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arXiv 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalos, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, CC BY-NC-ND.

关键词： Decision making

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Evolution of cooperation in spatial heterogeneous social dilemmas with dynamic self-adaptive matching

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Applied Mathematics and Computation 2026年 508卷

作者： Xiaopeng Li Xuexue Yang Xiuli Zhang Weiwei Han Yan Xu College of Big Data Statistics Guizhou University of Finance and Economics Guiyang Guizhou 550025 PR China Shanxi Key Laboratory of Intelligent Optimization Computing and Blockchain Technology Taiyuan Normal University Taiyuan Shanxi 030619 PR China School of Information Engineering Tianjin University of Commerce Tianjin 300134 PR China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin 300384 PR China

From the perspective of real-world interaction scenarios, the assumption of monotypic social dilemma engagement or fixed role allocations within heterogeneous social dilemmas contexts fundamentally misrepresents the dynamics of human social cognition. Empirical evidence consistently suggests that behavioral plasticity is achieved through adaptive identity switching across heterogeneous social dilemma environments. Herein, we develop an evolutionary game model simultaneously encompassing multiple social dilemmas (prisoner's dilemma game and snowdrift game), wherein agents engage in polymorphic game-environment governed by their own adaptive characteristic. Specifically, during the strategy update phase, if the agent x ′ s current strategy is retained, its strategy stickiness θ x increases one unit; otherwise, the metric reverts to the initial value of 0. Additionally, if θ x reaches the maximum value of 100, it signifies the termination of the old agent and the initialization of a new one, with the metric also reverting to 0. Moreover, agent whose strategy adherence is beyond predefined threshold θ t h is classified as possessing strong social dilemma resolution capacity, while that is below the threshold is allocated to mild-conflict scenarios. Subsequently, through sufficient Monte Carlo simulation, we systematically investigate the evolutionary trajectories and stable-state characteristics of cooperation in dynamically adaptive multi-social dilemma systems, yielding several insightful findings.

关键词： Evolution of cooperation Evolutionary dynamics Dynamic self-adaptive Heterogeneous social dilemmas

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Joint Deep Learning for RGB-D Action Recognition 33

Joint Deep Learning for RGB-D Action Recognition

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33rd IEEE International Conference on Visual Communications and Image Processing, VCIP 2018

作者： Qin, Xiaolei Ge, Yongxin Zhan, Liuwei Li, Guangrui Huang, Sheng Wang, Hongxing Chen, Feiyu School of Big Data and Software Engineering Chongqing University Chongqing China College of Optpelectronic Engineering Chongqing University Chongqing China

ISBN: (纸本)9781538644584

Recent approaches in RGB-based and depth-based human action recognition achieved outstanding performance respectively, which demonstrate the effectiveness of RGB and depth modalities for action classification, however it is infrequent to consider them both. Currently, available multimodal-based methods of action recognition suffer from some limitations, including non-end-to-end training, violent fusion and inefficiency. In this paper, we propose a novel joint deep learning (JDL) model which is capable of: 1) jointly optimizing the object of classification and feature extraction through a novel end-to-end two-stream deep learning model, 2) refining common-specific features via introducing the constraint of similarity loss in high-level, and 3) using 2D convolution kernel instead of 3D convolution kernel during feature extraction for gaining the efficiency. The experiments on two challenging datasets show the promising performance of our architecture. © 2018 IEEE.

关键词： Feature extraction

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Attribute-Based Weighted Keyword Search Scheme Supporting Multi-Search Mechanism in Fog Computing

Attribute-Based Weighted Keyword Search Scheme Supporting Mu...

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IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)

作者： Shulan Wang Yuan Li Haiyan Wang Xi Zhang Jianyong Chen College of Big Data and Internet Shenzhen University Shenzhen P.R. China College of Computer Science and Software Engineering Shenzhen University Shenzhen P.R. China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen P.R. China

ISBN: (数字)9781728165509

ISBN: (纸本)9781728165516

With the popularity of intelligent terminal, fog computing as a new generation of internet technology can integrate the computing power of terminals and improve the effective utilization of social resources. At the same time, more and more data owners store ciphertext in the network. Hence, the advent of searchable encryption (SE) scheme enables searchers to search ciphertext without downloading all of it. However, the existing SE scheme cannot identify the matching degree of keywords and ciphertext. Besides, these schemes can only provide one or two search mechanism. In this paper, we propose a ciphertext-policy attribute-based weighted keyword searchable encryption scheme (ABWKS-MSM) in fog computing in order to solve the matching degree issue and provide multi-search mechanism, so that it becomes more friendly to fog computing applications. Furthermore, the proposed weighted keyword model can reduce the computational and storage cost. Finally, the performance analysis is also given, which proves that our scheme is efficient and feasible in fog computing.

关键词： data security Searchable encryption Multisearch mechanism Ciphertext-policy attribute-based encryption Fog computing Ciphertext data Security computer information security Scheme Key Search searcher Downloading computational power effective utilization KEYWORD cryptography

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Deep semantic dictionary learning for multi-label image classification

arXiv

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arXiv 2020年

作者： Zhou, Fengtao Huang, Sheng Xing, Yun School of Big data & Software Engineering Chongqing University Shazheng street NO.174 Shapingba District Chongqing400044 China Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society Shazheng street NO.174 Shapingba District Chongqing400044 China

Compared with single-label image classification, multi-label image classification is more practical and challenging. Some recent studies attempted to leverage the semantic information of categories for improving multi-label image classification performance. However, these semantic-based methods only take semantic information as type of complements for visual representation without further exploitation. In this paper, we present an innovative path towards the solution of the multi-label image classification which considers it as a dictionary learning task. A novel end-to-end model named Deep Semantic Dictionary Learning (DSDL) is designed. In DSDL, an auto-encoder is applied to generate the semantic dictionary from class-level semantics and then such dictionary is utilized for representing the visual features extracted by Convolutional Neural Network (CNN) with label embeddings. The DSDL provides a simple but elegant way to exploit and reconcile the label, semantic and visual spaces simultaneously via conducting the dictionary learning among them. Moreover, inspired by iterative optimization of traditional dictionary learning, we further devise a novel training strategy named Alternately Parameters Update Strategy (APUS) for optimizing DSDL, which alternately optimizes the representation coefficients and the semantic dictionary in forward and backward propagation. Extensive experimental results on three popular benchmarks demonstrate that our method achieves promising performances in comparison with the state-of-the-arts. Our codes and models have been released1 © 2020, CC BY-NC-SA.

关键词： Image classification

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How good is query optimizer in Spark? 14th

How good is query optimizer in Spark?

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14th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2018

作者： Ren, Zujie Yun, Na Li, Youhuizi Wan, Jian Wang, Yuan Yu, Lihua Fan, Xinxin School of Computer Science Hangzhou Dianzi University Hangzhou China Department of Software Engineering Zhejiang University of Science and Technology Hangzhou China Key Enterprise Research Institute of NetEase Big Data of Zhejiang Province Netease Hangzhou Network Co. Ltd. Hangzhou China

ISBN: (纸本)9783030129804

In the big data community, Spark plays an important role and is used to process interactive queries. Spark employs a query optimizer, called Catalyst, to interpret SQL queries to optimized query execution plans. Catalyst contains a number of optimization rules and supports cost-based optimization. Although query optimization techniques have been well studied in the field of relational database systems, the effectiveness of Catalyst in Spark is still unclear. In this paper, we investigated the effectiveness of rule-based and cost-based optimization in Catalyst, meanwhile, we obtained a set of comparative experiments by varying the data volume and the number of nodes. It is found that even when applied query optimizations, the execution time of most TPC-H queries were slightly reduced. Some interesting observations were made on Catalyst, which can enable the community to have a better understanding and improvement of the query optimizer in Spark. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Query processing

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Automatic Recognition of Business Process Images

Automatic Recognition of Business Process Images

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IEEE International Conference on Computer Science and Educational Informatization (CSEI)

作者： Qingyi Kang Yong Yu Fei Dai School of Software Yunnan University Kunming China School of Software Key Laboratory in Software Engineering of Yunnan Province Yunnan University Kunming China School of Big Data and Intelligent Engineering Southwest Forestry University Kunming China

ISBN: (纸本)9781728123097

The Business Process Modelling Notation (BPMN) is a defacto standard for modelling business processes in the early phases of systems development. The increased adoption of business process management approaches has led organizations to model large collections of BPMN business process models. The mix of constructs in BPMN business process models can lead to semantic error. The ability to take a picture of BPMN business process models and analyze the semantic correctness of BPMN business process images is thus a desirable feature. In this paper, we propose an automatic recognition method to transform BPMN business process images into Petri nets, for which efficient analysis techniques are available. The proposed recognition has been implemented as a tool, in conjunction with existing Petri net-based tools, that enables the soundness analysis of business process images. This transformation can led to the identification of deficiencies in the BPMN business process images.

关键词： Task analysis Petri nets Logic gates Image edge detection Semantics

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A Framework of Blockchain-Based Security for WBANs

A Framework of Blockchain-Based Security for WBANs

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Smart BlockChain (SmartBlock), International Conference on

作者： Han Deng Xiangwei Meng Jun Guo Erhui Xi Hui Zhao Big Data Development and Research Center Guangzhou College of Technology and Business Guangzhou China School of Computer science and Engineering Hunan University of Science and Technology Xiangtan China School of Software Engineering Quanzhou University of Information Engineering Quanzhou China Guangzhou College of Technology and Business Guangzhou China Educational Information Technology Laboratory Henan University Kaifeng China

Purposed to collect and store human physiological data, WBANs as a telemedicine system play a significant role in sensor networks. The complex network environment presents a formidable challenge to the stability of the system and the security of the database. The traditional centralized architecture is to store the registration data of the sensor node and the patient's physiological data in the hub node. When the hub node is subjected to Denial of Service attacks (DoS) and Distributed Denial of Service attacks (DDoS), there will be a single point of failure, so that the sensor node cannot establish a communication connection with the hub node. In this paper, a cloud server layer architecture based on blockchain technology is proposed for WBNAs to ensure system stability and patient data security. Meanwhile, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme is used to ensure patient physiological data safety.

关键词： Wireless communication Cloud computing Architecture Distributed databases Blockchain Medical services Permission

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STAN: Towards Describing Bytecodes of Smart Contract

arXiv

引用

arXiv 2020年

作者： Li, Xiaoqi Chen, Ting Luo, Xiapu Zhang, Tao Yu, Le Xu, Zhou Department of Computing Hong Kong Polytechnic University Hong Kong Hong Kong Center for Cybersecurity University of Electronic Science and Technology of China Chengdu China Faculty of Information Technology Macau University of Science and Technology China School of Big Data and Software Engineering Chongqing University Chongqing China

More than eight million smart contracts have been deployed into Ethereum, which is the most popular blockchain that supports smart contract. However, less than 1% of deployed smart contracts are open-source, and it is difficult for users to understand the functionality and internal mechanism of those closed-source contracts. Although a few decompilers for smart contracts have been recently proposed, it is still not easy for users to grasp the semantic information of the contract, not to mention the potential misleading due to decompilation errors. In this paper, we propose the first system named STAN to generate descriptions for the bytecodes of smart contracts to help users comprehend them. In particular, for each interface in a smart contract, STAN can generate four categories of descriptions, including functionality description, usage description, behavior description, and payment description, by leveraging symbolic execution and NLP (Natural Language Processing) techniques. Extensive experiments show that STAN can generate adequate, accurate and readable descriptions for contract’s bytecodes, which have practical value for users. Copyright © 2020, The Authors. All rights reserved.

关键词： Smart contract

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Gesture Recognition Based on Accelerometer and Gyroscope and Its Application in Medical and Smart Homes

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Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint Conference on Web and big data, APWeb-WAIM 2018

作者： Su, Huaizhou Li, Yande Liu, Li School of Big Data and Software Engineering Chongqing University Chongqing China College of Information Science and Engineering Lanzhou University Lanzhou China

ISBN: (纸本)9783030012977

In recent years, with the rapid development of science and technology, artificial intelligence has gradually entered various fields, and medicine is no exception. For the patients with hand or leg disability after hand injury surgery, the rapid development of artificial intelligence undoubtedly also contributes to improving their life a lot. Lately, gesture-based human-computer interaction has further accelerated its research due to its natural and intuitive interaction, but building a powerful gesture recognition system is still based on traditional visual methods such as the one proposed in [1] based on multiple cameras which uses color matching for motion detection and tracking and applies it to vehicle control;and another gesture recognition algorithm using stereo imaging and color vision proposed in [2]. The popularity of accelerometers and gyroscopes opens up a new path for gesture recognition. This paper presents a gesture recognition algorithm based on neural network using accelerometer and gyroscope, and applies it to a simple mobile game and smart socket. The mobile game will be used in the hand rehabilitation training of patients after hand injury surgery, leg disability patients inconvenience, smart socket will help them more easily and quickly manage household electricity. © 2018, Springer Nature Switzerland AG.

关键词： Human computer interaction

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