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arXiv 2024年

作者： Bencomo, Nelly Cabot, Jordi Chechik, Marsha Cheng, Betty H.C. Combemale, Benoit Wąsowski, Andrzej Zschaler, Steffen Durham University Computer Science Durham Durham United Kingdom Luxembourg Institute of Science and Technology University of Luxembourg Esch-sur-Alzette Luxembourg University of Toronto Department of Computer Science TorontoON Canada Michigan State University Department of Computer Science and Engineering 428 S. Shaw Lane 4115 Engineering Building East LansingMI48824 United States University of Rennes IRISA and Inria Campus de Beaulieu Rennes35042 France IT University of Copenhagen Department of Computer Science Rued Langgaards Vej 7 Copenhagen2300 Denmark King’s College London Department of Informatics Bush House 30 Aldwych LondonWC2B 4BG United Kingdom

Modern software-based systems operate under rapidly changing conditions and face ever-increasing uncertainty. In response, systems are increasingly adaptive and reliant on artificial-intelligence methods. In addition to the ubiquity of software with respect to users and application areas (e.g., transportation, smart grids, medicine, etc.), these high-impact software systems necessarily draw from many disciplines for foundational principles, domain expertise, and workflows. Recent progress with lowering the barrier to entry for coding has led to a broader community of developers, who are not necessarily software engineers. As such, the field of software engineering needs to adapt accordingly and offer new methods to systematically develop high-quality software systems by a broad range of experts and non-experts. This paper looks at these new challenges and proposes to address them through the lens of Abstraction. Abstraction is already used across many disciplines involved in software development—from the time-honored classical deductive reasoning and formal modeling to the inductive reasoning employed by modern data science. The software engineering of the future requires Abstraction engineering—a systematic approach to abstraction across the inductive and deductive spaces. We discuss the foundations of Abstraction engineering, identify key challenges, highlight the research questions that help address these challenges, and create a roadmap for future research. © 2024, CC BY.

关键词： Application programs

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Dynamic hidden-variable network models

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Physical Review E 2021年第5期103卷 052307-052307页

作者： Harrison Hartle Fragkiskos Papadopoulos Dmitri Krioukov Network Science Institute Northeastern University Boston 02115 Massachusetts USA Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology 3036 Limassol Cyprus Northeastern University Departments of Physics Mathematics and Electrical & Computer Engineering Boston 02115 Massachusetts USA

Models of complex networks often incorporate node-intrinsic properties abstracted as hidden variables. The probability of connections in the network is then a function of these variables. Real-world networks evolve over time and many exhibit dynamics of node characteristics as well as of linking structure. Here we introduce and study natural temporal extensions of static hidden-variable network models with stochastic dynamics of hidden variables and links. The dynamics is controlled by two parameters: one that tunes the rate of change of hidden variables and another that tunes the rate at which node pairs reevaluate their connections given the current values of hidden variables. Snapshots of networks in the dynamic models are equivalent to networks generated by the static models only if the link reevaluation rate is sufficiently larger than the rate of hidden-variable dynamics or if an additional mechanism is added whereby links actively respond to changes in hidden variables. Otherwise, links are out of equilibrium with respect to hidden variables and network snapshots exhibit structural deviations from the static models. We examine the level of structural persistence in the considered models and quantify deviations from staticlike behavior. We explore temporal versions of popular static models with community structure, latent geometry, and degree heterogeneity. While we do not attempt to directly model real networks, we comment on interesting qualitative resemblances to real systems. In particular, we speculate that links in some real networks are out of equilibrium with respect to hidden variables, partially explaining the presence of long-ranged links in geometrically embedded systems and intergroup connectivity in modular systems. We also discuss possible extensions, generalizations, and applications of the introduced class of dynamic network models.

关键词： Complex systems Evolving networks Network structure Block models Evolving network models Fitness models

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Fitness dependent optimizer with neural networks for COVID-19 patients

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computer Methods and Programs in Biomedicine Update 2023年 3卷 100090页

作者： Abdulkhaleq, Maryam T. Rashid, Tarik A. Hassan, Bryar A. Alsadoon, Abeer Bacanin, Nebojsa Chhabra, Amit Vimal, S. Department of Computer Science and Engineering University of Kurdistan Hewler KRI Iraq Kurdistan Institution for Strategic Studies and Scientific Research Sulaimani KRI Iraq Department of Computer Networks Technical College of Informatics Sulaimani Polytechnic University Sulaimani KRI Iraq School of Computing and Mathematics Charles Sturt University Sydney Australia Information Technology Department Asia Pacific International College (APIC) Sydney Australia Singidunum University Danijelova 32 Belgrade 11000 Serbia Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India Dept of CSE Ramco Institute of Technology Tamilnadu Rajapalayam India

The Coronavirus, known as COVID-19, which appeared in 2019 in China, has significantly affected the global health and become a huge burden on health institutions all over the world. These effects are continuing today. One strategy for limiting the virus's transmission is to have an early diagnosis of suspected cases and take appropriate measures before the disease spreads further. This work aims to diagnose and show the probability of getting infected by the disease according to textual clinical data. In this work, we used five machine learning techniques (GWO_MLP, GWO_CMLP, MGWO_MLP, FDO_MLP, FDO_CMLP) all of which aim to classify Covid-19 patients into two categories (Positive and Negative). Experiments showed promising results for all used models. The applied methods showed very similar performance, typically in terms of accuracy. However, in each tested dataset, FDO_MLP and FDO_CMLP produced the best results with 100% accuracy. The other models' results varied from one experiment to the other. It is concluded that the models on which the FDO algorithm was used as a learning algorithm had the possibility of obtaining higher accuracy. However, it is found that FDO has the longest runtime compared to the other algorithms. The link to the Covid 19 models is found here: https://***/Tarik4Rashid4/covid19models © 2022

关键词： COVID 19 FDO Fitness dependent optimizer Machine learning Swarm intelligence

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Transformative Effects of ChatGPT on Modern Education: Emerging Era of AI Chatbots

arXiv

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arXiv 2023年

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, was released to provide coherent and useful replies based on analysis of large volumes of data. In this article, leading scientists, researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research seeks to improve our knowledge of ChatGPT capabilities and its use in the education sector, identifying potential concerns and challenges. Our preliminary evaluation concludes that ChatGPT performed differently in each subject area including finance, coding and maths. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within Generative AI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023, CC0.

关键词： Machine learning

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WcDT: World-centric Diffusion Transformer for Traffic Scene Generation

arXiv

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arXiv 2024年

作者： Yang, Chen He, Yangfan Tian, Aaron Xuxiang Chen, Dong Wang, Jianhui Shi, Tianyu Heydarian, Arsalan Liu, Pei Department of Computer Science and Informatics Cardiff University United Kingdom College of Libera Arts University of Minnesota - Twin Cities United States United States Agricultural & Biological Engineering Mississippi State University United States Information and Software Engineering University of Electronic Science and Technology of China China Transportation Research Institute University of Toronto Canada Link Lab & Civil and Environmental Engineering University of Virginia United States Intelligent Transportation Thrust The Hong Kong University of Science and Technology Guangzhou China

In this paper, we introduce a novel approach for autonomous driving trajectory generation by harnessing the complementary strengths of diffusion probabilistic models (a.k.a., diffusion models) and transformers. Our proposed framework, termed the "World-centric Diffusion Transformer"(WcDT), optimizes the entire trajectory generation process, from feature extraction to model inference. To enhance the scene diversity and stochasticity, the historical trajectory data is first preprocessed into "Agent Move Statement" and encoded into latent space using Denoising Diffusion Probabilistic Models (DDPM) enhanced with Diffusion with Transformer (DiT) blocks. Then, the latent features, historical trajectories, HD map features, and historical traffic signal information are fused with various transformer-based encoders that is used to enhance the interaction of agents with other elements in the traffic scene. The encoded traffic scenes are then decoded by a trajectory decoder to generate multimodal future trajectories. Comprehensive experimental results show that the proposed approach exhibits superior performance in generating both realistic and diverse trajectories, showing its potential for integration into automatic driving simulation systems. Our code is available at https://***/yangchen1997/WcDT. © 2024, CC BY.

关键词： Stochastic systems

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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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Automated Controller Placement for Software-Defined Networks to Resist DDoS Attacks

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computers, Materials & Continua 2021年第9期68卷 3147-3165页

作者： Muhammad Reazul Haque Saw Chin Tan Zulfadzli Yusoff Kashif Nisar Lee Ching Kwang Rizaludin Kaspin Bhawani Shankar Chowdhry Rajkumar Buyya Satya Prasad Majumder Manoj Gupta Shuaib Memon Faculty of Computing&Informatics Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Engineering Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu Sabah88400Malaysia Telekom Malaysia Research&Development TM Innovation CentreCyberjaya63000SelangorMalaysia National Center of Robotics and Automation Mehran University of Engineering&TechnologyJamshoroPakistan Department of Computer Science and Engineering Hanyang UniversitySeoul04763South Korea School of Electrical and Electronic Engineering Nanyang Technological University639798Singapore Cloud Computing and Distributed Systems Laboratory The University of MelbourneMelbourneVIC 3053Australia Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology(BUET)Dhaka1205Bangladesh Department of Electronics and Communication Engineering JECRC UniversityVidhaniJaipur303905India Auckland Institute of Studies Mt AlbertAucklandNew Zealand

In software-defined networks(SDNs),controller placement is a critical factor in the design and planning for the future Internet of Things(IoT),telecommunication,and satellite communication *** research has concentrated largely on factors such as reliability,latency,controller capacity,propagation delay,and energy ***,SDNs are vulnerable to distributed denial of service(DDoS)attacks that interfere with legitimate use of the *** ever-increasing frequency of DDoS attacks has made it necessary to consider them in network design,especially in critical applications such as military,health care,and financial services networks requiring high *** propose a mathematical model for planning the deployment of SDN smart backup controllers(SBCs)to preserve service in the presence of DDoS *** a number of input parameters,our model has two distinct ***,it determines the optimal number of primary controllers to place at specific locations or nodes under normal operating ***,it recommends an optimal number of smart backup controllers for use with different levels of DDoS *** goal of the model is to improve resistance to DDoS attacks while optimizing the overall cost based on the *** simulated results demonstrate that the model is useful in planning for SDN reliability in the presence of DDoS attacks while managing the overall cost.

关键词： SDN automated controller placement SBC ILP DDoS attack

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Impact of False Data Injection attacks on Decentralized Electric Vehicle Charging Protocols

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Transportation research Procedia 2021年 52卷 331-338页

作者： Nikos Piperigkos Aris S. Lalos Industrial Systems Institute ATHENA Research and Innovation Center 265 04 Platani Greece Department of Computer Engineering and Informatics University of Patras 265 04 Patras Greece Department of Electrical and Computer Engineering University of Patras 265 04 Patras Greece

Electric vehicles (EVs) gain great attention nowadays since the electrification of private and public transport has a great potential to reduce greenhouse gas emissions and mitigate oil dependency. However, the influx of a large number of electrical loads without any coordination could have adverse affects to the electrical grid. More importantly, the complexity in the coordination of a large number of EVs, pose critical challenges in ensuring overall system integrity. A typical attack found in the controllers of connected EVs is false data injection (FDI), which can be utilized to distort real energy demand and supply figures. Energy distribution requests may therefore be erroneous, which results in additional costs or more devastating hazards. The lack of a proper coordination scheme, robust to such cyber attacks could cause voltage magnitude drops and unacceptable load peaks. In this work, we study the impact of FDI attacks, on various decentralized charging protocols with reduced computational requirements. The proposed decentralized EV charging algorithms only require from each EV to solve a local problem, hence the proposed implementation require low computational resources. An extensive evaluation study highlights the strengths and weaknesses of the presented solutions which are based on iterative convex optimization solvers.

关键词： Electric Vehicles Coordinated Charging Decentralized Protocols False Data Injection Attacks

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MANAGING UNEXPECTED FAILURES OF NUCLEAR POWER PLANTS BY INCORPORATING ORGANIZATIONAL FACTORS INTO PROBABILISTIC RISK ASSESSMENT

MANAGING UNEXPECTED FAILURES OF NUCLEAR POWER PLANTS BY INCO...

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2021 International Topical Meeting on Probabilistic Safety Assessment and Analysis, PSA 2021

作者： Roy, Andrea Farshadmanesh, Pegah Sakurahara, Tatsuya Kee, Ernie Mohaghegh, Zahra Research Laboratory Department of Nuclear Plasma and Radiological Engineering Grainger College of Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Computer Science University of Illinois Urbana-Champaign United States Illinois Informatics Institute University of Illinois at Urbana-Champaign United States Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign United States

Organizational factors have been identified as key contributors to accidents and incidents in nuclear power plants (NPPs). The Data-Theoretic (DT) methodology, previously developed by some of the authors of this paper, helps quantify the impacts of organizational factors on plant risk scenarios in Probabilistic Risk Assessment (PRA). In the DT methodology, "data analytics" are guided by "theory," enhancing the completeness of "causality" derived from data and helping to avoid potentially misleading results from solely data-driven approaches. This paper advances the quantification of uncertainties (e.g., uncertainty associated with the PRA analyst's assessment of organizational factors) in the DT methodology. The DT methodology includes two submodules: (I) DT-BASE, which focuses on the development of detailed causal relationships based on a theory-building process and creates a baseline quantification utilizing analyst interpretation of information extracted from theoretical references and standards;(II) DT-SITE, which conducts data analytics (text mining) to update (add or delete links and nodes) the causal model structure developed in DT-BASE, quantify causal relationships using historical event databases, and update the results of baseline quantification in DT-BASE using Bayesian analysis. This paper adds uncertainty analysis to the DT-BASE submodule. The updated DT approach is implemented in a case study targeting NPP organizational "training quality" that influences the "training" performance shaping factor (PSF) in Human Reliability Analysis (HRA) associated with the plant PRA. © 2021 PSA. All Rights Reserved.

关键词： Nuclear power plants

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Resource Slicing Strategy for Services Co-Existence in Wireless Train Communication Network

Resource Slicing Strategy for Services Co-Existence in Wirel...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Qiao Ren Siyu Lin Yuanxuan Li Shichao Li Linghe Kong Shahid Mumtaz Bo Ai School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Locomotive and Car Research Institute China Academy of Railway Science Beijing China School of Information and Communication Guilin University of Electronic Technology Guilin China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Department of Applied Informatics Silesian University of Technology Akademicka Gliwice Poland Department of Engineering Nottingham Trent University Nottingham UK

Wireless train communication network (WLTCN) is a promising technology for intelligent rail vehicles. It is responsible for bearer of train control services (TCS) and passenger information services (PIS), with the latter mainly referring to multimedia services. The two services have notably different quality of service (QoS) requirements from traditional telecommunication services. To realize multiple services co-existence bearer with different quality of service (QoS) requirements in a single network, we propose a radio access network (RAN) slicing framework to fully utilize the bandwidth resource within a WLTCN in this paper. Based on the characteristics of TCS and PIS, the communications models for the two services are proposed. Next, the slicing strategy problem is formulated as the system bandwidth minimization problem, and then it is transformed into an equivalent problem as the non-convexity. We proposed a dual-decomposition based bandwidth allocation (DBA) algorithm to derive the closed-form expressions for the optimal resource allocation. Simulation results show that the proposed slicing strategy enables WLTCN to meet the QoS requirements for TCS and PIS with minimal bandwidth consumption.

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