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SmartHealth: An intelligent framework to secure IoMT service applications using machine learning

ICT EXPRESS 2024年第2期10卷 425-430页

作者： Rani, Sita Kumar, Sachin Kataria, Aman Min, Hong Guru Nanak Dev Engn Coll Dept Comp Sci & Engn Ludhiana Punjab India South Ural State Univ Natl Res Univ Big Data & Machine Learning Lab Chelyabinsk Russia Amity Univ Amity Inst Def Technol Noida CampusSect 125 Noida Uttar Pradesh India Gachon Univ Sch Comp Seongnam South Korea

Due to the complex functioning of Smart Healthcare Systems (SHS), many security concerns have been raised in the past. It provisions the attackers to hamper the working of SHS in a variety of ways, e.g., injection of false data to replace vital signs, tampering of medical devices to prevent informing critical situations, etc. In this work, a novel ML-based framework, i.e., SmartHealth is proposed to secure IoMT devices in SHS. SmartHealth watches the vital signs gathered through different IoMT to analyze the change in various body activities to differentiate between normal activities and dangerous security attacks. The performance of the SmartHealth is also analyzed for three different dangerous attacks. During performance analysis, it has been observed that SmartHealth can identify wicked activities in IoMT 92% times accurately with an F1-score of 90%. (c) 2023 The Authors. Published by Elsevier B.V. on behalf of The Korean Institute of Communications and Information Sciences. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Attacks Smart Healthcare Systems (SHS) Security Internet of Medical Things (ioMT) Service applications machine learning

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Dynamic Co-Embedding Model for Temporal Attributed Networks

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2024年第3期35卷 3488-3502页

作者： Tang, Shaowei Meng, Zaiqiao Liang, Shangsong Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Guangdong Key Lab Big Data Anal & Proc Guangzhou 510006 Peoples R China Univ Glasgow Sch Comp Sci Glasgow G12 8QQ Lanark Scotland Mohamed Bin Zayed Univ Artificial Intelligence Dept Machine Learning Abu Dhabi U Arab Emirates

In this article, we study the problem of embedding temporal attributed networks, with the goal of which is to learn dynamic low-dimensional representations over time for temporal attributed networks. Existing temporal network embedding methods only learn the representations for nodes, which are unable to capture the dynamic affinities between nodes and attributes. Moreover, existing co-embedding methods that learn the static embeddings of both nodes and attributes cannot be naturally utilized to obtain their dynamic embeddings for temporal attributed networks. To address these issues, we propose the dynamic co-embedding model for temporal attributed networks (DCTANs) based on the dynamic stochastic state-space framework. Our model captures the dynamics of a temporal attributed network by modeling the abstract belief states representing the condition of the nodes and attributes of current time step, and predicting the transitions between temporal abstract states of two successive time steps. Our model is able to learn embeddings for both nodes and attributes based on their belief states at each time step of the temporal attributed network, while the state transition tendency for predicting the future network can be tracked and the affinities between nodes and attributes can be preserved. Experimental results on real-world networks demonstrate that our model achieves substantial performance gains in several static and dynamic graph mining applications compared with the state-of-the-art static and dynamic models.

关键词： Task analysis Predictive models Stochastic processes Analytical models Codes Network analyzers Correlation Dynamic co-embedding network embedding temporal attributed network variational autoencoder (VAE)

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An efficient and secure CLAKA protocol for blockchain-aided wireless body area networks

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 242卷

作者： Singh, Anuj Kumar Kumar, Sachin Amity Univ Madhya Pradesh Amity Sch Engn & Technol Gwalior India South Ural State Univ Natl Res Univ Big Data & Machine Learning Lab Chelyabinsk Russia

The effective usage of a WBAN elaborated as a wireless body area network in remote healthcare services is made conceivable by the speedy progression of wireless transmission technologies. Wearable sensors are used in WBANs to gather patient health information and communicate it over a public channel to the controller node. Due to the wireless nature of communication, many attacks can be attempted over WBAN communication. This necessitates an appropriately authenticated key agreement mechanism and protected information transfer be-tween the user's device and the controller node. The key escrow issue plagues identity-based systems and is avoided by certificateless authenticated key agreement (CLAKA) protocols, which also eliminate certificateless operational overheads in public key infrastructure. This paper proposes an efficient and secure protocol named ES-CLAKA for WBANs based on blockchain, which satisfies multiple security features. Informal analysis confirms that the attributes of forward security, key security, known temporary key security, key control security, key escrow, anonymity, verifiability, consensus, immutability, and unknown key share are fulfilled by ES-CLAKA. The security of the authenticated key-establishment phase and the concealment of the established key have been established through a formal analysis of the ES-CLAKA protocol utilizing the BAN logic and the ROR model, respectively. Results show that compared to the other most effective CLAKA protocols, ES-CLAKA consumes 47.4% less computational time, 20% less communication bandwidth, and 16.67% less storage overhead under reasonable assumptions. Comparative assessment of the security attributes has revealed that, when equated to other analogous schemes, the proposed ES-CLAKA protocol offers the highest level of security.

关键词： Authentication WBAN Key-agreement Decentralized CLAKA Blockchain Security

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Relation-Aware Fine-Grained Reasoning Network for Textbook Question Answering

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2023年第1期34卷 15-27页

作者： Ma, Jie Liu, Jun Wang, Yaxian Li, Junjun Liu, Tongliang Xi An Jiao Tong Univ Sch Comp Sci & Technol Natl Engn Lab Big Data Analyt Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Comp Sci & Technol Shaanxi Prov Key Lab Satellite & Terr Network Tec Xian 710049 Peoples R China Univ Sydney Sch Comp Sci Fac Engn Trustworthy Machine Learning Lab Darlington NSW 2008 Australia

Textbook question answering (TQA) is a task that one should answer non-diagram and diagram questions accurately, given a large context which consists of abundant diagrams and essays. Although lots of studies have made significant progress in the natural image question answering (QA), they are not applicable to comprehending diagrams and reasoning over the long multimodal context. To address the above issues, we propose a relation-aware fine-grained reasoning (RAFR) network that performs fine-grained reasoning over the nodes of relation-based diagram graphs. Our method uses semantic dependencies and relative positions between nodes in the diagram to construct relation graphs and applies graph attention networks to learn diagram representations. To extract and reason over the multimodal knowledge, we first extract the text that is the most relevant to questions, options, and the instructional diagram which is the most relevant to question diagrams at the word-sentence level and the node-diagram level, respectively. Then, we apply instructional-diagram-guided attention and question-guided attention to reason over the node of question diagrams, respectively. The experimental results show that our proposed method achieves the best performance on the TQA dataset compared with baselines. We also conduct extensive ablation studies to comprehensively analyze the proposed method.

关键词： Semantics Cognition Knowledge discovery Task analysis Visualization Shape Frontal lobe Diagram understanding machine reading comprehension (MRC) multi-modality textbook question answering (TQA)

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Point-Based Learnable Query Generator for Human–Object Interaction Detection

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2023年 32卷 6469-6484页

作者： Lin, Wang-Kai Zhang, Hong-Bo Fan, Zongwen Liu, Jing-Hua Yang, Li-Jie Lei, Qing Du, Jixiang Huaqiao Univ Dept Comp Sci & Technol Xiamen 361000 Peoples R China Huaqiao Univ Fujian Prov Univ Key Lab Comp Vis & Machine Learning Xiamen 361000 Peoples R China Huaqiao Univ Xiamen Key Lab Comp Vis & Pattern Recognit Xiamen 361000 Peoples R China Huaqiao Univ Fujian Key Lab Big Data Intelligence & Secur Xiamen 361000 Peoples R China

Transformer-based and interaction point-based methods have demonstrated promising performance and potential in human-object interaction detection. However, due to differences in structure and properties, direct integration of these two types of models is not feasible. Recent Transformer-based methods divide the decoder into two branches: an instance decoder for human-object pair detection and a classification decoder for interaction recognition. While the attention mechanism within the Transformer enhances the connection between localization and classification, this paper focuses on further improving HOI detection performance by increasing the intrinsic correlation between instance and action features. To address these challenges, this paper proposes a novel Transformer-based HOI Detection framework. In the proposed method, the decoder contains three parts: learnable query generator, instance decoder, and interaction classifier. The learnable query generator aims to build an effective query to guide the instance decoder and interaction classifier to learn more accurate instance and interaction features. These features are then applied to update the query generator for the next layer. Especially, inspired by the interaction point-based HOI and object detection methods, this paper introduces the prior bounding boxes, keypoints detection and spatial relation feature to build the novel learnable query generator. Finally, the proposed method is verified on HICO-DET and V-COCO datasets. The experimental results show that the proposed method has the better performance compared with the state-of-the-art methods.

关键词： Human-object interaction detection learnable query generator prior bounding boxes point detection spatial relation feature

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Diffusion-Based Method with TTS Guidance for Foreign Accent Conversion

Diffusion-Based Method with TTS Guidance for Foreign Accent ...

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International Symposium on Chinese Spoken Language Processing

作者： Qibing Bai Shuai Wang Zhijun Liu Mingyang Zhang Wei Rao Yannan Wang Haizhou Li School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen Tencent Ethereal Audio Lab Tencent Machine Learning Lab (MLL) University of Bremen

ISBN: (数字)9798331516826

ISBN: (纸本)9798331516833

Accent conversion (AC) aims to alter the accent of spoken language while preserving the original content and speaker characteristics. While any accent can be selected as a target, foreign accent conversion (FAC) that focuses on L2 speakers is particularly noteworthy due to its wide-ranging applications. Compared to general voice conversion tasks, which focus on speaker conversion, research related to accent conversion is relatively scarce, and the audio quality is often limited. In this article, we introduce a diffusion decoder into the conventional TTS-guided accent conversion framework and propose a phoneme-level acoustic-linguistic alignment strategy. Subjective evaluations on the Chinese-accent source speech confirm that the proposed method outperforms the baseline in terms of speech naturalness, accentedness, and speaker similarity 1 1 Audio samples are available at ***..

关键词： Decoding

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LmTag: functional-enrichment and imputation-aware tag SNP selection for population-specific genotyping arrays

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BRIEFINGS IN BIOINFORMATICS 2022年第4期23卷 bbac252-bbac252页

作者： Dat Thanh Nguyen Quan Hoang Nguyen Nguyen Thuy Duong Vo, Nam S. Vingrp Big Data Inst Ctr Biomed Informat Hanoi Vietnam Univ Queensland UQ Inst Mol Biosci IMB Genom & Machine Learning Lab Brisbane Qld Australia Vin Univ Coll Engn & Comp Sci Vinhome Ocean Pk Hanoi Vietnam

Despite the rapid development of sequencing technology, single-nucleotide polymorphism (SNP) arrays are still the most cost-effective genotyping solutions for large-scale genomic research and applications. Recent years have witnessed the rapid development of numerous genotyping platforms of different sizes and designs, but population-specific platforms are still lacking, especially for those in developing countries. SNP arrays designed for these countries should be cost-effective (small size), yet incorporate key information needed to associate genotypes with traits. A key design principle for most current platforms is to improve genome-wide imputation so that more SNPs not included in the array (imputed SNPs) can be predicted. However, current tag SNP selection methods mostly focus on imputation accuracy and coverage, but not the functional content of the array. It is those functional SNPs that are most likely associated with traits. Here, we propose LmTag, a novel method for tag SNP selection that not only improves imputation performance but also prioritizes highly functional SNP markers. We apply LmTag on a wide range of populations using both public and in-house whole-genome sequencing databases. Our results show that LmTag improved both functional marker prioritization and genome-wide imputation accuracy compared to existing methods. This novel approach could contribute to the next generation genotyping arrays that provide excellent imputation capability as well as facilitate array-based functional genetic studies. Such arrays are particularly suitable for under-represented populations in developing countries or non-model species, where little genomics data are available while investment in genome sequencing or high-density SNP arrays is limited. LmTag is available at: https://***/datngu/LmTag.

关键词： tag SNP selection SNP array design linkage disequilibrium beam search

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learning Hierarchical Modular Networks for Video Captioning

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND machine INTELLIGENCE 2024年第2期46卷 1049-1064页

作者： Li, Guorong Ye, Hanhua Qi, Yuankai Wang, Shuhui Qing, Laiyun Huang, Qingming Yang, Ming-Hsuan Univ Chinese Acad Sci Sch Comp Sci & Technol Key Lab Big Data Min & Knowledge Management Beijing 100049 Peoples R China Univ Adelaide Australian Inst Machine Learning Adelaide SA 5005 Australia Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100045 Peoples R China Univ Calif Merced Merced CA 95343 USA Yonsei Univ Seoul 03722 South Korea Google Mountain View CA 94043 USA

Video captioning aims to generate natural language descriptions for a given video clip. Existing methods mainly focus on end-to-end representation learning via word-by-word comparison between predicted captions and ground-truth texts. Although significant progress has been made, such supervised approaches neglect semantic alignment between visual and linguistic entities, which may negatively affect the generated captions. In this work, we propose a hierarchical modular network to bridge video representations and linguistic semantics at four granularities before generating captions: entity, verb, predicate, and sentence. Each level is implemented by one module to embed corresponding semantics into video representations. Additionally, we present a reinforcement learning module based on the scene graph of captions to better measure sentence similarity. Extensive experimental results show that the proposed method performs favorably against the state-of-the-art models on three widely-used benchmark datasets, including microsoft research video description corpus (MSVD), MSR-video to text (MSR-VTT), and video-and-TEXt (VATEX).

关键词： Video captioning hierarchical modular network scene-graph reward reinforcement learning

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Supervised Feature Selection via Collaborative Neurodynamic Optimization

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2024年第5期35卷 6878-6892页

作者： Wang, Yadi Wang, Jun Pal, Nikhil R. Henan Univ Sch Comp & Informat Engn Henan Key Lab Big Data Anal & Proc Kaifeng 475004 Peoples R China Henan Univ Sch Comp & Informat Engn Inst Data & Knowledge Engn Kaifeng 475004 Peoples R China City Univ Hong Kong Dept Comp Sci Kowloon Hong Kong Peoples R China City Univ Hong Kong Sch Data Sci Kowloon Hong Kong Peoples R China Indian Stat Inst Ctr Artificial Intelligence & Machine Learning Kolkata 700108 India Indian Stat Inst Elect & Commun Sci Unit Kolkata 700108 India

As a crucial part of machine learning and pattern recognition, feature selection aims at selecting a subset of the most informative features from the set of all available features. In this article, supervised feature selection is at first formulated as a mixed-integer optimization problem with an objective function of weighted feature redundancy and relevancy subject to a cardinality constraint on the number of selected features. It is equivalently reformulated as a bound-constrained mixed-integer optimization problem by augmenting the objective function with a penalty function for realizing the cardinality constraint. With additional bilinear and linear equality constraints for realizing the integrality constraints, it is further reformulated as a bound-constrained biconvex optimization problem with two more penalty terms. Two collaborative neurodynamic optimization (CNO) approaches are proposed for solving the formulated and reformulated feature selection problems. One of the proposed CNO approaches uses a population of discrete-time recurrent neural networks (RNNs), and the other use a pair of continuous-time projection networks operating concurrently on two timescales. Experimental results on 13 benchmark datasets are elaborated to substantiate the superiority of the CNO approaches to several mainstream methods in terms of average classification accuracy with three commonly used classifiers.

关键词： Feature extraction Optimization Redundancy Neurodynamics Recurrent neural networks Collaboration Mutual information Biconvex optimization collaborative neurodynamic optimization (CNO) feature selection mixed-integer optimization

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Multi-UAV-Assisted Federated learning for Energy-Aware Distributed Edge Training

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2024年第1期21卷 280-294页

作者： Tang, Jianhang Nie, Jiangtian Zhang, Yang Xiong, Zehui Jiang, Wenchao Guizani, Mohsen Guizhou Univ State Key Lab Publ Big Data Guiyang 550025 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210000 Peoples R China Singapore Univ Technol & Design Pillar Informat Syst Technol & Design Singapore Singapore Mohamed Bin Zayed Univ Artificial Intelligence Machine Learning Dept Abu Dhabi U Arab Emirates

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has largely extended the border and capacity of artificial intelligence of things (AIoT) by providing a key element for enabling flexible distributed data inputs, computing capacity, and high mobility. To enhance data privacy for AIoT applications, federated learning (FL) is becoming a potential solution to perform training tasks locally on distributed IoT devices. However, with the limited onboard resources and battery capacity of each UAV node, optimization is required to achieve a large-scale and high-precision FL scheme. In this work, an optimized multi-UAV-assisted FL framework is designed, where regular IoT devices are in charge of performing training tasks, and multiple UAVs are leveraged to execute local and global aggregation tasks. An online resource allocation (ORA) algorithm is proposed to minimize the training latency by jointly deciding the selection decisions of clients and a global aggregation server. By leveraging the Lyapunov optimization technique, virtual energy queues are studied to depict the energy deficit. With the help of the actor-critic learning framework, a deep reinforcement learning (DRL) scheme is designed to improve per-round training performance. A deep neural network (DNN)-based actor module is designed to derive client selection decisions, and a critic module is proposed through a conventional optimization method to evaluate the obtained selection decisions. Moreover, a greedy scheme is developed to find the optimal global aggregation server. Finally, extensive simulation results demonstrate that the proposed ORA algorithm can achieve optimal training latency and energy consumption under various system settings.

关键词： UAV federated learning resource allocation client selection DRL

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