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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Li, Jiyang Vatanparvar, Korosh Gwak, Migyeong Zhu, Li Kuang, Jilong Gao, Alex Suny University at Buffalo Department of Computer Science and Engineering BuffaloNY United States Samsung Research America Digital Health Lab Mountain ViewCA United States

ISBN: (纸本)9798350371499

Measurement of cardiac pulse rate through image-based remote photoplethysmography (rPPG) is drawing attention to applications of continuous health monitoring. Meanwhile, extracting clean rPPG signals and reliable heart rate (HR) remotely is challenging especially in real-life scenarios where users can move freely. In this paper, we leverage head motion information in the video to increase tolerance of vital estimation against the motion. A motion artifact classification model relying on rPPG and real-time head motion signals is developed to identify motion artifacts and reject outliers. We handcrafted 106 features and selected 20 features from both time and frequency domains. The model and methodology are validated comprehensively in a dataset of 30 subjects with 25 motion tasks in three motion intensity levels: low-motion, medium-motion, and high-motion. The motion-aware pipeline achieves a mean absolute error of 4.03 bpm for high-motion intensity tasks, improved by 31% by removing artifacts with specificity over 75%. In addition, the pipeline is tested with various light intensities to show that the motion detection is robust in darker conditions. © 2024 IEEE.

关键词： Photoplethysmography

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An oceanographic data collection scheme using hybrid optimization for leakage detection during oil mining in mobility assisted UWSN

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Multimedia Tools and Applications 2024年第42期83卷 89723-89741页

作者： Choudhary, Monika Goyal, Nitin Gupta, Deepali Sharma, Bhanu Sharma, Nonita Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Seth Jai Parkash Mukandlal Institute of Engineering and Technology Haryana Radaur India Department of Computer Science and Engineering School of Engineering and Technology Central University of Haryana Haryana Mahendragarh India Department of Information Technology Indira Gandhi Delhi Technical University for Women Delhi India Immersive and Interactive Technology Lab Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

Data acquisition is the process of collecting, measuring and analysing information using standardised, validated techniques for application-specific tasks. In mobility-assisted underwater wireless sensor networks (UWSNs), where nodes are not fixed due to water current of 3 m/sec, data collection becomes an arduous task. There are few works that provide a mobile sink with optimised data transmission path planning and scheduling. These systems do not transmit the data fast enough to provide real-time data transmission as these methods do not consider the buffer occupancy rate and latency in data acquisition. In this paper, a stimulating transmission path planning technique for mobile sinks using the hybrid Grey Wolf Optimizer Whale Optimization Algorithm (GWOWOA) is proposed. In contrast to other optimization techniques, this hybrid technique includes a number of update processes such as random position update, prey search by the Grey Wolf Optimizer (GWO) and prey search by the Whale Optimization Algorithm (WOA). In this paper, the fitness function is calculated in terms of distance to the mobile sink, buffer occupancy rate, energy level and data acquisition latency. The use of these variables makes the proposed technique innovative. To prove the efficiency of the proposed system, GWOWOA is compared with existing systems. The simulation results show that the proposed system increases the residual energy and accuracy of the collected data and minimises the delay. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Data acquisition

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Improving Gloss-free Sign Language Translation by Reducing Representation Density 38

Improving Gloss-free Sign Language Translation by Reducing R...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Ye, Jinhui Wang, Xing Jiao, Wenxiang Liang, Junwei Xiong, Hui Guangzhou China Department of Computer Science and Engineering HKUST Hong Kong Guangzhou HKUST Fok Ying Tung Research Institute Hong Kong Tencent AI Lab China

Gloss-free sign language translation (SLT) aims to develop well-performing SLT systems with no requirement for the costly gloss annotations, but currently still lags behind gloss-based approaches significantly. In this paper, we identify a representation density problem that could be a bottleneck in restricting the performance of gloss-free SLT. Specifically, the representation density problem describes that the visual representations of semantically distinct sign gestures tend to be closely packed together in feature space, which makes gloss-free methods struggle with distinguishing different sign gestures and suffer from a sharp performance drop. To address the representation density problem, we introduce a simple but effective contrastive learning strategy, namely SignCL, which encourages gloss-free models to learn more discriminative feature representation in a self-supervised manner. Our experiments demonstrate that the proposed SignCL can significantly reduce the representation density and improve performance across various translation frameworks. Specifically, SignCL achieves a significant improvement in BLEU score for the Sign Language Transformer and GFSLT-VLP on the CSL-Daily dataset by 39% and 46%, respectively, without any increase of model parameters. Compared to Sign2GPT, a state-of-the-art method based on large-scale pre-trained vision and language models, SignCL achieves better performance with only 35% of its parameters. Implementation and Checkpoints are available at https://***/JinhuiYE/SignCL. © 2024 Neural information processing systems foundation. All rights reserved.

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MetaSTC: A Backbone Agnostic Spatio-Temporal Framework for Traffic Forecasting 24

MetaSTC: A Backbone Agnostic Spatio-Temporal Framework for T...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Xu, Kexin Yu, Zhemeng Gao, Yucen Zhang, Songjian Fang, Jun Gao, Xiaofeng Chen, Guihai Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Department of Computer Science and Engineering Shanghai China Didi Chuxing Technology Co. Beijing China

ISBN: (纸本)9798331506681

Traffic flow prediction is a critical issue in transportation engineering and presents distinct challenges when handling large-scale datasets in the real world. Existing complex spatio-temporal forecasting paradigms use the same parameters to fit traffic sequences with varying spatio-temporal features, and tend to train an average performance model over different time series. This approach greatly reduces their accuracy when applied to larger road networks. Moreover, the significant differences in traffic data distribution from one city to another can also pose great challenges. The same model may be excellent for one city and mediocre when applied to another. To this end, we propose a Meta Backbone Agnostic Spatio-Temporal Clustering Framework for Traffic Forecasting on Large-Scale Road Networks named MetaSTC. We tackle the disparities of spatio-temporal features of traffic flow through a spatio-temporal clustering-based strategy. We design meta-learner for large-scale road network that dynamically extracts the shared information across roads in the same sub-task. In this way, the model can represent task-specific details with a simpler model and make quick and accurate predictions. Our paradigm is backbone-agnostic and can be combined with different traffic prediction models, solving the problem caused by the difference in data distribution. Extensive experimental results conducted on real-world traffic dataset demonstrate the high accuracy and computational efficiency of our model over SOTA approaches. © 2024 IEEE.

关键词： Large datasets

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LAORA: Location-Aware Orientation Adjustment for MIMO Magnetic Wireless Charging System 21

LAORA: Location-Aware Orientation Adjustment for MIMO Magnet...

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21st Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2024

作者： Kong, Lingchang Wang, Xinyu Zhou, Hao Zhou, Fengyu Jiang, Shenyao Zhu, Peide Song, Qi Liu, Zhi LINKE Lab School of Computer Science and Technology University of Science and Technology of China China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China China Department of Computer Science City University of Hong Kong Hong Kong School of Mathematics and Statistics Fujian Normal University Fuzhou China Department of Computer and Network Engineering The University of Electro-Communications Tokyo182-8585 Japan

ISBN: (纸本)9798331519186

Wireless power transfer (WPT) systems using magnetic resonant coupling (MRC) have made significant progress recently, leading to various optimization methods in scenarios involving multiple-input multiple-output (MIMO) to improve charging performance. Adjusting the coil orientation of the power transmitter (TX) is a simple but effective method due to the directional nature of magnetic field distribution, but existing approaches often require unnecessary coil rotations. In this study, we introduce a Location-Aware Orientation Adjustment algorithm, known as LAORA, to address these inefficiencies. LAORA focuses on solving the problems of charging devices (RX) localization and location-based optimization. We begin by introducing the concept of Equivalent Impedance Distribution Image (EIDI) and transform the RX localization problem into a combined matching process involving EIDI. In addition, we establish a dynamic simulation framework to predict charging performance using RX-related knowledge, enabling us to obtain optimal TX orientations through reinforcement learning without needing to rotate on mechanical devices physically. We then implement the prototype and conduct extensive experiments. The results show that, compared to other existing orientation adjustment methods, LAORA achieves an average improvement of 186% while reducing the mechanical rotations by 83.3%. © 2024 IEEE.

关键词： Inductive power transmission

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Advancing the Arabic WordNet: Elevating Content Quality 6

Advancing the Arabic WordNet: Elevating Content Quality

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6th Workshop on Open-Source Arabic Corpora and Processing Tools, OSACT 2024 with Shared Tasks on Arabic LLMs Hallucination and Dialect to MSA Machine Translation

作者： Freihat, Abed Alhakim Khalilia, Hadi Bella, Gábor Giunchiglia, Fausto Department of Information Engineering and Computer Science University of Trento Italy Palestine Technical University - Kadoorie Palestine Lab-STICC CNRS UMR 628 IMT Atlantique Brest France

ISBN: (纸本)9782493814364

High-quality WordNets are crucial for achieving high-quality results in NLP applications that rely on such resources. However, the wordnets of most languages suffer from serious issues of correctness and completeness with respect to the words and word meanings they define, such as incorrect lemmas, missing glosses and example sentences, or an inadequate, Western-centric representation of the morphology and the semantics of the language. Previous efforts have largely focused on increasing lexical coverage while ignoring other qualitative aspects. In this paper, we focus on the Arabic language and introduce a major revision of the Arabic WordNet that addresses multiple dimensions of lexico-semantic resource quality. As a result, we updated more than 58% of the synsets of the existing Arabic WordNet by adding missing information and correcting errors. In order to address issues of language diversity and untranslatability, we also extended the wordnet structure by new elements: phrasets and lexical gaps. © 2024 ELRA Language Resource Association.

关键词： Semantics

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Geometry-Calibrated DRO: Combating Over-Pessimism with Free Energy Implications 41

Geometry-Calibrated DRO: Combating Over-Pessimism with Free ...

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41st International Conference on Machine Learning, ICML 2024

作者： Liu, Jiashuo Wu, Jiayun Wang, Tianyu Zou, Hao Li, Bo Cui, Peng Department of Computer Science and Technology Tsinghua University China Department of Industrial Engineering and Operations Research Columbia University United States Zhongguancun Lab China School of Economics and Management Tsinghua University China

Machine learning algorithms minimizing average risk are susceptible to distributional shifts. Distributionally Robust Optimization (DRO) addresses this issue by optimizing the worst-case risk within an uncertainty set. However, DRO suffers from over-pessimism, leading to low-confidence predictions, poor parameter estimations as well as poor generalization. In this work, we conduct a theoretical analysis of a probable root cause of over-pessimism: excessive focus on noisy samples. To alleviate the impact of noise, we incorporate data geometry into calibration terms in DRO, resulting in our novel Geometry-Calibrated DRO (GCDRO) for regression. We establish the connection between our risk objective and the Helmholtz free energy in statistical physics, and this free-energy-based risk can extend to standard DRO methods. Leveraging gradient flow in Wasserstein space, we develop an approximate minimax optimization algorithm with a bounded error ratio and elucidate how our approach mitigates noisy sample effects. Comprehensive experiments confirm GCDRO's superiority over conventional DRO methods. Copyright 2024 by the author(s)

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AI-driven models for Cold Spray deposition: transforming additive manufacturing for sustainability

AI-driven models for Cold Spray deposition: transforming add...

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2024 Ital-IA Intelligenza Artificiale - Thematic Workshops, Ital-IA 2024

作者： Citarella, Alessia Auriemma Carrino, Luigi De Marco, Fabiola Di Biasi, Luigi Perna, Alessia Serena Tortora, Genoveffa Viscusi, Antonio University of Salerno CAIS Lab Department of Computer Science Italy Department of Chemical Materials and Production Engineering University of Naples Federico II Piazzale V. Tecchio 80 Napoli80125 Italy

The integration of Artificial Intelligence (AI) techniques holds promise for advancing the optimization of industrial processes, such as the use of Cold Spray (CS) in the field of Additive Manufacturing (AM). This paper explores the intersection of AI and Cold Spray technology, highlighting its potential to enhance various aspects of AM, including material deposition, surface properties, and process efficiency. Through the utilization of Machine Learning (ML) and Deep Learning (DL) techniques, AI facilitates the analysis of vast datasets encompassing parameters such as powder properties, substrate characteristics, and process conditions, thereby enabling the identification of optimal deposition strategies. Furthermore, AI-driven predictive models offer insights into the complex interactions between process variables, leading to improved understanding and control of the CS process. © 2024 Copyright for this paper by its authors.

关键词： Deep learning

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SRender: Boosting Neural Radiance Field Efficiency via Sensitivity-Aware Dynamic Precision Rendering 57

SRender: Boosting Neural Radiance Field Efficiency via Sensi...

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57th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2024

作者： Song, Zhuoran He, Houshu Liu, Fangxin Hao, Yifan Song, Xinkai Jiang, Li Liang, Xiaoyao Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China Institute of Computing Technology Chinese Academy of Sciences State Key Lab of Processors Beijing China

ISBN: (纸本)9798350350579

Neural Radiance Field (NeRF) holds immense promise for generating photo-realistic images and videos. How-ever, the computation and memory demands significantly impede its applicability in real-Time tasks such as virtual reality and augmented reality. While accelerators dedicatedly designed for NeRF have emerged, they may not be the optimal solution as they only consider the optimization of full-precision NeRF models, leading to unsatisfactory memory and computation saving. This paper proposes Sensitivity-Aware Dynamic Precision Rendering, abbreviated as SRender, an algorithm-hardware co-design framework that borrows the idea of Adaptive Rendering commonly used in conventional graphics rendering to accelerate the rendering process of NeRF exploiting the fine-grained per-ray and per-point levels sensitivity. The fundamental idea behind SRender is that rays and points with high sensitivity require high-precision data types, while the insensitive rays and points do not, allowing them to be sacrificed for greater acceleration. Accordingly, the algorithm part of SRender dynamically configures the precision of the hash table and the multilayer perceptron (MLP), and the hardware part features a specialized accelerator that exploits such mixed-precision memory access and computation patterns. Specifically, we predict the sensitive rays and points based on low-precision hash encoding and feature computation. Then, we trigger a high-precision rendering again on the sensitive ones to guarantee the rendering quality. The hardware design of SRender includes a sensitivity prediction engine, coarse-Then-fine-grained encoding engines, and a recovery engine, which translates the computational saving in the algorithm to real speedup. Using the coarse-Then-fine-grained encoding engine, we maximize the data reuse on-chip, thereby reducing the stress of off-chip memory bandwidth. Experiments show that the edge-level SRender can achieve 3.94x and 34.06x speedup over state-of-The-Art NeRF

关键词： Encoding (symbols)

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Real-Time Implementation of Quadrotor UAV Control System Based on a Deep Reinforcement Learning Approach

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computers, Materials & Continua 2024年第12期81卷 4757-4786页

作者： Taha Yacine Trad Kheireddine Choutri Mohand Lagha Souham Meshoul Fouad Khenfri Raouf Fareh Hadil Shaiba Aeronautical Sciences Laboratory Aeronautical and Spatial Studies InstituteBlida 1 UniversityBlida0900Algeria Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Energy and Embedded Systems for Transportation Research Department ESTACA-LABMontigny-Le-Bretonneux78066France Department of Electrical Engineering University of SharjahSharjah27272United Arab Emirates Department of Computer Science College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The popularity of quadrotor Unmanned Aerial Vehicles(UAVs)stems from their simple propulsion systems and structural ***,their complex and nonlinear dynamic behavior presents a significant challenge for control,necessitating sophisticated algorithms to ensure stability and accuracy in *** strategies have been explored by researchers and control engineers,with learning-based methods like reinforcement learning,deep learning,and neural networks showing promise in enhancing the robustness and adaptability of quadrotor control *** paper investigates a Reinforcement Learning(RL)approach for both high and low-level quadrotor control systems,focusing on attitude stabilization and position tracking tasks.A novel reward function and actor-critic network structures are designed to stimulate high-order observable states,improving the agent’s understanding of the quadrotor’s dynamics and environmental *** address the challenge of RL hyper-parameter tuning,a new framework is introduced that combines Simulated Annealing(SA)with a reinforcement learning algorithm,specifically Simulated Annealing-Twin Delayed Deep Deterministic Policy Gradient(SA-TD3).This approach is evaluated for path-following and stabilization tasks through comparative assessments with two commonly used control methods:Backstepping and Sliding Mode Control(SMC).While the implementation of the well-trained agents exhibited unexpected behavior during real-world testing,a reduced neural network used for altitude control was successfully implemented on a Parrot Mambo mini *** results showcase the potential of the proposed SA-TD3 framework for real-world applications,demonstrating improved stability and precision across various test scenarios and highlighting its feasibility for practical deployment.

关键词： Deep reinforcement learning hyper-parameters optimization path following quadrotor twin delayed deep deterministic policy gradient and simulated annealing

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