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Web applications testing techniques: a systematic mapping study

International Journal of Web engineering and technology 2023年第4期17卷 372-412页

作者： Hanna, Samer Ahmad, Amro Al-Said Department of Software Engineering Faculty of Information Technology Philadelphia University Jordan School of Computing and Mathematics University of Keele United Kingdom

Due to the importance of web application testing techniques for detecting faults and assessing quality attributes, many research papers were published in this field. For this reason, it became essential to analyse, classify and summarise the research in the field. To achieve this goal, this research conducted a systematic mapping study on 98 research papers in the field of web applications testing published between 2008 and 2021. The results showed that the most commonly used web applications testing techniques in literature are model-based testing and security testing. Besides, the most commonly used models in model-based testing are finite-state machines. The most targeted vulnerability in security testing is SQL injection. Test automation is the most targeted testing goal in both model-based and security testing. For other web applications testing techniques, the main goals of testing were test automation, test coverage, and assessing security quality attributes. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Mapping

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A privacy and compliance in regulated anonymous payment system based on blockchain

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Journal of Ambient Intelligence and Humanized Computing 2024年第8期15卷 3141-3157页

作者： Elfadul, Issameldeen Wu, Lijun Elhabob, Rashad Elkhalil, Ahmed School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China School of Computer and Information Engineering Qilu Institute of Technology Shandong Jinan250200 China Faculty of Computer Science and Information Technology Karary University Khartoum Sudan

Decentralized Anonymous Payment Systems (DAP), often known as cryptocurrencies, stand out as some of the most innovative and successful applications on the blockchain. These systems have garnered significant attention in the financial industry due to their highly secure and reliable features. Regrettably, the DAP system can be exploited to fund illegal activities such as drug dealing and terrorism. Therefore, governments are increasingly worried about the illicit use of DAP systems, which poses a critical threat to their security. This paper proposes Privacy and Compliance in Regulated Anonymous Payment System Based on Blockchain (PCRAP), which provides government supervision and enforces regulations over transactions without sacrificing the essential idea of the blockchain, that is, without surrendering transaction privacy or anonymity of the participants. The key characteristic of the proposed scheme is using a ring signature and stealth address to ensure the anonymity of both the sender and receiver of the transaction. Moreover, a Merkle Tree is used to guarantee government supervision and enforce regulations. Our proposed scheme satisfies most of the stringent security requirements and complies with the standards of secure payment systems. Additionally, while our work supports government regulations and supervision, it guarantees unconditional anonymity for users. Furthermore, the performance analysis demonstrates that our suggested scheme still remains applicable and effective even when achieving complete anonymity. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Blockchain

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Wisdom Mining: Future of Data Mining

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Recent Patents on engineering 2023年第1期17卷 2-11页

作者： Khan, Salma Shaheen, Muhammad Department of Software Engineering Foundation University Islamabad Rawalpindi Campus Pakistan Faculty of Engineering & Information Technology Foundation University Islamabad Rawalpindi Campus Pakistan

Data mining has been instrumental in the extraction of some useful knowledge from data. The purpose of data mining has always been to focus on searching for methods to extract instead of revealing the implicit models of the data. The outcome of the process of data mining is the knowledge that is represented by different visualization techniques. Knowledge obtained through data mining is not effective without the intervention of a domain expert who uses that knowledge to make a decision. On the other hand, human participation has the potential to influence and predis-pose decisions. Human participation in the process of data mining is still subjective and cannot be automated. A possibility to look into this quandary is the conversion of these subjective factors into some measurable parameters. This predicament leads to the development of an area that can be re-ferred to as "Wisdom Mining," which will consist of procedures to add wisdom to the extracted knowledge. Wisdom mining, if it is proposed as an extension to data mining, exhibits the need for certain factors, methods, and measures beyond the methods and measures used in the data mining process. The factors proposed in this article for a seamless transition from data to wisdom mining are context, utility, time, and location. There are two possibilities to use these factors for the extraction of wisdom from data. One is to develop new algorithms for wisdom mining from scratch, keep-ing these four factors as major placeholders. The second approach is to add these four factors to the existing algorithms of data mining to get wise patterns as outcomes. The paper proposed a second approach for laying the foundation of this new domain of wisdom mining. © 2023 Bentham Science Publishers.

关键词： Data mining

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Challenge-Response Emotion Authentication Algorithm Using Modified Horizontal Deep Learning

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Intelligent Automation & Soft Computing 2023年第3期35卷 3659-3675页

作者： Mohamed Ezz Ayman Mohamed Mostafa Ayman Elshenawy College of Computer and Information Sciences Jouf UniversitySakaka72314Saudi Arabia Systems and Computers Engineering DEPT Faculty of EngineeringAl-Azhar UniversityCairo11651Egypt Software Engineering&Information Technology Faculty of Engineering&TechnologyEgyptian Chinese UniversityEgypt

Face authentication is an important biometric authentication method commonly used in security *** is vulnerable to different types of attacks that use authorized users’facial images and videos captured from social media to perform spooﬁng attacks and dynamic movements for penetrating secur-ity *** paper presents an innovative challenge-response emotions authentication model based on the horizontal ensemble *** proposed model provides high accurate face authentication process by challenging the authorized user using a random sequence of emotions to provide a speciﬁc response for every authentication trial with a different sequence of *** proposed model is applied to the KDEF dataset using 10-fold *** improvements are made to the proposed ***,the VGG16 model is applied to the seven common ***,the system usability is enhanced by analyzing and selecting only the four common and easy-to-use ***,the horizontal ensemble technique is applied to enhance the emotion recognition accuracy and minimize the error during authen-tication ***,the Horizontal Ensemble Best N-Losses(HEBNL)is applied using challenge-response emotion to improve the authentication efﬁ-ciency and minimize the computational *** successive improvements implemented on the proposed model led to an improvement in the accuracy from 92.1%to 99.27%.

关键词： Face authentication challenge-response authentication transfer learning and horizontal ensemble technique

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Intelligent Java Dataset Construction and Visualization Evaluation for Reliable software Development

Intelligent Java Dataset Construction and Visualization Eval...

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2023 International Conference on Frontiers of Robotics and software engineering, FRSE 2023

作者： Li, Dian Wang, Weidong Zhao, Yang Beijing University of Technology Faculty of Information Technology Department of Software Engineering Beijing China

ISBN: (纸本)9798350301113

As big data and artificial intelligence continue to gain importance, the reliable quality of datasets has be-come a crucial factor in algorithm performance and result reliability. However, many datasets lack standardization and quality control, which can lead to potential issues in software development and data analysis. In this paper, we propose an objective dataset evaluation algorithm that utilizes multiple metrics, including unified naming conventions and document annotations, for statistical analysis and filtering to ensure reliable data. Our approach scores datasets using reasonable criteria, and we use visualization techniques inspired by remote sensing to compare and visualize reliable dataset quality. Our research demonstrates that our evaluation method is feasible and can assist software developers in enhancing the reliable quality and efficiency of software development by improving dataset quality. Our study concentrates on datasets obtained from GitHub using a web crawler, and our approach establishes a standardized technique for evaluating reliable dataset quality. © 2023 IEEE.

关键词： Python

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Optimizing Clustering Approaches in Cloud Environments

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第19期17卷 70-94页

作者： Al-Ghuwairi, Abdel-Rahman Al-Fraihat, Dimah Sharrab, Yousef Kreishan, Yazeed Alsarhan, Ayoub Idhaim, Hasan Qahmash, Ayman Department of Software Engineering Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Software Engineering Faculty of Information Technology Isra University Amman Jordan Department of Data Science and Artificial Intelligence Faculty of Information Technology Isra University Amman Jordan Department of Information Technology Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Information Systems Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Information Systems Computer Science College King Khalid University Abha Saudi Arabia

This study focuses on the challenge of developing abstract models to differentiate various cloud resources. It explores the advancements in cloud products that offer specialized services to meet specific external needs. The study proposes a new approach to request processing in clusters, improving downtime, load distribution, and overall performance. A comparison of three clustering approaches is conducted: local single cluster, local multiple clusters, and multiple cloud clusters. Performance, scalability, fault tolerance, resource allocation, availability, and cost-effectiveness are evaluated through experiments with 50 requests. All three approaches achieve a 100% success rate, but processing times vary. The local single cluster has the longest duration, while the local multiple clusters and multiple cloud clusters perform better and offer faster processing, scalability, fault tolerance, and availability. From a cost perspective, the local single cluster and local multiple clusters incur capital and operational expenses, while the multiple cloud clusters follow a pay-as-you-go model. Overall, the local multiple clusters and multiple cloud clusters outperform the local single cluster in terms of performance, scalability, fault tolerance, resource allocation, availability, and cost-effectiveness. These findings provide valuable insights for selecting appropriate clustering strategies in cloud environments. © 2023 by the authors of this article. Published under CC-BY.

关键词： Fault tolerance

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Improved YOLOv8 for Complex Environmental Fish Detection 7

Improved YOLOv8 for Complex Environmental Fish Detection

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7th IEEE Advanced information technology, Electronic and Automation Control Conference, IAEAC 2024

作者： Cheng, Tonglin Li, Juan Luo, Jintao Li, Zhihui Beijing University of Technology Faculty of Information Technology China Beijing Engineering Research Center for LoT Software and Systems China

ISBN: (纸本)9798350339161

With the rapid development of computer vision and deep learning technologies, object detection plays a crucial role in various fields. Due to the complexity and diversity of underwater environments, underwater vision tasks face great challenges. In this paper, we improved the YOLOv8 detection model for the fish detection task in complex environments. Specifically, we proposed a multi-scale feature fusion method (MFANet) that combines multiscale weighted feature fusion with multiscale attention to efficiently capture multilevel features of fishes in complex environments. We designed a multiscale weighted feature fusion module that combines feature maps at different scales to enhance the model's representation of the target. By introducing a multi-scale attention mechanism, the model is able to pay more attention to important feature regions. We conducted experiments on real underwater complex environment datasets. The results show that our proposed improved method significantly enhances the performance of the YOLOv8 model in fish detection tasks and lays a good foundation for subsequent underwater detection tasks. © 2024 IEEE.

关键词： Fish

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Smart Glove with Mobile Application to Detect Static Arabic Hijaiyah Hand Code for Quran Recitation 21

Smart Glove with Mobile Application to Detect Static Arabic ...

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21st International Learning and technology Conference, L and T 2024

作者： Elshareif, Mohamed Elgilani Eltahir Ahmad Alias, Nurul Aswani Jomhari, Nazean Sofian, Hazrina University of Malaya Faculty of Computer Science and Information Technology Department of Software Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350393569

The use of assistive technology in the field of education is now a common practice in today's tech-driven era. The implementation is quite rampant in all levels and sections of education, including by special needs students with hearing impairments. With the help of technology or Internet of Things (IoT), they are offered with better opportunities to improve their communication and the teaching and learning process especially when it comes to sign language. However, the situation is quite different for Muslim deaf individuals. Sign language is not only used for communication but also as a form for reciting the Quran. Yet, the utilization of technology in the area of sign language for Quranic recitation is still limited. Hence, the goal of this study was to design and develop a sensor-based sign language recognition device consisting of a sensory smart glove and mobile application to detect Arabic sign language alphabets used in Quran recitation. The experimental result shows that the gesture recognition rate of 28 different Arabic alphabet signs, performed by 7 different signers, is 79.08%. The significance of the system lies in its mobility and capability to recognize the hand gestures regardless of the background noise. © 2024 IEEE.

关键词： Assistive technology

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Dialog generation for Arabic chatbot

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International Journal of information technology (Singapore) 2024年第2期16卷 881-890页

作者： Youness, Farida Madkour, Mohamed Ashraf Elshenawy, Ayman Computers and Systems Department Al-Azhar University Cairo Egypt Software Engineering and Information Technology Department Faculty of Engineering and Technology Egyptian Chinees University Cairo Egypt

Dialogue generation systems (DGS) is an important topic in the field of Natural Language Processing (NLP). It enables a wide range of real-world applications to interact with humans in various languages naturally and intelligently. Due to the complexity of the Arabic language, the development of an Arabic DGS is a challenge. In this paper, an Arabic DGS is developed using a sequence-to-sequence (Seq2Seq) framework comprised of Long-Short Term Memory (LSTM). A dataset of 12k conversations is translated from English to Arabic for the development process. The proposed DGS is analyzed, and its effectiveness is assessed using the Bilingual Evaluation Understudy (BLEU) score. The TensorFlow deep learning framework is used to test the proposed DGS. Also, the proposed DGS is tested and compared with a recently developed DGS. The Experiments revealed that the proposed Arabic DGS is effective in eliciting positive responses, with a BLEU score of 0.5988. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Artificial intelligence Chatbot Deep learning Dialogue generation Natural language preprocessing Seq2Seq

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Spatiotemporal crowds features extraction of infrared images using neural network

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Journal of Ambient Intelligence and Humanized Computing 2024年第4期15卷 2543-2556页

作者： Al-Oraiqat, Anas M. Drieiev, Oleksandr Drieieva, Hanna Meleshko, Yelyzaveta AlRawashdeh, Hazim Al-Oraiqat, Karim A. Hasan, Yassin M. Y. Maricar, Noor Khan, Sheroz Department of Computer Science College of Engineering and Information Technology Onaizah Colleges Qassim Saudi Arabia Department of Cybersecurity and Software Central Ukrainian National Technical University Kropyvnytskyi Ukraine Department of Artificial Intelligence Faculty of Information Technology Zarqa University Zarqa Jordan Department of Electrical Engineering College of Engineering and Information Technology Onaizah Colleges Qassim Saudi Arabia

Crowds can lead up to severe disasterous consequences resulting in fatalities. Videos obtained through public cameras or captured by drones flying overhead can be processed with artificial intelligence-based crowd analysis systems. Being a hot area of research over the past few years, the goal is not only to identify the presence of crowds but also to predict the probability of crowd-formation in order to issue timely warnings and preventive measures. Such systems will significantly reduce the probablity of the potential disasters. Developing effective systems is a challenging task, especially due to factors such as naturally occuring diverse conditions, variations in people or background pixel areas, noise, behaviors of individuals, relative amounts/distributions/directions of crowd movements, and crowd building reasons. This paper proposes an infrared video processing system based on U-Net convolutional neural network for crowd monitoring in infrared video frames to help estimate the people crowd with normal or abnormal trends. The proposed U-Net architecture aims to efficiently extract crowd features, achieve sufficient people marking-up accuracy, competitively with optimal network configurations in terms of the depth and number of filters to consequently minimise the number of coefficients. For further faster processing, hardware resources/implementation area savings, and lower power, the optimized network coefficients measured are represented in Canonic-Signed Digit with minimal number of nonzero (± 1) digits, minimizing the number of underlying shift-add/subtract operations of all multipliers. The achieved significantly reduced computational cost makes the proposed U-Net effectively suitable for resource-constrained and low power applications. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Convolutional neural networks

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