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International Conference on Electronics, Materials engineering and Nano-Technology (IEMENTech)

作者： Shafiul Islam Mazumder Sazzadul Islam Shovon Md Tanvir Chowdhury Minhajul Islam Habiba Azad Raisa Habibur Rahman Monjurul Islam Sumon Md. Khalid Mahbub Khan Department of Computer Science and Engineering American International University Dhaka Bangladesh Department of Information System Central Michigan University Michigan United States Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Computer Science and Engineering University of Information Technology & Sciences (UITS) Dhaka Bangladesh

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

The rapid growth of mobile applications, combined with an increasing reliance on these apps for a variety of purposes, has prompted serious concerns about user privacy and data security. This study aims to assess the privacy practices, data handling methods, and security vulnerabilities of mobile apps in a variety of categories, including social media, e-commerce, health and fitness, and financial management. This study identifies major privacy and security issues by examining privacy policies and terms of service, as well as how these apps handle sensitive data. The study also examines free and premium apps' privacy procedures, taking into account the implications for user data protection. The findings illustrate differences in data handling and security standards across different app categories, highlighting the importance of transparent privacy policies, strong security mechanisms, and limited data sharing to protect user information. The research helps to define standards and best practices for promoting secure app activity, increasing user confidence, and guaranteeing a safer digital environment.

关键词： Privacy Social networking (online) Data security Ecosystems Data protection Mobile applications Electronic commerce Protection Standards Best practices

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The Application of Regression Models to Market Rate of Return Prediction

The Application of Regression Models to Market Rate of Retur...

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2022 International Conference on Statistics, Data science, and Computational Intelligence, CSDSCI 2022

作者： Chen, Weizhi Fu, Jiawei Yang, Yu Yang, Zhenbang College of Computer Science and Technology Zhejiang University Hangzhou China College of Engineering and Applied Science University of Cincinnati 2901 Woodside Drive Cincinnati United States School of information and Electrical engineering Zhejiang University City College Hangzhou China School of Intelligent System engineering Sun Yat-sen University Shen Zhen China

ISBN: (纸本)9781510661325

Quant investing is very popular nowadays. To predict returns, there are lots of algorithm and models for financial market trading. In this case, with some new methods, quantitative researchers' ability to forecast an investment's return. In this paper, a new dataset obtained from a Kaggle competition called Ubiquant Market Prediction will be introduced and some classical method and model will be tested on it. This dataset has following characteristics. Firstly, its' target is return rate instead of the return, which means it could adapt to different scales of investment. Secondly, 300 dimensional anonymous features extracted are used in prediction so that this method could be applied in the prediction of different kinds of investments which have different kinds of features. Thirdly, this dataset considers the types and time periods of investment behavior. For the reasons above, the dataset is quite valuable. The classical methods tested in the experiment include Support Vector Regression (SVR), ensemble learning, k-Nearest Neighbor (KNN), Deep Neural Network (DNN), random forest, Stochastic Gradient Descent (SGD) regressor, passive aggressive regressor and Multilayer Perceptron (MLP) regressor. These methods are tested on this dataset and their performance are compared in the result and discussion section, aiming to figure out whether these classical approaches could have a good result in the dataset. © 2023 SPIE.

关键词： Investments

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The Power of Bamboo: On the Post-Compromise Security for Searchable Symmetric Encryption 30

The Power of Bamboo: On the Post-Compromise Security for Sea...

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30th Annual Network and Distributed system Security Symposium, NDSS 2023

作者： Chen, Tianyang Xu, Peng Picek, Stjepan Luo, Bo Susilo, Willy Jin, Hai Liang, Kaitai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Cluster and Grid Computing Lab School of Computer Science and Technology China Huazhong University of Science and Technology Wuhan430074 China Digital Security Group Radboud University Nijmegen Netherlands Department of EECS Institute of Information Sciences The University of Kansas LawrenceKS United States Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands

ISBN: (纸本)1891562835

Dynamic searchable symmetric encryption (DSSE) enables users to delegate the keyword search over dynamically updated encrypted databases to an honest-but-curious server without losing keyword privacy. This paper studies a new and practical security risk to DSSE, namely, secret key compromise (e.g., a user’s secret key is leaked or stolen), which threatens all the security guarantees offered by existing DSSE schemes. To address this open problem, we introduce the notion of searchable encryption with key-update (SEKU) that provides users with the option of non-interactive key updates. We further define the notion of post-compromise secure with respect to leakage functions to study whether DSSE schemes can still provide data security after the client’s secret key is compromised. We demonstrate that post-compromise security is achievable with a proposed protocol called "Bamboo". Interestingly, the leakage functions of Bamboo satisfy the requirements for both forward and backward security. We conduct a performance evaluation of Bamboo using a real-world dataset and compare its runtime efficiency with the existing forward-and-backward secure DSSE schemes. The result shows that Bamboo provides strong security with better or comparable performance. © 2023 30th Annual Network and Distributed system Security Symposium, NDSS 2023. All Rights Reserved.

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NASTCNN: a spatio-temporal convolutional neural network with non-local attention mechanism for image-induced brain activity pattern recognition

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Optoelectronics Letters 2025年

作者： TAN Xiangxin SHAO Mingxiong ZENG Ke GU Yue School of Computer Science and Engineering， Tianjin University of Technology Engineering Research Center of Learning-Based Intelligent System (Ministry of Education)， Tianjin University of Technology Krembil Research Institute， University Health Network

Deciphering the brain’s visual processing mechanisms has always been a hot topic. Using neural signal patterns to predict the corresponding external visual stimuli is crucial in understanding the interaction of real visual scenes. There are some challenges in the study of image-induced brain activity pattern recognition based on electroencephalogram (EEG)， e.g. how to fully utilize the effective features of EEG signals to improve the classification， and how the frequency and the length of EEG effect the model performance. We design an experimental paradigm that minimizes the interaction between visual stimuli， and propose a spatio-temporal convolutional neural network with non-local attention mechanism (NASTCNN) for image-induced brain activity pattern recognition. The framework utilizes non-local feature extraction operations to realize global information interaction between EEG signals over long distances and enhance the feature expression of some important information. Our model achieves the best performance on both MNIST-EEG and ImageNet-EEG datasets. The classification results are 24.00% and 50.10% respectively， higher than the random probability. We also analyze the effects of different EEG frequency bands and time subsequences of different lengths on the classification performance and which brain regions are activated over time. The results show that when full frequency band and entire length sequence are selected， the classification result is optimal. The occipital region is essential for processing visual tasks， and the temporal region and some frontal regions also play important role in the later stage of visual decoding. This research can help us better understand the visual processing mechanism of the brain.

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Multi-agent Deep Reinforcement Learning Based Multi-objective Charging Control for Electric Vehicle Charging Station 7th

Multi-agent Deep Reinforcement Learning Based Multi-objectiv...

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7th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2023

作者： Gong, Jiabao Fu, Weiming Kang, Yu Qin, Jiahu Xiao, Feng Department of Automation University of Science and Technology of China Hefei230027 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University Beijing102206 China

ISBN: (纸本)9789819733279

This paper proposes a multi-agent deep reinforcement learning (MADRL) based algorithm for charging control of multiple electric vehicles (EVs) in an electric vehicle charging station (EVCS) with dynamic operations. By taking the uncertainties of EV users’ behaviors and charging prices into consideration, a Markov game with multiple objectives is first established, where the individual objective of reducing EV charging costs and the collective objective of mitigating charging load fluctuations are both studied. Then, a multi-agent actor-double-critic (MAADC) algorithm with a unified advantage actor-critic framework is proposed, where two advantage functions are designed as the local and global critics, and the integrated actor is employed to balance the learning towards the individual and collective objectives. The simulation results obtained in a delicately built EVCS environment show the effectiveness of MAADC. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reinforcement learning

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Political Optimizer with Deep Learning-Enabled Tongue Color Image Analysis Model

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computer systems science & engineering 2023年第5期45卷 1129-1143页

作者： Anwer Mustafa Hilal Eatedal Alabdulkreem Jaber S.Alzahrani Majdy M.Eltahir Mohamed I.Eldesouki Ishfaq Yaseen Abdelwahed Motwakel Radwa Marzouk Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMecca24382Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt

Biomedical image processing is widely utilized for disease detection and classification of biomedical *** color image analysis is an effective and non-invasive tool for carrying out secondary detection at anytime and *** removing the qualitative aspect,tongue images are quantitatively inspected,proposing a novel disease classification model in an automated way is *** article introduces a novel political optimizer with deep learning enabled tongue color image analysis(PODL-TCIA)*** presented PODL-TCIA model purposes to detect the occurrence of the disease by examining the color of the *** attain this,the PODL-TCIA model initially performs image pre-processing to enhance medical image *** by,Inception with ResNet-v2 model is employed for feature ***,political optimizer(PO)with twin support vector machine(TSVM)model is exploited for image classification process,shows the novelty of the *** design of PO algorithm assists in the optimal parameter selection of the TSVM *** ensuring the enhanced outcomes of the PODL-TCIA model,a wide-ranging experimental analysis was applied and the outcomes reported the betterment of the PODL-TCIA model over the recent approaches.

关键词： Tongue color image analysis political optimizer twin support vector machine inception model deep learning

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Video image mosaic via multi-module cooperation

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Optoelectronics Letters 2021年第11期17卷 688-692页

作者： ZHANG Zhe XUE Wanli Engineering Research Center of Learning-Based Intelligent System Ministry of EducationSchool of Computer Science and EngineeringTianjin University of TechnologyTianjin 500384China

We propose a video image mosaic method based on multi-module cooperation. This method stitches the video into a panorama with a large field of view, divided into three modules: the key frame selection module, the image mosaic module, and the optimization module. The key frame selection module obtains key frames by comprehensively evaluating the overlap rate and image quality. The image mosaic module stitches the key frames into a panoramic image to generate an initial mosaic result. The optimization module makes the mosaic result more natural and eliminates ghosts by using object detection advantages. Our method is tested on videos taken in real scenes, and the results have a more comprehensive and natural description.

关键词： frame image Video image mosaic via multi-module cooperation

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X-Stream: A Flexible, Adaptive Video Transformer for Privacy-Preserving Video Stream Analytics

X-Stream: A Flexible, Adaptive Video Transformer for Privacy...

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

作者： Dou Feng Lin Wang Shutong Chen Lingching Tung Fangming Liu 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 Huazhong University of Science and Technology China 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 Paderborn University Germany 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 Guangxi University China 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 Peng Cheng Laboratory China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Video stream analytics (VSA) systems fuel many exciting applications that facilitate people’s lives, but also raise critical concerns about exposing too much individuals’ privacy. To alleviate these concerns, various frameworks have been presented to enhance the privacy of VSA systems. Yet, existing solutions suffer two limitations: (1) being scenario-customized, thus limiting the generality of adapting to multifarious scenarios, (2) requiring complex, imperative programming, and tedious process, thus largely reducing the usability of such systems. In this paper, we present X-Stream, a privacy-preserving video transformer that achieves flexibility and efficiency for a large variety of VSA tasks. X-Stream features three major novel designs: (1) a declarative query interface that provides a simple yet expressive interface for users to describe both their privacy protection and content exposure requirements, (2) an adaptation mechanism that dynamically selects the most suitable privacy-preserving techniques and their parameters based on the current video context, and (3) an efficient execution engine that incorporates optimizations for multi-task deduplication and inter-frame inference. We implement X-Stream and evaluate it with representative VSA tasks and public video datasets. The results show that X-Stream achieves significantly improved privacy protection quality and performance over the state-of-the-art, while being simple to use.

关键词： Privacy Transforms Streaming media Transformers Multitasking Real-time systems Task analysis

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UWM-Net: A Mixture Density Network Approach with Minimal Dataset Requirements for Underwater Image Enhancement

UWM-Net: A Mixture Density Network Approach with Minimal Dat...

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Artificial Intelligence (CAI), IEEE Conference on

作者： Jun Huang Zongze Li Ruihao Zheng Zhenkun Wang School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

The learning-based underwater image enhancement, which is suitable for batch processing, is a pivotal research direction in underwater image processing. Extensive paired image data are required in existing learning-based methods, which necessitate considerable preprocessing and hinder the application of these methods. To address these limitations, we propose a semi-supervised approach called UWM-Net: firstly, we use a compact dataset of underwater image pairs to train the Mixture Density Network (MDN) with an underwater scene setting; subsequently, U-Net can learn underwater image enhancement more efficiently. The MDN can transform standard images into underwater scenes, reducing the reliance on paired data and making much smaller training datasets. In experimental studies, UWM-Net using only 18 pairs of underwater image data achieves highly competitive results in terms of 3 metrics compared with advanced models.

关键词： Measurement Training Learning systems Image quality Image color analysis Refining Training data

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A Cost-Effective IoT Device for Protecting People from the Dangers of Environmental Risk in Bangladesh 8

A Cost-Effective IoT Device for Protecting People from the D...

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8th IEEE International Conference on Computing, engineering and Design, ICCED 2022

作者： Rimi, Mahmuda Mahfuz Ahmed, Md Rasel Hossan, Sakhawat Rahaman, Md Masudur Hossen Faysal, Md Anowar Ahmed Foysal, Md. Ferdouse Daffodil International University Department of Computer Information System Dhaka Bangladesh Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh Fiber@Home Ltd Dhaka Bangladesh

ISBN: (纸本)9781665453899

The factors of population growth, unplanned urbanization, vehicles, factories, and brick kilns are constantly making air quality concerns. In addition, due to lack of public awareness, people are continually facing various kinds of unintended accidents, for example, gas cylinder poisoning. The main objective of our paper is to create a low-cost Internet of Things (IoT) based system that can measure atmospheric air quality and gas leakage. Sensitive data will be stored on the server via the Internet of Things (IoT) device, which will visualize the data from the server via the website and the Android app. Moreover, by understanding the state of the environment, the user will be informed about the environment through Android apps and websites to protect himself from the grip of air pollution and protect himself before an accident like a gas cylinder. © 2022 IEEE.

关键词： Cost effectiveness

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