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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Huang, Wei Zhang, Deyu Luo, Wei Tang, Yin School of Computer Science and Engineering Central South University Changsha410083 China Big Data Institute Central South University Changsha410083 China

ISBN: (纸本)9798350368369

Edge server placement is a hot issue in mobile edge computing. It is a key prerequisite for deploying edge servers that can meet computing needs and improve resource utilization. This paper studies the joint location deployment problem of edge server and UAV, and proposes a dynamic placement solution for heterogeneous servers based on dynamically changing network environments and computing resource requirements, with the goal of minimizing migration costs. Extensive comparative experiments based on real datasets demonstrate that our algorithm outperforms other algorithms in minimizing costs. © 2025 IEEE.

关键词： Edge computing

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Evaluating the Impact of dataset Size on Univariate Prediction Techniques for Moroccan Agriculture

Evaluating the Impact of Dataset Size on Univariate Predicti...

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International Conference on Artificial Intelligence and Smart Environment, ICAISE 2022

作者： Ed-daoudi, Rachid Alaoui, Altaf Zerouaoui, Jad Ettaki, Badia Zerouaoui, Jamal Laboratory of Engineering Sciences and Modeling Faculty of Sciences Ibn Tofail University Campus Universitaire BP 133 Kenitra Morocco LyRICA: Laboratory of Research in Computer Science Data Sciences and Knowledge Engineering School of Information Sciences Rabat Rabat Morocco

ISBN: (纸本)9783031262531

Learning models used for prediction are mostly developed without taking into account the size of datasets that can produce models of high accuracy and better performance. Although, the general believe is that, large dataset is needed to construct a predictive learning model. To describe a data set as large in size depends on the circumstances and context of prediction. This means that what makes a dataset to be considered as being big or small is controversial. In this paper, the ability of the predictive model to adapt to a particular size of data in training is examined. The study experiments on three different sizes of Moroccan agricultural data using a variety of statistical and Machine Learning techniques, to create predictive models with a view to establishing if the size of data has any effect on the accuracy of a model. The output of each model is measured using the Mean Absolute Error (MAE) and r-squared, and comparisons are made. The results of training the models through the three partitioned dataset show that, the models trained with the smallest and largest size of training data appear to be less accurate, while the models trained with a medium sized dataset delivers a much better results. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Securely Fine-tuning Pre-trained Encoders Against Adversarial Examples

Securely Fine-tuning Pre-trained Encoders Against Adversaria...

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IEEE Symposium on Security and Privacy

作者： Ziqi Zhou Minghui Li Wei Liu Shengshan Hu Yechao Zhang Wei Wan Lulu Xue Leo Yu Zhang Dezhong Yao Hai Jin 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 School of Software Engineering Huazhong University of Science and Technology Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Huazhong University of Science and Technology School of Information and Communication Technology Griffith University

ISBN: (数字)9798350331301

ISBN: (纸本)9798350331318

With the evolution of self-supervised learning, the pre-training paradigm has emerged as a predominant solution within the deep learning landscape. Model providers furnish pre-trained encoders designed to function as versatile feature extractors, enabling downstream users to harness the benefits of expansive models with minimal effort through fine-tuning. Nevertheless, recent works have exposed a vulnerability in pre-trained encoders, highlighting their susceptibility to downstream-agnostic adversarial examples (DAEs) meticulously crafted by attackers. The lingering question pertains to the feasibility of fortifying the robustness of downstream models against DAEs, particularly in scenarios where the pre-trained encoders are publicly accessible to the *** this paper, we initially delve into existing defensive mechanisms against adversarial examples within the pre-training paradigm. Our findings reveal that the failure of current defenses stems from the domain shift between pre-training data and downstream tasks, as well as the sensitivity of encoder parameters. In response to these challenges, we propose Genetic Evolution-Nurtured Adversarial Fine-tuning (Gen-AF), a two-stage adversarial fine-tuning approach aimed at enhancing the robustness of downstream models. Gen-AF employs a genetic-directed dual-track adversarial fine-tuning strategy in its first stage to effectively inherit the pre-trained encoder. This involves optimizing the pre-trained encoder and classifier separately while incorporating genetic regularization to preserve the model’s topology. In the second stage, Gen-AF assesses the robust sensitivity of each layer and creates a dictionary, based on which the top-k robust redundant layers are selected with the remaining layers held fixed. Upon this foundation, we conduct evolutionary adaptability fine-tuning to further enhance the model’s generalizability. Our extensive experiments, conducted across ten self-supervised training methods and six d

关键词： Training Adaptation models Sensitivity Accuracy Self-supervised learning Genetics Feature extraction

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Application of constraint-based frequent closed itemsets Mining in TCM Clinical data Analysis

Application of constraint-based frequent closed itemsets Min...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Yu, Jinlong Zhang, Lei Xu, Ning Fa, Lifeng Yang, Kuo China Academy of Chinese Medical Science Institute of Information on Tcm Beijing China China Academy of Chinese Medical Sciences National Data Center of Tcm Beijing China Beijing Jiaotong University School of Computer and Information Technology Beijing China

ISBN: (纸本)9798350337488

Objective: To propose a frequent closed itemsets mining method based on item constraints to study the combination rules between symptoms, diagnoses, and herbs in clinical data of TCM. Methods: Based on the Charm algorithm, prune the itemsets which do not meet the constraint conditions firstly, design a constraint based frequent closed itemsets mining algorithm, which was used on the clinical dataset of TCM to confirm the effectiveness of the method and the clinical significance of the mining results. Results: Based on the Charm algorithm, a constraint based frequent closed itemsets mining algorithm was proposed. The results of the method for mining frequent closed itemsets from data of symptom, diagnosis and herbs were consistent with the relevant theories of ***: The combination of constraint conditions and frequent closed itemsets mining methods can effectively reduce the mining space. Using constraint based frequent closed itemsets mining methods to mine the combination rules between symptoms, diagnosis, and herbs in TCM clinical data maybe helpful for assisting clinical diagnosis and treatment. © 2023 IEEE.

关键词： Clinical data of TCM Constraints Frequent closed itemsets

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Contactless Respiratory Rate Monitoring For ICU Patients Based On Unsupervised Learning

Contactless Respiratory Rate Monitoring For ICU Patients Bas...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Liu, Zimeng Huang, Bin Lin, Chun-Liang Wu, Chieh-Liang Zhao, Changchen Chao, Wen-Cheng Wu, Yu-Cheng Zheng, Yadan Wang, Zhiru Beihang University Hangzhou Innovataion Institute China Beihang University School of Automation Science and Electrical Engineering China National Chung Hsing University College of Electrical Engineering and Computer Science Taiwan Taichung Veterans General Hospital Department of Critical Care Medicine Taiwan National Chung Hsing University College of Medicine Department of Post-Baccalaureate Medicine Taiwan National Chung Hsing University Big Data Center Taiwan Beijing University of Posts and Telecommunications Computer Science School China Beijing Foreign Studies University Graduate School of Translation and Interpretation China

ISBN: (纸本)9798350302493

Recently, the task of contactless physiological signal monitoring based on deep learning technologies has attracted a large number of scholars. However, few studies focus on the application of real-world scenarios, especially in clinical medicine scenes. In this paper, a novel video-based contactless respiratory rate measurement algorithm is developed for the Intensive Care Unit (ICU) patients. Firstly, a large-scale clinical real-world database towards ICU patient is collected in this study. Then, based on the dataset, the unsupervised learning is first introduced to extract the respiration waveform from the chest area of patients. Lastly, a respiratory rate estimator based on neural networks is proposed and trained on a periodical physiological signal simulation dataset, and utilizes the transfer learning technique to extract the respiratory rate from only 10-second respiration waveform. We obtained estimated respiratory rate with an MAE of 2.8 breaths/min and an STD of 3.0 breaths/min against the reference value computed from the specialized medical device. Extensive experiments demonstrate that our proposed methods achieve competitive results over state-of-the-art (SOTA) method in the real-world scenario. © 2023 IEEE.

关键词： Intensive care units

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Towards Insider Threat Resilience: A Proposed Mitigation Model

Towards Insider Threat Resilience: A Proposed Mitigation Mod...

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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： Navyansh Kothari Chanakya Bhardwaj Shruti Mishra Sandeep Kumar Satapathy Sung-Bae Cho Pradeep Kumar Mallick School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology Chennai Chennai India Centre for Advanced Data Science Vellore Institute of Technology Chennai Tamil Nadu India Department of Computer Science 50 Yonsei-ro Sudaemoon-gu Yonsei University Seoul South Korea School of Computer Engineeing KIIT University Odisha India

Businesses are increasingly concerned about insider threats, which highlights the need for effective mitigation strategies. This paper examines the variety of insider threats and provides a thorough plan for lowering this risk. Examining different internal threat types and their motives may help organizations identify any possible weak points. This paper also looks at technological advancements like user behavior analytics and anomaly detection as well as organizational practices like security culture promotion and employee training. It is advised to create a comprehensive framework for preventing insider threats that incorporates technology controls, organizational procedures, and human factors. In the implementation, a Windows application developed with Flutter and Dart takes user-input IP addresses to modify a Python script file using file handling. It also handles file name and path changes in the script, converts it into an executable file, and stores it with a data file. The application includes pages for inputting IP addresses, selecting files, and protecting the script. After protection, the executable file is saved in an executable folder, which can be further converted into a steganography file. This research focuses on evaluating the file’s behaviour on different networks to enhance security.

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LEARNING LONG-TERM MUSIC REPRESENTATIONS VIA HIERARCHICAL CONTEXTUAL CONSTRAINTS 22

LEARNING LONG-TERM MUSIC REPRESENTATIONS VIA HIERARCHICAL CO...

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22nd International Conference on Music Information Retrieval, ISMIR 2021

作者： Wei, Shiqi Xia, Gus School of Data Science Fudan University China Music X Lab Computer Science Department New York University Shanghai China

ISBN: (纸本)9781732729902

Learning symbolic music representations, especially disentangled representations with probabilistic interpretations, has been shown to benefit both music understanding and generation. However, most models are only applicable to short-term music, while learning long-term music representations remains a challenging task. We have seen several studies attempting to learn hierarchical representations directly in an end-to-end manner, but these models have not been able to achieve the desired results and the training process is not stable. In this paper, we propose a novel approach to learn long-term symbolic music representations through contextual constraints. First, we use contrastive learning to pre-train a long-term representation by constraining its difference from the short-term representation (extracted by an off-the-shelf model). Then, we fine-tune the long-term representation by a hierarchical prediction model such that a good long-term representation (e.g., an 8-bar representation) can reconstruct the corresponding short-term ones (e.g., the 2-bar representations within the 8-bar range). Experiments show that our method stabilizes the training and the fine-tuning steps. In addition, the designed contextual constraints benefit both reconstruction and disentanglement, significantly outperforming the baselines. © 2021 Proceedings of the 22nd International Conference on Music Information Retrieval, ISMIR 2021. All Rights Reserved.

关键词： Music

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Exfiltration-resistant Proxy Re-Encryption for IoT data Sharing in Unreliable Clouds

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Zhou, Yuyang Zhao, Liang Li, Fagen Takagi, Tsuyoshi School of Cyberspace Security Hainan University Haikou570000 China Department of Mathematical Informatics Graduate School of Information Science and Technology The university of Tokyo Tokyo113-8656 Japan School of Cyber Science and Engineering Sichuan University Chengdu610065 China Key Laboratory of Data Protection and Intelligent Management [Sichuan University Ministry of Education China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

The sharing of Internet of Things (IoT) data plays an extensive role in our everyday lives. Exploring secure and efficient methods for data sharing is a prominent area of research. Proxy re-encryption (PRE) in the cloud provides a solution. However, traditional PRE schemes are vulnerable to algorithm substitution attacks (ASA). Moreover, in most PRE schemes, the proxy can know the relevant identity information of the data owner or the data recipient through the re-encryption key, and some current PRE schemes cannot guarantee strong resistance to collision. To address the limitations of traditional PRE schemes and offer a robust solution for secure and efficient data sharing in IoT application, we propose a reverse firewall for proxy re-encryption (PRE-RF) scheme, which we implement using the JPBC library. The security analysis reveals that the PRE-RF scheme offers effective resistance against ASA, while also providing strong collision security, chosen plaintext attack security, and key-private security. Compared to similar PRE schemes with more security features, our scheme reduces the length of the re-encryption keys and ciphertext, thereby reducing the storage cost of the system;also, our scheme’s computational cost is lower, thereby improving the operational efficiency of the system. Furthermore, as security levels rise, the portion of time dedicated to reverse firewall operations within our scheme diminishes. These advantages collectively render it a secure and efficient choice for sharing IoT data in unreliable cloud environments. © 2004-2012 IEEE All rights reserved.

关键词： Ciphertext

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MatchNAS: Optimizing Edge AI in Sparse-Label data Contexts via Automating Deep Neural Network Porting for Mobile Deployment 24

MatchNAS: Optimizing Edge AI in Sparse-Label Data Contexts v...

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33rd ACM Web Conference, WWW 2024

作者： Huang, Hongtao Chang, Xiaojun Hu, Wen Yao, Lina School of Computer Science and Engineering University of New South Wales KensingtonNSW2052 Australia Faculty of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia CSIRO's Data61 University of New South Wales EveleighNSW2015 Australia

ISBN: (纸本)9798400701719

Recent years have seen the explosion of edge intelligence with powerful Deep Neural Networks (DNNs). One popular scheme is training DNNs on powerful cloud servers and subsequently porting them to mobile devices after being lightweight. Conventional approaches manually specialized DNNs for various edge platforms and retrain them with real-world data. However, as the number of platforms increases, these approaches become labour-intensive and computationally prohibitive. Additionally, real-world data tends to be sparse-label, further increasing the difficulty of lightweight models. In this paper, we propose MatchNAS, a novel scheme for porting DNNs to mobile devices. Specifically, we simultaneously optimise a large network family using both labelled and unlabelled data and then automatically search for tailored networks for different hardware platforms. MatchNAS acts as an intermediary that bridges the gap between cloud-based DNNs and edge-based DNNs. © 2024 ACM.

关键词： Network architecture

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Joint Sensing, Computation, and DAI Resource Management for Integrated Non-Terrestrial and Terrestrial Networks

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IEEE Network 2024年

作者： Gong, Yongkang Liu, Xiaonan Yao, Haipeng Xiong, Zehui Cheng, Xiuzhen Niyato, Dusit Shandong University School of Computer Science and Technology China University of Aberdeen School of Natural and Computing Science United Kingdom Beijing University of Posts and Telecommunications School of Information and Communication Engineering China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore Nanyang Technological University College of Computing and Data Science China

Integrated Non-Terrestrial and Terrestrial Networks (NTN-TN) have been emerging as a promising architecture to facilitate state sensing, computation migration, and privacy protection for terrestrial users, where both low, medium and geostationary orbit satellites collaboratively support various tasks and allocate resources effectively. Nevertheless, an integration of sensing, computation, and artificial intelligence (AI) in dynamic NTN-TNs has certain challenges in achieving high-efficient task offloading and guaranteeing robust privacy protection. To address these issues, we introduce an intelligent NTN-TN framework, focusing on sensing, computation migration, and privacy protection. Furthermore, we develop a versatile and scalable experimental platform for NTN-TNs to validate related network functions. Simulation results show that the proposed framework reduces network latency compared with advanced benchmarks. Finally, we present potential research directions and innovative solutions in future NTN-TNs. © 2024 IEEE.

关键词： Differential privacy

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