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arXiv 2020年

作者： Li, Feng Lam, Kwok-Yan Jia, Min Zhao, Jun Li, Xiuhua Wang, Li School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou310018 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China School of Big Data & Software Engineering Chongqing University Chongqing401331 China College of Marine Electrical Engineering Dalian Maritime University Dalian116026 China

This paper presents a blockchain-based approach for securing spectrum sharing in multi-beam satellite systems. Satellite spectrum is a scarce resource that requires highly efficient management schemes for optimized sharing by network users. However, spectrum sharing is vulnerable to attacks by malicious protocol participants. In order to ensure efficient spectrum management in the face of dishonest satellite users or cyber attackers, it is important for spectrum sharing mechanism to provide transparency and traceability of the trading process so as to enable the system to detect, and hence eliminate, unauthorized access by malicious users. We address these requirements by proposing the use of blockchain which, apart from its ability to provide transparency and traceability, ensures an immutable means for keeping track of user trading reputation. Besides, in order to address the practical constraints of heterogeneous user nodes, we also propose the use of edge computing to support users with limited computing power. In this paper, we propose a blockchain-based spectrum trading framework and, based on which, a multibeam satellite spectrum sharing algorithm for interference pricing and heterogeneous spectrum demands is devised to improve the efficiency of satellite spectrum. By leveraging on the system characteristics of blockchain, a dynamic spectrum sharing mechanism with traceability, openness and transparency for whole trading process is presented. Numerical results are also provided to evaluate the system benefits and spectrum pricing of the proposed mechanism. © 2020, CC BY.

关键词： Blockchain

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RETRACTED ARTICLE: Digital forensics system based on dynamic path prediction and the competitiveness of cross-border e-commerce SMEs

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Personal and Ubiquitous Computing 2021年第1期25卷 11-11页

作者： Wu, Chunqiong Yan, Bingwen Yu, Rongrui Yu, Baoqin Zhou, Xiukao Yu, Yanliang Chen, Na Business College Yango University Fuzhou City China Big Data Business Intelligence Engineering Research Center of Fujian University Fuzhou City China Software Engineering Institute Lanzhou Institute Technology Lanzhou City China

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Modeling and Simulation of Office Desk Illumination Using ZEMAX

Modeling and Simulation of Office Desk Illumination Using ZE...

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International Conference on Electrical, Communication, and Computer engineering (ICECCE)

作者： Awais Ahmed Muhammad Tanveer Riaz Ali Raza Aurang Zaib Muhammad Azeem Akbar Muhammad Bilal Sarwar School of electrical engineering The University of Faisalabad Faisalabad Pakistan UET Lahore Faisalabad Pakistan School of Big Data and Software Engineering Chongqing University Chongqing China

ISBN: (纸本)9781728138268

Different lighting techniques to laminate an office are enclosed, implemented and compared in this research article to describe the different lighting solutions with maximum achievable efficiency and minimum losses. Those sensitive parameters that can affect the efficiency of lighting are addressed and their drawbacks are compared. It is also shown that tackling with sensitive parameters in turn produces dramatic changes in output light efficiency, operating voltages and hence, operational costs.

关键词： Layout Optical design techniques Lighting Carbon dioxide

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Identity-aware attribute recognition via real-time distributed inference in mobile edge clouds

arXiv

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arXiv 2020年

作者： Xu, Zichuan Zhou, Pan Wu, Jiangkai Ren, Jiankang Xia, Qiufen Liang, Huizhi Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Dalian China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China School of Software Dalian University of Technology Dalian China School of Computer Science Dalian University of Technology Dalian China Key Laboratory for Ubiquitous Network Service Software of Liaoning Province International School of Information Science and Engineering Dalian University of Technology Dalian China Department of Computer Science University of Reading Reading United Kingdom

With the development of deep learning technologies, attribute recognition and person re-identification (re-ID) have attracted extensive attention and achieved continuous improvement via executing computing-intensive deep neural networks in cloud datacenters. However, the datacenter deployment cannot meet the real-time requirement of attribute recognition and person re-ID, due to the prohibitive delay of backhaul networks and large data transmissions from cameras to datacenters. A feasible solution thus is to employ mobile edge clouds (MEC) within the proximity of cameras and enable distributed inference. In this paper, we design novel models for pedestrian attribute recognition with re-ID in an MEC-enabled camera monitoring system. We also investigate the problem of distributed inference in the MEC-enabled camera network. To this end, we first propose a novel inference framework with a set of distributed modules, by jointly considering the attribute recognition and person re-ID. We then devise a learning-based algorithm for the distributions of the modules of the proposed distributed inference framework, considering the dynamic MEC-enabled camera network with uncertainties. We finally evaluate the performance of the proposed algorithm by Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile edge computing

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What is the focus of energy supply chain relationship management during geopolitical risks? Evidence from the stock market based on transaction cost economics

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Energy Economics 2025年 148卷

作者： Shizhen Bai Hao He Chunjia Han Mu Yang Wen-Long Shang Weijia Fan School of Management Harbin University of Commerce Harbin 150028 PR China Business School Birkbeck University of London Malet Street Bloomsbury London WC1E 7HX United Kingdom Centre for Transport Studies Imperial College London London SW7 2AZ United Kingdom Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 PR China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen 518060 PR China

This study examines B2B relationship management practices in energy supply chains under geopolitical risks and their impact on stock market volatility. We analyze U.S. energy firms' earnings conference calls across three dimensions: intra-firm managerial behavior, inter-firm relationship management, and relationship evaluation. Employing Word2Vec and structural topic modeling, we identify specific B2B relationship management strategies deployed during geopolitical uncertainties. Our findings reveal varied effects of these strategies on market volatility. Controls on energy prices, carbon-neutral technological alliances, and ESG assessments contribute to increased volatility. Conversely, strategies focused on maintaining demand in changing markets, long-term shareholder return management, strategic industry communication, and enhanced technological R&D partnerships reduce volatility. Notably, R&D cooperation in LNG technology, customer interaction improvements, and operational efficiency initiatives show no significant effect. Furthermore, market attention moderates the relationship between B2B relationship management and market volatility. Disclosures emphasizing long-term investments correlate with decreased market share and reduced volatility, while short-term investment communications increase both market share and volatility. It suggests energy firms must balance short-term performance with long-term stability in their disclosure strategies. This research advances the application of artificial intelligence in B2B relationship management under geopolitical uncertainty, enriching theoretical foundations in energy supply chain management while providing practical guidance for firms navigating complex global risks.

关键词： Business-to-business (B2B) relationship management Energy supply chain Geopolitical risks Stock market volatility Stock market attention Disruptive technologies Earning conference call

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Multi-Scale Hierarchical Context-Aware Survival Prediction Network based on whole slide images

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Biomedical Signal Processing and Control 2025年 110卷

作者： Jinmiao Song Yatong Hao Hui Zhai Shuang Zhao Tao Ning Shan Huang Xiaodong Duan School of Software Xinjiang University Urumqi 830046 Xinjiang China School of Computer Science and Engineering Dalian Minzu University Dalian 116650 Liaoning China State Ethnic Affairs Commission Key Laboratory of Big Data Applied Technology Dalian Minzu University Dalian 116650 Liaoning China Dalian Key Laboratory of Digital Technology for Minzu Culture Dalian Minzu University Dalian 116650 Liaoning China The First Affiliated Hospital Xinjiang Medical University Urumqi 830011 Xinjiang China

In high-resolution whole slide images (WSIs), multi-scale information is crucial for survival prediction. However, due to the ultra-large sizes of WSIs, existing methods have not fully utilized the multi-scale information at the gigapixel scale. Additionally, WSI-based survival prediction, as a patient-level multiple instance learning (MIL) task, is far more complex than WSI-level MIL which presents a significant challenge. To address these challenges, we propose a Multi-Scale Hierarchical Context-Aware Survival Prediction Network (MSASurv). This network progressively explores the tumor microenvironment, tumor-associated tissue structures, and patient-level tumor heterogeneity in WSIs. We validated our approach using five types of cancer from The Cancer Genome Atlas (TCGA), including 3,068 H&E-stained WSIs. Experimental results demonstrate that our proposed MSASurv algorithm outperforms previous weakly supervised methods by 3.3% to 16.8%. The code and models are publicly available at https://***/yatonghao/MSASurv .

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A Brief Review of Explainable Artificial Intelligence in Healthcare

SSRN

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SSRN 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 Pardalo, 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 Swinburne University of Technology HawthornVIC3122 Australia 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, The Authors. All rights reserved.

关键词： Decision making

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Weighted Linear Multiple Kernel Learning for Saliency Detection 2nd

Weighted Linear Multiple Kernel Learning for Saliency Detect...

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2nd EAI International Conference on Robotic Sensor Networks, ROSENET 2018

作者： Zhou, Quan Wu, Jinwen Fan, Yawen Zhang, Suofei Wu, Xiaofu Zheng, Baoyu Jin, Xin Lu, Huimin Latecki, Longin Jan National Engineering Research Center of Communications and Networking Nanjing University of Posts and Telecommunications Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China College of Information Engineering China University of Geosciences Wuhan China School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China National Engineering Research Center of Communications and Networking Nanjing University of Posts and Telecommunications Nanjing China Department of Cyber Security Beijing Electronic Science and Technology Institute Beijing China CETC Big Data Research Institute Co. Ltd. Guizhou Guiyang China Department of Mechanical and Control Engineering Kyushu Institute of Technology Kitakyushu Japan Department of Computer and Information Sciences Temple University PhiladelphiaPA United States

ISBN: (纸本)9783030177621

This paper presents a novel saliency detection method based on weighted linear multiple kernel learning (WLMKL), which is able to adaptively combine different contrast measurements in a supervised manner. Three commonly used bottom-up visual saliency operations are first introduced, including corner-surround contrast (CSC), center-surround contrast (CESC), and global contrast (GC). Then these contrast measures are fed into our WLMKL framework to produce the final saliency map. We show that the assigned weights for each contrast feature maps are always normalized in our WLMKL formulation. In addition, the proposed approach benefits from the advantages of the contribution of each individual contrast operation, and thus produces more robust and accurate saliency maps. The extensive experimental results show the effectiveness of the proposed model, and demonstrate the combination is superior to individual subcomponent. © 2020, Springer Science and Business Media Deutschland GmbH. All rights reserved.

关键词： Center-surround contrast Corner-surround contrast Global contrast Multiple kernel learning Saliency detection

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Deep well construction of big data platform based on multi-source heterogeneous data fusion

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International Journal of Internet Manufacturing and Services 2019年第4期6卷 371-388页

作者： Zhang, Yu Wang, Yange Ding, Hongwei Li, Yongzhen Bai, Yanping School of Electrical and Information Engineering Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture No. 1 Zhanlanguan Road Xicheng District Beijing100044 China China University of Mining and Technology No. 16 Qinghua Road Haidian District Beijing100083 China School of Software Beijing University of Technology Beijing100124 China Network Information Center Beijing University of Civil Engineering and Architecture Beijing100044 China College of Management Capital Normal University Beijing100048 China

At present, energy saving and emission reduction had become a problem of great concern for mankind. At the same time, there were some problems in the mining industry, such as waste of resources, low efficiency and easy occurrence of industrial accidents. Therefore, this paper had designed a deep well construction big data platform. The high precision and bear great pressure sensors were added to the system to solve the difficult problem of collecting information in deep wells by ordinary sensors. The multi-source heterogeneous data fusion algorithm was added to the system to solve the problem that the format of the data acquisition was different. In conclusion, the completion of the platform could achieve data monitoring in the process of mines. It not only helps to enhance the safety of mine construction, but also provides data analytical tools for further theoretical research of mine construction. © 2019 Inderscience Enterprises Ltd.

关键词： big data

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Practice and Study of Training Model for Professional Master Degree in software engineering

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计算机教育 2018年第12期 7-12页

作者： Yi Zhang Xinyi Yang Jun Liao Junhao Wen Qingyu Xiong Department of application software School of big data & software engineering Chongqing University

The culture of professional degree graduate students is a new form of postgraduate education in China. It focuses on cultivating high-level and applied talents compared with original academic degree graduate students. Considering about the source of full-time professional degree graduate students in domain of software engineering and the current college educational system, this paper makes a few beneficial explorations about curriculum, practice teaching, process management and puts forward the mode and method to improve full-time professional degree graduate education in domain of software engineering.

关键词： professional degree graduate students training mode software talents quality assurance

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