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arXiv 2025年

作者： Papazafeiropoulos, Anastasios Kourtessis, Pandelis Kaklamani, Dimitra I. Venieris, Iakovos S. Communications and Intelligent Systems Research Group University of Hertfordshire HatfieldAL10 9AB United Kingdom Microwave and Fiber Optics Laboratory Greece Intelligent Communications and Broadband Networks Laboratory School of Electrical and Computer Engineering National Technical University of Athens Zografou Athens15780 Greece

The recent combination of the rising architectures, known as stacked intelligent metasurface (SIM) and holographic multiple-input multiple-output (HMIMO), drives toward breakthroughs for next-generation wireless communication systems. Given the fact that the number of elements per surface of the SIM is much larger than the base station (BS) antennas, the acquisition of the channel state information (CSI) in SIM-aided multi-user systems is challenging, especially when a line-of-sight (LoS) component is present. Thus, in this letter, we address the channel procedure under conditions of Rician fading by proposing a protocol in terms of a minimum mean square error (MMSE) estimator for wave-based design in a single phase. Moreover, we derive the normalized mean square error (NMSE) of the suggested estimator, and provide the optimal phase shifts minimising the NMSE. Numerical results illustrate the performance of the new channel estimation protocol. © 2025, CC BY.

关键词： Mean square error

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Empirical Validation of software Integration Practices in Global software Development

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SN computer Science 2020年第3期1卷 1-23页

作者： Ilyas, Muhammad Khan, Siffat Ullah Rashid, Nasir Software Engineering Research Group (SERG_UOM) Department of Computer Science & IT University of Malakand Lower Dir District Chakdara Pakistan

software complexity accrues, as modern societies are highly dependent on large-scale, software-intensive systems that increasingly operate within an environment of continuous availability. Global software development (GSD) vendors strive to reduce this complexity by decomposing the target software product into various components that are developed in-house, outsourced or purchased as commercial off-the-shelf (COTS) components. Subsequently, these components are integrated into a final working product. However, there is a lack of specific practices to be followed by GSD vendors in the software integration phase. In our previous study, we identified a list of nine software integration critical success factors (CSFs) for GSD vendors. In order to assist GSD vendors in adoption of the identified CSFs, we conducted another Systematic Literature Review (SLR) for identification of practices and validated the results using questionnaire survey. We identified 116 software integration practices/solutions that will help GSD vendors in trimming down the complexity of their software integration process. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Empirical study Global software development Practices software integration Solutions Systematic literature review

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Towards Evidence-Based Cybersecurity Assessment of Programmable Systems to Ensure the Protection of Critical IT Infrastructure

Towards Evidence-Based Cybersecurity Assessment of Programma...

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IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications

作者： Oleg Illiashenko Vyacheslav Kharchenko Oleg Odarushchenko Department of computer systems and networks National aerospace university “KhAI” Kharkiv Ukraine Software Engineering & Dependable Computing Laboratory Institute of Information Science and Technologies “Alessandro Faedo”—ISTI CNR Pisa Italy Research and Production Enterprise RPC Radics LLC Kropyvnytskyi Ukraine

The paper is devoted to developing scientific principles, methods, means, and information technology of model-oriented verification and evidence-based assessment using functional safety and cybersecurity cases for programmable systems of critical applications (PSCA). In particular, information and control systems important for the safety of nuclear power plants (NPPs), aerospace systems, railway domains, etc., developed using Field Programmable Gate Arrays (FPGAs) and hardware platforms are reviewed. The goal is to ensure the guaranteed completeness and reliability of its functional safety and cybersecurity assessment by developing and implementing a set of formal and semi-formal methods and tools that consider defects of different nature – physical, design, trojans, and vulnerabilities that can be attacked and lead to a fatal system failure, which results in damaging the critical IT infrastructure. The methods shown are based on integrating algebraic, tabular, graph models, and case assessment methodology. The methods are implemented as appropriate technologies for evidence-based verification and evaluation of PSCA. For formal methods, a prototype of a translator of Very High-Speed Integrated Circuits Hardware Description Language (VHDL) code is developed into an algebra of behaviors. It provides evidence-based verification and a framework and tools to design reports on assessing the cybersecurity and functional safety of programmable systems.

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Towards Anti-Collision Coordination for UAVs with Serverless Edge Computing

Towards Anti-Collision Coordination for UAVs with Serverless...

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IEEE International Conference on Cloud engineering (IC2E)

作者： Tobias Pfandzelter David Bermbach Robert Vilter Ingo Friese Sergiy Melnyk Qiuheng Zhou Hans D. Schotten Scalable Software Systems Group Technische Universität Berlin & Einstein Center Digital Future Berlin Germany Aviation Engineering Group Technical University of Applied Sciences Wildau Wildau Germany Deutsche Telekom AG Berlin Germany Intelligent Networks German Research Center for Artificial Intelligence (DFKI) Kaiserslautern Germany Institute for Wireless Communication and Navigation University of Kaiserslautern (RPTU) Kaiserslautern Germany

ISBN: (数字)9798331528690

ISBN: (纸本)9798331528706

Autonomously flying unmanned aerial vehicles in logistics, agriculture, and other domains will take up considerable airspace in the future. In areas with a high amount of air traffic, ground coordination to detect and mitigate encounters with collision risks is required. We propose a serverless approach to build and deploy such a system using edge computing resources. Serverless abstractions allow domain experts to focus on implementing the application logic while the underlying platform manages constrained resources and low-latency service access.

关键词： Autonomous aerial vehicles Agriculture Logic Low latency communication Edge computing Logistics

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Uplink Control Information Based Clustered Access and Discontinuous Reception Management for mMTC

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IEEE Communications Letters 2025年

作者： Wang, Shao-Di Wang, Chang-Long Wang, Hui-Ming Zhou, Feng Leung, Victor C. M. Xidian University Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education Xi’an710071 China Xi’an Jiaotong University School of Information and Communication Engineering Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an710049 China Shenzhen MSU BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Uplink control information (UCI) and discontinuous reception (DRX) play important roles for massive machine type communication (mMTC). Despite their standalone significance, a conspicuous gap exists in comprehensively exploring the synergy between UCI and DRX concerning both energy conservation and latency reduction for mMTC. In this letter, we propose a UCI-based clustered access and DRX management (UCADM) scheme that strikes a balance between these two critical performance metrics, reducing energy usage while preserving low latency in uplink-downlink interaction for mMTC. Specifically, we present a strictly feasible solution for the proposed UCADM scheme by using the spectral radius and the constraint projection, which can effectively solve DRX switching operations as indicated by scheduling information from UCI. Simulation results confirm the effectiveness of our scheme. © 1997-2012 IEEE.

关键词： Information management

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A Comprehensive Approach to Identify Stroke based on Real-time Medical Data

A Comprehensive Approach to Identify Stroke based on Real-ti...

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Cognitive Robotics and Intelligent Systems (ICC - ROBINS), International Conference on

作者： M. Maragatharajan Shanmugapriya S M. B. Sudhan Deeptha R. Sankar Muthu Paramasivam School of Computing Science and Engineering VIT Bhopal University Kothrikalan Madhya Pradesh India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Srivilliputhur Tamilnadu India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai Tamilnadu Department of Information Technology SRM Institute of Science and Technology Ramapuram Chennai Tamilnadu India Distinguished Software Engineer Palo Alto Networks Inc Santa Clara California USA

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

Strokes or cardiovascular disorders continue to be a major global health concern. Proactive intervention and timely detection greatly reduce the morbidity and death related to strokes. The goal of this research is to build a prediction model using machine learning techniques to assess a person’s risk of stroke based on a variety of sociodemographic and health characteristics. The dataset used in this study consists of a large collection of medical records. It contains important details including age, gender, BMI, smoking habits, heart disease, hypertension, and the type of residential property. Using this information, a Decision Tree Classifier is trained to predict the likelihood of strokes, enabling early detection and customized treatment plans. A Decision Tree Classifier with a predetermined maximum depth is used in the proposed methodology’s rigorous data preprocessing, which includes handling missing values, encoding categorical features, and developing models. Both the training and testing datasets are used to evaluate the model’s performance using evaluation measures including accuracy, precision, and recall. The model’s insights provide important characteristics that influence the prediction of stroke risk, assisting medical professionals in focused risk assessment and preventive intervention. The proposed method has the potential to improve healthcare practices by facilitating prompt interventions and personalized treatment for stroke risk individuals. The proposed model has resulted in 86.78% accuracy for this dataset by using the traditional machine learning algorithms. This work highlights the value of predictive modelling in the healthcare industry by demonstrating how it may improve early detection techniques and lessen the impact of strokes on public health.

关键词： Training Machine learning algorithms Machine learning Predictive models Stroke (medical condition) Real-time systems Risk management

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An Efficient Multi-band Infrared Small Objects Detection Approach for Low-Altitude Artificial Intelligence of Thing

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IEEE Internet of Things Journal 2025年

作者： Peng, Yuhuai Wang, Jing Wang, Wenqian Liu, Lei Atiquzzaman, Mohammed Guizani, Mohsen Dustdar, Schahram Northeastern University School of Computer Science and Engineering Shenyang110819 China Zhiyuan Research Institute Hangzhou310024 China Northeastern University School of Computer Science and Engineering Shenyang110819 China Xidian University State Key Laboratory of Integrated Service Networks Xi'an710071 China University of Oklahoma School of Computer Science NormanOK73019 United States Mohamed Bin Zayed University of Artificial Intelligence Department of Machine Learning Masdar United Arab Emirates Technische Universität Wien Distributed Systems Group Vienna1040 Austria

As a cutting-edge technology of low-altitude Artificial Intelligence of Thing (AIoT), UAV object detection significantly enhances the surveillance services capabilities of low-altitude AIoT. However, the difficulty of object detection is exacerbated by the high proportion of small and obscure objects in the captured images. To address the mentioned challenges, we present an efficient multi-band infrared small object detection approach for low-altitude intelligent surveillance services. Firstly, we propose the Multi-band infrared Image Fusion algorithm based on Cascade-GAN (MIF-CGAN), which produces fused images with high information entropy and high contrast. Then, the Transformer-based Multi-scale Dense Small Object Detection (MsDSOD) algorithm is proposed. The algorithm consists of the Global-Local Object Detection (G-LOD) network, the Object Dense Area Extraction (O-DAE) module, and the Weighted Boxes Fusion (WBF) module. It extracts small objects features at different scales from infrared images and fuses the global and local detection results to accurately identify small objects in dense scenes. Furthermore, compared to the traditional algorithms, the mean Average Precision (mAP) of MsDSOD is improved by 0.80% and the Average Precision in small object detection (APs) is improved by 0.72%. The proposed algorithm is optimally suited to deal with complex scenes with dense small objects and background occlusion. © 2014 IEEE.

关键词： Infrared imaging

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A Model-Driven Approach for IoT-Based Monitoring Systems in Industry 4.0 4

A Model-Driven Approach for IoT-Based Monitoring Systems in ...

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4th International Conference on Smart Cities, Internet of Things and Applications, SCIoT 2020

作者： Ziaei, Mahdi Zamani, Bahman Bohlooli, Ali University of Isfahan Mdse Research Group Department of Software Engineering Isfahan Iran University of Isfahan Faculty of Computer Engineering Isfahan Iran

ISBN: (纸本)9781728196114

Industry 4.0 provides a framework for applying new technologies in industrial environments to boost the efficiency and intelligence. A recently blossomed technology in Industry 4.0 is Internet of Things (IoT), which allows us to create a smart environment by connecting various equipment. One of the main applications of IoT in a smart factory is to design monitoring systems, which helps put the behavior of devices under permanent and comprehensive supervision. However, the rapid growth and change in the monitoring facilities creates a big challenge for people who either want to use that equipment in Industry 4.0, or want to update the systems to benefit from this technology. To address this problem, this paper presents new approach based on model-driven engineering paradigm, for simplifying the design and development of real-Time monitoring systems in an industrial environment. Our approach includes a domain-specific modeling language, a graphical editor, and model-To-code transformations that generate a hardware descriptive code, a mobile application, and a web application for a monitoring system. To evaluate the applicability of our approach, a scenario in the power industry has been designed, which offers user a VHDL code, a mobile application, and a web application for monitoring processes of the plant. © 2020 IEEE.

关键词： Modeling languages

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Cross-Domain Disentangled Learning for E-Commerce Live Streaming Recommendation

Cross-Domain Disentangled Learning for E-Commerce Live Strea...

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International Conference on Data engineering

作者： Yixin Zhang Yong Liu Hao Xiong Yi Liu Fuqiang Yu Wei He Yonghui Xu Lizhen Cui Chunyan Miao School of Software Shandong University China Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR) Shandong University China Alibaba Group China Alibaba-NTU Singapore Joint Research Institute Singapore School of Computer Science and Engineering Nanyang Technological University Singapore

E-commerce live streaming as an increasingly popular sales model has generated a significant amount of gross merchandise value (GMV) for e-commerce platforms. Live streaming recommendation systems (LSRS) of e-commerce aim to recommend the most appropriate live channels for users to motivate them to buy products. Existing LSRS methods focus only on the user’s interaction behaviors on the live channel (live domain) while ignoring the user’s behaviors and intentions on the e-commerce product (product domain). As a result, the user’s consistent purchase intentions in the cross-domain are not being fully captured, especially when user present differentiated purchase intentions in the cross-domain. How to disentangle user’s consistent intentions and domain-specific intentions in the cross-domain poses a challenge to the LSRS of e-commerce platforms. In this paper, we present a live channel recommendation method, named eLiveRec, developed for Taobao, one of the largest e-commerce platform in the world. Specifically, eLiveRec employs the disentangled encoder module to learn user’s cross-domain consistent intentions and domain-specific intentions. Then, an adaptive multi-task learning framework is developed to jointly optimize the multiple objectives (e.g., stay time, click goods bag, and click products after entering channel) related to live streaming recommendation. In this way, the performance of live streaming recommendation can be further improved and con-form to standard industry RS paradigms. Extensive experiments are conducted on a large-scale industry dataset collected from Taobao Live platform have been performed. Both online and offline experimental results indicate that eLiveRec consistently outperforms existing state-of-the-art baseline methods.

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Towards Fully Decoupled End-to-End Person Search

arXiv

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arXiv 2023年

作者： Zhang, Pengcheng Zheng, Jin Bai, Xiao Ning, Xin School of Computer Science and Engineering State Key Laboratory of Software Development Environment Jiangxi Research Institute Beihang University Beijing China Institute of Semiconductors Chinese Academy of Sciences Cognitive Computing Technology Joint Laboratory Wave Group Beijing China

End-to-end person search aims to jointly detect and re-identify a target person in raw scene images with a unified model. The detection task unifies all persons while the re-id task discriminates different identities, resulting in conflict optimal objectives. Existing works proposed to decouple end-to-end person search to alleviate such conflict. Yet these methods are still sub-optimal on one or two of the sub-tasks due to their partially decoupled models, which limits the overall person search performance. In this paper, we propose to fully decouple person search towards optimal person search. A task-incremental person search network is proposed to incrementally construct an end-to-end model for the detection and re-id sub-task, which decouples the model architecture for the two sub-tasks. The proposed task-incremental network allows task-incremental training for the two conflicting tasks. This enables independent learning for different objectives thus fully decoupled the model for persons earch. Comprehensive experimental evaluations demonstrate the effectiveness of the proposed fully decoupled models for end-to-end person search. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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