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Multidisciplinary engineering and Applied Science (ICMEAS), International Conference on

作者： Yusuf Olayinka Imam-Fulani Nasir Faruk Aliyu D. Usman Abubakar Abdulkarim Abdoulie M.S Tekanyi Abdulmalik S. Yaro Emmanuel Alozie Salisu Garba Kayode S. Adewole Abdulkarim A. Oloyede Olugbenga A. Sowande Bashir Abdullahi Baba Abdulwaheed Musa Agbotiname Lucky Imoize Yinusa A. Adediran Hawau I. Olagunju Lawan S. Taura Department of Telecommunication Science University of Ilorin Ilorin Nigeria Department of Electronics and Telecommunication Engineering Ahmadu Bello University Zaria Nigeria Department of Information Technology Sule Lamido University Kafin Hausa Nigeria Directorate of Information and Communication Technology Sule Lamido University Kafin Hausa Nigeria Department of Electrical Engineering Ahmadu Bello University Zaria Nigeria Department of Electrical and Telecommunication Engineering Kampala International University Kampala Uganda Department of Informatics and Qualitative Methods Faculty of Informatics and Management University of Hradec Králové Hradec Králové Czech Republic Department of Software Engineering Sule Lamido University Kafin Hausa Nigeria Department of Computer Science University of Ilorin Ilorin Nigeria Department of Electrical and Computer Engineering Kwara State University Malete Nigeria Institute for Intelligent Systems University of Johannesburg Johannesburg South Africa Department of Electrical and Electronics Engineering University of Lagos Lagos Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum Germany Department of Electrical and Electronics Engineering University of Ilorin Ilorin Nigeria Department of Physics Sule Lamido University Kafin Hausa Nigeria

5G communication systems provide an end-to-end wireless connection to billions of users and devices across the globe. The quality of signals received during radio communication is notably influenced by the behavior of the radio propagation channel. A major parameter used in characterizing this channel is the Path Loss Exponent (PLE). Several works that have estimated and analyzed the PLE mostly considered the effect of distance and carrier frequency. However, the effect of base station antenna height and channel bandwidth on the PLE for 5G networks have not been adequately considered. To address this, the impact of antenna height of base station and channel bandwidth on the PLE within the 5G Frequency Range 1 (FR1) frequencies was investigated in this study, specifically at 800, 3500, and 5900 MHz. The licensed EDX Signal Pro software® with Cirrus high resolution global terrain and clutter data base was utilized to model, simulate and analyze the PLE for Kano City, Nigeria. Results showed that for the tested frequencies, an increase in either base station height or channel bandwidth leads to a significant reduction in the PLE. This study can be utilized by network planning engineers and the wireless research community to further improve network implementation and optimization toward understanding the behavior of signal propagation in 5G networks and beyond.

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A Multilevel Intelligent Assistant for Multilevel Social Network Analysis 10

A Multilevel Intelligent Assistant for Multilevel Social Net...

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10th IEEE International Conference on Intelligent Systems, IS 2020

作者： Tianxing, Man Zhukova, Nataly Tsochev, Georgi ITMO University St. Petersburg Russia Laboratory of Computer-Information Systems and Software Engineering St. Petersburg Institute for Informatics and Automation Russian Academy of Sciences St. Petersburg Russia Bulgarian Academy of Sciences Laboratory of Telematics Sofia Bulgaria

ISBN: (纸本)9781728154565

In recent years, social network data analysis is an emerging field. Multilevel social network analysis could help researchers to figure out comprehensive influencing factors. But the raw data extracted from life is complicated and inoperable so that the entire analysis process is multi-step and changeable. The characteristics of social network datasets at different levels are diverse. There is no general data analysis algorithms for each level. The characteristics of the data and the requirements of the task are important basis for choosing suitable analysis methods. But it is hard for non-expert researchers. This paper proposes an intelligent multilevel assistant for multilevel social network analysis and implements it based on ontology technology. Algorithms at different levels process the corresponding data forms until the appropriate output model is generated. The use of ontology technology makes this framework extensible and understandable. Such a framework is significant for non-experts. © 2020 IEEE.

关键词： Ontology

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Visualizing Multi-dimensional State Spaces Using Selective Abstraction

Visualizing Multi-dimensional State Spaces Using Selective A...

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Euromicro Conference on software engineering and Advanced Applications (SEAA)

作者： Christian Burghard Luca Berardinelli AVL List GmbH Graz Austria Institute of Business Informatics -Software Engineering Johannes Kepler University Linz Austria

ISBN: (数字)9781728195322

ISBN: (纸本)9781728195339

Domain-specific languages (DSLs) are popular for many reasons, such as increasing productivity for developers and improving communication with domain experts. Both textual and graphical DSLs are viable solutions with complementary pros and cons: while graphical DSLs shorten the learning curve and facilitate documentation and communication, textual DSLs aim at higher productivity thanks to more efficient editor functionalities. This paper presents the industrial experience on the adoption of a hybrid approach combining an existing textual DSL with a read-only graphical state machine representation (visualization), equipped with a selective abstraction functionality that offers user-specific, highly configurable views on states and transitions. Our approach is the result of an evolutionary process to improve the modelling experience, relying on frequent user feedback. We argue that a well-tailored visualization is a suitable way to shorten the learning curve and ease the adoption of model-driven approaches in industrial settings.

关键词： Unified modeling language DSL Visualization software Testing Data models Analytical models

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Fuzzy Attack Tree: Assessing Cyberattack Risk Using Attack Tree and Fuzzy Logic

Fuzzy Attack Tree: Assessing Cyberattack Risk Using Attack T...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Nitin Naik Paul Jenkins Paul Grace Dishita Naik Jingping Song Shaligram Prajapat Durgesh Mishra Longzhi Yang Tossapon Boongoen Natthakan Iam-On School of Informatics and Digital Engineering Aston University United Kingdom Cardiff School of Technologies Cardiff Metropolitan University United Kingdom Birmingham City University United Kingdom Software College Northeastern University China International Institute of Professional Studies Devi Ahilya University India School of Computer Science & Information Technology Symbiosis University of Applied Sciences India Department of Computer and Information Sciences Northumbria University United Kingdom Department of Computer Science Aberystwyth University United Kingdom

Organisations and users have been experiencing significant rises in cyberattacks and their severity, which means that they require a greater awareness and understanding of the anatomy of cyberattacks, to prevent and mitigate their effects. In analysing cyberattacks, there are a number of different approaches that may be used to assess their potential risks and effects. However, these are utilised in specific types of cyberattacks and their analysis, which means they cannot be applied in every situation or cyberattack. Moreover, several other factors may influence the decision to use these approaches, such as cost, complexity, skills and adaptability. As a result, continuous research to design and enhance these approaches is undertaken to produce a generic, cost-effective, easy and adaptable approach. This paper presents one such approach to assess the risk of cyberattacks utilising an attack tree and fuzzy logic. An attack tree is a systematic and illustrative method for describing an attack on a system and analysing its taxonomy and other aspects. Subsequently, the probability and risk of each leaf node in the attack tree are calculated using the proposed formulas. Finally, fuzzy logic enables decision making based on imprecise data and heuristics to obtain the overall risk of attack. This proposed approach comprises systematic steps to accomplish an assessment of any cyberattack and its associated risks in an uncomplicated and effective manner, enabling its prevention and mitigation to be determined. The paper illustrates an application of the proposed approach to assess the risk of an information theft attack on an organisation, which can then be utilised to assess the risk of other cyberattacks.

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Towards Automating the Industrial Design of CPS: The Experience of Volvo Construction Equipment

SSRN

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SSRN 2023年

作者： Bergelin, Johan Berardinelli, Luca Bilic, Damir Bruneliere, Hugo Cicchetti, Antonio Dehghani, MohammadHadi Sipio, Claudio Di Miranda, James Rahimi, Abbas Rubei, Riccardo Mälardalen University Universitetsplan 1 P.O. Box 883 Västerås72123 Sweden Institute of Business Informatics - Software Engineering Johannes Kepler University Altenberger Straße 69 Linz4040 Austria Volvo Construction Equipment Bolindervägen 5 Eskilstuna63185 Sweden Università degli Studi dell’Aquila Via Vetoio 2 L’Aquila66010 Italy 4 Rue Alfred Kastler Nantes44000 France

Supporting the design of more and more complex Cyber-Physical Systems (CPSs) frequently poses new challenges to both industrial practitioners and academics. In this context, the adoption of cutting-edge paradigms and associated techniques, such as MDE and AI, can offer new opportunities for improving the automation of CPS design. While such paradigms and techniques are already widely used in the research community to support system design activities, there is a need to fill the gap between academia and industrial practitioners. Indeed, system specification is still performed largely manually in a majority of real industrial projects. In this paper, we report on the ongoing collaboration between industrial and academic partners of the AIDOaRt European project towards a model-based framework for CPS engineering at Volvo Construction Equipment. We identified key challenges and corresponding solutions to enhance the automation of the CPS design process via prescriptive modeling, model transformations, model views, modeling process mining, and AI-based modeling recommendations. As a proof of concept, the resulting software architecture is applied to a real case study that allows for discussion of the proposed solution’s contributions and current limitations. © 2023, The Authors. All rights reserved.

关键词： Cyber Physical System

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PhenoProfiler: Advancing Phenotypic Learning for Image-based Drug Discovery

arXiv

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arXiv 2025年

作者： Li, Bo Zhang, Bob Zhang, Chengyang Zhou, Minghao Huang, Weiliang Wang, Shihang Wang, Qing Li, Mengran Zhang, Yong Song, Qianqian PAMI Research Group Department of Computer and Information Science University of Macau Taipa China Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial Intelligence Beijing University of Technology Beijing China Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida Florida United States Faculty of Applied Sciences Macao Polytechnic University Taipa China School of Intelligent Systems Engineering Sun Yat-sen University Guangdong China Department of Cancer Biology Wake Forest School of Medicine Winston SalemNC United States

In the field of image-based drug discovery, capturing the phenotypic response of cells to various drug treatments and perturbations is a crucial step. This process involves transforming high-throughput cellular images into quantitative representations for downstream analysis. However, existing methods require computationally extensive and complex multi-step procedures, which can introduce inefficiencies, limit generalizability, and increase potential errors. To address these challenges, we present PhenoProfiler, an innovative model designed to efficiently and effectively extract morphological representations, enabling the elucidation of phenotypic changes induced by treatments. PhenoProfiler is designed as an end-to-end tool that processes whole-slide multi-channel images directly into low-dimensional quantitative representations, eliminating the extensive computational steps required by existing methods. It also includes a multi-objective learning module to enhance robustness, accuracy, and generalization in morphological representation learning. PhenoProfiler is rigorously evaluated on large-scale publicly available datasets, including over 230,000 whole-slide multi-channel images in end-to-end scenarios and more than 8.42 million single-cell images in non-end-to-end settings. Across these benchmarks, PhenoProfiler consistently outperforms state-of-the-art methods by up to 20%, demonstrating substantial improvements in both accuracy and robustness. Furthermore, PhenoProfiler uses a tailored phenotype correction strategy to emphasize relative phenotypic changes under treatments, facilitating the detection of biologically meaningful signals. UMAP visualizations of treatment profiles demonstrate PhenoProfiler’s ability to effectively cluster treatments with similar biological annotations, thereby enhancing interpretability. These findings establish PhenoProfiler as a scalable, generalizable, and robust tool for phenotypic learning, offering transformative advancement

关键词： Benchmarking

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Detection and evaluation of abnormal user behavior based on quantum generation adversarial network

arXiv

引用

arXiv 2022年

作者： Pan, Minghua Wang, Bin Tao, Xiaoling Zheng, Shenggen Situ, Haozhen Li, Lvzhou Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin541004 China Peng Cheng Laboratory Shenzhen518055 China College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China Institute of Quantum Computing and Software School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China

Quantum computing holds tremendous potential for processing high-dimensional data, capitalizing on the unique capabilities of superposition and parallelism within quantum states. As we navigate the noisy intermediate-scale quantum (NISQ) era, the exploration of quantum computing applications has emerged as a compelling frontier. One area of particular interest within the realm of cyberspace security is Behavior Detection and Evaluation (BDE). Notably, the detection and evaluation of internal abnormal behaviors pose significant challenges, given their infrequent occurrence or even their concealed nature amidst vast volumes of normal data. In this paper, we introduce a novel quantum behavior detection and evaluation algorithm (QBDE) tailored for internal user analysis. The QBDE algorithm comprises a Quantum Generative Adversarial Network (QGAN) in conjunction with a classical neural network for detection and evaluation tasks. The QGAN is built upon a hybrid architecture, encompassing a Quantum Generator (GQ) and a Classical Discriminator (DC). GQ, designed as a parameterized quantum circuit (PQC), collaborates with DC, a classical neural network, to collectively enhance the analysis process. To address the challenge of imbalanced positive and negative samples, GQ is employed to generate negative samples. Both GQ and DC are optimized through gradient descent techniques. Through extensive simulation tests and quantitative analyses, we substantiate the effectiveness of the QBDE algorithm in detecting and evaluating internal user abnormal behaviors. Our work not only introduces a novel approach to abnormal behavior detection and evaluation but also pioneers a new application scenario for quantum algorithms. This paradigm shift underscores the promising prospects of quantum computing in tackling complex cybersecurity challenges. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Pareto-aware Neural Architecture Generation for Diverse Computational Budgets

arXiv

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arXiv 2022年

作者： Guo, Yong Chen, Yaofo Zheng, Yin Chen, Qi Zhao, Peilin Chen, Jian Huang, Junzhou Tan, Mingkui The School of Software Engineering South China University of Technology China Max Planck Institute for Informatics Germany The Weixin Group Tencent China The School of Computer Science The University of Adelaide Australia Tencent AI Lab Tencent China The Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China The Pazhou Laboratory Guangzhou China

Designing feasible and effective architectures under diverse computational budgets, incurred by different applications/devices, is essential for deploying deep models in real-world applications. To achieve this goal, existing methods often perform an independent architecture search process for each target budget, which is very inefficient yet unnecessary. More critically, these independent search processes cannot share their learned knowledge (i.e., the distribution of good architectures) with each other and thus often result in limited search results. To address these issues, we propose a Pareto-aware Neural Architecture Generator (PNAG) which only needs to be trained once and dynamically produces the Pareto optimal architecture for any given budget via inference. To train our PNAG, we learn the whole Pareto frontier by jointly finding multiple Pareto optimal architectures under diverse budgets. Such a joint search algorithm not only greatly reduces the overall search cost but also improves the search results. Extensive experiments on three hardware platforms (i.e., mobile device, CPU, and GPU) show the superiority of our method over existing methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Pareto principle

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CS-REP: MAKING SPEAKER VERIFICATION NETWORKS EMBRACING RE-PARAMETERIZATION

arXiv

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arXiv 2021年

作者： Zhang, Ruiteng Wei, Jianguo Lu, Wenhuan Zhang, Lin Ji, Yantao Xu, Junhai Lu, Xugang College of Intelligence and Computing Tianjin University Tianjin China Computer College Qinghai Nationalities University Xining China National Institute of Informatics Tokyo Japan School of Software Engineering Xi’an Jiaotong University Xi’an China National Institute of Information and Communications Technology Kyoto Japan

Automatic speaker verification (ASV) systems, which determine whether two speeches are from the same speaker, mainly focus on verification accuracy while ignoring inference speed. However, in real applications, both inference speed and verification accuracy are essential. This study proposes cross-sequential re-parameterization (CS-Rep), a novel topology re-parameterization strategy for multi-type networks, to increase the inference speed and verification accuracy of models. CS-Rep solves the problem that existing re-parameterization methods are not suitable for typical ASV backbones. When a model applies CS-Rep, the training-period network utilizes a multi-branch topology to capture speaker information, whereas the inference-period model converts to a time-delay neural network (TDNN)-like plain backbone with stacked TDNN layers to achieve the fast inference speed. Based on CS-Rep, an improved TDNN with friendly test and deployment called Rep-TDNN is proposed. Compared with the state-of-the-art model ECAPA-TDNN, Rep-TDNN increases the actual inference speed by about 50% and reduces the EER by 10%. Copyright © 2021, The Authors. All rights reserved.

关键词： Speed

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Potential Indicator for Continuous Emotion Arousal by Dynamic Neural Synchrony 4th

Potential Indicator for Continuous Emotion Arousal by Dyna...

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4th International Workshop on Human Brain and Artificial Intelligence, HBAI 2024

作者： Pan, Guandong Wu, Zhaobang Yang, Yaqian Wang, Xin Liu, Longzhao Zheng, Zhiming Tang, Shaoting School of Computer Science and Engineering Beihang University Beijing100191 China Institute of Artificial Intelligence Beihang University Beijing100191 China Key Laboratory of Mathematics Informatics and Behavioral Semantics Beihang University Beijing100191 China Institute of Medical Artificial Intelligence Binzhou Medical University Yantai264003 China Zhongguancun Laboratory Beijing100094 China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang University Beijing100191 China PengCheng Laboratory Shenzhen518055 China State Key Lab of Software Development Environment Beihang University Beijing100191 China

ISBN: (纸本)9789819640003

The need for automatic and high-quality emotion annotation is paramount in applications such as continuous emotion recognition and video highlight detection, yet achieving this through manual human annotations is challenging. Inspired by inter-subject correlation (ISC) utilized in neuroscience, this study introduces a novel Electroencephalography (EEG) based ISC methodology that leverages a single-electrode and feature-based dynamic approach. Our contributions are three folds: Firstly, we reidentify two potent emotion features suitable for classifying emotions-first-order difference (FD) an differential entropy (DE). Secondly, through the use of overall correlation analysis, we demonstrate the heterogeneous synchronized performance of electrodes. This performance aligns with neural emotion patterns established in prior studies, thus validating the effectiveness of our approach. Thirdly, by employing a sliding window correlation technique, we showcase the significant consistency of dynamic ISCs across various features or key electrodes in each analyzed film clip. Our findings indicate the method’s reliability in capturing consistent, dynamic shared neural synchrony among individuals, triggered by evocative film stimuli. This underscores the potential of our approach to serve as an indicator of continuous human emotion arousal. The implications of this research are significant for advancements in affective computing and the broader neuroscience field, suggesting a streamlined and effective tool for emotion analysis in real-world applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electroencephalography (EEG) Emotion Annotation Inter-subject Correlation

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