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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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Towards Transmission-Friendly and Robust CNN Models over Cloud and Device

arXiv

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arXiv 2022年

作者： Ding, Chuntao Lu, Zhichao Juefei-Xu, Felix Boddeti, Vishnu Naresh Li, Yidong Cao, Jiannong The School of Computer and Information Technology Beijing Jiaotong University Beijing China The School of Software Engineering Sun Yat-sen University Zhuhai519082 China Alibaba Group United States The Department of Computer Science and Engineering Michigan State University East LansingMI48824 United States The Department of Computing The Hong Kong Polytechnic University Hong Kong

Deploying deep convolutional neural network (CNN) models on ubiquitous Internet of Things (IoT) devices has attracted much attention from industry and academia since it greatly facilitates our lives by providing various rapid-response services. Due to the limited resources of IoT devices, cloud-assisted training of CNN models has become the mainstream. However, most existing related works suffer from a large amount of model parameter transmission and weak model robustness. To this end, this paper proposes a cloud-assisted CNN training framework with low model parameter transmission and strong model robustness. In the proposed framework, we first introduce MonoCNN, which contains only a few learnable filters, and other filters are nonlearnable. These nonlearnable filter parameters are generated according to certain rules, i.e., the filter generation function (FGF), and can be saved and reproduced by a few random seeds. Thus, the cloud server only needs to send these learnable filters and a few seeds to the IoT device. Compared to transmitting all model parameters, sending several learnable filter parameters and seeds can significantly reduce parameter transmission. Then, we investigate multiple FGFs and enable the IoT device to use the FGF to generate multiple filters and combine them into MonoCNN. Thus, MonoCNN is affected not only by the training data but also by the FGF. The rules of the FGF play a role in regularizing the MonoCNN, thereby improving its robustness. Experimental results show that compared to state-of-the-art methods, our proposed framework can reduce a large amount of model parameter transfer between the cloud server and the IoT device while improving the performance by approximately 2.2% when dealing with corrupted data. The code is available at https://***/zhichao-lu/mono-cnn-pytorch. Copyright © 2022, The Authors. All rights reserved.

关键词： Internet of things

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Improving software Project Management by Applying Agile Methodologies: A Case Study 2nd

Improving Software Project Management by Applying Agile Meth...

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2nd International Conference on applied Technologies, ICAT 2020

作者： Quiña-Mera, Antonio Chamorro Andrade, Lincon Montaluisa Yugla, Javier Chicaiza Angamarca, Doris Guevara-Vega, Cathy Pamela Engineering in Applied Sciences Faculty Department of Software Universidad Técnica del Norte 17 de Julio Avenue Ibarra100150 Ecuador Network Science Research Group e-CIER 17 de Julio Avenue Ibarra100150 Ecuador Universidad de Investigación de Tecnología Experimental Yachay Tech Urcuquí Ecuador Department of Software Engineering Universidad de Las Fuerzas Armadas ESPE Quijano-Ordónez Y Hermanas Páez Street Latacunga050150 Ecuador

ISBN: (纸本)9783030715021

In the industry, the demand for building custom software is continuous and pressing;this responsibility falls directly on the institutional software provider, whether internal or external. It is common to observe that development teams have problems managing work and delays in project delivery and software delivery with quality problems during the software engineering process. Thus, this study aims to implement a methodology to improve software development management and answer the research question: What success factors were found when implementing the methodology? The research structure was established as follows: 1) Comparative analysis between agile development methodologies most used in the community. 2) Conducting a case study at Yachay Tech University 3) Determine the critical success factors in adopting the methodology. 4) Statistical analysis of the results. The results obtained from the case study's critical success factors were an improvement of 60.50% in human factors, 50.27% in organizational factors, and 32.32% in other factors. © 2021, Springer Nature Switzerland AG.

关键词： Project management

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Empirical Analysis on Productivity Prediction and Locality for Use Case Points Method

arXiv

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arXiv 2021年

作者： Azzeh, Mohammad Nassif, Ali Bou Martín, Cuauhtémoc López Department of Software Engineering Applied Science Private University Jordan Department of Computer Engineering University of Sharjah UAE United Arab Emirates Department of Information Systems Universidad de Guadalajara ZapopanJalisco Mexico

Use Case Points (UCP) method has been around for over two decades. Although, there was a substantial criticism concerning the algebraic construction and factors assessment of UCP, it remains an efficient early size estimation method. Predicting software effort from UCP is still an ever-present challenge. The earlier version of UCP method suggested using productivity as a cost driver, where fixed or a few pre-defined productivity ratios have been widely agreed. While this approach was successful when no enough historical data is available, it is no longer acceptable because software projects are different in terms of development aspects. Therefore, it is better to understand the relationship between productivity and other UCP variables. This paper examines the impact of data locality approaches on productivity and effort prediction from multiple UCP variables. The environmental factors are used as partitioning factors to produce local homogeneous data either based on their influential levels or using clustering algorithms. Different machine learning methods, including solo and ensemble methods, are used to construct productivity and effort prediction models based on the local data. The results demonstrate that the prediction models that are created based on local data surpass models that use entire data. Also, the results show that conforming the hypothetical assumption between productivity and environmental factors is not necessarily a requirement for success of locality. © 2021, CC BY.

关键词： Productivity

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An efficient artificial intelligence approach for early detection of cross-site scripting attacks

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Decision Analytics Journal 2024年 11卷

作者： Younas, Faizan Raza, Ali Thalji, Nisrean Abualigah, Laith Zitar, Raed Abu Jia, Heming Department of Computer Science & Information Technology The University Of Lahore Lahore 54000 Pakistan Department of Software Engineering The University Of Lahore Lahore 54000 Pakistan Department of Robotics and Artificial Intelligence Jadara University Irbid Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk 71491 Saudi Arabia MEU Research Unit Middle East University Amman 11831 Jordan Applied science research center Applied science private university Amman 11931 Jordan School of Engineering and Technology Sunway University Malaysia Petaling Jaya 27500 Malaysia Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi Abu Dhabi 38044 United Arab Emirates School of Information Engineering Sanming University Sanming 365004 China

Cross-Site Scripting (XSS) attacks continue to pose a significant threat to web applications, compromising the security and integrity of user data. XSS is a web application vulnerability where malicious scripts are injected into websites, allowing attackers to execute arbitrary code in the victim's browser. The consequences of XSS attacks can be severe, ranging from financial losses to compromising sensitive user information. XSS attacks enable attackers to deface websites, distribute malware, or launch phishing campaigns, compromising the trust and reputation of affected organizations. This study proposes an efficient artificial intelligence approach for the early detection of XSS attacks, utilizing machine learning and deep learning approaches, including Long Short-Term Memory (LSTM). Additionally, advanced feature engineering techniques, such as the Term Frequency-Inverse Document Frequency (TFIDF), are applied and compared to evaluate results. We introduce a novel approach named LSTM-TFIDF (LSTF) for feature extraction, which combines temporal and TFIDF features from the cross-site scripting dataset, resulting in a new feature set. Extensive research experiments demonstrate that the random forest method achieved a high performance of 0.99, outperforming state-of-the-art approaches using the proposed features. A k-fold cross-validation mechanism is utilized to validate the performance of applied methods, and hyperparameter tuning further enhances the performance of XSS attack detection. We have applied Explainable Artificial Intelligence (XAI) to understand the interpretability and transparency of the proposed model in detecting XSS attacks. This study makes a valuable contribution to the growing body of knowledge on XSS attacks and provides an efficient model for developers and security practitioners to enhance the security of web applications. © 2024 The Author(s)

关键词： Artificial intelligence Cross-site scripting attacks Deep learning Feature engineering Feature fusion Machine learning

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Analysis of Twitter Data Using Evolutionary Clustering during the COVID-19 Pandemic

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computers, Materials & Continua 2020年第10期65卷 193-203页

作者： Ibrahim Arpaci Shadi Alshehabi Mostafa Al-Emran Mahmoud Khasawneh Ibrahim Mahariq Thabet Abdeljawad Aboul Ella Hassanien Department of Computer Education and Instructional Technology Tokat Gaziosmanpasa UniversityTokatTurkey Department of Computer Engineering University of Turkish Aeronautical AssociationAnkaraTurkey Applied Computational Civil and Structural Engineering Research Group Faculty of Civil EngineeringTon Duc Thang UniversityHo Chi MinhVietnam College of Engineering and Technology American University of the Middle EastKuwaitKuwait Department of Mathematics and General Sciences Prince Sultan UniversityRiyadh66833Saudi Arabia Department of Medical Research China Medical UniversityTaichung40402Taiwan Department of Computer Science and Information Engineering Asia UniversityTaichungTaiwan Faculty of Computers and AI Cairo UniversityGizaEgypt Scientific Research Group in Egypt CairoEgypt

People started posting textual tweets on Twitter as soon as the novel coronavirus(COVID-19)*** these tweets can assist institutions in better decision-making and prioritizing their ***,this study aimed to analyze 43 million tweets collected between March 22 and March 30,2020 and describe the trend of public attention given to the topics related to the COVID-19 epidemic using evolutionary clustering *** results indicated that unigram terms were trended more frequently than bigram and trigram terms.A large number of tweets about the COVID-19 were disseminated and received widespread public attention during the *** high-frequency words such as“death”,“test”,“spread”,and“lockdown”suggest that people fear of being infected,and those who got infection are afraid of *** results also showed that people agreed to stay at home due to the fear of the spread,and they were calling for social distancing since they become aware of the *** can be suggested that social media posts may affect human psychology and *** results may help governments and health organizations to better understand the psychology of the public,and thereby,better communicate with them to prevent and manage the panic.

关键词： Twitter social media evolutionary clustering COVID-19 coronavirus

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Disk2Planet: A Robust and Automated Machine Learning Tool for Parameter Inference in Disk-Planet Systems

arXiv

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arXiv 2024年

作者： Mao, Shunyuan Dong, Ruobing Yi, Kwang Moo Lu, Lu Wang, Sifan Perdikaris, Paris Department of Physics and Astronomy University of Victoria VictoriaBCV8P 5C2 Canada Kavli Institute for Astronomy and Astrophysics Peking University Beijing100871 China Department of Computer Science University of British Columbia VancouverBCV6T 1Z4 Canada Department of Statistics and Data Science Yale University New HavenCT06511 United States Graduate Group in Applied Mathematics and Computational Science University of Pennsylvania PhiladelphiaPA19104 United States Institute for Foundations of Data Science Yale University New HavenCT06520 United States Department of Mechanical Engineering and Applied Mechanics University of Pennsylvania PhiladelphiaPA19104 United States

We introduce Disk2Planet, a machine learning-based tool to infer key parameters in disk-planet systems from observed protoplanetary disk structures. Disk2Planet takes as input the disk structures in the form of two-dimensional density and velocity maps, and outputs disk and planet properties, that is, the Shakura-Sunyaev viscosity, the disk aspect ratio, the planet-star mass ratio, and the planet's radius and azimuth. We integrate the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), an evolutionary algorithm tailored for complex optimization problems, and the Protoplanetary Disk Operator Network (PPDONet), a neural network designed to predict solutions of disk-planet interactions. Our tool is fully automated and can retrieve parameters in one system in three minutes on an Nvidia A100 graphics processing unit. We empirically demonstrate that our tool achieves percent-level or higher accuracy, and is able to handle missing data and unknown levels of noise. Copyright © 2024, The Authors. All rights reserved.

关键词： Open source software

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Continuous Evolution of Digital Twins using the DarTwin Notation

arXiv

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arXiv 2024年

作者： Mertens, Joost Klikovits, Stefan Bordeleau, Francis Denil, Joachim Haugen, Øystein Faculty of Applied engineering: Electronics and ICT University of Antwerp Groenenborgerlaan 171 Antwerp Antwerp2020 Belgium Institute for Business Informatics - Software Engineering Johannes Kepler University Altenberger Strasse 69 Linz4040 Austria Montreal Canada Flanders Make @UAntwerpen University of Antwerp Groenenborgerlaan 171n Antwerpen Antwerp2020 Belgium Department of Computer Science and Communication Østfold University College B R A veien 4 Halden1757 Norway

Despite best efforts, various challenges remain in the creation and maintenance processes of digital twins (DTs). One of those primary challenges is the constant, continuous and omnipresent evolution of systems, their user’s needs and their environment, demanding the adaptation of the developed DT systems. DTs are developed for a specific purpose, which generally entails the monitoring, analysis, simulation or optimization of a specific aspect of an actual system, referred to as the actual twin (AT). As such, when the twin system changes, that is either the AT itself changes, or the scope/purpose of a DT is modified, the DTs usually evolve in close synchronicity with the AT. As DTs are software systems, the best practices or methodologies for software evolution can be leveraged. This paper tackles the challenge of maintaining a (set of) DT(s) throughout the evolution of the user’s requirements and priorities and tries to understand how this evolution takes place. In doing so, we provide two contributions: (i) we develop DarTwin, a visual notation form that enables reasoning on a twin system, its purposes, properties and implementation, and (ii) we introduce a set of architectural transformations that describe the evolution of DT systems. The development of these transformations is driven and illustrated by the evolution and transformations of a family home’s DT, whose purpose is expanded, changed and re-prioritized throughout its ongoing lifecycle. Additionally, we evaluate the transformations on a lab-scale gantry crane’s DT. © 2024, CC BY.

关键词： Smart homes

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A performance comparison of graph analytic methods for supporting enterprise architecture model maintenance

A performance comparison of graph analytic methods for suppo...

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2019 Practice of Enterprise Modelling Conference Forum, PoEM 2019 Forum

作者： Rajashekar, Nikhitha Hacks, Simon Silva, Nuno Research Group Software Construction RWTH Aachen University Germany Division of Network and Systems Engineering KTH Royal Institute of Technology Sweden Department of Computer Science and Engineering Technical University of Lisbon Lisbon Portugal

Enterprise Architecture (EA) models help enterprise architects to make business decisions and to support organisations to understand and analyse its structure. Creating and maintaining such an EA model is expensive and time-consuming in large organisations. In this paper, the authors provide a review of how graph analytic methods can be used to support EA model maintenance. To achieve the best results for the duplicate identification, it is necessary to evaluate and optimise different graph analytic and Machine Learning (ML) algorithms to support the maintenance process. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Machine learning

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The Global Environmental Effects During and Beyond COVID-19 1

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丛书名： Studies in Systems, Decision and Control

2021年

作者： Aboul Ella Hassanien Ashraf Darwish Benji Gyampoh Alaa Tharwat Abdel-Monaim Ahmed M. Anter

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