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23rd International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2024

作者： Yang, Guoyin Zhang, Haotian Yao, Junfeng Cai, Chengyou Song, Jun Xu, Yuelin Deng, Bin School of Computer Engineer and Science Shanghai University Shanghai China The 32nd Research Institute of China Electronics Technology Group Corporation China CSSC Seago System Technology Co. Ltd. Shanghai200010 China Department of Geography Hong Kong Baptist University Hong Kong Science and Technology on Near-Surface Detection Laboratory Wuxi China

ISBN: (纸本)9781643685380

Advances in computer science and technology, coupled with the growing societal demand for enhanced safety, have paved the way for innovative smart home environments. While current smart home systems can send alarms and notify emergency contacts during a crisis, they do not provide timely and convenient on-site emergency assistance, potentially delaying critical aid and compromising user safety. This paper introduces the Intelligent Collaborative Emergency system (ICERS), which addresses this gap through three integrated subsystems: Fall Detection, Vital Signs Monitoring, and Central Dispatch. The Fall Detection module leverages camera video data to classify human positions and estimate potential falls. The Vital Signs Monitoring module employs millimeter-wave radar technology to provide non-contact, real-time health monitoring. The Central Dispatch system receives alert signals from on-edge devices and relays them to medical facilities and volunteer organizations for a coordinated emergency response. By utilizing multimodal data for collaborative efforts, the ICERS ensures that individuals receive timely notifications and coordinated emergency assistance during unforeseen situations. © 2024 IOS Press. All rights reserved.

关键词： Fall detection

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Imaging flow cytometry with a real-time throughput beyond 1,000,000 events per second

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Light(science & Applications) 2025年第4期14卷 931-947页

作者： Jiehua Zhou Liye Mei Mingjie Yu Xiao Ma Dan Hou Zhuo Yin Xun Liu Yan Ding Kaining Yang Ruidong Xiao Xiandan Yuan Yueyun Weng Mengping Long Taobo Hu Jinxuan Hou Yu Xu Liang Tao Sisi Mei Hui Shen Yaxiaer Yalikun Fuling Zhou Liang Wang Du Wang Sheng Liu Cheng Lei The Institute of Technological Sciences Wuhan UniversityWuhan 430072China School of Computer Science Hubei University of TechnologyWuhan 430068China Division of Materials Science Nara Institute of Science and TechnologyTakayama-cho 8916-5Japan School of Science Hubei University of TechnologyWuhan 430068China Department of Pathology Peking University Cancer HospitalBeijing 100142China Department of Breast Surgery Peking University People’s HospitalBeijing 100044China Department of Thyroid and Breast Surgery Zhongnan HospitalWuhan UniversityWuhan 430071China Department of Radiation and Medical Oncology Zhongnan HospitalWuhan UniversityWuhan 430071China People’s Hospital of Anshun City Guizhou Province Anshun 561000China Department of Hematology Zhongnan HospitalWuhan UniversityWuhan 430071China National Engineering Laboratory for Next Generation Internet Access System School of Optics and Electronic InformationHuazhong University of Science and TechnologyWuhan 430074China Suzhou Institute of Wuhan University Suzhou 215000China Shenzhen Institute of Wuhan University Shenzhen 518057China

Imaging flow cytometry(IFC)combines the imaging capabilities of microscopy with the high throughput of flow cytometry,offering a promising solution for high-precision and high-throughput cell analysis in fields such as biomedicine,green energy,and environmental ***,due to limitations in imaging framerate and realtime data processing,the real-time throughput of existing IFC systems has been restricted to approximately 1000-10,000 events per second(eps),which is insufficient for large-scale cell *** this work,we demonstrate IFC with real-time throughput exceeding 1,000,000 eps by integrating optical time-stretch(OTS)imaging,microfluidic-based cell manipulation,and online image *** flowing at speeds up to 15 m/s are clearly imaged with a spatial resolution of 780 nm,and images of each individual cell are captured,stored,and *** capabilities and performance of our system are validated through the identification of malignancies in clinical colorectal *** work sets a new record for throughput in imaging flow cytometry,and we believe it has the potential to revolutionize cell analysis by enabling highly efficient,accurate,and intelligent measurement.

关键词： real time data processing flow cytometryoffering spatial resolution imaging flow cytometry ifc combines high throughput cell analysis cell analysis imaging flow cytometry optical time stretch imaging

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Privacy-preserving data aggregation achieving completeness of data queries in smart grid

Privacy-preserving data aggregation achieving completeness o...

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作者： Li, Xinyang Zhao, Meng Ding, Yong Yang, Changsong Wang, Huiyong Liang, Hai Wang, Yujue Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin University of Electronic Technology Guilin China Hangzhou Innovation Institute of Beihang University Hangzhou China Institute of Cyberspace Technology HKCT Institute for Higher Education Hong Kong School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin China

In smart grid systems, the control center formulates strategies and provides services by analyzing electricity consumption data. However, ensuring the privacy and security of user data is a critical concern. While traditional data aggregation schemes can provide a certain level of privacy protection for users, they also impose limitations on the control center's access to fine-grained data. To address these challenges, we propose a privacy-preserving data aggregation scheme supporting data query (PAQ). We designed a multi-level data aggregation mechanism based on Paillier semi-homomorphic encryption to achieve efficient aggregation of user data in the control center. Additionally, a data query mechanism based on electricity consumption intervals is introduced, allowing the control center to query aggregated ciphertexts for different user categories from outsourced data on the cloud server. Security analysis demonstrates that PAQ design effectively solves security issues in data aggregation and query processes. Performance analysis indicates that the proposed scheme outperforms existing solutions in terms of efficiency. © 2024 John Wiley & Sons, Ltd.

关键词： Smart power grids

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Investigating the Potential of AI-Driven Data Analytics for eARMS: A Survey-Based Research 7

Investigating the Potential of AI-Driven Data Analytics for ...

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7th International Conference on Internet Applications, Protocols, and services, NETAPPS 2024

作者： Khan, Nauman Ali Bashir, Ijaz Khan, Mudassar Ali Wazir, Waqas Din, Ikram Ud Hassan, Suhaidi Department of CSE Military College of Signals National University of Sciences & Technology Islamabad Pakistan Department of Information Technology The University of Haripur Pakistan Department of Computer Science COMSATS University Islamabad Islamabad Pakistan InterNetWorks Research Laboratory School of Computing Universiti Utara Malaysia UUM Sintok06010 Malaysia

ISBN: (纸本)9798331519513

The aim of this paper is on the role of AI powered data analysis in the decision-making processes of information systems such as ERP systems. We reviewed the literature, technological aspects, and case studies that pertain to the area of embedding AI analytics into ERP systems, or in any compliance with the ERP design. We consider the weights and downsides of the suggested AI analytics sales model, potential ethical issues, privacy concerns, other technical issues, and foresight research. We focus on bridging this information gap by increasing the knowledge of intersection analysis and artificial intelligence that structures ERP systems, describing how ERP systems can benefit along with the shortcomings in this area and how they can be addressed in further research. At the same time, we address the practical aspects of the implementation of AI-guided methods in everyday business activities and evaluate their applicability to various sectors. By analyzing the latest trends, we intend to add value to the existing body of knowledge by addressing emerging opportunities and challenges in the effective utilization of AI in enterprise systems. Our review also indicates the necessity of a cross-discipline agenda in order to resolve current issues and shape the growth of this fast-changing domain. © 2024 IEEE.

关键词： Data Analytics

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A Four-Pronged Defense Against Byzantine Attacks in Federated Learning

arXiv

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arXiv 2023年

作者： Wan, Wei Hu, Shengshan Li, Minghui Lu, Jianrong Zhang, Longling Zhang, Leo Yu Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia 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 Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab

Federated learning (FL) is a nascent distributed learning paradigm to train a shared global model without violating users' privacy. FL has been shown to be vulnerable to various Byzantine attacks, where malicious participants could independently or collusively upload well-crafted updates to deteriorate the performance of the global model. However, existing defenses could only mitigate part of Byzantine attacks, without providing an all-sided shield for FL. It is difficult to simply combine them as they rely on totally contradictory assumptions. In this paper, we propose FPD, a four-pronged defense against both non-colluding and colluding Byzantine attacks. Our main idea is to utilize absolute similarity to filter updates rather than relative similarity used in existingI works. To this end, we first propose a reliable client selection strategy to prevent the majority of threats in the bud. Then we design a simple but effective score-based detection method to mitigate colluding attacks. Third, we construct an enhanced spectral-based outlier detector to accurately discard abnormal updates when the training data is not independent and identically distributed (non-IID). Finally, we design update denoising to rectify the direction of the slightly noisy but harmful updates. The four sequentially combined modules can effectively reconcile the contradiction in addressing non-colluding and colluding Byzantine attacks. Extensive experiments over three benchmark image classification datasets against four state-of-the-art Byzantine attacks demonstrate that FPD drastically outperforms existing defenses in IID and non-IID scenarios (with 30% improvement on model accuracy). © 2023, CC BY.

关键词： Classification (of information)

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Stealthy Backdoor Attack Towards Federated Automatic Speaker Verification

Stealthy Backdoor Attack Towards Federated Automatic Speaker...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Longling Zhang Lyqi Liu Dan Meng Jun Wang Shengshan Hu School of Cyber Science and Engineering Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security OPPO Research Institute China

Automatic speech verification (ASV) authenticates individuals based on distinct vocal patterns, playing a pivotal role in many applications such as voice-based unlocking systems for devices. The ASV system comprises three stages: training, registration, and validation. The model refines using voice data in training, extracts vocal features in registration, and contrasts these with speech patterns in validation. Modern ASV models, primarily grounded in DNN architectures, require extensive data for training. Federated learning (FL) fosters model-sharing across multiple clients while ensuring data privacy. Due to its open architecture, FL is vulnerable to backdoor attacks. However, training a stealthy backdoor attack in FL presents challenges, including diminished attack generalization owing to data heterogeneity, and conspicuous triggers that render them easily detectable. In this paper, we propose a Federated Stealthy Backdoor Attack method ($FedSBA$). FedSBA aims to improve the attack model’s generalization, enhance its persistence, and elude anomaly detection under the heterogeneous data distribution. FedSBA constructs an attack model based on a personalized transformer and encompasses a stealthy trigger. Moreover, we also propose a defensive strategy that utilizes an adaptive weight aggregation scheme. The stealthiness and effectiveness of FedSBA are demonstrated by exhibiting superior performance in comparison to previous works.

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Adaptive Data Transport Mechanism for UAV Surveillance Missions in Lossy Environments 22

Adaptive Data Transport Mechanism for UAV Surveillance Missi...

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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Mehrabi, Niloufar Boroujeni, Sayed Pedram Haeri Hofseth, Jenna Razi, Abolfazl Cheng, Long Kaur, Manveen Martin, James Amin, Rahul School of Computing Clemson University ClemsonSC United States California State University Computer Science Department Los AngelesCA United States Massachusetts Institute of Technology Lincoln Laboratory LexingtonMA United States

ISBN: (纸本)9798331508050

Unmanned Aerial Vehicles (UAVs) play an increasingly critical role in Intelligence, Surveillance, and Reconnaissance (ISR) missions such as border patrolling and criminal detection due to their ability to access remote areas and transmit real-time imagery to servers. However, UAVs face limitations in payload, power, and communication bandwidth, necessitating selective data transmission strategies. While traditional methods strive to preserve maximal information in transferred video frames, missing the fact that only certain parts of images/video frames are relevant for Object Detection and Tracking (OD/OT) in ISR missions. This paper adopts a different perspective and offers an alternative AI-driven scheduling policy that prioritizes selecting regions of the image that significantly contribute to the mission objective. The key idea is tiling the image into small patches and developing a Deep Reinforcement Learning (DRL) framework that assigns higher transmission probabilities to patches that present higher overlaps with the detected object of interest while penalizing sharp transitions over consecutive frames to promote smooth scheduling shifts. Although we used YOLOv8 object detection and UDP transmission protocols as a benchmark testing scenario, the idea is general and applicable to different transmission protocols and OD/OT methods. To further boost the system's performance and avoid OD errors for cluttered image patches, we integrate it with inter-frame interpolations. With this method, we achieved about 45% improvement in terms of OD accuracy for the proposed method (F1 score:98%) compared to random selection (F1 score: 53%) when the transmission budget is 50% (we afford sending half of the image patches). Under an extremely constrained transmission budget (5%), this gain can be as high as 87%. The only cost for such improvement is a feedback channel from the ground server to drones. © 2025 IEEE.

关键词： Drones

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Optimal Method Combining Blockchain and Multi-Agent system to Ensure Data Integrity and Deduplication in the Cloud Environment

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第10期18卷 90-105页

作者： El Ghazouani, Mohamed Ikidid, Abdelouafi Zaouiat, Charafeddine Ait Aziz, Layla Ichahane, Moulay Youssef Er-Rajy, Latifa ESIM Polydisciplinary Faculty of Sidi Bennour Chouaîb Doukkali University El Jadida Morocco Laboratory of System Analysis Information Processing and Industrial Management EST Salé Mohammed V University Salé Morocco Information Technology Laboratory National School of Applied Sciences Chouaîb Doukkali University El Jadida Morocco Computer Science Department Laboratory of Information Systems Engineering Cadi Ayyad University Marrakesh Morocco

Cloud computing (CC) refers to the transmission, storage, and processing of any type of information at a location that is not owned or controlled by the information owner. This information can be stored and accessed anytime, from anywhere, and using any device. However, several challenges threaten the deployment of cloud computing technology within organizations, including user privacy, data integrity, and managing the retention of the vast amount of data that will be stored and analyzed in this environment. In recent years, various data integrity checking systems and data storage mechanisms have been proposed to address these diverse challenges. Nevertheless, some protocols do not guarantee data confidentiality against auditors during the auditing process, while others do not provide batch auditing. Additionally, the majority of these protocols do not support the deduplication technique. Therefore, in this research work, the security features of blockchain technology and those of multi-agent systems (MAS) have prompted us to propose models that enable both data deduplication, saving storage space, and data integrity auditing, efficiently verifying the accuracy of data outsourced to CC environments. Implementation findings indicate that the suggested system is highly feasible and applicable in real-world scenarios. © 2024 by the authors of this article.

关键词： Multi agent systems

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Why Does Little Robustness Help? A Further Step Towards Understanding Adversarial Transferability

arXiv

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arXiv 2023年

作者： Zhang, Yechao Hu, Shengshan Zhang, Leo Yu Shi, Junyu Li, Minghui Liu, Xiaogeng Wan, Wei Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia 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 Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab

Adversarial examples for deep neural networks (DNNs) have been shown to be transferable: examples that successfully fool one white-box surrogate model can also deceive other black-box models with different architectures. Although a bunch of empirical studies have provided guidance on generating highly transferable adversarial examples, many of these findings fail to be well explained and even lead to confusing or inconsistent advice for practical use. In this paper, we take a further step towards understanding adversarial transferability, with a particular focus on surrogate aspects. Starting from the intriguing "little robustness" phenomenon, where models adversarially trained with mildly perturbed adversarial samples can serve as better surrogates for transfer attacks, we attribute it to a trade-off between two dominant factors: model smoothness and gradient similarity. Our research focuses on their joint effects on transferability, rather than demonstrating the separate relationships alone. Through a combination of theoretical and empirical analyses, we hypothesize that the data distribution shift induced by off-manifold samples in adversarial training is the reason that impairs gradient similarity. Building on these insights, we further explore the impacts of prevalent data augmentation and gradient regularization on transferability and analyze how the trade-off manifest in various training methods, thus building a comprehensive blueprint for the regulation mechanisms behind transferability. Finally, we provide a general route for constructing superior surrogates to boost transferability, which optimizes both model smoothness and gradient similarity simultaneously, e.g., the combination of input gradient regularization and sharpness-aware minimization (SAM), validated by extensive experiments. In summary, we call for attention to the united impacts of these two factors for launching effective transfer attacks, rather than optimizing one while ignoring the other,

关键词： Deep neural networks

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IoT-Assisted Blockchain-Based Car Rental system Supporting Traceability 28th

IoT-Assisted Blockchain-Based Car Rental System Supporting T...

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28th International Conference on Database systems for Advanced Applications, DASFAA 2023

作者： Chen, Lipan Wang, Yujue Ding, Yong Liang, Hai Yang, Changsong Wang, Huiyong Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Hangzhou Innovation Institute Beihang University Hangzhou China Cyberspace Security Research Center Pengcheng Laboratory Shenzhen China School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9783031306778

The majority of existing car rental systems rely on a centralized database to manage the data. Although these centralized architectures could make the system design and operation costs are reduced, there are many security problems in real world applications. If the information of the car obtained by the tenant is not completely credible, it will lead to security risks in the rental process. This paper proposes a blockchain-based car rental system with the Internet of Things (IoT), where the blockchain is engaged to store the use history of the car, so that the car condition can be traced to the source and the risk of using the car for the tenant can be reduced. The IoT technology is employed to enable renters to flexibly rent and unlock platform cars. Our system also provides visual operation interface. The evaluation results demonstrate the practicality of our system in the online car rental market with higher security and more flexible rental methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Internet of things

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