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2024 Ieee Global Communications Conference, GLObeCOM 2024

作者： Yu, Yantao Guan, Yunguo Xue, Xiaoping Lu, Rongxing Tongji University College of Electronic and Information Engineering Shanghai201804 China Eastern Michigan University School of Information Security and Applied Computing YpsilantiMI48197 United States University of New Brunswick Faculty of Computer Science FrederictionNBE3B 5A3 Canada

ISBN: (纸本)9798350351255

Aggregation query, which is one of the most crucial data analysis tools applied to vehicular crowdsensed data, is expected to extract valuab.e insights and support smart decision-making. Meanwhile, the edge servers are deployed to cope with the escalation of service scale, which however raises privacy concerns about the query requests and reported data. Previously reported privacy-preserving aggregation query schemes are either tailored for static datasets or lack an index structure for deployed queries, which makes them impractical in vehicular crowdsensing (VCS) scenarios characterized by large volumes of real-time data and high data update frequencies. To tackle these challenges, we propose an efficient privacy-preserving edge-based dynamic aggregation query scheme with an encrypted tree-based index. Concretely, we first design two building blocks, namely the balanced spatial encoding quadtree (b.eQtree) and a predicate encryption scheme for membership testing (PeMT), which enab.e the edge servers to obliviously match data and queries by traversal over the encrypted b.eQtree. based on the above blocks and arithmetic secret sharing (ASS), we construct our scheme, in which the edge servers can efficiently and securely aggregate newly reported data to related query results. Our security analysis shows the privacy preservation of our scheme, and the experiment results on a real dataset validate the efficiency of our scheme. © 2024 Ieee.

关键词： Data aggregation

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AN AUTOeNCODeR AND GeNeRATIVe ADVeRSARIAL NeTWORKS APPROACH FOR MULTI-OMICS DATA IMbALANCeD CLASS HANDLING AND CLASSIFICATION

arXiv

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arXiv 2024年

作者： Al-Hurani, Ibrahim Alkhateeb, Abedalrhman Ikki, Salama Lakehead University Department of Electrical and Computer Engineering Thunder BayONP7B 5E1 Canada Lakehead University Department of Computer Science Thunder BayONP7B 5E1 Canada

In the relentless efforts in enhancing medical diagnostics, the integration of state-of-the-art machine learning methodologies has emerged as a promising research area. In molecular biology, there has b.en an explosion of data generated from multi-omics sequencing. The advent sequencing equipment can provide large number of complicated measurements per one experiment. Therefore, traditional statistical methods face challenging tasks when dealing with such high dimensional data. However, most of the information contained in these datasets is redundant or unrelated and can be effectively reduced to significantly fewer variab.es without losing much information. Dimensionality reduction techniques are mathematical procedures that allow for this reduction;they have largely b.en developed through statistics and machine learning disciplines. The other challenge in medical datasets is having an imbalanced number of samples in the classes, which leads to biased results in machine learning models. This study, focused on tackling these challenges in a neural network that incorporates autoencoder to extract latent space of the features, and Generative Adversarial Networks (GAN) to generate synthetic samples. Latent space is the reduced dimensional space that captures the meaningful features of the original data. Our model starts with feature selection to select the discriminative features before feeding them to the neural network. Then, the model predicts the outcome of cancer for different datasets. The proposed model outperformed other existing models by scoring accuracy of 95.09% for bladder cancer dataset and 88.82% for the b.east cancer dataset. © 2024, CC bY.

关键词： Classification (of information)

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Secure Transmission Design for Aerial IRS Assisted Wireless Networks

Secure Transmission Design for Aerial IRS Assisted Wireless ...

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作者： Wei, Wenjing Pang, Xiaowei Tang, Jie Zhao, Nan Wang, Xianbin Nallanathan, Arumugam Dalian University of Technology School of Information and Communication Engineering Dalian116024 China South China University of Technology School of Electronic and Information Engineering Guangzhou510640 China Western University Department of Electrical and Computer Engineering LondonONN6A 5B9 Canada Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom

Combining intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) offers a new degree of freedom to improve the coverage performance. However, it is more challenging to secure the air-ground transmission, due to the line-of-sight (LoS) links established by UAV. Since IRS is a promising solution for wireless environment reconfiguration, in this paper, we propose an aerial IRS-assisted secure transmission design in wireless networks. In particular, an access point (AP) equipped with a uniform planar array serves several single-antenna legitimate users in the presence of multiple single-antenna eavesdroppers, whose precise positions are unknown. An IRS is mounted on the UAV to help establish desired virtual LoS links between the AP and legitimate users, while ensuring their security. We aim to maximize the worst-case sum secrecy rate by jointly optimizing the hovering position of UAV, the transmit beamforming of AP and the phase shifts of IRS, sub.ect to the requirement of minimum rate for legitimate users. To tackle this non-convex prob.em, we first decompose it into three subprob.ems, which are transformed into convex ones via successive convex approximation. An alternating algorithm is then proposed to solve them iteratively. Simulation results show the effectiveness of the proposed scheme and the security improvement by the joint optimization. © 1972-2012 Ieee.

关键词： Wireless networks

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backdoor Attacks against Image-to-Image Networks

arXiv

引用

arXiv 2024年

作者： Jiang, Wenbo Li, Hongwei He, Jiaming Zhang, Rui Xu, Guowen Zhang, Tianwei Lu, Rongxing The School of Computer Science and Engineering University of Electronic Science and Technology of China China The School of Computer Science and Cyber Security Chengdu University of Technology China City University of Hong Kong Hong Kong The School of Computer Science and Engineering Nanyang Technological University Singapore The Faculty of Computer Science University of New Brunswick FrederictonNBE3B 5A3 Canada

Recently, deep learning-based Image-to-Image (I2I) networks have become the predominant choice for I2I tasks such as image super-resolution and denoising. Despite their remarkab.e performance, the backdoor vulnerability of I2I networks has not b.en explored. To fill this research gap, we conduct a comprehensive investigation on the susceptibility of I2I networks to backdoor attacks. Specifically, we propose a novel backdoor attack technique, where the compromised I2I network behaves normally on clean input images, yet outputs a predefined image of the adversary for malicious input images containing the trigger. To achieve this I2I backdoor attack, we propose a targeted universal adversarial perturbation (UAP) generation algorithm for I2I networks, where the generated UAP is used as the backdoor trigger. Additionally, in the backdoor training process that contains the main task and the backdoor task, multitask learning (MTL) with dynamic weighting methods is employed to accelerate convergence rates. In addition to attacking I2I tasks, we extend our I2I backdoor to attack downstream tasks, including image classification and ob.ect detection. extensive experiments demonstrate the effectiveness of the I2I backdoor on state-of-the-art I2I network architectures, as well as the robustness against different mainstream backdoor defenses. Copyright © 2024, The Authors. All rights reserved.

关键词： Ob.ect detection

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enhancing Wireless Sensor Network Longevity and Security: A Quad-LeACH Approach

Enhancing Wireless Sensor Network Longevity and Security: A ...

引用

emerging Technologies in computer science for Interdisciplinary Applications (ICeTCS), International Conference on

作者： J J Lohith S Shreya J L Hamsa Priya Dept. of Computer Science and Engineering B.M.S. College of Engineering Bangalore India Dept. of Information Science and Engineering D.S.C.E. Bangalore India Dept. of Electronics and Telecommunications B.M.S. College of Engineering Bangalore India

ISBN: (数字)9798350372502

ISBN: (纸本)9798350372519

Researchers have increasingly focused on Wireless Sensor Networks (WSNs) in recent years, recognizing their versatile applications. The primary focus within WSNs is enhancing network longevity for efficient resource utilization, coupled with a strong emphasis on security and authentication. This paper specifically delves into the implementation of Quad LeACH for homogeneous networks, aiming to substantially improve network lifetime, stability, and throughput. The proposed protocol achieves this by employing a cluster head selection process and integrating security and authentication measures between cluster heads in the Quad-LeACH routing protocol. The research addresses the integration of a security agent within WSNs using the NS-2 tool, contributing to enhanced authentication. The implementation is carried out through the simulation software NS 2, where the security agent is seamlessly combined with the WSN infrastructure. The ob.ective of this research is to incorporate a reliab.e authentication and security agent into NS-2 in conjunction with the LeACH protocol, which is the standard for wireless sensors networks (WSNs). The efficacy of the suggested methodology will be validated through simulated outcomes.

关键词： Wireless sensor networks Authentication Throughput Stability analysis Software Routing protocols Software reliability

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Unlocking efficiency in Agricultural Supply Chains: A Secure and Transparent Approach through blockchain Technology

Unlocking Efficiency in Agricultural Supply Chains: A Secure...

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emerging Technologies in computer science for Interdisciplinary Applications (ICeTCS), International Conference on

作者： J J Lohith S Shreya J L Hamsa Priya Dept. of Computer Science and Engineering B.M.S. College of Engineering Bangalore India Dept. of Information Science and Engineering D.S.C.E Bangalore India Dept. of Electronics and Telecommunications B.M.S. College of Engineering Bangalore India

ISBN: (数字)9798350372502

ISBN: (纸本)9798350372519

Supply Chain Management (SCM) involves orchestrating various processes that extend from acquiring raw materials to delivering finished products to end consumers. The key ob.ectives of a proposed supply chain in agriculture, emphasizes information-driven processes, cost reduction, and customer impact. It creates a versatile system capab.e of managing the supply chain for diverse commodities within a country, while also addressing the crucial need for increased transparency and traceability in data throughout the chain of activities. In our proposed approach to agricultural supply chain management (SCM), the focus is on creating a secure, transparent, and traceab.e SCM. To achieve this, we leverage cutting-edge technologies like the ethereum blockchain and the Interplanetary File System (IPFS). The resulting software is a Decentralized Application (DApp) that orchestrates a series of activities within the agricultural industry, using cryptocurrencies to establish a seamless and efficient supply chain management system. This software platform is fortified by the secure algorithms of blockchain, providing an added layer of reliability. Additionally, our application incorporates transparency and traceability, ensuring user-friendly functionality and making it more accessib.e for users.

关键词： Supply chain management Costs Supply chains Software algorithms Decentralized applications Software blockchains

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Reinforcement Learning With Selective exploration for Interference Management in mmWave Networks

IEEE Transactions on Machine Learning in Communications and ...

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Ieee Transactions on Machine Learning in Communications and Networking 2025年 3卷 280-295页

作者： Dinh-Van, Son Nguyen, van-Linh Cebecioglu, berna bulut Masaracchia, Antonino Higgins, Matthew D. The University of Warwick School of Engineering Warwick Manufacturing Group CoventryCV4 7AL United Kingdom Advanced Institute of Manufacturing With High-Tech Innovations Chiayi Minxiong621301 Taiwan Birmingham City University School of Computing and Digital Technology BirminghamB4 7XG United Kingdom Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom

The next generation of wireless systems will leverage the millimeter-wave (mmWave) bands to meet the increasing traffic volume and high data rate requirements of emerging applications (e.g., ultra HD streaming, metaverse, and holographic telepresence). In this paper, we address the joint optimization of beamforming, power control, and interference management in multi-cell mmWave networks. We propose novel reinforcement learning algorithms, including a single-agent-based method (bPC-SA) for centralized settings and a multi-agent-based method (bPC-MA) for distributed settings. To tackle the high-variance rewards caused by narrow antenna beamwidths, we introduce a selective exploration method to guide the agent towards more intelligent exploration. Our proposed algorithms are well-suited for scenarios where beamforming vectors require control in either a discrete domain, such as a codebook, or in a continuous domain. Furthermore, they do not require channel state information, extensive feedback from user equipments, or any searching methods, thus reducing overhead and enhancing scalability. Numerical results demonstrate that selective exploration improves per-user spectral efficiency by up to 22.5% compared to scenarios without it. Additionally, our algorithms significantly outperform existing methods by 50% in terms of per-user spectral effciency and achieve 90% of the per-user spectral efficiency of the exhaustive search approach while requiring only 0.1% of its computational runtime. © 2025 The Authors.

关键词： Reinforcement learning

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Automatic Plane Pose estimation for Cardiac Left Ventricle Coverage estimation via Deep Adversarial Regression Network

IEEE Transactions on Artificial Intelligence

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Ieee Transactions on Artificial Intelligence 2024年第9期5卷 4738-4752页

作者： Zhang, Le bronik, Kevin Piechnik, Stefan K. Lima, Joao A. C. Neubauer, Stefan Petersen, Steffen e. Frangi, Alejandro F. University of Birmingham School of Engineering College of Engineering and Physical Sciences BirminghamB15 2TT United Kingdom University of Oxford Department of Engineering Science OxfordOX1 3PJ United Kingdom Division of Cardiovascular Medicine OxfordOX3 9DU United Kingdom Johns Hopkins University School of Medicine Division of Cardiology BaltimoreMD21287 United States Queen Mary University of London Centre for Cardiovascular Medicine The William Harvey Research Institute LondonE1 1BB United Kingdom Barts Health Nhs Trust Barts Heart Center LondonE1 1BB United Kingdom The University of Manchester Division of Informatics Imaging and Data Science School of Health Sciences The Department of Computer Science School of Engineering ManchesterM13 9PL United Kingdom

Accurate segmentation of the ventricles plays a crucial role in determining cardiac functional parameters such as ventricular volume, ventricular mass, and ejection fraction. However, poor image quality, such as inadequate coverage of the left ventricle (LV) and right ventricle (RV) in cardiac magnetic resonance (CMR) image sequences, can significantly affect the assessment of cardiac function. This study investigates issues related to missing or corrupted imaging planes, which often lead to incomplete ventricle coverage. To address the challenge of estimating ventricle coverage in CMR images regardless of variations in imaging parameters such as device type, magnetic field strength, and protocol execution, we introduce a novel convolutional neural network (CNN) based on adversarial learning. Additionally, we integrate supplementary information (e.g., cross-view image data) as privileged information to enhance the interpretability of our model's predictions and identify potential biases or inaccuracies. This research represents the first attempt to automatically estimate ventricular coverage by identifying missing slices and plane orientations in CMR images using a dataset-agnostic approach. The effectiveness of the proposed model is demonstrated through the evaluation of datasets from three diverse and sizab.e image acquisition cohorts, demonstrating superior performance compared to existing methods. © 2020 Ieee.

关键词： Image quality

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TrustChain: A blockchain Framework for Auditing and Verifying Aggregators in Decentralized Federated Learning

arXiv

引用

arXiv 2025年

作者： Hallaji, ehsan Razavi-Far, Roozbeh Saif, Mehrdad Department of Electrical and Computer Engineering University of Windsor WindsorONN9B 3P4 Canada Faculty of Computer Science University of New Brunswick FrederictonNBE3B 5A3 Canada

The server-less nature of Decentralized Federated Learning (DFL) requires allocating the aggregation role to specific participants in each federated round. Current DFL architectures ensure the trustworthiness of the aggregator node upon selection. However, most of these studies overlook the possibility that the aggregating node may turn rogue and act maliciously after being nominated. To address this prob.em, this paper proposes a DFL structure, called TrustChain, that scores the aggregators before selection based on their past behavior and additionally audits them after the aggregation. To do this, the statistical independence between the client updates and the aggregated model is continuously monitored using the Hilbert-Schmidt Independence Criterion (HSIC). The proposed method relies on several principles, including blockchain, anomaly detection, and concept drift analysis. The designed structure is evaluated on several federated datasets and attack scenarios with different numbers of byzantine nodes. © 2025, CC bY-NC-ND.

关键词： Federated learning

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Applications in Home Improvement Retailer, Koctas 2

Applications in Home Improvement Retailer, Koctas

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2nd International Conference on business Analytics for Technology and Security, ICbATS 2023

作者： Mourad, Nahia Raza, Hammad Amjad, Muhamamd Ullah, Muhammed Ubaid Sabri elmitwally, Nouh University in Dubai College of Engineering & IT British Dubai United Arab Emirates College of Business Administration and Economics Department of Computer Science National Lahore Pakistan NCBA&E Lahore School of Computer Science Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan Minhaj University Lahore Pakistan Birmingham City University School of Computing and Digital Technology BirminghamB4 7XG United Kingdom Cairo University Faculty of Computers and Artifcial Intelligence Department of Computer Science Giza12613 Egypt

ISBN: (纸本)9798350335644

It sounds like Koçtaş is a leader in the home improvement sector in Turkey, and they are focused on providing the best service and customer experience possib.e. They are also actively working to accelerate their digital investments and use their vast amount of customer data to innovate in the industry. One way they are using this data is by collecting and analyzing video camera images using AI. This allows them to detect humans and identify which products and shelves are most viewed in their stores. This information can then be used to optimize store layout and product placement for a better customer experience. Another way Koçtaş is innovating is through the implementation of kiosks that use Natural Language Processing (NLP) to interact with customers. These kiosks can understand and respond to questions asked by customers using AI, providing a more personalized and human-like ***, Koçtaş is using Dynamic Creative Optimization to create personalized advertisements for their customers. This method allows them to optimize the content and format of their ads based on the individual preferences and behavior of their customers, leading to more effective ***, Koçtaş is using technology and data to drive innovation and provide a better customer experience in the home improvement industry. © 2023 Ieee.

关键词： Video cameras

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