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Deep Learning Approaches for Stress Detection: A Survey

IEEE Transactions on Affective Computing 2024年第2期16卷 499-517页

作者： Kyrou, Maria Kompatsiaris, IoannisSenior Petrantonakis, Panagiotis C. Aristotle University of Thessaloniki Artificial Intelligence and Information Analysis Laboratory Dept. of Informatics GR 54124 Greece Information Technologies Institute Centre for Research and Technology Hellas GR 57001 Greece Aristotle University of Thessaloniki Dept. of Electrical and Computer Engineering GR 54124 Greece

Stress has a severe impact on individuals irrespective of age, sex, work, or background. The reliable development of stress detection techniques enhances the social, educational, physical, economic, and professional quality of life, preventing chronic stress and proposing alleviation strategies. research studies examine psychological, cognitive, behavioral, and physiological reactions to identify stress adequately. Deep Learning (DL) has received significant attention in recent years as it deals with high-dimensional, heterogeneous data and automatically learns representative features. This paper presents a survey on stress detection with recent DL approaches, leveraging data from all possible sources (physiological, speech, facial expressions, gestures, and social media content). The methodological outlines, the best results, and the main contributions of each study are discussed. We also describe publicly available datasets used by several of the presented works. Finally, we emphasize various open issues within the field of research and highlight key directions for future work © 2024 IEEE.

关键词： Social psychology

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PANDEMIC IMPACT ON ARGENTINIAN HIGHER EDUCATION. THE CASE OF THE NATIONAL UNIVERSITY OF LA PLATA 17

PANDEMIC IMPACT ON ARGENTINIAN HIGHER EDUCATION. THE CASE OF...

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17th International Conference on e-Learning and Digital Learning 2023, ELDL 2023 and 11th International Conference on Sustainability, Technology and Education 2023, STE 2023

作者： Díaz, Francisco Javier Osorio, María Alejandra Amadeo, Ana Paola Kimura, Isabel LINTI – Research Laboratory in New Computer Technologies School of Information Technology National University of La Plata La Plata Argentina

ISBN: (纸本)9789898704511

This article analyzes the impact of the pandemic in Argentina, considering that the National Government and provincial governments issued isolation resolutions throughout the 2020 academic year and mandatory social distancing measures in 2021, which prevented in-person activities at National Universities, such as classes and exams. The analysis focuses on the use of virtual means (such as Moodle, Zoom, Webex, etc.) by students and the graduation rate. Official statistics from the Secretary of University Policies of the Ministry of National Education are used, comparing them with those of the National University of La Plata. The study includes a gender analysis to determine if the pandemic's impact in our country maintained or modified the proportions of incoming students, enrolled students, and graduates. The results show that while the pandemic changed habits, it did not have such a negative impact as expected on the overall university population. © 2023 17th International Conference on e-Learning and Digital Learning 2023, ELDL 2023 and 11th International Conference on Sustainability, Technology and Education 2023, STE 2023. All rights reserved.

关键词： Students

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Visible and Infrared Image Fusion for Object Detection: A Survey

Visible and Infrared Image Fusion for Object Detection: A Su...

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International Conference on Image, Vision and Intelligent Systems, ICIVIS 2023

作者： Sun, Yuxuan Meng, Yuanqin Wang, Qingbo Tang, Minghua Shen, Tao Wang, Qingwang School of Information Engineering and Automation Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming China Naval Research Academy Beijing China

ISBN: (纸本)9789819708543

Thanks to the advancements of Deep Learning (DL) Algorithms. DL-based object detection models has witnessed remarkable success in the past few years. By leveraging deep convolutional neural networks (CNNs) and other deep learning models, rich feature representations can be effectively extracted from visible (RGB) images for object detection. However, for challenging scenarios such as low-light conditions and haze, the performance of object detection using visible images is often not satisfactory. On the other hand, thermal camera, which is unaffected by lighting conditions, can penetrate through low-light and hazy environments to capture object images. However, infrared images lack edge and texture information of objects. Recognizing the complementary nature of visible and infrared images, researchers have explored the fusion of visible and infrared images for object detection, yielding promising research outcomes. This paper provides an analysis of the current research status of visible and infrared image fusion for object detection. Firstly, the fusion models are categorized into three kinds: pixel-level fusion, feature-level fusion, and decision-level fusion. Several models within each category are discussed. Furthermore, this paper summarizes five datasets that can be utilized for training RGB-Infrared object detection models and compares the experimental results of selected models on the KAIST dataset. Lastly, the paper concludes with a summary of existing challenges in the field and offers some reflections on future directions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Object detection

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EMARec: a sequential recommendation with exponential moving average

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Neural Computing and Applications 2024年第21期36卷 12917-12933页

作者： Chen, Rui Wang, Zonglin Tang, Cundong Zhang, Jianwei Li, Pu Kong, Xiangjie Huang, Min College of Software Engineering Zhengzhou University of Light Industry Zhengzhou450001 China Henan Joint International Research Laboratory of Computer Animation Implementation Technologies Zhengzhou450001 China School of Information Science and Technology Northwest University Xi’an710127 China Faculty of Information and Engineering Xuchang Vocational Technical College Xuchang461000 China College of Computer Science and Technology Zhejiang University of Technology Hangzhou310023 China

Capturing dynamic preference features from user historical behavioral data is widely applied to improve the accuracy of recommendations in sequential recommendation tasks. However, existing deep neural network-based sequential recommendation methods often ignore the noise information in user behavioral data that influence recommendation effectiveness, making recommendation models sensitive to noisy data. Additionally, these deep learning-based recommendation models often require a large number of parameters to capture user behavior patterns, leading to higher time complexity and susceptibility to overfitting due to noise fluctuations. To address these issues, in this paper, we apply the moving average concept from the field of time-series analysis to sequential recommendation tasks. The approach smooths sequence data, removes noise, making the data more stable and reliable, increases the model’s insensitivity to noise information, and better understands the evolution of user interests and behavioral patterns. Specifically, in our experiments, the moving average algorithm effectively denoises sequential data with low time complexity. Therefore, we propose a Sequential Recommendation with Exponential Moving Average with MLP architecture that makes the model more competitive in terms of time complexity. Experimental results conducted on two datasets demonstrate that EMARec outperforms state-of-the-art sequential recommendation methods across various common evaluation metrics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Recommender systems

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A self-supervised graph convolutional model for recommendation with exponential moving average

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Neural Computing and Applications 2025年第9期37卷 6777-6793页

作者： Chen, Rui Pang, Kangning Wang, Zonglin Liu, Qingfang Tang, Cundong Chang, Yanshuo Huang, Min College of Software Engineering Zhengzhou University of Light Industry Zhengzhou450001 China Henan Joint International Research Laboratory of Computer Animation Implementation Technologies Zhengzhou450001 China Information Center Shijiazhuang Posts and Telecommunications Technical College Shijiazhuang050021 China School of Information Science and Technology Northwest University Xi’an710127 United States Insititute for Silk Road Research Xi’an University of Finance and Economics Xi’an710100 China

Recommendation algorithms based on graph convolutional networks can integrate user and item node information along with their interaction topology, better capturing the intricate relationships between users and items, thereby enhancing the accuracy of recommender systems. However, existing methods often overlook the impact of noise in user behavior data on recommendation performance. Additionally, when there are too many convolutional layers in the graph, the node representations tend to smoothing, resulting in an inability to accurately distinguish user preferences. To address these issues, we propose a self-supervised graph convolutional model for recommendation with exponential moving average (SGCERec). Specifically, we first employ exponential moving average (EMA) techniques from the field of time-series analysis to denoise the raw user interaction data. Then, by applying layer filtering technique to update the propagation of information and the representation of nodes within the graph convolutional network, we effectively deepen the model hierarchy, enabling the model to gain a deeper understanding of the features and structures of the graph data, thereby improving the performance and effectiveness of the recommender systems. Finally, experimental results on three real datasets show that SGCERec outperforms state-of-the-art recommendation methods across various common evaluation metrics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Recommender systems

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A research on cross-chain and interoperation methods of fusion protocol

IET Blockchain

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IET Blockchain 2024年第1期4卷 18-29页

作者： Ming, Li Wenpeng, Song Ankai, Li Ziming, Zhou Mianchen, Liu Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Shanghai Engineering Research Center of Blockchain Shanghai China China Electronics Standardization Institute Beijing China School of Economics and Management Beihang University Beijing China Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operations Beijing China

At present, cross-chain technology and cross-chain system pay more attention to cross-chain security and performance, and have shown great improvement in security, scalability and efficiency. However, the generality of cross-chain technology in different scenarios is poor, and it may not fit in different business fields. In order to solve the generality problem of cross-chain technology, this paper sorts out and analyzes the research of blockchain cross-chain protocol, and proposes a fusion protocol cross-chain method based on relay-chain technology. This paper will introduce the cross-chain framework and cross-chain transaction process of fusion protocol. Cross-chain framework includes blockchain cross-chain interaction architecture, cross-chain interaction governance mechanism and cross-chain privacy protection scheme. The transaction process is divided into three different stages. Finally, combined with cross-chain key technologies and landing application requirements, the future research direction of blockchain cross-chain technology is given. The fusion protocol cross-chain method based on relay-chain technology proposed here is a groundbreaking innovation in the field of blockchain cross-chain technology. By integrating the advantages of different blockchain systems and protocols, the fusion protocol is able to achieve a high degree of generality and compatibility, making it suitable for various business scenarios and applications. © 2023 The Authors. IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Blockchain

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Predictive model for daily risk alerts in sepsis patients in the ICU:visualization and clinical analysis of risk indicators

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Precision Clinical Medicine 2025年第1期8卷 28-39页

作者： Hao Yang Jiaxi Li Chi Zhang Alejandro Pazos Sierra Bairong Shen Information Center West China Hospital of Sichuan UniversityChengdu 610041China Department of Computer Science and Information Technologies Research Center for Information and Communications TechnologiesUniversity of A CoruñaBiomedical Research Institute of a CoruñaA Coruña 15071Spain Department of Clinical Laboratory Medicine Jinniu Maternity and Child Health Hospital of ChengduChengdu 610031China Joint Laboratory of Artificial Intelligence for Critical Care Medicine Department of Critical Care Medicine and Institutes for Systems GeneticsFrontiers Science Center for Disease-related Molecular NetworkWest China HospitalSichuan UniversityChengdu 610041China

This study introduces a novel Transformer-based time-series framework designed to revolutionize risk stratification in Intensive Care Units(ICUs)by predicting patient outcomes with high temporal *** sequential data from the eICU database,our two-stage architecture dynamically captures evolving health trajectories throughout a patient’s ICU stay,enabling real-time identification of high-risk individuals and actionable insights for personalized *** model demonstrated exceptional predictive power,achieving a progressive AUC increase from 0.87(±0.021)on admission day to 0.92(±0.009)by day 5,reflecting its capacity to assimilate longitudinal physiological *** external validation across geographically diverse cohorts—including an 81.8%accuracy on Chinese sepsis data(AUC=0.73)and 76.56%accuracy on MIMIC-IV-3.1(AUC=0.84)—confirmed robust ***,SHAP-derived temporal heatmaps unveiled mortality-associated feature dynamics over time,bridging the gap between model predictions and clinically interpretable *** findings establish a new paradigm for ICU prognostics,where data-driven temporal modeling synergizes with clinician expertise to optimize triage,reduce diagnostic latency,and ultimately improve survival outcomes in critical care.

关键词： sepsis Transformer time-series visualization predicting mortality

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Physical and System Level Power Reduction Technique for NoC Architectures: Overview of State of the Art

Physical and System Level Power Reduction Technique for NoC ...

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2023 International Conference on Decision Aid Sciences and Applications, DASA 2023

作者： Belkebir, Djalila Lati, Abdelhai Belkebir, Malak Faculty of New Information and Communication Technologies Ouargla30000 Algeria University of Oum El Bouaghi Research Laboratory on Computer Science's Complex Systems Dept. Mathematics and Computer Science Oum El Bouaghi Algeria

ISBN: (纸本)9798350342055

Power dissipation and energy consumption are key factors for the performance improvement of modern technology systems. In recent years, several strategies have been proposed to solve the power management issues on embedded systems. In this paper, we classify the state of the art of those techniques. Classification is done based on run-time, activation, and idle state of the different components of the network on chip (NoC). We illustrate the requirement of power management based on several important parameters. A highlight is put on the drawbacks and benefits. Also, classification is done based on different NoC abstraction levels. However, by this paper, we intend to guide the researchers and application developers in getting insights about the strategies of power management techniques that will lead to the design of more efficient high performances in embedded systems of the future. © 2023 IEEE.

关键词： Network-on-chip

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Correction to: Improving time and energy efficiency in multi-UAV coverage operations by optimizing the UAVs’ initial positions (International Journal of Intelligent Robotics and Applications, (2024), 10.1007/s41315-024-00333-2)

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International Journal of Intelligent Robotics and Applications 2024年第1期9卷 365-373页

作者： Stefanopoulou, Aliki Raptis, Emmanuel K. Apostolidis, Savvas D. Gkelios, Socratis Kapoutsis, Athanasios Ch. Chatzichristofis, Savvas A. Vrochidis, Stefanos Kosmatopoulos, Elias B. Information Technologies Institute The Centre for Research & ampTechnology Hellas 6th km Harilaou - Thermis Thessaloniki57001 Greece Department of Electrical and Computer Engineering Democritus University of Thrace Kimmeria Campus Xanthi67100 Greece Intelligent Systems Laboratory Department of Computer Science Neapolis University Pafos 2 Danais Avenue PafosCY 8042 Cyprus

In the original publication, the author has found few errors in the figures 2, 4, 11 and 12 which significantly impact both the paper's quality and the reputation of the journal. These issues undermine the clarity of the methodology and the novelty of the research. 1. Each column represents a different case in the experiment, corresponding to the number of UAVs in both the obstacle-free and obstacle areas. However, in Fig. 2, the first row contains identical subfigures across all columns, while the subfigures in the remaining rows are identical to each other The incorrect Fig. 2 appears as below: (Figure presented.) However the corrected Fig. 2 should appear as below. (Figure presented.) 2. In Fig. 4, every row showcase the average QoF for the testing scenarios (obstacle-free and obstacle area). However the first row has the exact same figures with the second row. The incorrect Fig. 4 appears as below: (Figure presented.) However the corrected Fig. 4 should appear as below. (Figure presented.) 3. Figures 11 and 12 showcase the distribution of each Quality of Flight (QoF) metric value for every UAV in the conducted trials during the testing scenarios. However, Fig. 11 is the same with Fig. 12. The incorrect Figs. 11 and 12 appears as below: (Figure presented.) However the corrected Figs. 11 and 12 should appear as below. (Figure presented.) Distribution of each Quality of Flight metric value for every UAV in the conducted trials during the coverageof the obstacle-free area Distribution of each Quality of Flight metric value for every UAV in the conducted trials during the coverageof the obstacle area The original article has been corrected. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

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PerfMon: Measuring Application-Level Performance in a Large-Scale Campus Wireless Network

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China Communications 2023年第3期20卷 316-335页

作者： Weizhen Dang Tao Yu Haibo Wang Jing’An Xue Fenghua Li Jilong Wang Institute for Network Sciences and Cyberspace Tsinghua UniversityBeijing 100084China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Beijing National Research Center for Information Science and Technology(BNRist) Beijing 100084China Huawei Technologies Beijing 100085China Zhongguancun Laboratory Beijing 100094China

WiFi has become one of the most popular ways to access the ***,in large-scale campus wireless networks,it is challenging for network administrators to provide optimized access quality without knowledge on fine-grained traffic characteristics and real network *** this paper,we implement PerfMon,a network performance measurement and diagnosis system,which integrates collected multi-source datasets and analysis *** on PerfMon,we first conduct a comprehensive measurement on application-level traffic patterns and behaviors from multiple dimensions in the wireless network of T university(TWLAN),which is one of the largest campus wireless *** we systematically study the application-level network *** observe that the application-level traffic behaviors and performance vary greatly across different locations and device *** performance is far from satisfactory in some *** diagnose these problems,we distinguish locations and device types,and further locate the most crucial factors that affect the *** results of case studies show that the influential factors can effectively characterize performance changes and explain for performance degradation.

关键词： WiFi traffic patterns network manage-ment performance measurement network diagnosis

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