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International Journal of Interactive Mobile Technologies 2022年第4期16卷 30-45页

作者： Abuaddous, Hayfa. Y. Saleh, Ashraf Mousa Enaizan, Odai Ghabban, Fahad Al-Badareen, Anas Bassam Faculty of Computer Sciences and Informatics Amman Arab University Amman Jordan Department of Management Information System University of Tabuk Tabuk Saudi Arabia College of Computer Science and Engineering Taibah University Medina Saudi Arabia Department of Software Engineering Aqaba University of Technology Aqaba Jordan

User Experience (UX) evaluation has a significant importance for any interactive application. Mobile device applications have additional limitations to convey good user experiences (UX) due to the usage and features of mobile devices in ubiquitous computing contexts. There are a lot of automated tools available for evaluating, such applications particularly developed for Android and iOS. Automated UX evaluation provides a number of strengths which are able to overcome the limitations of non-automated (moderated) counterparts. Automated UX evaluation is regarded as unexplored field, especially when it is adopted for the UX evaluation of complex mobile application. Despite these, automated UX evaluation tools are unexplored and should only be used in coupling with traditional UX evaluation techniques. Hence, this study aims to discuss the literature in automated UX evaluation and to highlight the UX measurements that merit further investigation. This paper presents the most fre-quently used automated UX evaluation tools for mobile applications. Therefore, the study analyzes existing automated UX tools and frameworks and identifies which UX attributes and measurements of automated UX evaluation are likely to be of use in future research. Moreover, we discuss the strengths and limitations of automated UX evaluation methods for mobile applications. Finally, the paper provides an overview of automated UX evaluation techniques and tools. It also presents the strengths and limitations for comparing various evaluation methods. © 2022,International Journal of Interactive Mobile Technologies. All Rights Reserved.

关键词： Automation

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Effective and Imperceptible Adversarial Textual Attack Via Multi-objectivization

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ACM Transactions on Evolutionary Learning and Optimization 2024年第3期4卷 1-23页

作者： Liu, Shengcai Lu, Ning Hong, Wenjing Qian, Chao Tang, Ke Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen College of Engineering South Tower 1088 Xueyuan Avenue Guangdong Shenzhen518055 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong999077 Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen 10F Zhizhen Building Canghai Campus 3688 Nanhai Avenue Guangdong Shenzhen China National Key Laboratory for Novel Software Technology School of Artificial Intelligence Nanjing University Nanjing International College Area Xianlin Campus 163 Xianlin Avenue Jiangsu Nanjing China

The field of adversarial textual attack has significantly grown over the past few years, where the commonly considered objective is to craft adversarial examples (AEs) that can successfully fool the target model. However, the imperceptibility of attacks, which is also essential for practical attackers, is often left out by previous studies. In consequence, the crafted AEs tend to have obvious structural and semantic differences from the original human-written text, making them easily perceptible. In this work, we advocate leveraging multi-objectivization to address such an issue. Specifically, we reformulate the problem of crafting AEs as a multi-objective optimization problem, where the attack imperceptibility is considered as an auxiliary objective. Then, we propose a simple yet effective evolutionary algorithm, dubbed HydraText, to solve this problem. HydraText can be effectively applied to both score-based and decision-based attack settings. Exhaustive experiments involving 44,237 instances demonstrate that HydraText consistently achieves competitive attack success rates and better attack imperceptibility than the recently proposed attack approaches. A human evaluation study also shows that the AEs crafted by HydraText are more indistinguishable from human-written text. Finally, these AEs exhibit good transferability and can bring notable robustness improvement to the target model by adversarial training. © 2024 Copyright held by the owner/author(s).

关键词： Multiobjective optimization

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BMSAD: Behavior Feature and Multi-Scenario -Based Sybil Attack Detection Method in Vehicular networks 3

BMSAD: Behavior Feature and Multi-Scenario -Based Sybil Atta...

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3rd International Seminar on Artificial Intelligence, Networking, and Information Technology, AINIT 2022

作者： Luo, Jie Li, Zhong College of Information Science and Technology Donghua University Shanghai201620 China Key Laboratory of the Ministry of Education for Embedded System and Service Computing Department of Computer Science Tongji University Shanghai201804 China

ISBN: (数字)9781510662971

ISBN: (纸本)9781510662964

Vehicular networks improve traffic safety and efficiency by wireless communications among vehicles and infrastructures. However, security has always been a challenge to vehicular networks, which may cause severe harm to the intelligent transportation systems. Sybil attack is considered as a serious security threat to vehicular networks since the attacker can disseminate false messages with multiple forged identities. In this paper, we designed a behavior feature and multi-scenario-based Sybil attack detection method, named BMSAD in vehicular networks. In BMSAD, we propose solutions for two scenarios: normal traffic and traffic congestion. A node is allowed to verify the authenticity of another node by estimating their geographic distance based on received signal strength, and compare them to its claimed localizations. Then we design long- and short-term pattern to analyze the similarity of vehicles' behavior features and trajectory for Sybil nodes detection, which act as the second line of defense in normal traffic scenario. At last, the Sybil nodes of the traffic congestion scenario are detected by logical inference based on kinematics theory. And experiment results demonstrate that the proposed scheme achieves high detection rate and low false positive rate. © 2023 SPIE.

关键词： Traffic congestion

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Meta-Heuristic Based Multipath Joint Routing and Scheduling of Time-Triggered Traffic for Time-Sensitive Networking in IIoT 2

Meta-Heuristic Based Multipath Joint Routing and Scheduling ...

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2nd International Conference on Green Communication, Network, and Internet of Things, CNIoT 2022

作者： Chen, Chen Li, Zhong The College of Information Science and Technology Donghua University Shanghai201620 China The Key Laboratory of the Ministry of Education for Embedded System and Service Computing Department of Computer Science Tongji University Shanghai201804 China

ISBN: (纸本)9781510662940

Time-Sensitive networking (TSN) meets the needs of industrial internet of things (IIoT). It solves the challenges of deterministic transmission and reliable communication of time sensitive data streams. Traffic scheduling is the core mechanism of time-sensitive networks. Many excellent researches have explored and optimized the method of scheduling time-triggered streams in TSN. However, the existing time-triggered streams scheduling in TSN mostly separates routing and scheduling, which limits the scalability of scheduling. Many researches are based on fixed routing to schedule streams. Due to the interaction between scheduling and routing, the quality of fixed route solution is inferior to that of joint routing and scheduling. In this paper, we propose a meta-heuristic based multipath joint routing and scheduling method for time-triggered traffic in TSN, named MMRS. We set path selection as a variable and consider multipath routing for fault tolerance. At the same time, we establish the integer linear programming formulation and use meta-heuristic to obtain high-quality solutions. The evaluations show that compared with other excellent routing and scheduling methods, the runtime of 60 streams of MMRS method is reduced by 65.6%, and the schedulability is improved by 21% on average. The experiments verify that our proposed scheduling method can obtain high-quality solutions in an acceptable solution time. © 2023 SPIE.

关键词： Internet of things

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Sine Cosine Optimization with Deep Learning-Based Applied Linguistics for Sentiment Analysis on COVID-19 Tweets

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computers, Materials & Continua 2023年第6期75卷 4767-4783页

作者： Abdelwahed Motwakel Hala J.Alshahrani Abdulkhaleq Q.A.Hassan Khaled Tarmissi Amal S.Mehanna Ishfaq Yaseen Amgad Atta Abdelmageed Mohammad Mahzari Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of English College of Science and Arts at MahayilKing Khalid UniversityMuhayil63763Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Applied linguistics is an interdisciplinary domain which identifies,investigates,and offers solutions to language-related real-life *** new coronavirus disease,otherwise known as Coronavirus disease(COVID-19),has severely affected the everyday life of people all over the ***,since there is insufficient access to vaccines and no straight or reliable treatment for coronavirus infection,the country has initiated the appropriate preventive measures(like lockdown,physical separation,and masking)for combating this extremely transmittable ***,individuals spent more time on online social media platforms(i.e.,Twitter,Facebook,Instagram,LinkedIn,and Reddit)and expressed their thoughts and feelings about coronavirus *** has become one of the popular social media platforms and allows anyone to post *** study proposes a sine cosine optimization with bidirectional gated recurrent unit-based senti-ment analysis(SCOBGRU-SA)on COVID-19 *** SCOBGRU-SA technique aimed to detect and classify the various sentiments in Twitter data during the COVID-19 *** SCOBGRU-SA technique follows data pre-processing and the Fast-Text word embedding process to accomplish ***,the BGRU model is utilized to recognise and classify sen-timents present in the ***,the SCO algorithm is exploited for tuning the BGRU method’s hyperparameter,which helps attain improved classification *** experimental validation of the SCOBGRU-SA technique takes place using a benchmark dataset,and the results signify its promising performance compared to other DL models.

关键词： Applied linguistics deep learning sentiment analysis COVID-19 pandemic sine cosine optimization twitter

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AI in Education: Improving Quality for Both Centralized and Decentralized Frameworks 14

AI in Education: Improving Quality for Both Centralized and ...

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14th IEEE Global Engineering Education Conference, EDUCON 2023

作者： Madathil, Nisha Thorakkattu Alrabaee, Saed Al-Kfairy, Mousa Damseh, Rafat Belkacem, Abdelkader N College of IT United Arab Emirates University Department of Information Systems and Security United Arab Emirates College of Technological Innovation Zayed University Dubai United Arab Emirates College of IT Department of Computer Science and Software Engineering United Arab Emirates University United Arab Emirates College of IT Department of Computer Engineering and Networks United Arab Emirates University United Arab Emirates

ISBN: (纸本)9798350399431

Education is essential for achieving many Sustainable Development Goals (SDGs). Therefore, the education system focuses on empowering more educated people and improving the quality of the education system. One of the latest technologies to enhance the quality of education is Artificial Intelligence (AI)-based Machine Learning (ML). As a result, ML has a significant influence on the education system. ML is currently widely applied in the education system for various tasks, such as creating models by monitoring student performance and activities that accurately predict student outcomes, their engagement in learning activities, decision-making, problem-solving capabilities, etc. In this research, we provide a survey of machine learning frameworks for both distributed (clusters of schools and universities) and centralized (university or school) educational institutions to predict the quality of students' learning outcomes and find solutions to improve the quality of their education system. Additionally, this work explores the application of ML in teaching and learning for further improvements in the learning environment for centralized and distributed education systems. © 2023 IEEE.

关键词： Sustainable development

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Leveraging Adaptive Algorithms for Real-Time Data Analysis 15

Leveraging Adaptive Algorithms for Real-Time Data Analysis

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sahoo, Girija Shankar Hannah Jessie Rani, R. Goyal, Manish Kumar Mohammed, Vazeer Ali Jana, D Little Femilin Wasatkar, Namrata N. Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Lewis University Illinois Department of Computer & Information System United States Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350370249

This paper proposes the use of adaptive algorithms for real-time data analysis. Adaptive algorithms are a set of practical methods used for acting information-driven optimization and studying. They have a wide variety of applications and may be implemented to a selection of facts analysis obligations. This paper offers an overview of existing adaptive algorithms and descriptions of several new programs in the area of actual-time facts analysis. In particular, the paper discusses the notion of adaptive thresholding, which allows for practical real-time evaluation of large datasets. The paper also provides a novel technique for making use of these algorithms in an online learning environment. Results from simulations illustrate the ability of the proposed method in real-international packages. Sooner or later, the paper presents numerous key lessons discovered and affords recommendations for future research and packages in this area. © 2024 IEEE.

关键词： Adaptive algorithms

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⁠Marine Ecosystem Monitoring Based on Remote Sensing Using Underwater Image Analysis for Biodiversity Conservation Model

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Remote Sensing in Earth systems sciences 2024年第4期7卷 309-318页

作者： Rao, Chandana Narasimha Rao, A. Venkateswara Shanmugasundar, G. Hamid, Junainah Abd Haldorai, Anandakumar Naidu, G. Rama Sapthami, I. Department of Chemistry Koneru Lakshmaiah Education Foundation Green Fields Vaddeswaram Andhra Pradesh Guntur 522302 India Department of Mechanical Engineering Sri Sairam Institute of Technology Chennai 600044 India Management and Science University Selangor Shah Alam Malaysia Center for Future Networks and Digital Twin Department of Computer Science and Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore 641202 India Department of Electronics & amp Communication Engineering Aditya University Surampalem India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

The growing focus on underwater observation and use of marine resources in recent years has led to an active research area in underwater image processing and analysis. Because of its importance in marine engineering as well as aquatic robotics, underwater picture enhancement serves as a preprocessing step to increase performance of high-level vision tasks like underwater object detection as well as recognition. While a number of studies demonstrate that underwater image enhancement methods (synthetic underwater environment image) increase the detectors’ F-measure, no research has examined the connection between these two jobs. This research proposed novel technique in remote sensing–based underwater image analysis in ecosystem monitoring in biodiversity conservation model using machine learning techniques. Here, the underwater-based remote sensing image based on marine ecosystem has been collected and analysed for noise removal with normalisation. The processed image feature extracted and classified using Gaussian spatial gradient neural network (GSGNN) and multilayer pyramid Bayes network (MPBN). The output images show coral surface texture. The experimental analysis has been carried out for various underwater dataset in terms of kappa coefficient, classification accuracy, random precision, F-measure, and normalised square error. Proposed technique kappa coefficient is 94%, classification accuracy is 98%, random precision is 95%, F-measure is 93%, and Normalised square error is 63%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Biodiversity conservation model Ecosystem monitoring Machine learning Remote sensing Underwater image analysis

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Optimizing Connections:Applied Shortest Path Algorithms for MANETs

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computer Modeling in Engineering & sciences 2024年第10期141卷 787-807页

作者： Ibrahim Alameri Jitka Komarkova Tawfik Al-Hadhrami Abdulsamad Ebrahim Yahya Atef Gharbi Faculty of Medical Sciences Jabir Ibn Hayyan Medical UniversityAlkufaNajaf54001Iraq Faculty of Economics and Administration University of PardubiceStudentskaPardubice53210Czech Republic Computer Science Department School of Science and TechnologyNottingham Trent UniversityNottinghamNG118NSUK Department of Information Technology College of Computing and Information TechnologyNorthern Border UniversityArar91431Saudi Arabia Department of Information System College of Computing and Information TechnologyNorthern Border UniversityArar91431Saudi Arabia

This study is trying to address the critical need for efficient routing in Mobile Ad Hoc networks(MANETs)from dynamic topologies that pose great challenges because of the mobility of *** objective was to delve into and refine the application of the Dijkstra’s algorithm in this context,a method conventionally esteemed for its efficiency in static ***,this paper has carried out a comparative theoretical analysis with the Bellman-Ford algorithm,considering adaptation to the dynamic network conditions that are typical for *** paper has shown through detailed algorithmic analysis that Dijkstra’s algorithm,when adapted for dynamic updates,yields a very workable solution to the problem of real-time routing in *** results indicate that with these changes,Dijkstra’s algorithm performs much better computationally and 30%better in routing optimization than Bellman-Ford when working with configurations of sparse *** theoretical framework adapted,with the adaptation of the Dijkstra’s algorithm for dynamically changing network topologies,is novel in this work and quite different from any traditional *** adaptation should offer more efficient routing and less computational overhead,most apt in the limited resource environment of ***,from these findings,one may derive a conclusion that the proposed version of Dijkstra’s algorithm is the best and most feasible choice of the routing protocol for MANETs given all pertinent key performance and resource consumption indicators and further that the proposed method offers a marked improvement over traditional *** paper,therefore,operationalizes the theoretical model into practical scenarios and also further research with empirical simulations to understand more about its operational effectiveness.

关键词： Dijkstra’s algorithm optimization complexity analysis shortest path first comparative algorithm analysis nondeterministic polynomial(NP)-complete

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Public Key Infrastructure Approaches Based on Blockchain

Public Key Infrastructure Approaches Based on Blockchain

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IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Dena S. Alani Ali M Sagheer College of Education for Women University of Anbar Anbar Iraq Department of Computer Networks Systems College of Computer Science and Information Technology University of Anbar Anbar Iraq

ISBN: (数字)9798350374131

ISBN: (纸本)9798350374148

Public-key infrastructure or PKI, is the fundamental technology that makes safe online information sharing possible. But PKI is vulnerable to failures of Certificate Authorities (CAs), which could allow illegal certificates to be issued to the end users. The security of the corresponding end users is at danger if a CA is compromised, as seen by numerous recent breaches. Blockchain technology is a new solution that has the ability to address the issues with traditional PKI systems, specifically the removal of single points of failure and the quick response to CA flaws. Blockchain technology facilitates the management and archiving of digital certificates in an unchangeable, publicly accessible ledger, producing a history log that can be completely tracked. In this article, the classical and modern blockchain-based PKI strategies were explained in addition to illustrating the limitations of each strategy. Finally, this article analyzed PKI methods by comparing their features according to a number of parameters.

关键词： Privacy Scalability Public key Information sharing Authentication Blockchains Encryption

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