basic automata called prime automata. They are the building blocks of all automata, analogously to prime numbers being the building blocks of all natural numbers. We show that Past LTL corresponds to cascades of one kind of prime automata called flip-flops. In particular, the temporal operators of Past LTL are captured by flip-flops, and they cannot capture any other prime automaton, confining the expressivity within the star-free regular languages. We propose novel temporal operators that can capture other prime automata, and hence extend the expressivity of Past LTL. Such operators are infinitely-many, and they yield an infinite number of logics capturing an infinite number of distinct fragments of the regular languages. The result is a yet unexplored landscape of extensions of Past LTL, that we call Krohn-Rhodes Logics, each of them with the potential of matching the expressivity required by specific applications.
[ { "version": "v1", "created": "Wed, 19 Apr 2023 13:24:04 GMT" } ]
2023-04-20T00:00:00
[ [ "Ronca", "Alessandro", "" ] ]
new_dataset
0.996893
2304.09653
Sitong Wang
Sitong Wang, Samia Menon, Tao Long, Keren Henderson, Dingzeyu Li, Kevin Crowston, Mark Hansen, Jeffrey V. Nickerson, Lydia B. Chilton
ReelFramer: Co-creating News Reels on Social Media with Generative AI
null
null
null
null
cs.HC cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Short videos on social media are a prime way many young people find and consume content. News outlets would like to reach audiences through news reels, but currently struggle to translate traditional journalistic formats into the short, entertaining videos that match the style of the platform. There are many ways to frame a reel-style narrative around a news story, and selecting one is a challenge. Different news stories call for different framings, and require a different trade-off between entertainment and information. We present a system called ReelFramer that uses text and image generation to help journalists explore multiple narrative framings for a story, then generate scripts, character boards and storyboards they can edit and iterate on. A user study of five graduate students in journalism-related fields found the system greatly eased the burden of transforming a written story into a reel, and that exploring framings to find the right one was a rewarding process.
[ { "version": "v1", "created": "Wed, 19 Apr 2023 13:44:35 GMT" } ]
2023-04-20T00:00:00
[ [ "Wang", "Sitong", "" ], [ "Menon", "Samia", "" ], [ "Long", "Tao", "" ], [ "Henderson", "Keren", "" ], [ "Li", "Dingzeyu", "" ], [ "Crowston", "Kevin", "" ], [ "Hansen", "Mark", "" ], [ "Nickerson", "Jeffrey V.", "" ], [ "Chilton", "Lydia B.", "" ] ]
new_dataset
0.999675
2304.09660
Liang Zhang
Liang Zhang, Anwen Hu, Jing Zhang, Shuo Hu, Qin Jin
MPMQA: Multimodal Question Answering on Product Manuals
null
null
null
null
cs.CL
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Visual contents, such as illustrations and images, play a big role in product manual understanding. Existing Product Manual Question Answering (PMQA) datasets tend to ignore visual contents and only retain textual parts. In this work, to emphasize the importance of multimodal contents, we propose a Multimodal Product Manual Question Answering (MPMQA) task. For each question, MPMQA requires the model not only to process multimodal contents but also to provide multimodal answers. To support MPMQA, a large-scale dataset PM209 is constructed with human annotations, which contains 209 product manuals from 27 well-known consumer electronic brands. Human annotations include 6 types of semantic regions for manual contents and 22,021 pairs of question and answer. Especially, each answer consists of a textual sentence and related visual regions from manuals. Taking into account the length of product manuals and the fact that a question is always related to a small number of pages, MPMQA can be naturally split into two subtasks: retrieving most related pages and then generating multimodal answers. We further propose a unified model that can perform these two subtasks all together and achieve comparable performance with multiple task-specific models. The PM209 dataset is available at https://github.com/AIM3-RUC/MPMQA.
[ { "version": "v1", "created": "Wed, 19 Apr 2023 13:48:14 GMT" } ]
2023-04-20T00:00:00
[ [ "Zhang", "Liang", "" ], [ "Hu", "Anwen", "" ], [ "Zhang", "Jing", "" ], [ "Hu", "Shuo", "" ], [ "Jin", "Qin", "" ] ]
new_dataset
0.999023
2304.09787
Seung Wook Kim
Seung Wook Kim, Bradley Brown, Kangxue Yin, Karsten Kreis, Katja Schwarz, Daiqing Li, Robin Rombach, Antonio Torralba, Sanja Fidler
NeuralField-LDM: Scene Generation with Hierarchical Latent Diffusion Models
CVPR 2023
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Automatically generating high-quality real world 3D scenes is of enormous interest for applications such as virtual reality and robotics simulation. Towards this goal, we introduce NeuralField-LDM, a generative model capable of synthesizing complex 3D environments. We leverage Latent Diffusion Models that have been successfully utilized for efficient high-quality 2D content creation. We first train a scene auto-encoder to express a set of image and pose pairs as a neural field, represented as density and feature voxel grids that can be projected to produce novel views of the scene. To further compress this representation, we train a latent-autoencoder that maps the voxel grids to a set of latent representations. A hierarchical diffusion model is then fit to the latents to complete the scene generation pipeline. We achieve a substantial improvement over existing state-of-the-art scene generation models. Additionally, we show how NeuralField-LDM can be used for a variety of 3D content creation applications, including conditional scene generation, scene inpainting and scene style manipulation.
[ { "version": "v1", "created": "Wed, 19 Apr 2023 16:13:21 GMT" } ]
2023-04-20T00:00:00
[ [ "Kim", "Seung Wook", "" ], [ "Brown", "Bradley", "" ], [ "Yin", "Kangxue", "" ], [ "Kreis", "Karsten", "" ], [ "Schwarz", "Katja", "" ], [ "Li", "Daiqing", "" ], [ "Rombach", "Robin", "" ], [ "Torralba", "Antonio", "" ], [ "Fidler", "Sanja", "" ] ]
new_dataset
0.993329
2304.09831
Kyle Stachowicz
Kyle Stachowicz, Dhruv Shah, Arjun Bhorkar, Ilya Kostrikov, Sergey Levine
FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing
null
null
null
null
cs.RO cs.AI cs.LG
http://creativecommons.org/licenses/by/4.0/
We present a system that enables an autonomous small-scale RC car to drive aggressively from visual observations using reinforcement learning (RL). Our system, FastRLAP (faster lap), trains autonomously in the real world, without human interventions, and without requiring any simulation or expert demonstrations. Our system integrates a number of important components to make this possible: we initialize the representations for the RL policy and value function from a large prior dataset of other robots navigating in other environments (at low speed), which provides a navigation-relevant representation. From here, a sample-efficient online RL method uses a single low-speed user-provided demonstration to determine the desired driving course, extracts a set of navigational checkpoints, and autonomously practices driving through these checkpoints, resetting automatically on collision or failure. Perhaps surprisingly, we find that with appropriate initialization and choice of algorithm, our system can learn to drive over a variety of racing courses with less than 20 minutes of online training. The resulting policies exhibit emergent aggressive driving skills, such as timing braking and acceleration around turns and avoiding areas which impede the robot's motion, approaching the performance of a human driver using a similar first-person interface over the course of training.
[ { "version": "v1", "created": "Wed, 19 Apr 2023 17:33:47 GMT" } ]
2023-04-20T00:00:00
[ [ "Stachowicz", "Kyle", "" ], [ "Shah", "Dhruv", "" ], [ "Bhorkar", "Arjun", "" ], [ "Kostrikov", "Ilya", "" ], [ "Levine", "Sergey", "" ] ]
new_dataset
0.99978
2304.09856
Xianbiao Qi
Xianbiao Qi, Jianan Wang, Yihao Chen, Yukai Shi, Lei Zhang
LipsFormer: Introducing Lipschitz Continuity to Vision Transformers
To appear in ICLR 2023, our code will be public at https://github.com/IDEA-Research/LipsFormer
null
null
null
cs.CV cs.AI cs.LG
http://creativecommons.org/licenses/by/4.0/
We present a Lipschitz continuous Transformer, called LipsFormer, to pursue training stability both theoretically and empirically for Transformer-based models. In contrast to previous practical tricks that address training instability by learning rate warmup, layer normalization, attention formulation, and weight initialization, we show that Lipschitz continuity is a more essential property to ensure training stability. In LipsFormer, we replace unstable Transformer component modules with Lipschitz continuous counterparts: CenterNorm instead of LayerNorm, spectral initialization instead of Xavier initialization, scaled cosine similarity attention instead of dot-product attention, and weighted residual shortcut. We prove that these introduced modules are Lipschitz continuous and derive an upper bound on the Lipschitz constant of LipsFormer. Our experiments show that LipsFormer allows stable training of deep Transformer architectures without the need of careful learning rate tuning such as warmup, yielding a faster convergence and better generalization. As a result, on the ImageNet 1K dataset, LipsFormer-Swin-Tiny based on Swin Transformer training for 300 epochs can obtain 82.7\% without any learning rate warmup. Moreover, LipsFormer-CSwin-Tiny, based on CSwin, training for 300 epochs achieves a top-1 accuracy of 83.5\% with 4.7G FLOPs and 24M parameters. The code will be released at \url{https://github.com/IDEA-Research/LipsFormer}.
[ { "version": "v1", "created": "Wed, 19 Apr 2023 17:59:39 GMT" } ]
2023-04-20T00:00:00
[ [ "Qi", "Xianbiao", "" ], [ "Wang", "Jianan", "" ], [ "Chen", "Yihao", "" ], [ "Shi", "Yukai", "" ], [ "Zhang", "Lei", "" ] ]
new_dataset
0.956404
2010.00600
Emilio Ferrara
Emily Chen, Ashok Deb, Emilio Ferrara
#Election2020: The First Public Twitter Dataset on the 2020 US Presidential Election
Our dataset is available at: https://github.com/echen102/us-pres-elections-2020
null
10.1007/s42001-021-00117-9
null
cs.SI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The integrity of democratic political discourse is at the core to guarantee free and fair elections. With social media often dictating the tones and trends of politics-related discussion, it is of paramount important to be able to study online chatter, especially in the run up to important voting events, like in the case of the upcoming November 3, 2020 U.S. Presidential Election. Limited access to social media data is often the first barrier to impede, hinder, or slow down progress, and ultimately our understanding of online political discourse. To mitigate this issue and try to empower the Computational Social Science research community, we decided to publicly release a massive-scale, longitudinal dataset of U.S. politics- and election-related tweets. This multilingual dataset that we have been collecting for over one year encompasses hundreds of millions of tweets and tracks all salient U.S. politics trends, actors, and events between 2019 and 2020. It predates and spans the whole period of Republican and Democratic primaries, with real-time tracking of all presidential contenders of both sides of the isle. After that, it focuses on presidential and vice-presidential candidates. Our dataset release is curated, documented and will be constantly updated on a weekly-basis, until the November 3, 2020 election and beyond. We hope that the academic community, computational journalists, and research practitioners alike will all take advantage of our dataset to study relevant scientific and social issues, including problems like misinformation, information manipulation, interference, and distortion of online political discourse that have been prevalent in the context of recent election events in the United States and worldwide. Our dataset is available at: https://github.com/echen102/us-pres-elections-2020
[ { "version": "v1", "created": "Thu, 1 Oct 2020 18:00:03 GMT" } ]
2023-04-19T00:00:00
[ [ "Chen", "Emily", "" ], [ "Deb", "Ashok", "" ], [ "Ferrara", "Emilio", "" ] ]
new_dataset
0.999833
2103.01403
Qing Li
Qing Li, Siyuan Huang, Yining Hong, Yixin Zhu, Ying Nian Wu, Song-Chun Zhu
A Minimalist Dataset for Systematic Generalization of Perception, Syntax, and Semantics
ICLR 2023. website: https://liqing-ustc.github.io/HINT
null
null
null
cs.LG cs.AI cs.CV
http://creativecommons.org/licenses/by/4.0/
Inspired by humans' exceptional ability to master arithmetic and generalize to new problems, we present a new dataset, Handwritten arithmetic with INTegers (HINT), to examine machines' capability of learning generalizable concepts at three levels: perception, syntax, and semantics. In HINT, machines are tasked with learning how concepts are perceived from raw signals such as images (i.e., perception), how multiple concepts are structurally combined to form a valid expression (i.e., syntax), and how concepts are realized to afford various reasoning tasks (i.e., semantics), all in a weakly supervised manner. Focusing on systematic generalization, we carefully design a five-fold test set to evaluate both the interpolation and the extrapolation of learned concepts w.r.t. the three levels. Further, we design a few-shot learning split to determine whether or not models can rapidly learn new concepts and generalize them to more complex scenarios. To comprehend existing models' limitations, we undertake extensive experiments with various sequence-to-sequence models, including RNNs, Transformers, and GPT-3 (with the chain of thought prompting). The results indicate that current models struggle to extrapolate to long-range syntactic dependency and semantics. Models exhibit a considerable gap toward human-level generalization when evaluated with new concepts in a few-shot setting. Moreover, we discover that it is infeasible to solve HINT by merely scaling up the dataset and the model size; this strategy contributes little to the extrapolation of syntax and semantics. Finally, in zero-shot GPT-3 experiments, the chain of thought prompting exhibits impressive results and significantly boosts the test accuracy. We believe the HINT dataset and the experimental findings are of great interest to the learning community on systematic generalization.
[ { "version": "v1", "created": "Tue, 2 Mar 2021 01:32:54 GMT" }, { "version": "v2", "created": "Tue, 20 Sep 2022 02:16:59 GMT" }, { "version": "v3", "created": "Tue, 18 Apr 2023 07:54:24 GMT" } ]
2023-04-19T00:00:00
[ [ "Li", "Qing", "" ], [ "Huang", "Siyuan", "" ], [ "Hong", "Yining", "" ], [ "Zhu", "Yixin", "" ], [ "Wu", "Ying Nian", "" ], [ "Zhu", "Song-Chun", "" ] ]
new_dataset
0.999857
2203.07908
Josip \v{S}ari\'c
Josip \v{S}ari\'c, Marin Or\v{s}i\'c, Sini\v{s}a \v{S}egvi\'c
Panoptic SwiftNet: Pyramidal Fusion for Real-time Panoptic Segmentation
Code available at: https://github.com/jsaric/panoptic-swiftnet
Remote Sensing. 2023, 15(8), 1968;
10.3390/rs15081968
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Dense panoptic prediction is a key ingredient in many existing applications such as autonomous driving, automated warehouses or remote sensing. Many of these applications require fast inference over large input resolutions on affordable or even embedded hardware. We propose to achieve this goal by trading off backbone capacity for multi-scale feature extraction. In comparison with contemporaneous approaches to panoptic segmentation, the main novelties of our method are efficient scale-equivariant feature extraction, cross-scale upsampling through pyramidal fusion and boundary-aware learning of pixel-to-instance assignment. The proposed method is very well suited for remote sensing imagery due to the huge number of pixels in typical city-wide and region-wide datasets. We present panoptic experiments on Cityscapes, Vistas, COCO and the BSB-Aerial dataset. Our models outperform the state of the art on the BSB-Aerial dataset while being able to process more than a hundred 1MPx images per second on a RTX3090 GPU with FP16 precision and TensorRT optimization.
[ { "version": "v1", "created": "Tue, 15 Mar 2022 13:47:40 GMT" }, { "version": "v2", "created": "Tue, 18 Apr 2023 14:46:07 GMT" } ]
2023-04-19T00:00:00
[ [ "Šarić", "Josip", "" ], [ "Oršić", "Marin", "" ], [ "Šegvić", "Siniša", "" ] ]
new_dataset
0.985067
2204.06776
Haolong Li
Haolong Li and Joerg Stueckler
Visual-Inertial Odometry with Online Calibration of Velocity-Control Based Kinematic Motion Models
Accepted by IEEE Robotics and Automation Letters (RA-L) 2022
null
10.1109/LRA.2022.3169837
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Visual-inertial odometry (VIO) is an important technology for autonomous robots with power and payload constraints. In this paper, we propose a novel approach for VIO with stereo cameras which integrates and calibrates the velocity-control based kinematic motion model of wheeled mobile robots online. Including such a motion model can help to improve the accuracy of VIO. Compared to several previous approaches proposed to integrate wheel odometer measurements for this purpose, our method does not require wheel encoders and can be applied when the robot motion can be modeled with velocity-control based kinematic motion model. We use radial basis function (RBF) kernels to compensate for the time delay and deviations between control commands and actual robot motion. The motion model is calibrated online by the VIO system and can be used as a forward model for motion control and planning. We evaluate our approach with data obtained in variously sized indoor environments, demonstrate improvements over a pure VIO method, and evaluate the prediction accuracy of the online calibrated model.
[ { "version": "v1", "created": "Thu, 14 Apr 2022 06:21:12 GMT" }, { "version": "v2", "created": "Fri, 22 Apr 2022 15:50:59 GMT" }, { "version": "v3", "created": "Tue, 18 Apr 2023 09:45:42 GMT" } ]
2023-04-19T00:00:00
[ [ "Li", "Haolong", "" ], [ "Stueckler", "Joerg", "" ] ]
new_dataset
0.958961
2207.01249
Bohan Yang
Bohan Yang, Bo Lu, Wei Chen, Fangxun Zhong, and Yun-Hui Liu
Model-Free 3D Shape Control of Deformable Objects Using Novel Features Based on Modal Analysis
Accepted by the IEEE Transactions on Robotics. The paper will appear in the IEEE Transactions on Robotics. IEEE copyright
null
null
null
cs.RO
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Shape control of deformable objects is a challenging and important robotic problem. This paper proposes a model-free controller using novel 3D global deformation features based on modal analysis. Unlike most existing controllers using geometric features, our controller employs a physically-based deformation feature by decoupling 3D global deformation into low-frequency mode shapes. Although modal analysis is widely adopted in computer vision and simulation, it has not been used in robotic deformation control. We develop a new model-free framework for modal-based deformation control under robot manipulation. Physical interpretation of mode shapes enables us to formulate an analytical deformation Jacobian matrix mapping the robot manipulation onto changes of the modal features. In the Jacobian matrix, unknown geometry and physical properties of the object are treated as low-dimensional modal parameters which can be used to linearly parameterize the closed-loop system. Thus, an adaptive controller with proven stability can be designed to deform the object while online estimating the modal parameters. Simulations and experiments are conducted using linear, planar, and solid objects under different settings. The results not only confirm the superior performance of our controller but also demonstrate its advantages over the baseline method.
[ { "version": "v1", "created": "Mon, 4 Jul 2022 08:15:10 GMT" }, { "version": "v2", "created": "Tue, 18 Apr 2023 08:23:02 GMT" } ]
2023-04-19T00:00:00
[ [ "Yang", "Bohan", "" ], [ "Lu", "Bo", "" ], [ "Chen", "Wei", "" ], [ "Zhong", "Fangxun", "" ], [ "Liu", "Yun-Hui", "" ] ]
new_dataset
0.995755
2208.08636
Haoran Xie
Yichen Peng, Chunqi Zhao, Haoran Xie, Tsukasa Fukusato, Kazunori Miyata, Takeo Igarashi
DualMotion: Global-to-Local Casual Motion Design for Character Animations
10 pages, 10 figures, under submission, video is here https://youtu.be/-tk8q8LSiL0
null
10.1587/transinf.2022IIP0011
null
cs.GR cs.HC
http://creativecommons.org/licenses/by/4.0/
Animating 3D characters using motion capture data requires basic expertise and manual labor. To support the creativity of animation design and make it easier for common users, we present a sketch-based interface DualMotion, with rough sketches as input for designing daily-life animations of characters, such as walking and jumping.Our approach enables to combine global motions of lower limbs and the local motion of the upper limbs in a database by utilizing a two-stage design strategy. Users are allowed to design a motion by starting with drawing a rough trajectory of a body/lower limb movement in the global design stage. The upper limb motions are then designed by drawing several more relative motion trajectories in the local design stage. We conduct a user study and verify the effectiveness and convenience of the proposed system in creative activities.
[ { "version": "v1", "created": "Thu, 18 Aug 2022 05:11:11 GMT" } ]
2023-04-19T00:00:00
[ [ "Peng", "Yichen", "" ], [ "Zhao", "Chunqi", "" ], [ "Xie", "Haoran", "" ], [ "Fukusato", "Tsukasa", "" ], [ "Miyata", "Kazunori", "" ], [ "Igarashi", "Takeo", "" ] ]
new_dataset
0.999059