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1st IEEE International Conference on Artificial Intelligence for Medicine, Health and Care, AIMHC 2024

作者： Ahmad, Asiyah Roupe, Griffin Samsa, Leigh Ann Krishnamurthy, Ashok Hubal, Robert Computer Science University of North Carolina at Chapel Hill Chapel Hill United States School of Data Science and Society University of North Carolina at Chapel Hill Chapel Hill United States Renaissance Computing Institute Chapel Hill United States

ISBN: (纸本)9798350371987

The NIH RADx data Hub serves as an access platform for COVID-19 research studies funded by the U.S. National Institutes of Health (NIH). The RADx data Hub contains a range of patient-related information, including diagnostic test data, demographic details, and clinical records. Controlled access to data safeguards patient privacy, but also presents challenges for users unfamiliar with the platform, developers seeking to test processes, and curators aiming to showcase its features. Meanwhile, using actual patient data raises confidentiality concerns and timing challenges, such as needing to get requisite approval. To address these limitations, synthetic datasets support generation of simulated patients, enabling effective learning and testing of the system with data closely resembling real study data. This paper describes a dynamic synthetic data generator for COVID-19 research data based on RADx codebooks and external sources. The generator empowers RADx data Hub users to model diverse patient cohorts, generate hypotheses, and attain a better grasp of existing COVID-19 data that they may access through the RADx data Hub. ©2024 IEEE.

关键词： COVID-19

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DRL-Based Time-Varying Workload Scheduling With Priority and Resource Awareness

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2838-2852页

作者： Liu, Qifeng Fan, Qilin Zhang, Xu Li, Xiuhua Wang, Kai Xiong, Qingyu Chongqing University School of Big Data and Software Engineering Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing400044 China Nanjing University School of Electronic Science and Engineering Nanjing210023 China Chongqing University School of Big Data and Software Engineering Chongqing400044 China Haihe Laboratory of Information Technology Application Innovation Tianjin300072 China Harbin Institute of Technology School of Computer Science and Technology Weihai264209 China Shandong Key Laboratory of Industrial Network Security Weihai264209 China

With the proliferation of cloud services and the continuous growth in enterprises’ demand for dynamic multidimensional resources, the implementation of effective strategy for time-varying workload scheduling has become increasingly significant. In this paper, we propose a deep reinforcement learning (DRL)-based method for time-varying workload scheduling, aiming to allocate resources efficiently across servers in the cluster. Specifically, we integrate a classifier and queue scorer to construct a priority queue that exploits temporal resource utilization patterns across different workload classes. Then, we design parallel graph attention layers to capture the dimensional features and temporal dynamics of cloud server cluster. Moreover, we propose a DRL algorithm to generate scheduling strategies that can adapt to dynamic environments. Validation on real-world traces from Google cluster demonstrates that our method outperforms existing approaches in key metrics of cloud server cluster management. © 2004-2012 IEEE.

关键词： Cloud platforms

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Autonomous Emergency Landing on 3D Terrains: Approaches for Monocular Vision-based UAVs 8

Autonomous Emergency Landing on 3D Terrains: Approaches for ...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Du, Weiming Lin, Junmou Du, Binqing Borhan, Uddin Md. Li, Jianqiang Chen, Jie College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China

ISBN: (纸本)9798350300178

With the increasing use of unmanned aerial vehicles (UAVs) in a variety of applications, their safety has become a critical concern. UAVs face numerous emergencies during missions, in these situations, the UAVs need to autonomously find a suitable landing site, plan flight routes, and avoid obstacles in unstructured environments. However, due to the limitations of computing power and sensors, it is challenging to achieve this goal. This research aims to explore the monocular emergency autonomous landing algorithm. Specifically, this work focuses on extracting depth and vision information. A topology information extractor is designed to convert image to graph and assess the connectivity of terrain. Additionally, a depth information extractor is designed to calculate the slope and roughness of the ground. A 3D topology optimizer is designed to optimize the graph by depth information and evaluate the landing suitability by a heuristic strategy. To make the action decision, a hybrid decision network (DGN) based on 3D topology information is proposed. Finally, this work build a simulated scenario based on real scene to verify the efficiency of DGN. The results of the experiment show that DGN outperforms its counterparts in terms of action prediction accuracy and landing success rate. © 2023 IEEE.

关键词： Landing

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SAIF: A Comprehensive Framework for Evaluating the Risks of Generative AI in the Public Sector

arXiv

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arXiv 2025年

作者： Lee, Kyeongryul Kim, Heehyeon Whang, Joyce Jiyoung Graduate School of Data Science KAIST Korea Republic of School of Computing KAIST Korea Republic of

The rapid adoption of generative AI in the public sector, encompassing diverse applications ranging from automated public assistance to welfare services and immigration processes, highlights its transformative potential while underscoring the pressing need for thorough risk assessments. Despite its growing presence, evaluations of risks associated with AI-driven systems in the public sector remain insufficiently explored. Building upon an established taxonomy of AI risks derived from diverse government policies and corporate guidelines, we investigate the critical risks posed by generative AI in the public sector while extending the scope to account for its multimodal capabilities. In addition, we propose a Systematic data generatIon Framework for evaluating the risks of generative AI (SAIF). SAIF involves four key stages: breaking down risks, designing scenarios, applying jailbreak methods, and exploring prompt types. It ensures the systematic and consistent generation of prompt data, facilitating a comprehensive evaluation while providing a solid foundation for mitigating the risks. Furthermore, SAIF is designed to accommodate emerging jailbreak methods and evolving prompt types, thereby enabling effective responses to unforeseen risk scenarios. We believe that this study can play a crucial role in fostering the safe and responsible integration of generative AI into the public sector. Copyright © 2025, The Authors. All rights reserved.

关键词： Energy policy

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CryptoASCII-ASCII based cryptosystem for cloud data storage

CryptoASCII-ASCII based cryptosystem for cloud data storage

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International Conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

作者： Pavithra, M. Prakash, M. Vennila, V. Tamil Nadu Komarapalayam India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Computer Science and Engineering K. S. R. College of Engineering Tamil Nadu Tiruchengode India

ISBN: (纸本)9781032669663

Cloud computing plays a huge role in IT industry, Education sector, government sector, medical field, defense management, Natural disaster control, weather reporting, road traffic maintenances, e-money transaction etc. The cloud provides wide amount of services can include delivery of data, software, hardware, infrastructure, platform, networking and so on. The merits of cloud computing are numerous to the user for data storing, accessing, maintenance in the cloud from anywhere at any time at low cost. On the other hands, the data in the cloud were not maintained by the direct owner. Despite, the data security has always been big challenge in cloud storage. data integrity, confidentiality and privacy protection are the prominent factors of the user’s side from the cloud environment. In this proposed paper, we introducing a new method CryptoASCII to provide data security in the cloud storage. By CryptoASCII, we confirmed that the user’s data are maintained with integrity and confidentiality. CryptoASCII can provide encryption and decryption algorithms to ensure data privacy as well as it provide redundancy checking proof for data integrity. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Redundancy

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Set pair three-way overlapping community discovery algorithm for weighted social internet of things

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Digital Communications and Networks 2023年第1期9卷 3-13页

作者： Chunying Zhang Jing Ren Lu Liu Shouyue Liu Xiaoqi Li Liya Wang College of Science North China University of Science and TechnologyTangshanHebeiChina Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and TechnologyTangshanHebeiChina Hebei Key Laboratory of Data Science and Application North China University of Science and TechnologyTangshanHebeiChina The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and TechnologyTangshanHebeiChina Tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and TechnologyTangshanHebeiChina

There are many problems in Social Internet of Things(IoTs),such as complex topology information,different degree of association between nodes and overlapping *** idea of set pair information grain computing and clustering is introduced to solve the above problems so as to accurately describe the similarity between nodes and fully explore the multi-community structure.A Set Pair Three-Way Overlapping Community Discovery Algorithm for Weighted Social Internet of Things(WSIoT-SPTOCD)is *** the local network structure,which fully considers the topological information between nodes,the set pair connection degree is used to analyze the identity,difference and reverse of neighbor *** similarity degree of different neighbor nodes is defined from network edge weight and node degree,and the similarity measurement method of set pair between nodes based on the local information structure is *** to the number of nodes'neighbors and the connection degree of adjacent edges,the clustering intensity of nodes is defined,and an improved algorithm for initial value selection of k-means is *** nodes are allocated according to the set pair similarity between nodes and different ***-way community structures composed of a positive domain,boundary domain and negative domain are generated ***,the overlapping node set is generated according to the calculation results of community node ***,experiments are carried out on artificial networks and real *** results show that WSIoT-SPTOCD performs well in terms of standardized mutual information,overlapping community modularity and F1.

关键词： Social internet of things Set pair analysis K-means Local information structure Overlapping community

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UAV-enabled Collaborative Secure data Transmission via Hybrid-Action Multi-Agent Deep Reinforcement Learning

UAV-enabled Collaborative Secure Data Transmission via Hybri...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Liu, Saichao Sun, Geng Teng, Siyu Li, Jiahui Zhang, Chuang Wang, Jiacheng Du, Hongyang Liu, Yinqiu College of Computer Science and Technology Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Hong Kong Baptist University Kowloon Hong Kong Waytous Inc. Beijing China

ISBN: (纸本)9798350351255

With the advancement of smart cities, smart manufacturing, and smart transportation, the Internet of Things (IoT) big data platform operating on wireless networks has emerged as a pivotal sector. In such systems, unmanned aerial vehicles (UAVs) play an indispensable support due to their flexibility and adaptability, but the energy sensitivity and limited communication capabilities pose further challenges. In this paper, we study a UAV-assisted secure communication system, where a UAV-enabled virtual antenna array (UVAA) consisting of multiple UAVs communicates with a remote mobile user (MU) by executing collaborative beamforming (CB), and then an eavesdropper exists for eavesdropping the transmission data from UVAA to MU. Then, a UAV-enabled secure communication optimization problem is formulated to maximize the total secrecy rate between the UVAA and the MU by optimizing the roles, locations and excitation current weights of UAVs. Since the considered scenario is dynamic and the UAVs need to cooperate with each other, we propose a hybrid-action multi-agent deep reinforcement learning (MADRL) algorithm (HMAPPO) to efficiently solve the optimization problem. Simulation results verify the effectiveness of the HMAPPO and illustrate that it learns the best strategy compared with other baseline methods. © 2024 IEEE.

关键词： Deep reinforcement learning

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Debiasing Computer Vision Models using data Augmentation based Adversarial Techniques 3

Debiasing Computer Vision Models using Data Augmentation bas...

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3rd Workshop on Bias, Risk, Ethical AI, Explainability and the Role of Logic and Logic Programming, BEWARE 2024

作者： Kumar, Teerath Mandal, Abhishek Leavy, Susan Little, Suzanne Mileo, Alessandra Bendechache, Malika CRT-AI School of Computing Dublin City University Dublin Ireland Insight SFI Research Center for Data Analytics School of Computing Dublin City University Dublin Ireland Insight SFI Research Center for Data Analytics School of Information and Communication Studies University College Dublin Dublin Ireland ADAPT & Lero Research Centres School of Computer Science University of Galway Galway Ireland

Deep learning models in computer vision, such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), have been found to exhibit significant biases related to factors such as gender and ethnicity. These biases often originate from inherent imbalances in training data predominantly sourced from the internet. In this study, we aim to address gender bias in computer vision models by curating a specialized dataset that highlights gender-related disparities. Additionally, we measure dataset diversity across six datasets (FFHQ, WIKI, IMDB, LFW, UTK Faces, diverse dataset), five professions (CEO, engineer, nurse, politician, and teacher) and different query retrieval tasks using the Image Similarity Score (ISS). To reduce learned gender biases and increase data diversity, we propose adversarial data augmentation techniques that specifically target facial regions within images. These techniques, named Partial Mix (PM), that partially mixes two gendered faces in a squared pattern, and Noise Addition (NA), that adds noise to the facial region, are designed to mitigate bias. Our experimental results demonstrate increased data diversity across the six datasets and professions, along with reduction in gender bias for CNN-based models. However, these adversarial techniques were less effective in reducing bias for Vision Transformers. This discrepancy highlights the unique challenges for bias mitigation posed by ViTs. Consistent with prior research, our findings indicate that ViTs learn from a broader set of visual cues compared to CNNs. This increased sensitivity makes ViTs more prone to amplifying biases, emphasizing the need for tailored bias mitigation strategies when deploying these models in real-world applications. © 2024 Copyright for this paper by its authors.

关键词： Adversarial machine learning

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Region detection and image clustering via sparse Kronecker product decomposition

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Computational Statistics and data Analysis 2025年 211卷

作者： Yang, Guang Feng, Long Department of Statistics The Chinese University of Hong Kong China School of Computing and Data Science The University of Hong Kong China

Image clustering is usually conducted by vectorizing image pixels, treating them as independent, and applying classical clustering approaches to the obtained features. However, as image data is often of high-dimensional and contains rich spatial information, such treatment is far from satisfactory. For medical image data, another important characteristic is the region-wise sparseness in signals. That is to say, there are only a few unknown regions in the medical image that differentiate the images associated with different groups of patients, while other regions are uninformative. Accurately detecting these informative regions would not only improve clustering accuracy, more importantly, it would also provide interpretations for the rationale behind them. Motivated by the need to identify significant regions of interest, we propose a general framework named Image Clustering via Sparse Kronecker Product Decomposition (IC-SKPD). This framework aims to simultaneously divide samples into clusters and detect regions that are informative for clustering. Our framework is general in the sense that it provides a unified treatment for matrix and tensor-valued samples. An iterative hard-thresholded singular value decomposition approach is developed to solve this model. Theoretically, the IC-SKPD enjoys guarantees for clustering accuracy and region detection consistency under mild conditions on the minimum signals. Comprehensive simulations along with real data analysis further validate the superior performance of IC-SKPD on clustering and region detection. © 2025 The Authors

关键词： Image segmentation

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Learnable Broad Learning for Semi-Supervised Specific Emitter Identification in the Internet of Everything

Learnable Broad Learning for Semi-Supervised Specific Emitte...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Zhang, Yibin Peng, Yang Wang, Qin Lin, Yun Gui, Guan Niyato, Dusit Adachi, Fumiyuki College of Telecommunications and Information Engineering Njupt Nanjing China College of Information and Communication Engineering Harbin Engineering University Harbin China College of Computing and Data Science Nanyang Technological University Singapore International Research Institute of Disaster Science Tohoku University Sendai Japan

ISBN: (纸本)9798350368369

Specific emitter identification (SEI) is crucial in the Internet of Everything (IoE). Over the past decade, deep learning (DL) and broad learning (BL)-enabled SEI technologies have emerged. Recently, many researchers have begun exploring semi-supervised learning techniques to address the semi-supervised SEI (SS-SEI) problem with limited labeled RF signals. However, existing SS-SEI solutions often prioritize identification performance, leading to high computational overheads and lacking iterability. To overcome these challenges, this paper proposes a novel SS-SEI solution based on a learnable broad learning network (LBL). Initially, a pretrained DL-based SEI model is downloaded to the edge device. Meanwhile, an updatable BL-based SEI method is deployed locally on the edge device to identify unlabelled signals. When the LBL solution is operational, edge devices capture real-time unlabelled RF signals. The pretrained DL-based SEI method and the locally BL-based SEI method jointly identify these RF signals. The identification results and the new real-time RF signals are then used to update the weights of the BL-based SEI method at the edge devices. The LBL SS-SEI solution is validated using an open-source, large-scale, real-world automatic dependent surveillance-broadcast (ADS-B) dataset. Experimental results demonstrate that the proposed LBL solution offers significant advantages regarding SS-SEI performance. © 2025 IEEE.

关键词： Semi-supervised learning

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