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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： Mekruksavanich, Sakorn Hnoohom, Narit Phaphan, Wikanda Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakhon Pathom Thailand King Mongkut's University of Technology North Bangkok Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science Bangkok 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)9798331543273

With the rise of wearable sensors and smart devices, human activity recognition (HAR) has become a vital research area in ubiquitous computing. Although many studies report high accuracy using k-fold cross-validation, these results often do not reflect actual generalization interpretation due to subject-dependent data leakage, where models test on activities from the subjects they were trained. This study compares traditional k-fold cross-validation with leave-one-subject-out (LOSO) validation using the WISDM dataset, highlighting the importance of proper validation techniques in HAR systems. We implemented and evaluated five advanced deep learning models - convolutional neural network (CNN), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and bidirectional GRU (BiGRU) - on windowed raw sensor data. Our experiments showed significant differences between validation methods. The BiGRU model achieved 97.91% accuracy with k-fold cross-validation and 98.02% with LOSO validation, while the CNN model achieved only 92.55% and 93.44%, respectively. These results underscore the impact of both model architecture and validation approach on performance. Our findings stress the need for subject-independent validation strategies like LOSO to develop truly generalizable HAR systems. © 2025 IEEE.

关键词： Convolutional neural networks

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Improved Guess and Determine attack on the MASHA stream cipher

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science China(information sciences) 2021年第9期64卷 247-248页

作者： Lin DING Dawu GU Lei WANG Chenhui JIN Jie GUAN Department of Computer Science and Engineering Shanghai Jiao Tong University Zhengzhou Information Science and Technology Institute Westone Cryptologic Research Center

Dear editor,K2 is a secure and high-performance stream cipher and has been standardized by ISO/IEC 18033-4. The MASHA stream cipher is a successor of K2 with integrated MAC functionality proposed in 2011. Based on optimizing the guess and determination process, we present an improved Guess and Determine attack on MASHA with time complexity of 2224, reducing the time complexity of the existing attack by a factor of 296. To the best of our knowledge, this is the best attack on MASHA so far.

关键词： information Systems and Communication Service

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Using DSCB: A Depthwise Separable Convolution Block Rebuild MTCNN for Face Detection 2022

Using DSCB: A Depthwise Separable Convolution Block Rebuild ...

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5th International Conference on Image and Graphics Processing, ICIGP 2022

作者： Wang, Qiang Cui, Jingru Qin, Zunying An, Ninggang Ma, Xiaofei Li, Guodong Network Information Center Xi'an Jiaotong University China School of Computer Science and Technology Xi'an Jiaotong University China Network Information Center Xi'an Jiaotong University China School of Software Engineering Xi'an Jiaotong University China

ISBN: (纸本)9781450395465

Nowadays, there are huge demands of face detection in images and videos for surveillance, education, autonomous driving and health care. These application scenarios need high accuracy and efficiency of face detection. However, in some scene, unconstrained pose variation, occlusion, large number of faces and illumination bring great challenges to existing face detection methods. In view of above problems, we propose a depthwise separable convolution block (DSCB) which can maintain the speed of training and improve the accuracy at the same time. Then, using the proposed DSCB, we design a face detection model based on MTCNN (Multi-task Convolution Neural network) to improve performance of occlusion, unconstrained pose variation, large numbers of small targets. In order to better evaluate the proposed method, we built a new dataset which is derived from the classroom teaching scene for training and evaluating. Our dataset consists of 7168 images and 294924 face bounding boxes with occlusion, unconstrained pose variation, and large numbers of small targets. The comparative experiments on our dataset show that the proposed method is superior to other state-of-the-art methods in accuracy and speed of face detection. Compared with the original MTCNN, the face detection method we proposed can bring about 3.9%, 8.66% and 1.39 times overall performance improvement on precision, recall and detection speed respectively. © 2022 ACM.

关键词： Convolution

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Emotion Recognition Using EEG Signals in Human Brain Waves and Deep Learning Approaches 10

Emotion Recognition Using EEG Signals in Human Brain Waves a...

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作者： Mekruksavanich, Sakorn Hnoohom, Narit Phaphan, Wikanda Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Thailand Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakhon Pathom Thailand King Mongkut's University of Technology North Bangkok Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science Bangkok 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)9798331543273

Emotion recognition using EEG signals has gained growing attention in recent years due to its promising applications in human-computer interaction, affective computing, and mental health assessment. This study investigates the performance of various deep learning architectures in classifying emotional states from EEG recordings. We utilize the Emotion EEG dataset as a benchmark and evaluate five prominent deep learning models. The classification task targets three emotion categories: neu-tral, positive, and negative. Experimental results reveal notable differences in model performance. Among the tested models, BiGRU achieves the highest classification accuracy of 97.70% (±0.69%), followed closely by BiLSTM with 97.37% (±0.17%). These bidirectional architectures significantly outperform the CNN model, which attains an accuracy of only 61.59% (±1.22%). The LSTM and GRU models yield intermediate results, nearing 92% accuracy. The findings highlight the effectiveness of bidirectional recurrent neural networks in modeling the temporal characteristics of EEG signals for emotion detection. This research contributes to advancing EEG-based emotion recognition by identifying the most suitable deep learning strategies, thus supporting the development of more intelligent and emotion-aware computing systems. © 2025 IEEE.

关键词： Recurrent neural networks

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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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Data-dependent bounds for online portfolio selection without lipschitzness and smoothness 23

Data-dependent bounds for online portfolio selection without...

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Proceedings of the 37th International Conference on Neural information Processing Systems

作者： Chung-En Tsai Ying-Ting Lin Yen-Huan Li Department of Computer Science and Information Engineering National Taiwan University Department of Computer Science and Information Engineering Department of Mathematics Center for Quantum Science and Engineering National Taiwan University

This work introduces the first small-loss and gradual-variation regret bounds for online portfolio selection, marking the first instances of data-dependent bounds for online convex optimization with non-Lipschitz, non-smooth losses. The algorithms we propose exhibit sublinear regret rates in the worst cases and achieve logarithmic regrets when the data is "easy," with per-round time almost linear in the number of investment alternatives. The regret bounds are derived using novel smoothness characterizations of the logarithmic loss, a local norm-based analysis of following the regularized leader (FTRL) with self-concordant regularizers, which are not necessarily barriers, and an implicit variant of optimistic FTRL with the log-barrier.

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A Security Enhanced Chaotic-Map-Based Authentication Protocol for Internet of Drones

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IEEE Internet of Things Journal 2024年第12期11卷 22301-22309页

作者： Mahmood, Khalid Ghaffar, Zahid Farooq, Muhammad Yahya, Khalid Das, Ashok Kumar Chaudhry, Shehzad Ashraf National Yunlin University Of Science And Technology Graduate School Of Intelligent Data Science Douliu64002 Taiwan National Yunlin University Of Science And Technology Graduate School Of Engineering Science And Technology Douliu64002 Taiwan Abu Dhabi University Department Of Mathematics And Statistics College Of Arts And Sciences Abu Dhabi United Arab Emirates Istanbul Gelisim University Department Of Electrical And Electronics Engineering Istanbul34310 Turkey Sabratah University Department Of Electrical Engineering Faculty Of Engineering Regdalen Libya Center For Security Theory And Algorithmic Research Hyderabad500 032 India Abu Dhabi University Department Of Computer Science And Information Technology College Of Engineering Abu Dhabi United Arab Emirates Nisantasi University Faculty Of Engineering And Architecture Department Of Software Engineering Istanbul34398 Turkey

The Internet of Drones (IoD) extends the capabilities of unmanned aerial vehicles, enabling them to participate in a connected network. In IoD infrastructure, drones communicate not only among themselves but also with users and a control center. This interconnected communication framework holds promise for various applications, from collaborative decision-making to real-time data exchange. However, the expansion of communication in IoD also introduces new challenges, particularly in terms of security, privacy and authentication. Unfortunately, the current authentication protocols are inadequate in offering robust security features against various attacks in the IoD environment. To address these security issues and limitations, we proposed a secure protocol for the IoD environment using chaotic maps and hash functions. In addition, we also employed a physically unclonable function in the development of the proposed protocol. We assess the security of the protocol through both informal and formal security analysis. The formal security analysis is conducted through a widely used random or real (RoR) model. The informal analysis shows the rigorous security features against various attacks, such as masquerading, anonymity violation, and physical cloning attacks. Moreover, we compare the performance of the devised protocol with similar existing protocols across important performance parameters, such as communication overhead, computation overhead, and security features. The devised protocol provides a 67.86% and 17.80% reduction in computation and communication overheads, respectively, as compared to related protocols. The analysis demonstrates the proposed protocol's capacity to support secure communication in the IoD environment and satisfy desirable security attributes. © 2014 IEEE.

关键词： Drones

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Age of information Based User Scheduling and Data Assignment in Multi-User Mobile Edge Computing networks:An Online Algorithm

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China Communications 2024年第5期21卷 153-165页

作者： Ge Yiyang Xiong Ke Dong Rui Lu Yang Fan Pingyi Qu Gang School of Computer and Information Technology Beijing Jiaotong UniversityBeijing 100044China Engineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education Beijing 100044China Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing 100044China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China Beijing National Research Center for Information Science and Technology Tsinghua UniversityBeijing 100084China Department of Electrical and Computer Engineering College ParkMarylandUSA

This paper investigates the age of information(AoI)-based multi-user mobile edge computing(MEC)network with partial offloading *** weighted sum AoI(WSA)is first analyzed and derived,and then a WSA minimization problem is formulated by jointly optimizing the user scheduling and data *** to the non-analytic expression of the WSA *** optimization variables and the unknowability of future network information,the problem cannot be solved with known solution ***,an online Joint Partial Offloading and User Scheduling Optimization(JPOUSO)algorithm is proposed by transforming the original problem into a single-slot data assignment subproblem and a single-slot user scheduling sub-problem and solving the two sub-problems *** analyze the computational complexity of the presented JPO-USO algorithm,which is of O(N),with N being the number of *** results show that the proposed JPO-USO algorithm is able to achieve better AoI performance compared with various baseline *** is shown that both the user’s data assignment and the user’s AoI should be jointly taken into account to decrease the system WSA when scheduling users.

关键词： age of information(aoi) mobile edge computing(mec) user scheduling

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Integration of STAR-RIS With Index Modulation: Novel Attraction Solutions for IoT Applications

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IEEE Internet of Things Journal 2025年

作者： Yue, Ming Peng, Yuyang Ye, Runlong AL-Hazemi, Fawaz Mirza, Mohammad Meraj Lee, Juho Macau University of Science and Technology School of Computer Science and Engineering China University of Jeddah Department of Computer and Network Engineering Jeddah21959 Saudi Arabia Taif University College of Computers and Information Technology Department of Computer Science Taif21944 Saudi Arabia Samsung Electronics Samsung Research Seoul06765 Korea Republic of

The integration of the Sixth-Generation (6G) communication technology with the Internet of Things (IoT) is one of the directions in achieving a digitalized world, aiming to provide ultra-high-speed communication services that exhibit higher energy-efficiency, lower latency, and heightened reliability and availability. In this paper, we investigate the use of the Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS), one of the candidate technologies for 6G communication, along with the Index Modulation (IM) in multi-antennas Ambient Backscatter Communication System (ABCS) to form the STAR-RIS-assisted ABCS with Spatial Modulation (STAR-RIS-ABCS-SM). In particular, through the utilization of STAR-RIS and backscatter communication, we meet the demand for low-power wireless communication devices in the green communication, which is also a crucial characteristic of the IoT. Specifically, we introduce STAR-RIS into ABCS, which not only significantly boost the signal strength but also achieve full communication coverage. Meanwhile, we apply the SM technology in the multi-antennas Backscatter Tags (BTs), located on both sides of the STAR-RIS, and use joint detection at the reader to enhance the system’s Spectral Efficiency (SE) as well as its Bit Error Rate (BER) performance. Furthermore, we divide the STAR-RIS into various subsurfaces to form patterns that further enhance SE, which is termed STAR-RIS-assisted ABCS with Pattern Index and SM (STAR-RIS-ABCS-PISM) scheme. Finally, theoretical BER analytical expressions for both schemes are derived and presented. Simulation and theoretical results demonstrate the enhanced performance of the proposed schemes as well as the validity of the system model. © 2014 IEEE.

关键词： Spectrum efficiency

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Energy-Efficient Multi-Trip Routing for Municipal Solid Waste Collection by Contribution-Based Adaptive Particle Swarm Optimization

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Complex System Modeling and Simulation 2023年第3期3卷 202-219页

作者： Xiaoning Shen Hongli Pan Zhongpei Ge Wenyan Chen Liyan Song Shuo Wang Collaborative Innovation Center of Atmospheric Environment and Equipment Technology and also with the Jiangsu Key Laboratory of Big Data Analysis TechnologySchool of AutomationNanjing University of Information Science and TechnologyNanjing 210044China School of Automation Nanjing University of Information Science and TechnologyNanjing 210044China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation the Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China School of Computer Science University of BirminghamBirminghamB152TTUK

Waste collection is an important part of waste management *** costs and carbon emissions can be greatly reduced by proper vehicle ***,each vehicle can work again after achieving its capacity limit and unloading the *** this,an energy-efficient multi-trip vehicle routing model is established for municipal solid waste collection,which incorporates practical factors like the limited capacity,maximum working hours,and multiple trips of each *** both economy and environment,fixed costs,fuel costs,and carbon emission costs are minimized *** solve the formulated model effectively,contribution-based adaptive particle swarm optimization is *** strategies named greedy learning,multi-operator learning,exploring learning,and exploiting learning are specifically designed with their own searching *** assessing the contribution of each learning strategy during the process of evolution,an appropriate one is selected and assigned to each individual adaptively to improve the searching efficiency of the ***,an improved local search operator is performed on the trips with the largest number of waste sites so that both the exploiting ability and the convergence accuracy of the algorithm are *** of the proposed algorithm is tested on ten waste collection instances,which include one real-world case derived from the Green Ring Company of Jiangbei New District,Nanjing,China,and nine synthetic instances with increasing scales generated from the commonly-used capacitated vehicle routing problem benchmark *** with five state-of-the-art algorithms show that the proposed algorithm can obtain a solution with a higher accuracy for the constructed model.

关键词： municipal solid waste collection energy conservation multi-trip contribution particle swarm optimization

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