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Crowdsourced Requirements engineering Challenges and Solutions:A Software Industry Perspective

computer systems science & engineering 2021年第11期39卷 221-236页

作者： Huma Hayat Khan Muhammad Noman Malik Youseef Alotaibi Abdulmajeed Alsufyani Saleh Alghamdi Department of Software Engineering National University of Modern LanguagesIslamabadPakistan Department of Computer Science National University of Modern LanguagesIslamabadPakistan Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityPO Box 11099TaifSaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia

Software crowdsourcing(SW CS)is an evolving software development paradigm,in which crowds of people are asked to solve various problems through an open call(with the encouragement of prizes for the top solutions).Because of its dynamic nature,SW CS has been progressively accepted and adopted in the software ***,issues pertinent to the understanding of requirements among crowds of people and requirements engineers are yet to be clarified and *** the requirements are not clear to the development team,it has a significant effect on the quality of the software *** study aims to identify the potential challenges faced by requirements engineers when conducting the SW–CS based requirements engineering(RE)***,solutions to overcome these challenges are also *** data analysis is performed on the interview data collected from software industry ***,20 SW–CS based RE challenges and their subsequent proposed solutions are devised,which are further grouped under seven *** study is beneficial for academicians,researchers and practitioners by providing detailed SW–CS based RE challenges and subsequent solutions that could eventually guide them to understand and effectively implement RE in SW CS.

关键词： Software crowdsourced requirements engineering software industry software development survey challenges

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Sampling-Based Distributed Control for Quantized Consensus Tracking Under Denial-of-Service Attacks

Sampling-Based Distributed Control for Quantized Consensus T...

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International Symposium on Autonomous systems (ISAS)

作者： Xinhe Wang Linan Wang Shuai Wang School of Cyber Science and Engineering Southeast University Nanjing China Department of Systems Science Southeast University Nanjing China School of Computer Science and Engineering Beihang University Beijing China

ISBN: (数字)9798350363173

ISBN: (纸本)9798350363180

In this paper, the quantized consensus tracking problem for continuous-time multi-agent systems (MASs) under denial-of-service (DoS) attacks is studied. Based on the dynamical quantized strategy, a sampling-based control protocol is developed, which is suitable for the high-order MASs under the general directed communication topology with limited bandwidth. To resist the DoS attacks, the scaling function is designed with a zooming-in and zooming-out approach. Sufficient conditions for consensus tracking problem under DoS attacks are provided, and the quantizer is unsaturated under the specified quantization level. Finally, simulation examples are provided to validate the theoretical results.

关键词： Resistance Sufficient conditions Quantization (signal) Protocols Decentralized control Bandwidth Resists

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Mining the Chatbot Brain to Improve COVID-19 Bot Response Accuracy

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computers, Materials & Continua 2022年第2期70卷 2619-2638页

作者： Mukhtar Ghaleb Yahya Almurtadha Fahad Algarni Monir Abdullah Emad Felemban Ali M.Alsharafi Mohamed Othman Khaled Ghilan Department of Information Systems College of Sciences and ArtsUniversity of BishaAl Namas67392Saudi Arabia Department of Computer Science College of Computing and Information TechnologyUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia Computer Engineering Department Umm Al-Qura UniversityMakkah21955Saudi Arabia Department of Communication Technology and Network Universiti Putra MalaysiaSelangor43400Malaysia Faculty of Public Health and Tropical Medicine Jazan UniversityJazan45142Saudi Arabia

People often communicate with auto-answering tools such as conversational agents due to their 24/7 availability and unbiased ***,chatbots are normally designed for specific purposes and areas of experience and cannot answer questions outside their *** employ Natural Language Understanding(NLU)to infer their *** is a need for a chatbot that can learn from inquiries and expand its area of experience with *** chatbot must be able to build profiles representing intended topics in a similar way to the human brain for fast *** study proposes a methodology to enhance a chatbot’s brain functionality by clustering available knowledge bases on sets of related themes and building representative *** used a COVID-19 information dataset to evaluate the proposed *** pandemic has been accompanied by an“infodemic”of fake *** chatbot was evaluated by a medical doctor and a public trial of 308 real *** obtained and statistically analyzed tomeasure effectiveness,efficiency,and satisfaction as described by the ISO9214 *** proposed COVID-19 chatbot system relieves doctors from answering *** provide an example of the use of technology to handle an infodemic.

关键词： Machine learning text classification e-health chatbot COVID-19 awareness natural language understanding

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Improving the Detection of Kidney Tumours Using Firefly Algorithm Optimisation

Improving the Detection of Kidney Tumours Using Firefly Algo...

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Intelligent Cyber Physical systems and Internet of Things (ICoICI), International Conference on

作者： Rajagopal K V. Sheeja Kumari Saraswathy S S. Ponmaniraj V. Suresh Kumar A. Deepa Department of Computer Science and Engineering Saveetha Institute of Medical and Technical Sciences SIMATS University Thandalam Chennai Department of Computer Science and Business Systems Sri Sairam Engineering College Chennai Department of Computational Intelligence Institute of ECE Saveetha Institute of Medical and Technical Sciences SIMATS University Thandalam Chennai Department of Computer Science and Engineering Saveetha Engineering College Thandalam Chennai

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

Accurate and timely diagnosis of kidney tumors is crucial for effective treatment. This study proposes a novel approach utilizing deep learning techniques and the firefly algorithm (FA) to enhance kidney tumor segmentation. By optimizing feature selection and CNN hyperparameters, FA improves the accuracy and efficiency of the detection system. The proposed method involves pre-processing medical images, extracting features using CNNs, and fine-tuning the model with FA. Experiments on a public dataset demonstrate significant improvements in classification metrics compared to traditional methods. The FA-optimized model provides clinicians with a valuable tool for accurate kidney tumor detection and classification, leading to improved patient outcomes.

关键词： Deep learning Image segmentation Accuracy Feature extraction Classification algorithms Convolutional neural networks Kidney Optimization Medical diagnostic imaging Tumors

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An asynchronous P system for solving the minimum Steiner tree with branch and bound

An asynchronous P system for solving the minimum Steiner tre...

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International Symposium on Computing and Networking Workshops (CANDARW)

作者： Reo Ueno Akihiro Fujiwara Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Fukuoka Japan

Membrane computing, which is also known as a P system, is a computational model inspired by the activity of living cells. Several P systems, which work in a polynomial number of steps, have been proposed for solving computationally hard problems. However, most of the proposed algorithms use an exponential number of membranes, and reduction of the number of membranes must be considered in order to make a P system a more realistic *** the present paper, we propose an asynchronous P system using branch and bound for solving the minimum Steiner tree. The proposed P system solves the minimum Steiner tree with n vertices and m edges in O(n 2 ) parallel steps or O(2 m n 2 ) sequential *** evaluate the number of membranes used in the proposed P system using experimental simulations. Our experimental results show validity and efficiency of the proposed P system.

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Energy-Efficient Routing Using Novel Optimization with Tabu Techniques for Wireless Sensor Network

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computer systems science & engineering 2023年第5期45卷 1711-1726页

作者： Manar Ahmed Hamza Aisha Hassan Abdalla Hashim Dalia H.Elkamchouchi Nadhem Nemri Jaber S.Alzahrani Amira Sayed A.Aziz Mnahel Ahmed Ibrahim Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University MalaysiaKuala Lumpur53100Malaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt College of Science and Humanities Studies in Alquwayiyah Shaqra UniversitySaudi Arabia

Wireless Sensor Network(WSN)consists of a group of limited energy source sensors that are installed in a particular region to collect data from the *** the energy-efficient data collection methods in largescale wireless sensor networks is considered to be a difficult area in the *** node clustering is a popular approach for ***,the sensor nodes are grouped to form clusters in a cluster-based WSN *** battery performance of the sensor nodes is likewise *** a result,the energy efficiency of WSNs is *** specific,the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station(BS).Therefore,energy efficiency and load balancing are very essential in *** the proposed method,a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques(GW-IPSO-TS)was *** selection of Cluster Heads(CHs)and routing path of every CH from the base station is enhanced by the proposed *** provides the best routing path and increases the lifetime and energy efficiency of the ***-to-end delay and packet loss rate have also been *** proposed GW-IPSO-TS method enhances the evaluation of alive nodes,dead nodes,network survival index,convergence rate,and standard deviation of sensor *** to the existing algorithms,the proposed method outperforms better and improves the lifetime of the network.

关键词： Wireless sensor networks energy-efficient load balancing energy consumption network’s lifetime cluster heads grey wolf optimization tabu search particle swarm optimization

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Learning and Motivational Impact of Game-Based Learning: Comparing Face-To-Face and Online Formats on computer science Education

arXiv

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arXiv 2024年

作者： López-Fernández, Daniel Gordillo, Aldo Pérez, Jennifer Tovar, Edmundo Computer Science Department Universidad Politécnica de Madrid Madrid Spain Computer Languages and Systems and Software Engineering Department Universidad Politécnica de Madrid Madrid Spain

Contribution: This article analyzes learning and motivational impact of teacher-authored educational video games on computer science education and compares its effectiveness in both face-to-face and online (remote) formats. This work presents comparative data and findings obtained from 217 students who played the game in a face-to-face format (control group) and 104 students who played the game in an online format (experimental group). Background: Serious video games have been proven effective at computer science education, however it is still unknown whether the effectiveness of these games is the same regardless of their format, face-to-face or online. Moreover, the usage of games created through authoring tools has barely been explored. Research questions: Are teacher-authored educational video games effective in terms of learning and motivation for computer science students? Does the effectiveness of teacher-authored educational video games depend on whether they are used in a face-to-face or online format? Methodology: A quasi-experiment has been conducted by using three instruments (pre-test, post-test and questionnaire) with the purpose of comparing the effectiveness of Game-Based Learning in face-to-face and online formats. A total of 321 computer science students played a teacher-authored educational video game aimed to learn about software design. Findings: The results reveal that teacher-authored educational video games are highly effective in terms of knowledge acquisition and motivation both in face-to-face and online formats. The results also show that some students’ perceptions were more positive when a face-to-face format was used. Copyright © 2024, The Authors. All rights reserved.

关键词： Motivation

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Dermoscopic Skin Lesion Classification using Color Features Based on Fine-Tuning of Resnet50 and Densenet121 Transfer Learning Models

Dermoscopic Skin Lesion Classification using Color Features ...

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Intelligent Algorithms for Computational Intelligence systems (IACIS), International Conference on

作者： T. Kujani V. Dhilip Kumar Bhuvanya. R A Kavitha Sathya T Sathiya Priya S Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Chennai India Computer Engineering Government Polytechnic College Karur India Computer Science and Business Systems Sri Sairam Engineering College Chennai India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

Skin cancer, which stands as the most prevalent form of human malignancy, is primarily identified through visual examination. The procedure for identifying skin lesions typically starts with an initial clinical assessment, which might be followed by dermoscopic analysis, a biopsy, and a subsequent histopathological checkup. Image-based Skin lesion classification presents a significant challenge due to the subtle and intricate variations in their appearance. Deep Convolutional Neural Network (DCNN) show promising results in managing diverse and intricate tasks involving a wide array of finely detailed object categories. Skin lesions Classification is shown in this work initially using a six-layered CNN performed on the skin cancer dataset to classify seven classes of lesions with 10015 images. Exploratory data analysis of the dataset is performed to identify any outliers and to verify the balance of samples in each class. It was observed after 50 epochs, the training accuracy was 98.03% and the testing accuracy was 67.40%. To avoid the overfitting observed in the CNN approach, the pre-trained transfer learning models, Resnet50 and Densenet121 were finetuned with data augmentation to handle the imbalance of samples present in seven classes. The results obtained prove that the Densenet121 model achieves high performance compared with the other Resnet50 model after augmentation.

关键词： Visualization Accuracy Computational modeling Transfer learning Data augmentation Skin Lesions Convolutional neural networks Residual neural networks Skin cancer

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Continual Machine Reading Comprehension via Uncertainty-aware Fixed Memory and Adversarial Domain Adaptation

Continual Machine Reading Comprehension via Uncertainty-awar...

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2022 Findings of the Association for Computational Linguistics: NAACL 2022

作者： Wu, Zhijing Xu, Hua Fang, Jingliang Gao, Kai State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing100084 China China School of Information Science and Engineering Hebei University of Science and Technology China

ISBN: (纸本)9781955917766

Continual Machine Reading Comprehension aims to incrementally learn from a continuous data stream across time without access the previous seen data, which is crucial for the development of real-world MRC systems. However, it is a great challenge to learn a new domain incrementally without catastrophically forgetting previous knowledge. In this paper, MA-MRC, a continual MRC model with uncertainty-aware fixed Memory and Adversarial domain adaptation, is proposed. In MA-MRC, a fixed size memory stores a small number of samples in previous domain data along with an uncertainty-aware updating strategy when new domain data arrives. For incremental learning, MA-MRC not only keeps a stable understanding by learning both memory and new domain data, but also makes full use of the domain adaptation relationship between them by adversarial learning strategy. The experimental results show that MA-MRC is superior to strong baselines and has a substantial incremental learning ability without catastrophically forgetting under two different continual MRC settings. © Findings of the Association for Computational Linguistics: NAACL 2022 - Findings.

关键词： Continuous time systems

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Throughput and Latency of Network Coding in Line Networks with Outages

Throughput and Latency of Network Coding in Line Networks wi...

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IEEE International Symposium on Information Theory

作者： Yanyan Dong Shenghao Yang Jie Wang Fan Cheng School of Science and Engineering The Chinese University of Hong Kong Stewart School of Industrial and Systems Engineering Georgia Institute of Technology Department of Computer Science and Engineering Shanghai Jiao Tong University

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

Wireless communications are often affected by out-age events caused by fading and interference. This paper focuses on investigating the communication throughput and latency in a line-topology, multi-hop network where outages may occur on network links. We focus on three types of intermediate network node schemes: random linear network coding (RLNC), store-and-forward (SF), and hop-by-hop retransmission. The analytical formulas for the maximum throughput and the end-to-end latency are provided for each scheme. To gain a more explicit understanding, we conducted a scalability analysis of the maximum throughput and latency as the network length $L$ increases. We observed that the same order of throughput/latency holds across a wide range of outage functions for each scheme. Specifically, the SF scheme achieves at most $\Theta(\frac{1}{L})$ throughput, while retransmission and RLNC achieve a constant throughput. However, the retransmission scheme relies on ideal feedback, which is rarely satisfied in practice, whereas RLNC does not. We conducted latency comparisons among various schemes under several constraints regarding the volume of data for transmission.

关键词： Wireless communication Fading channels Scalability Spread spectrum communication Interference Network coding Throughput

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