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IEEE Transactions on Sustainable computing 2025年

作者： Li, Dun Li, Hongzhi Crespi, Noel Minerva, Roberto Li, Ming Liang, Wei Li, Kuan-Ching Tsinghua University Department of Industrial Engineering China Institut Polytechnique de Paris Samovar Telecom SudParis Palaiseau91120 France Chizhou University College of Big Data and Artificial Intelligence Chizhou247100 China The Hong Kong Polytechnic University Research Institute for Advanced Manufacturing Hung Hom Hong Kong Hunan University of Science and Technology School of Computer Science and Engineering Hunan University of Science and Technology Sanya Research Institute Hunan Key Laboratory for Service Computing and Novel Software Technology China Providence University Dept. of Computer Science and Information Engineering Taiwan

In recent years, the refinements in industrial processes and the increasing complexity of managing privacy-sensitive data in Industrial Internet of Things (IIoT) devices have highlighted the need for secure, robust, and adaptive data management solutions. In this work, we propose a smart contract-assisted access control scheme for IIoT, which employs the Attribute-Based Access Control (ABAC) model to set access permissions for different industrial components. Besides, we defined a storage model and data format for private data by designing and deploying smart contracts to manage system operations and access policies. In addition, the bloom filter component is deployed to optimize the efficiency of contract management and system performance. Experimental results show that in real-world simulations, Hyper-IIoT demonstrates excellent contract execution time control, stable system throughput, and rapid consensus processes that are capable of handling high throughput and efficient consensus in distributed systems, even in large-scale request scenarios. © 2016 IEEE.

关键词： Smart contract

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BMEye: Public Health-Oriented Body Mass Index Monitoring Using Commodity WiFi

BMEye: Public Health-Oriented Body Mass Index Monitoring Usi...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Kiran Davuluri Khairul Mottakin Zheng Song Jin Lu Mark Allison Dept. of Computer and Information Science University of Michigan-Dearborn MI USA School of Computing University of Georgia GA USA College of Innovation and Technology University of Michigan-Flint MI USA

ISBN: (数字)9798350311617

ISBN: (纸本)9798350311624

Body Mass Index (BMI) is a commonly used measure of body fat based on an individual's height and weight. Public health authorities and organizations monitor BMI at a population level to assess the prevalence and trends of under-weight and obesity within communities. This helps identify at-risk populations, evaluate the effectiveness of interventions, and develop targeted strategies to promote healthy weight and prevent associated health problems. However, there is a lack of BMI monitoring methods that do not require active cooperation or cause privacy concerns to human subjects. This paper introduces a BMI classification approach based on passive WiFi sensing. Our proposed approach is inspired by the observation that different body shapes cause dissimilar impacts on WiFi signal propagation, which can be reflected by Channel State information (CSI) readings. Leveraging state-of-the-art machine learning algorithms, we developed a BMI sensing system that classifies the BMI of human subjects when they move in a WiFi range. Our evaluation results show that our approach could be an effective tool for public-health-oriented BMI monitoring, yielding a precision rate of 70.86%, a recall rate of 71.21 %, and an F1 score of 73.66%.

关键词： Weight measurement Shape Sociology Stability criteria Sensors Statistics Public healthcare

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Enhancing Patient Data Security Through Blockchain Adoption in Fiji's Healthcare System

Enhancing Patient Data Security Through Blockchain Adoption ...

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Sustainable technology and Engineering (i-COSTE), International Conference on

作者： Jashnil Kumar ABM Shawkat Ali Anal Kumar Shavnil Kumar Department of Computing Sciences and Information Systems School of Mathematical and Computing Science Fiji National University Natabua Lautoka Fiji Department of Computer Science and Mathematics School of Science and Technology The University of Fiji Saweni Lautoka Fiji Department of Maths Physics and Department of Computer Studies Pandit Vishnu Deo Memorial College Saweni Lautoka Fiji

Ensuring the security of patient data is of utmost importance in healthcare systems worldwide, and Fiji is no exception to this concern. With healthcare providers in Fiji gradually transitioning to digital health records and information sharing, the need to protect the confidentiality, integrity, and accessibility of patient data becomes a top priority. While there is a wealth of literature discussing the utilization of blockchain technology in the healthcare sector, the novelty of this study lies in its focus on developing a blockchain-based information system tailored explicitly for Fiji to bolster the security of patients' data. This research endeavors to explore the potential of blockchain technology in enhancing the security of patient data, building upon previous studies in this field. The study critically assesses Fiji's existing healthcare data management system and its challenges. It considers the unique requirements of Fiji's healthcare landscape and endeavors to develop and propose a blockchain-based system, accompanied by recommendations for its implementation. In conclusion, this research provides a brief summary of its key findings and delves into the broader implications for patient data security and healthcare in Fiji. Furthermore, it suggests future avenues for research in this dynamic and evolving field. In an era where reliance on digital health data is growing, blockchain technology emerges as a promising solution for safeguarding patient information in Fiji, laying the groundwork for continued exploration of healthcare information security.

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Real-Time Counterfactual Explanations For Robotic Systems With Multiple Continuous Outputs

arXiv

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arXiv 2022年

作者： Gjærum, Vilde B. Strümke, Inga Lekkas, Anastasios M. Miller, Tim Miller, Timothy Department of Engineering Cybernetics Norwegian University of Science and Technology Trondheim Norway Department of Computer Science Norwegian University of Science and Technology Trondheim Norway School of Computing and Information Systems University of Melbourne Melbourne Australia

Although many machine learning methods, especially from the field of deep learning, have been instrumental in addressing challenges within robotic applications, we cannot take full advantage of such methods before these can provide performance and safety guarantees. The lack of trust that impedes the use of these methods mainly stems from a lack of human understanding of what exactly machine learning models have learned, and how robust their behaviour is. This is the problem the field of explainable artificial intelligence aims to solve. Based on insights from the social sciences, we know that humans prefer contrastive explanations, i.e. explanations answering the hypothetical question "what if?". In this paper, we show that linear model trees are capable of producing answers to such questions, so-called counterfactual explanations, for robotic systems, including in the case of multiple, continuous inputs and outputs. We demonstrate the use of this method to produce counterfactual explanations for two robotic applications. Additionally, we explore the issue of infeasibility, which is of particular interest in systems governed by the laws of physics. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Beyond MLE: Convex Learning for Text Generation

arXiv

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arXiv 2023年

作者： Shao, Chenze Ma, Zhengrui Zhang, Min Feng, Yang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China School of Future Science and Engineering Soochow University China

Maximum likelihood estimation (MLE) is a statistical method used to estimate the parameters of a probability distribution that best explain the observed data. In the context of text generation, MLE is often used to train generative language models, which can then be used to generate new text. However, we argue that MLE is not always necessary and optimal, especially for closed-ended text generation tasks like machine translation. In these tasks, the goal of model is to generate the most appropriate response, which does not necessarily require it to estimate the entire data distribution with MLE. To this end, we propose a novel class of training objectives based on convex functions, which enables text generation models to focus on highly probable outputs without having to estimate the entire data distribution. We investigate the theoretical properties of the optimal predicted distribution when applying convex functions to the loss, demonstrating that convex functions can sharpen the optimal distribution, thereby enabling the model to better capture outputs with high probabilities. Experiments on various text generation tasks and models show the effectiveness of our approach. It enables autoregressive models to bridge the gap between greedy and beam search, and facilitates the learning of non-autoregressive models with a maximum improvement of 9+ BLEU points. Moreover, our approach also exhibits significant impact on large language models (LLMs), substantially enhancing their generative capability on various tasks. Source code is available at https://***/ictnlp/Convex-Learning. Copyright © 2023, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

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Provably secure fog-based authentication protocol for VANETs

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Computer Networks 2024年 246卷

作者： Awais, Syed Muhammad Yucheng, Wu Mahmood, Khalid Badar, Hafiz Muhammad Sanaullah Kharel, Rupak Das, Ashok Kumar School of Microelectronics and Communication Engineering Chongqing University Chongqing China Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Yunlin Douliu64002 Taiwan Department of Emerging Computing Technologies Emerson University Multan Pakistan School of Computing and Engineering University of Huddersfield HuddersfieldHD1 3DH United Kingdom The Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad500 032 India

The integration of fog computing and vehicular ad-hoc networks (VANETs) has become increasingly important due to advancements in cloud computing, Internet of Things (IoT) technologies, and intelligent transportation systems. However, ensuring secure communication in fog-based VANETs poses a significant challenge. To address this challenge, we propose a novel authenticated key agreement protocol that achieves mutual authentication, generates a secure session key for confidential communication, and ensures privacy protection without relying on bilinear pairing. We provide rigorous security proofs for our protocol, specifically tailored for fog-based VANETs, demonstrating its ability to meet their stringent security requirements. Simulations using NS3 allow for a comprehensive performance evaluation, providing insights into the protocol's efficiency and scalability. Additionally, our protocol is highly efficient, featuring low computation and communication costs, thereby offering a practical and scalable solution for secure communication in fog-based VANETs. © 2024

关键词： Fog

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Simulating a Finite-State Markov Chain for Rayleigh Block-Fading Channel based on its Mathematical Model, and Practical Representation

Simulating a Finite-State Markov Chain for Rayleigh Block-Fa...

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information technology (ACIT)

作者： Hani Attar Jafar Ababneh Mohammad Al-Hihi Ali Rachini Ahmed Solyman Faculty of Engineering Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Cyber Security Department Faculty of Information Technology Zarqa University Zarqa Jordan Independent Research Erlangen Germany Department of Computer Science and Information Technology Holy Spirit University of Kaslik (USEK) Jounieh Lebanon School of Computing Engineering and Built Environment Glasgow Caledonian University Glasgow UK

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

In the proposed paper, simulating the Finite State Markov Chain (FSMC) to be implemented as Rayleigh Block-fading channel. Indeed, FSMC is regarded as a practical fading channel because of its random choice for future transmission probability. Accordingly, this paper introduces the FSMC technology with the theoretical limits and equations; moreover, the uplink and downlink Rayleigh Block-fading channel scenarios are also investigated and supported by the simulation set-up. The results for the Rayleigh fading channel for the Long-Term Evaluation-Advanced network were obtained as an example of the channel's attitude. The findings demonstrate that FSMC provides such reliable results and is suitable for use as a practical fading channel, particularly in terms of burst error behavior.

关键词： Fading channels Bit error rate Quality of service Reliability theory Mathematical models Software Mirrors Uplink MATLAB information technology

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Chat2Brain: A Method for Mapping Open-Ended Semantic Queries to Brain Activation Maps

Chat2Brain: A Method for Mapping Open-Ended Semantic Queries...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yaonai Wei Tianyang Zhong Songyao Zhang Xiao Li Tuo Zhang Lin Zhao Zhengliang Liu Muheng Shang Tianming Liu Han Zhang Chong Ma Lei Du Junwei Han School of Automation Northwestern Polytechnical University Xi’an China School of Information Science&Technology Northwest University Xi’an China School of Computing University of Georgia GA USA Network and Data Center Northwest University Xi’an China

Over decades, neuroscience has accumulated a wealth of research results in the text modality that can be used to explore cognitive processes. Meta-analysis is a typical method that successfully establishes a link from text queries to brain activation maps using these research results, but it still relies on an ideal query environment. In practical applications, text queries used for meta-analyses may encounter issues such as semantic redundancy and ambiguity, resulting in an inaccurate mapping to brain images. On the other hand, large language models (LLMs) like ChatGPT have shown great potential in tasks such as context understanding and reasoning, displaying a high degree of consistency with human natural language. Hence, LLMs could improve the connection between text modality and neuroscience, resolving existing challenges of meta-analyses. In this study, we propose a method called Chat2Brain that combines LLMs to basic text-2-image model, known as Text2Brain, to map open-ended semantic queries to brain activation maps in data-scarce and complex query environments. By utilizing the understanding and reasoning capabilities of LLMs, the performance of the mapping model is optimized by transferring text queries to semantic queries. We demonstrate that Chat2Brain can synthesize anatomically plausible neural activation patterns for more complex tasks of text queries.

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Coarse-to-Fine Latent Diffusion for Pose-Guided Person Image Synthesis

arXiv

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arXiv 2024年

作者： Lu, Yanzuo Zhang, Manlin Ma, Andy J. Xie, Xiaohua Lai, Jianhuang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Guangzhou China

Diffusion model is a promising approach to image generation and has been employed for Pose-Guided Person Image Synthesis (PGPIS) with competitive performance. While existing methods simply align the person appearance to the target pose, they are prone to overfitting due to the lack of a high-level semantic understanding on the source person image. In this paper, we propose a novel Coarse-to-Fine Latent Diffusion (CFLD) method for PGPIS. In the absence of image-caption pairs and textual prompts, we develop a novel training paradigm purely based on images to control the generation process of a pre-trained text-to-image diffusion model. A perception-refined decoder is designed to progressively refine a set of learnable queries and extract semantic understanding of person images as a coarse-grained prompt. This allows for the decoupling of fine-grained appearance and pose information controls at different stages, and thus circumventing the potential overfitting problem. To generate more realistic texture details, a hybrid-granularity attention module is proposed to encode multi-scale fine-grained appearance features as bias terms to augment the coarse-grained prompt. Both quantitative and qualitative experimental results on the DeepFashion benchmark demonstrate the superiority of our method over the state of the arts for PGPIS. Code is available at https://***/YanzuoLu/CFLD. Copyright © 2024, The Authors. All rights reserved.

关键词： Textures

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DQFORMER: Dynamic Query Transformer for Lane Detection

DQFORMER: Dynamic Query Transformer for Lane Detection

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

作者： Hao Yang Shuyuan Lin Runqing Jiang Yang Lu Hanzi Wang Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Xiamen China College of Information Science and Technology / College of Cyber Security Jinan University Guangzhou China

Lane detection is one of the most important tasks in self-driving. The critical purpose of lane detection is the prediction of lane shapes. Meanwhile, it is challenging and difficult to determine lane instance positions before predicting lane shapes in an image. In this paper, we propose a top-down method called Dynamic Query Transformer (DQFormer), which uses a Dynamic Lane Queries (DLQs) module to predict lane shapes. Specifically, to accurately predict lane shapes, we propose a new framework for generating dynamic weights based on DLQs, which can focus on the context of lane shapes dynamically. Unlike existing transformer-based methods, the proposed DQFormer does not require setting a fixed number of lane queries, so it is suitable for various scenes. In addition, we further propose a Line Voting Module (LVM) which collects votes from other lanes to enhance lane features, to determine lane instance positions. Extensive experiments demonstrate that DQFormer outperforms several state-of-the-art methods on two popular lane detection benchmarks (i.e., CULane and TuSimple).

关键词： Shape Lane detection Signal processing Benchmark testing Transformers Acoustics Task analysis

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