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The Journal of Machine Learning Research 2024年第1期25卷 17738-17789页

作者： Gabriel Rioux Ziv Goldfeld Kengo Kato Center for Applied Mathematics Cornell University Ithaca NY School of Electrical and Computer Engineering Cornell University Ithaca NY Department of Statistics and Data Science Cornell University Ithaca NY

The Gromov-Wasserstein (GW) distance quantifies discrepancy between metric measure spaces and provides a natural framework for aligning heterogeneous datasets. Alas, as exact computation of GW alignment is NP-complete, entropic regularization provides an avenue towards a computationally tractable proxy. Leveraging a recently derived variational representation for the quadratic entropic GW (EGW) distance, this work derives the first efficient algorithms for solving the EGW problem subject to formal, nonasymptotic convergence guarantees. To that end, we derive smoothness and convexity properties of the objective in this variational problem, which enables its resolution by the accelerated gradient method. Our algorithms employ Sinkhorn's fixed point iterations to compute an approximate gradient, which we model as an inexact oracle. We furnish convergence rates towards local and even global solutions (the latter holds under a precise quantitative condition on the regularization parameter), characterize the effects of gradient inexactness, and prove that stationary points of the EGW problem converge towards a stationary point of the unregularized GW problem, in the limit of vanishing regularization. We provide numerical experiments that validate our theory and empirically demonstrate the state-of-the-art empirical performance of our algorithm.

关键词： Algorithms convergence rate global guarantees Gromov-Wasserstein distances entropic regularization inexact gradient methods

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Classifying Activities of Electrical Line Workers Based on Deep Learning Approaches Using Wrist-Worn Sensor 20

Classifying Activities of Electrical Line Workers Based on D...

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20th International Joint Conference on computer Science and Software engineering, JCSSE 2023

作者： Mekruksavanich, Sakorn Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350300505

The field o f human activity recognition (HAR) is a priority for cutting-edge study because of its potential to revolutionize the way we understand and improve our everyday lives. A large variety of ordinary, everyday tasks has been classified using H AR. Nevertheless, inc ontrast to basic human actions, the increasing demands of numerous real-world applications have attracted the interest of the HAR area of study. Electrical line workers (ELWs) face a variety of challenges, including long hours, working in isolated locations, and performing particularly hazardous tasks. Wearable sensor-based HAR allows for unobtrusive tracking of ELW efficiency and security. This study explores deep learning strategies for automatically categorizing ELWs' complicated actions through sensor data collected through a wrist-worn device. We propose ResNeXt, a deep residual neural network, and evaluate it with other deep learning networks for their ability to categorize ELW activities effectively. We employ a publicly available benchmark dataset that includes 10 ELW tasks. The results of the experiment demonstrate that the proposed ResNeXt achieved the highest accuracy (98.74%) and F1-score (98.81%) compared to other deep learning networks studied. © 2023 IEEE.

关键词： Wearable sensors

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Bounded Future MS-TCN++ for Surgical Gesture Recognition 17th

Bounded Future MS-TCN++ for Surgical Gesture Recognition

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17th European Conference on computer Vision, ECCV 2022

作者： Goldbraikh, Adam Avisdris, Netanell Pugh, Carla M. Laufer, Shlomi Applied Mathematics Department Technion – Israel Institute of Technology Haifa3200003 Israel School of Computer Science and Engineering The Hebrew University of Jerusalem Jerusalem Israel School of Medicine Stanford Stanford University StanfordCA United States Faculty of Industrial Engineering and Management Technion – Israel Institute of Technology Haifa3200003 Israel

ISBN: (纸本)9783031250651

In recent times there is a growing development of video-based applications for surgical purposes. Part of these applications can work offline after the end of the procedure, other applications must react immediately. However, there are cases where the response should be done during the procedure but some delay is acceptable. In the literature, the online-offline performance gap is known. Our goal in this study was to learn the performance-delay trade-off and design an MS-TCN++-based algorithm that can utilize this trade-off. To this aim, we used our open surgery simulation data-set containing 96 videos of 24 participants that perform a suturing task on a variable tissue simulator. In this study, we used video data captured from the side view. The Networks were trained to identify the performed surgical gestures. The naive approach is to reduce the MS-TCN++ depth, as a result, the receptive field is reduced, and also the number of required future frames is also reduced. We showed that this method is sub-optimal, mainly in the small delay cases. The second method was to limit the accessible future in each temporal convolution. This way, we have flexibility in the network design and as a result, we achieve significantly better performance than in the naive approach. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Gesture recognition

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Semi-Markov Telecommunication Networks Model

Semi-Markov Telecommunication Networks Model

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2024 International Conference on Actual Problems of Electron Devices engineering, APEDE 2024

作者： Svetlov, Mikhail S. L’vov, Alexey A. Martirosyan, Arman M. Martirosyan, David M. Bagaev, Igor S. Khasanov, Semen T. Laboratory of Control and Automation Problems in Mechanical Engineering Institute of Precision Mechanics and Control of RAS Saratov Russia Institute of Institute of Electronic Engineering and Instrumentation Yuri Gagarin State Technical University of Saratov Saratov Russia Institute of Applied Information Technologies and Communications Yuri Gagarin State Technical University of Saratov Saratov Russia Phystech-School of Applied Mathematics and Computer Science Moscow Institute of Physics and Technology Moscow Russia

ISBN: (纸本)9798350393316

This paper proposes a semi-Markov model of telecommunication network (TCN). The variant of dynamic traffic adaptive control of queuing system as a special case of TCN is considered. The main purpose of control is to minimize the average cost per unit of time to service the incoming flow of information (packets). This takes into account the different bandwidth of the channels, the processing speed of information in the channel and the information capacity of the buffers. The approach to the organization of dynamic control taking into account noise immunity (information reliability) and information security is discussed. © 2024 IEEE.

关键词： Bandwidth

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Ensuring Zero Trust IoT Data Privacy: Differential Privacy in Blockchain using Federated Learning

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IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Hussain, Altaf Akbar, Wajahat Hussain, Tariq Bashir, Ali Kashif Dabel, Maryam M. Al Ali, Farman Yang, Bailin School of Electronic and Control Engineering Chang’an University Xi’an China School of Computer Science and Technology and School of Mathematics and Statistics Zhejiang Gongshang University Hangzhou China Department of Computing and Mathematics Manchester Metropolitan University UK Department of Computer Science and Engineering College of Computer Science and Engineering University of Hafr Al Batin Saudi Arabia Department of Applied AI School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul South Korea

In the increasingly digitized world, the privacy and security of sensitive data shared via IoT devices are paramount. Traditional privacy-preserving methods like k-anonymity and ldiversity are becoming outdated due to technological advancements. In addition, data owners often worry about misuse and unauthorized access to their personal information. To address this, we propose a secure data-sharing framework that uses local differential privacy (LDP) within a permissioned blockchain, enhanced by federated learning (FL) in a zero-trust environment. To further protect sensitive data shared by IoT devices, we use the Interplanetary File System (IPFS) and cryptographic hash functions to create unique digital fingerprints for files. We mainly evaluate our system based on latency, throughput, privacy accuracy, and transaction efficiency, comparing the performance to a benchmark model. The experimental results show that the proposed system outperforms its counterpart in terms of latency, throughput, and transaction efficiency. The proposed model achieved a lower average latency of 4.0 seconds compared to the benchmark model’s 5.3 seconds. In terms of throughput, the proposed model achieved a higher throughput of 10.53 TPS (transactions per second) compared to the benchmark model’s 8 TPS. Furthermore, the proposed system achieves 85% accuracy, whereas the counterpart achieves only 49%. IEEE

关键词： Anonymity

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A Deep Multi-Task Learning Network for Activity Recognition and User Identification Using Smartphone Sensors 16

A Deep Multi-Task Learning Network for Activity Recognition ...

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16th International Conference on ENTERprise Information Systems, CENTERIS 2024 - 12th International Conference on Project MANagement, ProjMAN 2024 - 14th International Conference on Health and Social Care Information Systems and Technologies, HCist 2024

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao56000 Thailand Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok10800 Thailand

Human activity recognition (HAR) and user identification utilizing smartphone sensors are crucial in domains such as wellness tracking, tailored services, and security. Conventional approaches handle these activities as distinct entities, leading to less-Than-ideal performance and restricted applicability. This research presents a novel deep multi-Task learning network that simultaneously performs activity recognition and user identification using smartphone sensor data. By leveraging shared representations and task-specific information, the network enhances accuracy. It combines a shared convolutional neural network (CNN) with bidirectional long short-Term memory (BiLSTM) layers to extract features from raw sensor data. The CNN identifies patterns, while the BiLSTM captures sequential dependencies and contextual information. These features are processed by fully connected layers for both activity recognition and user identification. A multi-Task loss function is proposed to optimize both tasks based on their complexity and importance. The approach is tested on the UCI-HAR dataset, containing diverse activities from multiple users. Experimental results show that the proposed CNN-BiLSTM model achieves 97.64% accuracy in activity recognition and 82.59% in user identification, surpassing current state-of-The-Art methods. This demonstrates the effectiveness of the multi-Task learning framework and the advantage of using the shared CNN-BiLSTM architecture for improving both tasks. © 2025 The Author(s).

关键词： Convolutional neural networks

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LowResIR-Net: Lightweight Residual Network for ADL Recognition Using Low-Resolution Infrared Sensor Data 16

LowResIR-Net: Lightweight Residual Network for ADL Recogniti...

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Detecting activities of daily living (ADL) is crucial for supported living and medical monitoring. Traditional approaches rely on high-resolution sensors and computationally intensive algorithms, limiting their scalability and practical use in real-world settings. This research introduces LowResIR-Net, a compact deep residual network architecture specifically tailored for recognizing ADL utilizing low-resolution infrared sensor data. LowResIR-Net efficiently extracts key features from low-dimensional data by leveraging the advantages of infrared sensing, such as privacy protection and light resilience. Its architecture combines residual connections with depth-wise convolutions, enhancing feature learning while maintaining a compact model size. The model’s performance is evaluated using the Coventry-2018 dataset, containing low-resolution infrared data from three residential sensor locations. Our tests have shown that in all three cases, LowResIR-Net consistently achieves superior performance compared to other cutting-edge deep learning models, such as CNN, LSTM, BiLSTM, GRU, and BiGRU. LowResIR-Net attains the most excellent accuracies of 90.18%, 93.75%, and 93.30%, and F1-scores of 81.81%, 89.55%, and 88.92% for scenarios I, II, and III, respectively. Furthermore, it achieves these results with fewer parameters than most other models. LowResIR-Net’s strong performance highlights its ability to accurately capture complex patterns in low-resolution infrared data for ADL detection. This study demonstrates the potential of lightweight deep learning and low-resolution infrared sensors in developing privacy-focused and computationally efficient wellness monitoring systems. © 2025 The Author(s).

关键词： Infrared detectors

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Negative Binomial Regression Model for Predicting the Number of Tourists in National Parks of Thailand 10

Negative Binomial Regression Model for Predicting the Number...

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10th International Conference on Digital Arts, Media and Technology and 8th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Thepsumritporn, Sthaporn Jitpattanakul, Anuchit Mekruksavanich, Sakorn Phaphan, Wikanda King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Applied Statistics Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Thailand University of Phayao School of Information and Communication Department of Computer Engineering Phayao56000 Thailand Research Group in Statistical Learning and Inference Thailand

ISBN: (纸本)9798331543273

This article examines the use of count data models to predict the number of tourists visiting Thailand's national parks. The dataset, encompassing observations from 2016 to 2022 across 146 national parks, exhibits characteristics such as discreteness, over dispersion, and non-negative integer values. Two models - Poisson (POI), and Negative Binomial (NB) - were compared to identify the most effective approach for modeling and prediction. Model evaluation metrics, including the Akaike Information Criterion (AIC) and log-likelihood, were employed. Results demonstrate that the Negative Binomial regression model best captures the data's overdispersion compared to the POI model. The NB model achieved the lowest AIC and highest log-likelihood, affirming its superior performance for this application. Our findings underscore the NB model's utility in predicting tourism-related count data and suggest its broader applicability to other domains characterized by overdispersed count data. © 2025 IEEE.

关键词： Logistic regression

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Deep Learning Approaches for Recognizing Daily Human Activities Using Smart Home Sensors 8

Deep Learning Approaches for Recognizing Daily Human Activit...

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Joint 8th International Conference on Digital Arts, Media and Technology and 6th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2023

作者： Sanpote, Worrakit Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit Mekruksavanich, Sakorn School of Information and Communication Technology Department of Computer Science University of Phayao Phayao Thailand School of Information and Communication Technology Department of Computer Engineering University of Phayao Phayao Thailand Mahidol University Image Information and Intelligence Laboratory Faculty of Engineering Department of Computer Engineering Nakorn Pathom Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350396546

Nowadays, one of the most important objectives in health-related research is the improvement of the living condition and well-being of people. Smart home systems can provide health protection for residents based on the results of daily activity recognition. Recent advances and developments in sensor technology have increased the need for sensor-compatible goods and services in smart homes. Consequently, the ever-increasing volume of data requires the field of deep learning (DL) for auto-matic human motion recognition. Recent research has modeled spatiotemporal sequences gathered by smart home sensors using long short-term memory networks. In this work, ResNeXt-based models that learn to classify human activities in smart homes were proposed to improve recognition performance. Experiments conducted on Center for Advanced Studies in Adaptive Systems (CASAS) data, a publicly available benchmark dataset, shows that the proposed ResNeXt-based techniques are significantly superior to the existing DL methods and provide better results compared to the existing literature. The ResNeXt model achieved the averaged accuracy over the benchmark method to 84.81%, 93.57%, and 90.38% for the CASAS-Cairo, CASAS-Milan and CASAS-Kyoto3 datasets, respectively. © 2023 IEEE.

关键词： Deep learning

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Leveraging Semantic Parsing using Text Embeddings and Reinforcement Learning

Leveraging Semantic Parsing using Text Embeddings and Reinfo...

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Latin America Congress on Computational Intelligence (LA-CCI)

作者： Jessenia Piza-Londoño Edgar J. Andrade-Lotero Applied Mathematics and Computer Science Universidad del Rosario Bogotá Colombia School of Engineering Science and Technology Universidad del Rosario Bogotá Colombia

ISBN: (数字)9798350374575

ISBN: (纸本)9798350374582

Representing natural language information is a key challenge in Artificial Intelligence and Cognitive Science, requiring the transformation of unstructured data into formats suitable for computational tasks. While logical formalisms offer robust methods for information representation, their complexity often limits widespread adoption. Conversely, transformer architectures provide strong generalization capabilities but struggle with logical inference tasks. To address both the need for generalization and reliable logical inference, we propose a novel approach using deep reinforcement learning, enabling agents to autonomously learn the rules of semantic parsing. Our preliminary results indicate successful generation of appropriate representations for simple queries. Future work will extend the environment to handle a wider range of real-world sentences.

关键词： Translation Semantics computer architecture Information representation Linguistics Transformers Stability analysis Complexity theory Reliability Thermal stability

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