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arXiv 2025年

作者： Xie, Haoyu Li, Haoxuan Zheng, Chunyuan Yuan, Haonan Liao, Guorui Liao, Jun Liu, Li School of Big Data and Software Engineering Chongqing University China Center for Data Science Peking University China School of Mathematical Sciences Peking University China School of Computer Science and Engineering Beihang University China

Wearable Human Activity Recognition (WHAR) is a prominent research area within ubiquitous computing. Multi-sensor synchronous measurement has proven to be more effective for WHAR than using a single sensor. However, existing WHAR methods use shared convolutional kernels for indiscriminate temporal feature extraction across each sensor variable, which fails to effectively capture spatio-temporal relationships of intra-sensor and inter-sensor variables. We propose the DecomposeWHAR model consisting of a decomposition phase and a fusion phase to better model the relationships between modality variables. The decomposition creates high-dimensional representations of each intra-sensor variable through the improved Depth Separable Convolution to capture local temporal features while preserving their unique characteristics. The fusion phase begins by capturing relationships between intra-sensor variables and fusing their features at both the channel and variable levels. Long-range temporal dependencies are modeled using the State Space Model (SSM), and later cross-sensor interactions are dynamically captured through a self-attention mechanism, highlighting inter-sensor spatial correlations. Our model demonstrates superior performance on three widely used WHAR datasets, significantly outperforming state-of-the-art models while maintaining acceptable computational efficiency. Our codes and supplementary materials are available at https://***/Anakin2555/DecomposeWHAR. © 2025, CC BY.

关键词： Spatio-temporal data

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IoHT System Authentication Through the Blockchain Technology: A Review

IoHT System Authentication Through the Blockchain Technology...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Muwafaq Jawad Ali A. Yassin Hamid Ali Abed Al-Asadi Zaid Ameen Abduljabbar Vincent Omollo Nyangaresi Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah Iraq Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and Technology Bando Kenya Department of Applied Electronics Saveetha School of Engineering SIMATS Chennai Tamilnadu India

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

The growing adoption of the Internet of Things (IoT) terminology in recent years in several domains, including healthcare, has yielded multiple benefits. One of the roles is to support professionals in delivering outstanding and suitable healthcare for patients, while also empowering patients to independently track their health state. However, this wide growth is accompanied by many significant security concerns and cyber risks, including unauthorized access, data leakage, information modification, and data erasure. This article outlines the advantages of employing groundbreaking blockchain technology to provide robust and verified authentication methods for different systems. Additionally, it imparts a foundational comprehension of the technologies employed in the procedure of constructing and advancing blockchain-based platforms. In addition, we explore tools like Scythe and Avispa that are employed to assess the system’s resilience against different types of attacks.

关键词： Terminology Reviews Instruments Authentication Medical services Throughput Blockchains Internet of Things Information technology Resilience

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Eliciting Requirements from Stakeholders’ Responses Using Natural Language Processing

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computer Modeling in engineering & sciences 2021年第4期127卷 99-116页

作者： Mohammed Lafi Bilal Hawashin Shadi AlZu’bi Department of Software Engineering Faculty of Science and Information TechnologyAl-Zaytoonah University of JordanAmman11733Jordan Department of Computer Information Systems Faculty of Science and Information TechnologyAl-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science Faculty of Science and Information TechnologyAl-Zaytoonah University of JordanAmman11733Jordan

Most software systems have different stakeholders with a variety of *** process of collecting requirements from a large number of stakeholders is vital but *** propose an efficient,automatic approach to collecting requirements from different stakeholders’responses to a specific *** use natural language processing techniques to get the stakeholder response that represents most other stakeholders’*** study improves existing practices in three ways:Firstly,it reduces the human effort needed to collect the requirements;secondly,it reduces the time required to carry out this task with a large number of stakeholders;thirdly,it underlines the importance of using of data mining techniques in various software engineering *** approach uses tokenization,stop word removal,and word lemmatization to create a list of frequently accruing *** then creates a similarity matrix to calculate the score value for each response and selects the answer with the highest *** experiments show that using this approach significantly reduces the time and effort needed to collect requirements and does so with a sufficient degree of accuracy.

关键词： software requirements requirements elicitation natural language processing

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Combining Strategy and Roles to Formally Analyze Multi-Agent Reinforcement Learning

Combining Strategy and Roles to Formally Analyze Multi-Agent...

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Electronic Information engineering and computer science (EIECS), 2021 International Conference on

作者： Xiaoyan Wang Yujuan Zhang Bing Li College of Computer Science and Technology Jilin University Changchun China Software Engineering Jilin University Changchun China

A novel semantics for MARL called neural concurrent game structure (NCGS) is introduced, which extends CGS with neural network and roles where the agents are implemented via feed-forward ReLU neural networks. To formally verify concrete NCGS systems and reduce the complexity, multi-role strategy logic(mrSL) was put forward. mrSL is suitable for the properties of the NCGS, regardless of which agent is responsible for the specific task. Parameterized model checking(PMC) is used to solve the NCGS verification problem against mrSL. Finally an algorithm for MILP-based verification process is introduced, and report the experimental results.

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A Hybrid CNN-LSTM Model for Virtual Machine Workload Forecasting in Cloud Data Center 18

A Hybrid CNN-LSTM Model for Virtual Machine Workload Forecas...

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18th International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2021

作者： Leka, Habte Lejebo Fengli, Zhang Kenea, Ayantu Tesfaye Tegene, Abebe Tamrat Atandoh, Peter Hundera, Negalign Wake University of Electronic Science and Technology of China School of Information and Software Engineering China Adama Science and Technology University School of Electrical Engineering and Computing Department of Computer Science and Engineering Ethiopia University of Electronic Science and Technology of China School of Computer Science and Engineering China

ISBN: (纸本)9781665413640

It is vital to precisely forecast the workload of Virtual Machines (VMs) to achieve efficient cloud resources management and reduce power consumption. In this research study, a deep learning-based hybrid strategy for VM workload prediction is proposed. To create an accurate prediction, the suggested prediction model integrated a convolutional neural network (CNN) architecture and a long-short-term memory (LSTM) neural network. The CNN component is used to elicit complex distinctive attributes of the VM workload data, while the LSTM component models temporal information and predict the future VM workload. Experimental results on real-world dataset have shown that the proposed CNN-LSTM model is effective on VM workload prediction, and when compared to frequently used workload prediction models, the proposed approach enhances workload forecasting performance. © 2021 IEEE.

关键词： Long short-term memory

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Semi-Asynchronous Federated Learning with Trajectory Prediction for Vehicular Edge Computing

Semi-Asynchronous Federated Learning with Trajectory Predict...

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International Workshop on Quality of Service

作者： Yuxuan Deng Xiuhua Li Chuan Sun Qilin Fan Xiaofei Wang Victor C. M. Leung School of Big Data & Software Engineering Chongqing University Chongqing China School of Computer Science and Engineering Nanyang Technological University Singapore TKLAN College of Intelligence & Computing Tianjin University Tianjin China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Department of Electrical & Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9798350350128

ISBN: (纸本)9798350350135

Federated learning, as a distributed machine learning paradigm, offers promising solutions for vehicular edge computing (VEC) networks. However, federated learning in VEC with classification tasks still faces two key challenges: i) Delayed data labeling hampers supervised training; ii) Dynamic vehicle behavior complicates training scheduling and model uploads to edge servers. In this paper, we propose a semi-asynchronous federated learning algorithm for VEC. Specifically, it utilizes knowledge distillation to generate soft labels from raw data for supervised training, and estimates model training and uploading time through trajectory prediction. We further logically group vehicles based on the characteristics of their dynamic behavior. We then employ synchronous aggregation within groups and asynchronous aggregation between groups to optimize model performance while reducing latency. Finally, we conduct separate comparative experiments for all components, demonstrating that each component possesses unique advantages. Experiment results show that the proposed algorithm outperforms existing schemes in terms of accuracy and latency. The code is available at: https://***/dyxcode/Semi-Asynchronous-Federated-Learning.

关键词： Training Federated learning Heuristic algorithms Predictive models Prediction algorithms Data models Trajectory

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Ensemble based unsupervised anomaly detection with concept drift adaptation for time series data

Ensemble based unsupervised anomaly detection with concept d...

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Smart World Congress (SWC), IEEE

作者： Danlei Li Nirmal Nair Kevin I-Kai Wang Kouichi Sakurai Department of Electrical Computer and Software Engineering University of Auckland Auckland New Zealand Department of Informatics Faculty of Information Science and Electrical Engineering Kyushu University Fukuoka Japan

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

Anomaly detection is the identification of instances that substantially deviate from the majority of the data and do not conform to a well-defined normal behavior. Investigating time series anomalies has become increasingly popular in recent years due to widely deployed IoT devices and sensors. Although most existing methods apply image anomaly detection algorithms directly on time series and commonly assume the data is IID (independent and identically distributed). However, in most realworld applications, the underlying data distribution alters over time. Changes in data over time are referred to as concept drift and can significantly degrade the efficacy of an anomaly detection model. Existing research also indicates that the majority of anomaly detection frameworks ignored concept drift, resulting in poor long-term model performance. This paper presents an ensemble-based, unsupervised anomaly detection framework that is adaptive to the concept drift in time series data. Both distribution and performance-based techniques are used to detect concept drift and update the model to ensure the framework’s longterm performance reliability. The proposed method is evaluated using a real-world univariate time series data that captures sea surface temperature data over a 2 -year period. By comparing our proposed framework to other state-of-the-art algorithms, we demonstrate our proposed method has obvious benefits in accuracy and long-term stability.

关键词： Adaptation models Accuracy Concept drift Time series analysis Stability analysis Sensors Sea surface temperature Reliability Anomaly detection Thermal stability

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A Framework to Analyze the Impact of Maps on VANET Simulation

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Chinese Journal of Electronics 2023年第3期20卷 401-406页

作者： Xiaoming LI Jingxuan WANG Xin JIN Tong ZHAO Wei YAN State Key Laboratory of Software Development Environment Beihang University Beijing China School of Electronic Engineering and Computer Science Peking University Beijing China

Mobility models play an important role on VANET simulation, which has been discussed in many studies. However, few studies have analyzed the impact of maps. We find that maps can greatly influence simulation results. We present a framework to help researchers analyze the impact of maps. We regard intersections in the map as virtual nodes and propose Virtual node coverage (VNC) and Virtual connected components (VCC) as two metrics to estimate the impact of maps. Through the-oretical analysis and extensive simulations, we show that our approach can be applied to various scenarios and researchers can use our approach to analyze the impact of maps on VANET simulation.

关键词： Measurement Analytical models Electric potential Mobility models Simulation Vehicular ad hoc networks Research and development

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Efficient Face Mask Detection Method Using YOLOX: An Approach to Reduce Coronavirus Spread 5

Efficient Face Mask Detection Method Using YOLOX: An Approac...

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5th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2022

作者： Endris, Ahmed Yang, Shangming Zenebe, Yutra Abel Gashaw, Biniyam Mohammed, Juhar Bayisa, Leta Yobsan Abera, Ali Ebrahim University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China Office Shanghai365 China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China University of Electronic Science and Technology of China Information and Communication Technology Adiss Abeba Ethiopia

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

Many lives have been put in danger since the start of the COVID-19 outbreak. According to WHO (World Health Organization)'s statements, breathing without a mask is highly dangerous in public and crowded places. Wearing a mask is essential as long as the vaccination is not widely used and does not fully protect everyone. Wearing masks does minimize the chances of becoming infected and the transmission of Coronavirus. Due to this, many public service providers may mandate customers to wear masks in order to get ***, manually monitoring whether people are wearing a mask or not in public is inefficient and difficult. This paper proposes replacing manual inspection with a deep learning-based method using YOLOX, the most powerful objection detection algorithm. The results of the experiments show that the algorithm described in this work can efficiently detect face masks and enable more effective personnel monitoring than other YOLO series models. In addition, as compared to other models, our technique has significant benefits in terms of speed and accuracy in small and crowded areas. © 2022 IEEE.

关键词： COVID-19

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Performance Evaluation of Popular Deep Neural Networks for Neural Machine Translation 20

Performance Evaluation of Popular Deep Neural Networks for N...

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20th International Conference on Frontiers of Information Technology, FIT 2023

作者： Naeem, Muhammad Siddique, Abu Bakar Ali, Raja Hashim Arshad, Usama Abideen, Zain Ul Ali Khan, Talha Shah, Muhammad Huzaifa Zeeshan Ijaz, Ali Ali, Nisar Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350395785

The field of Neural Machine Translation (NMT) has shown impressive performance for quick and easy communication in various languages spoken all over the world. NMT helps us by improving communication between different languages. For this purpose, different sequential models are used such as Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Units (GRU). Analysis among these different models are important for making language translation better and choose the best model for the right job. This research investigates the performance of these models on two distinct language datasets, English-to-German and English-to-Urdu. Based on accuracy metrics, the findings reveal that GRU having test accuracy (88.22% ) outperforms RNN (87.21% ), and LSTM (85.70% )demonstrating the highest translation accuracy, followed by RNN and LSTM exhibiting comparatively lower accuracy levels. © 2023 IEEE.

关键词： Long short-term memory

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