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International Journal of Interactive Mobile Technologies 2022年第21期16卷 49-70页

作者： Islam, Gazi Zahirul Hossain, Md. Mobarak Faruk, Md. Nur, Fernaz Narin Hasan, Nayeem Khan, Khalid Mahbub Tumpa, Zerin Nasrin Department of Computer Science and Engineering Southeast University Dhaka Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering Notre Dame University Bangladesh Dhaka Bangladesh School of Computer Science Western Illinois University IL United States Department of Computer Science and Engineering University of Information Technology & Sciences Dhaka Bangladesh

Gas Leakage and its fatal effects are a great concern throughout the world, especially in developing countries like Bangladesh. Every year lots of people died and countless damages to assets occur due to the fire caused by the gas leakage. Not only that but gas leakage and explosion are also very harmful to the climate. Thus, a system to detect gas leakage and preventive measures is of utmost importance. In this project, we design and implement an intelligent IoT prototype to detect gas leakage, and the fire caught by gas leakage. Our goal is to minimize the effect of gas leakage by taking some protective measures. When the gas sensor, detects the gas leakage, the solenoid valve shuts off the gas line, and the exhaust fan starts to run. Again, when the flame sensor detects a fire, the sucker throws the fire extinguisher balls at the fire. The GSM SIM module notifies the user by sending a message to his smartphone. The buzzer sounds when a mishap occurs and the LCD monitor always shows the status of the system. In this way, we have efficiently designed and implemented a low-cost and intelligent gas leak detection and fire suppression system. © 2022, International Journal of Interactive Mobile Technologies. All Rights Reserved.

关键词： Internet of things

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Adaptive Addax-based Deep Fuzzy Clustering for Face Mask Detection

Adaptive Addax-based Deep Fuzzy Clustering for Face Mask Det...

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International Conference on Green Energy, Computing and Intelligent technology 2024, GEn-CITy 2024

作者： Anuradha, B. Gupta, Sudhanshu Kumar, Akhil Bennett University Greater Noida India CMR College of Engineering & Technology Hyderabad India School of Computer Science Engineering and Technology Bennett University India

ISBN: (纸本)9781837242689

Respiratory illness Diseases transmitted by droplets highlighted the necessity of face masks. The traditional methods to ensure mask usage faced challenges like the environment, partial masks, low-resolution, real-time processing, and human error, which hinder accuracy and efficiency dynamically. To handle these challenges, efficient automated mask detection systems are required in real-time across various environments. This work develops such an innovative model named Adaptive Addax Optimization Algorithm enabled Deep Fuzzy Clustering (Adaptive AOA_DFC). In this method, preprocessing is done using a Gaussian filter then undergoes Region of Interest (RoI) extraction to isolate the face region. Subsequently, the mask detection is performed using Deep Fuzzy Clustering (DFC) further trained through the proposed Adaptive AOA method. This algorithm is obtained by integrating the Addax Optimization Algorithm (AOA) with an adaptive mechanism to enhance detection accuracy. The performance evaluation metrics are Accuracy, Dice coefficient, and Jaccard coefficient, which yield the values of 92.39%, 0.950, and 0.951, respectively. © The Institution of engineering & technology 2024.

关键词： Clustering algorithms

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Design and beam dynamics study of a magnet system for an 11 MeV superconducting isochronous cyclotron

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Nuclear science and Techniques 2025年第6期 193-202页

作者： Pan-Pan Zheng Xiang-Hui Wang Zi-Feng He Yue Wu Zhi-Min Dai Wei-Shi Wan School of Physical Science and Technology ShanghaiTech University Shanghai Institute of Applied Physics Chinese Academy of Sciences School of Computer Engineering Wefang University

In recent years, due to the scarcity of domestic radioisotopes, the Chinese government has strongly supported the development of dedicated radioisotope production facilities. This paper presents conceptual design simulations of an 11 MeV, 50 μA,H-compact superconducting cyclotron for radioisotope production. This paper focuses primarily on four aspects: magnet system design, central region configuration, beam dynamics analysis, and extraction system design. This paper outlines the cyclotron's primary parameters and key steps in the development process.

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Meta-Auto-Decoder:a Meta-Learning-Based Reduced Order Model for Solving Parametric Partial Differential Equations

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Communications on Applied Mathematics and Computation 2024年第2期6卷 1096-1130页

作者： Zhanhong Ye Xiang Huang Hongsheng Liu Bin Dong Beijing International Center for Mathematical Research Peking UniversityBeijing 100871China School of Computer Science and Technology University of Science and Technology of ChinaHefei 230027AnhuiChina Central Software Institute Huawei Technologies Co.LtdHangzhou 310007ZhejiangChina Center for Machine Learning Research Peking UniversityBeijing 100871China

Many important problems in science and engineering require solving the so-called parametric partial differential equations(PDEs),i.e.,PDEs with different physical parameters,boundary conditions,shapes of computational domains,*** reduced order modeling techniques accelerate the solution of the parametric PDEs by projecting them onto a linear trial manifold constructed in the ofline *** methods often need a predefined mesh as well as a series of precomputed solution snapshots,and may struggle to balance between the efficiency and accuracy due to the limitation of the linear *** the nonlinear representation of neural networks(NNs),we propose the Meta-Auto-Decoder(MAD)to construct a nonlinear trial manifold,whose best possible performance is measured theoretically by the decoder *** on the meta-learning concept,the trial manifold can be learned in a mesh-free and unsupervised way during the pre-training *** adaptation to new(possibly heterogeneous)PDE parameters is enabled by searching on this trial manifold,and optionally fine-tuning the trial manifold at the same *** numerical experiments show that the MAD method exhibits a faster convergence speed without losing the accuracy than other deep learning-based methods.

关键词： Parametric partial differential equations(PDEs) Meta-learning Reduced order modeling Neural networks(NNs) Auto-decoder

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MeHyper: Accelerating Hypergraph Neural Networks by Exploring Implicit Dataflows 31

MeHyper: Accelerating Hypergraph Neural Networks by Explorin...

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31st IEEE International Symposium on High Performance computer Architecture, HPCA 2025

作者： Zhao, Wenju Yao, Pengcheng Chen, Dan Zheng, Long Liao, Xiaofei Wang, Qinggang Ma, Shaobo Li, Yu Liu, Haifeng Xiao, Wenjing Sun, Yufei Zhu, Bing Jin, Hai Xue, Jingling Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China National University of Singapore School of Computing 119077 Singapore Guangxi University School of Computer Electronics and Information NanNing530004 China University of New South Wales School of Computer Science and Engineering SydneyNSW2052 Australia

ISBN: (纸本)9798331506476

Hypergraph Neural Networks (HGNNs) are increasingly utilized to analyze complex inter-entity relationships. Traditional HGNN systems, based on a hyperedge-centric dataflow model, independently process aggregation tasks for hyperedges and vertices, leading to significant computational redundancy. This redundancy arises from recalculating shared information across different tasks. For the first time, we identify and harness implicit dataflows (i.e., dependencies) within HGNNs, introducing the microedge concept to effectively capture and reuse intricate shared information among aggregation tasks, thereby minimizing redundant computations. We have developed a new microedge-centric dataflow model that processes shared information as fine-grained microedge aggregation tasks. This dataflow model is supported by the Read-Process-Activate-Generate execution model, which aims to optimize parallelism among these tasks. Furthermore, our newly developed MeHyper, a microedge-centric HGNN accelerator, incorporates a decoupled pipeline for improved computational parallelism and a hierarchical feature management strategy to reduce off-chip memory accesses for large volumes of intermediate feature vectors generated. Our evaluation demonstrates that MeHyper substantially outperforms the leading CPUbased system PyG-CPU and the GPU-based system HyperGef, delivering performance improvements of 1,032.23 × and 10.51 ×, and energy efficiencies of 1,169.03 × and 9.96 ×, respectively. © 2025 IEEE.

关键词： Redundancy

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WFGuard: an Effective Fuzzing-testing-based Traffic Morphing Defense against Website Fingerprinting

WFGuard: an Effective Fuzzing-testing-based Traffic Morphing...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Zhen Ling Gui Xiao Lan Luo Rong Wang Xiangyu Xu Guangchi Liu School of Computer Science and Engineering Southeast University China School of Cyber Science and Engineering Southeast University China School of Computer Science and Technology Anhui University of Technology China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Website fingerprinting (WF) attack is a type of traffic analysis attack. It enables a local and passive eavesdropper situated between the Tor client and the Tor entry node to deduce which websites the client is visiting. Currently, deep learning (DL) based WF attacks have overcome a number of proposed WF defenses, demonstrating superior performance compared to traditional machine learning (ML) based WF attacks. To mitigate this threat, we present WFGuard, a fuzzing-testing-based traffic morphing WF defense technique. WFGuard employs fine-grained neuron information within WF classifiers to design a joint optimization function and then applies gradient ascent to maximize both neurons value and misclassification possibility in DL-based WF classifiers. During each traffic mutation cycle, we propose a gradient based dummy traffic injection pattern generation approach, continuously mutating the traffic until a pattern emerges that can successfully deceive the classifier. Finally, the pattern present in successful variant traces are extracted and applied as defense strategies to Tor traffic. Extensive evaluations reveal that WFGuard can effectively decrease the accuracy of DL-based WF classifiers (e.g., DF and Var-CNN) to a mere 4.43%, while only incurring an 11.04% bandwidth overhead. This highlights the potential efficacy of our approach in mitigating WF attacks.

关键词： Deep learning Privacy Accuracy Neurons Bandwidth Fingerprint recognition Fuzzing

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Evolutionary Multilabel Adversarial Examples: An Effective Black-Box Attack

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第3期4卷 562-572页

作者： Kong, Linghao Luo, Wenjian Zhang, Hongwei Liu, Yang Shi, Yuhui Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Shenzhen518055 China Peng Cheng Laboratory Shenzhen518055 China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation School of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Studies have shown that deep neural networks (DNNs) are vulnerable to adversarial attack. Minor malicious modifications of examples will lead to the DNN misclassification. Such maliciously modified examples are called adversarial examples. So far, the work on adversarial examples is mainly focused on multiclass classification tasks;there is less work in the field of multilabel classification. In this article, for the first time, a differential evolution (DE) algorithm that can effectively generate multilabel adversarial examples is proposed, which is called MLAE-DE. Different from traditional DE, we designed a complementary mutation operator for MLAE-DE, which can improve attack performance and reduce the number of fitness evaluations. As a black-box attack, MLAE-DE does not need to access model parameters and only uses model outputs to generate adversarial examples. Experiments on two typical multilabel classification models and three typical datasets under the black-box settings are conducted in this article. Experimental results demonstrate that, comparing with the existing black-box attack algorithms for multilabel classification models, the attack success rate of our proposed algorithm is much better. © 2020 IEEE.

关键词： Deep neural networks

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A Differential Privacy Protection Protocol Based on Location Entropy

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Tsinghua science and technology 2023年第3期28卷 452-463页

作者： Ping Guo Baopeng Ye Yuling Chen Tao Li Yixian Yang Xiaobin Qian Xiaomei Yu State Key Laboratory of Public Big Data School of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China Information Technology Innovation Service Center of Guizhou Province Guiyang 550025China School of Cyberspace Security Beijing University of Posts and TelecommnuicationsBeijing 100000China Guizhou CoVision Science&Technology Co. Ltd.Guiyang 550025China School of Information Science and Engineering Shandong Normal UniversityJinan 250000China

A Location-Based Service(LBS)refers to geolocation-based services that bring both convenience and *** an increase in the scale and value of data,most existing location privacy protection protocols cannot balance privacy and *** solve the revealing problems in LBS,we propose a differential privacy protection protocol based on location ***,we design an algorithm of the best-assisted user selection for constructing anonymity ***,we employ smart contracts to evaluate the credibility of participants,which ensures the honesty of ***,we provide a comprehensive experiment;the theoretical analysis and experiments show that the proposed protocol effectively resists background knowledge ***,our protocol improves data ***,it realizes user-controllable privacy protection,which improves privacy protection and strengthens security.

关键词： Location-Based Services(LBS) smart contract location entropy differential privacy privacy protection

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Video Streaming Caching and Transcoding for Heterogeneous Mobile Users 29

Video Streaming Caching and Transcoding for Heterogeneous Mo...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Cai, Jinbo Xiao, Mingjun Sun, He Shao, Junjie Zhao, Yu Zhang, Tongxiao University of Science and Technology of China School of Computer Science and Technology Suzhou Institute for Advanced Study Hefei China Northeastern University at Qinhuangdao School of Computer and Communication Engineering China

ISBN: (纸本)9798350330717

With the increasing prevalence of video streaming media, users have higher requirements for the quality of video streams, which can be quantified as Quality of Experience (QoE). Caching and transcoding video chunks on edge servers are effective methods to improve users' QoE and reduce the cost of retrieving video chunks. In this paper, we investigate how to cache and transcode video chunks for mobile users to maximize their QoE, while taking into consideration the cooperation between edge servers, users' mobility, and heterogeneous preferences, simultaneously. We employ a Multi-Agent Reinforcement Learning (MARL) framework and propose an MARLbased Cache replacement and Transcoding (MACT) mechanism. More specifically, we formulate the problem as a multi-agent game and prove this game is a state-based potential game. Then, we decompose this multi-agent game into individual agent decision problems to be solved. Through extensive simulations, we demonstrate that the proposed MACT mechanism achieves about 19.3% performance improvement, compared to the state-of-the-art works. © 2023 IEEE.

关键词： edge caching multi-agent reinforcement learning quality of experience Video streaming

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Alzheimer's Disease Prediction from MRI image using Convolutional Deep Neural Network

Alzheimer's Disease Prediction from MRI image using Convolut...

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2024 International Conference on Intelligent Computing and Sustainable Innovations in technology, IC-SIT 2024

作者： Panda, Sanghamitra Sahu, Rekha Dash, Satya Ranjan Centurion University of Technology and Management Department of Computer Science and Engineering Odisha Bhubaneswar India Silicon University Department of Computer Science and Engineering Odisha Bhubaneswar India KIIT Deemed to Be University School of Computer Application Odisha Bhubaneswar India

ISBN: (纸本)9798350369175

Alzheimer's disease is one sort of neurological disorder which affects the daily activities and it is incurable. But, if it can be detected early, then it may be prevented or made to progress slowly. For detection, deep learning has played a great role now a days. In this paper, we have taken 6400 MRI images of both dementia and non-dementia subjects. The dataset is spilt into ${8 0 \%}$ for training a model and ${2 0 \%}$ for validating and accessing the achievement of the trained model. We have recommended a two-dimensional convolutional neural network architecture in order to apply on the MRI dataset to generate a model for classifying and predicting Alzheimer's disease patients. The model generated using the proposed two-dimensional architecture has given approximately $98 \%$ classification accuracy. Besides, we have presented roc-auc curve, f1-score, recall, support, precision and confusion matrix to show the execution of the model. Again, at training procedure, model loss and accuracy also depicted according to epoch times. The researches already performed on prediction of Alzheimer's disease has given poor performance of ${9 1 \%}$ classification accuracy in comparison to our model. © 2024 IEEE.

关键词： Convolutional neural networks

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