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Identity Based Encryption and Biometric Authentication Scheme for Secure Data Access in Cloud Computing

Chinese Journal of Electronics 2023年第2期21卷 254-259页

作者： Hongbing Cheng Chunming Rong Zhenghua Tan Qingkai Zeng State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Information Engineering Jiangsu City Vocation College Nanjing China Department of Electronic Engineering and Computer Science University of Stavanger Stavanger Norway Department of Electronic Systems Aalborg University Aalborg Denmark

Cloud computing will be a main information infrastructure in the future; it consists of many large datacenters which are usually geographically distributed and heterogeneous. How to design a secure data access for cloud computing platform is a big challenge. In this paper, we propose a secure data access scheme based on identity-based encryption and biometric authentication for cloud computing. Firstly, we describe the security concern of cloud computing and then propose an integrated data access scheme for cloud computing, the procedure of the proposed scheme include parameter setup, key distribution, feature template creation, cloud data processing and secure data access control. Finally, we compare the proposed scheme with other schemes through comprehensive analysis and simulation. The results show that the proposed data access scheme is feasible and secure for cloud computing.

关键词： Access control Cloud computing Identity-based encryption Biometrics (access control) Authentication Process control Memory

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Heterogeneous Domain Adaptation via Correlative and Discriminative Feature Learning

引用

IEEE Transactions on Multimedia 2025年

作者： Lu, Yuwu Lin, Dewei Shen, Linlin Zhou, Yicong Pan, Jiahui South China Normal University School of Artificial Intelligence Foshan528225 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518055 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China University of Nottingham Ningbo China Department of Computer Science Ningbo315100 China University of Macau Department of Computer and Information Science China

Heterogeneous domain adaptation seeks to learn an effective classifier or regression model for unlabeled target samples by using the well-labeled source samples but residing in different feature spaces and lying different distributions. Most recent works have concentrated on learning domain-invariant feature representations to minimize the distribution divergence via target pseudo-labels. However, two critical issues need to be further explored: 1) new feature representations should be not only domain-invariant but also category-correlative and discriminative, and 2) alleviating the negative transfer caused by the incorrect pseudo-labeling target samples could boost the adaptation performance during the iterative learning process. To address these issues, in this paper, we put forward a novel heterogeneous domain adaptation method to learn category-correlative and discriminative representations, referred to as correlative and discriminative feature learning (CDFL). Specifically, CDFL aims to learn a feature space where class-specific feature correlations between the source and target domains are maximized, the divergences of marginal and conditional distribution between the source and target domains are minimized, and the distances of inter-class distribution are forced to be maximized to ensure the discriminative ability. Meanwhile, a selective pseudo-labeling procedure based on the correlation coefficient and classifier prediction is introduced to boost class-specific feature correlation and discriminative distribution alignment in an iteration way. Extensive experiments certify that CDFL outperforms the state-of-the-art algorithms on five standard benchmarks. © 1999-2012 IEEE.

关键词： Contrastive Learning

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HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud Computing System 14

HealthEdge: A Machine Learning-Based Smart Healthcare Framew...

引用

14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Hennebelle, Alain Materwala, Huned Ismail, Leila Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates Lab School of Computing and Information Systems The University of Melbourne Australia

Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. Numerical experiments and comparative analysis were carried out between the two most used machine learning algorithms in the literature, Random Forest (RF) and Logistic Regression (LR), using two real-life diabetes datasets. The results show that RF predicts diabetes with 6% more accuracy on average compared to LR. © 2023 Elsevier B.V.. All rights reserved.

关键词： Edge computing

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Insights into software Development Approaches: Mining Q&A Repositories

arXiv

引用

arXiv 2023年

作者： Khan, Arif Ali Khan, Javed Ali Akbar, Muhammad Azeem Zhou, Peng Fahmideh, Mahdi M3S Empirical Software Engineering Research Unit University of Oulu OuluFI-90014 Finland Department of Software Engineering University of Science and Technology Bannu28100 Pakistan Software Engineering Department Lappeenranta-Lahti University of Technology Lappeenranta15210 Finland College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Business University of Southern Queensland ToowoombaQLD4350 Australia

Context: software practitioners adopt approaches like DevOps, Scrum, and Waterfall for high-quality software development. However, limited research has been conducted on exploring software development approaches concerning practitioners’ discussions on Q&A forums. Objective: We conducted an empirical study to analyze developers’ discussions on Q&A forums to gain insights into software development approaches in practice. Method: We analyzed 13,903 developers’ posts across Stack Overflow (SO), software engineering Stack Exchange (SESE), and Project Management Stack Exchange (PMSE) forums. A mixed method approach, consisting of the topic modeling technique (i.e., Latent Dirichlet Allocation (LDA)) and qualitative analysis, is used to identify frequently discussed topics of software development approaches, trends (popular, difficult topics), and the challenges faced by practitioners in adopting different software development approaches. Findings: We identified 15 frequently mentioned software development approaches topics on Q&A sites and observed an increase in trends for the top-3 most difficult topics requiring more attention. Finally, our study identified 49 challenges faced by practitioners while deploying various software development approaches, and we subsequently created a thematic map to represent these findings. Conclusions: The study findings serve as a useful resource for practitioners to overcome challenges, stay informed about current trends, and ultimately improve the quality of software products they develop. © 2023, CC BY.

关键词： Project management

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Video2Reward: Generating Reward Function from Videos for Legged Robot Behavior Learning

arXiv

引用

arXiv 2024年

作者： Zeng, Runhao Zhou, Dingjie Liang, Qiwei Liu, Junlin Li, Hui Huang, Changxin Li, Jianqiang Hu, Xiping Sun, Fuchun Artificial Intelligence Research Institute Shenzhen MSU-BIT University China College of Mechatronics and Control Engineering Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer Science and Technology Tsinghua University China

Learning behavior in legged robots presents a significant challenge due to its inherent instability and complex constraints. Recent research has proposed the use of a large language model (LLM) to generate reward functions in reinforcement learning, thereby replacing the need for manually designed rewards by experts. However, this approach, which relies on textual descriptions to define learning objectives, fails to achieve controllable and precise behavior learning with clear directionality. In this paper, we introduce a new video2reward method, which directly generates reward functions from videos depicting the behaviors to be mimicked and learned. Specifically, we first process videos containing the target behaviors, converting the motion information of individuals in the videos into keypoint trajectories represented as coordinates through a video2text transforming module. These trajectories are then fed into an LLM to generate the reward function, which in turn is used to train the policy. To enhance the quality of the reward function, we develop a video-assisted iterative reward refinement scheme that visually assesses the learned behaviors and provides textual feedback to the LLM. This feedback guides the LLM to continually refine the reward function, ultimately facilitating more efficient behavior learning. Experimental results on tasks involving bipedal and quadrupedal robot motion control demonstrate that our method surpasses the performance of state-of-the-art LLM-based reward generation methods by over 37.6% in terms of human normalized score. More importantly, by switching video inputs, we find our method can rapidly learn diverse motion behaviors such as walking and running. Copyright © 2024, The Authors. All rights reserved.

关键词： Robots

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Grammar Normalization to Support Automated Merging 1

Grammar Normalization to Support Automated Merging

引用

1st Intermountain engineering, Technology and Computing, IETC 2020

作者： Roberts, Rosetta Griffith, Isaac Empirical Software Engineering Laboratory Informatics and Computer Science Idaho State University PocatelloID83208 United States

ISBN: (纸本)9781728142913

Introduction: Current solutions to multilingual parsing, for programming languages, are flawed. Current implementations are either limited in scope or difficult to develop and maintain. The development of multilingual parsers requires the combination of multiple base grammars, leading to a maintenance headache as these grammars evolve. Such a repetitive process should be automated. Objective: Develop an approach to normalize a grammar in such a way that the grammar is equivalent to the original, but in a state which reduces the effort to merge grammars by reducing ambiguity in automated merge decision making. Methods: This normalization procedure transforms grammars such that each production is one of two forms. Additionally, the normalized grammars maintain a set of additional constraints we identified as useful. A pilot study demonstrating this approach was conducted on three existing grammars. Results: The normalization algorithm was shown to correctly normalize the three grammars. Conclusions: This work presents a normalization method towards easing the development of automatically merging programming language grammars. © 2020 IEEE.

关键词： Merging

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The Dataflow Runtime Environment of DFC 1

引用

21st International Conference on Parallel and Distributed Computing, Applications, and Technologies, PDCAT 2020

作者： Zhang, Jing Li, Jinrong Du, Zheng Shu, Jiwu Luo, Qiuming NHPCC/Guangdong Key Laboratory of Popular HPC College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9783030692445

ISBN: (纸本)9783030692438

In this paper, we introduce the DFC dataflow language and its runtime environment. DFC runtime library is in charge of constructing the DAG of the dataflow graph, firing the DFC tasks and the synchronizations between the tasks of successive passes. Basing on an elaborately implemented thread pool and queued Active Data, DFC runtime shows an ideal performance comparing with DSPatch. The experiment of a simple dataflow graph shows that DFC has better performance for the cases that the parallelism beneath the core number, while DSPatch shows a better scalability for the cases of the parallelism exceed the core number. As DFC is still a prototype language, it lacks the ability to construct the DAG dynamically, which leads to low efficiency when coding for a well-structured dataflow graph. © 2021, Springer Nature Switzerland AG.

关键词： Data flow analysis

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State/Action-based Fairness Verification for Non-determinism

引用

Chinese Journal of Electronics 2023年第4期20卷 603-606页

作者： XU Guangquan PANG Shanchen GAO Honghao XIAO Yingyuan LI Xiaohong FENG Zhiyong School of Computer Science and Technology Tianjin University Tianjin China Department of Computer Science and Technology Tsinghua University Beijing China College of Information Science and Engineering Shandong University of Science and Technology Qingdao China School of Computer Engineering and Science Shanghai University Shanghai China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China

Most of the existing work about fairness verification was carried out by checking the fairness based on state, action, or process respectively. It is hard to resolve the fairness issue for complex software systems, especially in life-critical systems and safety-critical systems. In this paper, we verify the fairness based on both state and action, which is realized mainly by introducing an extended Kripke structure and four kinds of fairness (weak global/local fairness and strong global/local fairness). The experimental results show the superiority of our method by the savings in memory (2.08%) and time consumption (26.45%).

关键词： Memory management software systems

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An Approach towards Merging Grammars 1

An Approach towards Merging Grammars

引用

1st Intermountain engineering, Technology and Computing, IETC 2020

作者： Griffith, Isaac D. Roberts, Rosetta Empirical Software Engineering Laboratory Informatics and Computer Science Idaho State University PocatelloID83208 United States

ISBN: (纸本)9781728142913

Introduction: Since the introduction of Island Grammars, they have been successfully used for a variety of tasks, including impact analysis, multi-lingual parsing, and source code identification. However, there has been no attempt to automate the generation of Island Grammars. Objective: This research considers the development of a method to automate the merging of Island Grammar components. The goal of this is to facilitate the development of an approach to automate the creation of Island Grammars. The result of this is the reduction in initial effort and maintenance effort required for Island Grammar engineering. Methods: We develop an automated approach to merge the components of grammars. To evaluate this approach, we conducted two experiments, each using a factorial design of five replications each. We randomly selected pairs of grammars from each of three size categories to assess the effects of the merging process on the maintenance effort and complexity of the generated grammars. Results: We found that in nearly all cases, the application of this merging approach reduces the maintenance effort and complexity of the grammars. Limitations: The primary limitation of this research is that this approach is currently limited to grammars written in the Antlr4 grammar format. Conclusions: This work presents the initial steps towards the automated construction of Island and Tolerant Grammars. We have shown that this approach to merging grammar components follows suit with the expectations of Island and Tolerant grammars (reduction in maintenance effort and complexity). © 2020 IEEE.

关键词： Merging

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The Compiler of DFC: A Source Code Converter that Transform the Dataflow Code to the Multi-threaded C Code 1

引用

21st International Conference on Parallel and Distributed Computing, Applications, and Technologies, PDCAT 2020

作者： Du, Zheng Zhang, Jing Li, Jinrong Du, Haixin Shu, Jiwu Luo, Qiuming Department of Computer Science and Technology Tsinghua University Beijing China NHPCC/Guangdong Key Laboratory of Popular HPC College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9783030692445

ISBN: (纸本)9783030692438

The working principle of DFC compiler is introduced in this article. DFC is a grammatical extension of standard C language, with special DF function which describe the dependence of computing DAG. DFC compiler, dfcc, is used to convert the DFC source codes to standard C codes with the assistance of multi-threaded library. The lexical rules and grammatical rules are used to setup the AST of DFC codes, and then it is converted to the AST of standard C without DF function nodes. The derived AST is printed into a text file, a normal C file, and finally is processed by GCC to obtain the executable file. The experiment gives some demonstration of that converting details, and the memory footprint is studied to show an ideal scalability. © 2021, Springer Nature Switzerland AG.

关键词： Program compilers

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