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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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DOA-Aware Audio-Visual Self-Supervised Learning for Sound Event Localization and Detection

arXiv

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arXiv 2024年

作者： Fujita, Yoto Bando, Yoshiaki Imoto, Keisuke Onishi, Masaki Yoshii, Kazuyoshi Graduate School of Informatics Kyoto University Japan National Institute of Advanced Industrial Science and Technology Japan Faculty of Science and Engineering Doshisha University Japan

This paper describes sound event localization and detection (SELD) for spatial audio recordings captured by first-order ambisonics (FOA) microphones. In this task, one may train a deep neural network (DNN) using FOA data annotated with the classes and directions of arrival (DOAs) of sound events. However, the performance of this approach is severely bounded by the amount of annotated data. To overcome this limitation, we propose a novel method of pretraining the feature extraction part of the DNN in a self-supervised manner. We use spatial audio-visual recordings abundantly available as virtual reality contents. Assuming that sound objects are concurrently observed by the FOA microphones and the omni-directional camera, we jointly train audio and visual encoders with contrastive learning such that the audio and visual embeddings of the same recording and DOA are made close. A key feature of our method is that the DOA-wise audio embeddings are jointly extracted from the raw audio data, while the DOA-wise visual embeddings are separately extracted from the local visual crops centered on the corresponding DOA. This encourages the latent features of the audio encoder to represent both the classes and DOAs of sound events. The experiment using the DCASE2022 Task 3 dataset of 20 hours shows non-annotated audio-visual recordings of 100 hours reduced the error score of SELD from 36.4 pts to 34.9 pts. Copyright © 2024, The Authors. All rights reserved.

关键词： Microphones

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A Framework for Democratizing Open-Source Decision-Making using Decentralized Autonomous Organization

A Framework for Democratizing Open-Source Decision-Making us...

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Computing and informatics (ICCI), International Conference on

作者： Khalifa H. Afifi Khaled M. Fouad Heba K. Aslan Faculty of Information Technology and Computer Science Nile University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Faculty of Computers and Engineering New Mansoura University Egypt Informatics Department Electronics Research Institute (ERI) Cairo Egypt Center of Informatics Science Faculty of Information Technology and Computer Science Nile University Cairro Egypt

The open Source Software (OSS) became the backbone of the most heavily used technologies, including operating systems, cloud computing, AI, Blockchain, Bigdata Systems, IoT, and many more. Although the OSS individual contributors are the primary power for developing the OSS projects, they do not contribute to the OSS project's decisionmaking as much as their contributions in the OSS Projects development. This paper proposes a framework to democratize the OSS Project's decision-making using a blockchain-related technology called Decentralized Autonomous Organization (DAO). Using DAO, contributors get incentive tokens as a reward in return for contributions. The earned tokens can be used in the process of decision-making governance. This governance model tends to allow OSS individual contributors to have a public voice in the OSS project's development roadmap in particular, and the OSS decision making governance in general.

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An Efficient Algorithm for Software Reliability Prediction via Harris Hawks Optimization 2nd

An Efficient Algorithm for Software Reliability Prediction v...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Kong, Li Sheng Jasser, Muhammed Basheer Issa, Bayan Ajibade, Samuel-Soma M. Majeed, Anwar P. P. Abdul Luo, Yang Department of Smart Computing and Cyber Resilience School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Department of Data Science and Artificial Intelligence School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Faculty of Informatics Engineering University of Aleppo Aleppo Syria School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

Software reliability has become an increasingly important characteristic as software systems continue to be integrated into all parts of our lives. While the occurrence of failure after a period of time is within the nature of all software systems, the ability to estimate and predict failure is greatly beneficial to software development and maintenance. Hence, Software Reliability Growth Models (SRGMs) were developed to fulfill this requirement. Conventional parameter estimation methods of SRGM functions were complex and left more to be desired. Consequently, metaheuristics such as Swarm Intelligence algorithms were introduced to estimate and optimize the parameters of SRGM functions. Likewise, Swarm Intelligence algorithms were found to be exceptionally effective in estimating and optimizing the parameters of SRGM functions. This paper presents a novel approach to parameter estimation on three different SRGM functions through Harris’ Hawks Optimization (HHO). The proposed HHO design is compared against numerous different variants of Swarm Intelligence algorithms and is shown to be significantly more efficient than the majority of the compared designs. Moreover, as the proposed design is a base variant of Swarm Intelligence algorithms, various avenues of future improvements have also been presented to improve its overall prediction accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Harris’ Hawks Optimization Parameter Estimation Software Reliability Growth Models Software Reliability Prediction Swarm Intelligence

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Arabic Sentiment Analysis of Food Delivery Services Reviews

Arabic Sentiment Analysis of Food Delivery Services Reviews

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2023 International Symposium on Networks, Computers and Communications, ISNCC 2023

作者： Mustafa, Dheya Khabour, Safaa M. Shatnawi, Ahmed S. Taqieddin, Eyad The Hashemite University Faculty of Engineering Dept. of Computer Engineering Zarqa13133 Jordan Yarmouk University Faculty of Information Technology and Computer Sciences Dept. of Information Systems Irbid21163 Jordan Jordan University of Science & Technology Dept. of Software Engineering Irbid21110 Jordan School of Computing and Informatics Dept. of Cyber Security Al-Hussein Technical University Amman Jordan

ISBN: (纸本)9798350335590

Customer reviews on online platforms have grown to become an important source of insight into a company's performance. Food delivery services (FDS) companies aim to effectively use customers' feedback to identify areas for improvement of customer satisfaction. Although Arabic is becoming one of the most widely used languages on the Internet, only a few studies have focused on Arabic sentiment analysis to date. The present study conducts an extensive emotion mining and sentiment analysis on FDS-related reviews in Arabic, exploiting natural language processing, and machine learning techniques to extract information that reflects customers' subjective viewpoints, recognize their feelings, and determine polarity in the FDS domain. This work begins with collecting the FDS dataset (Talabat), and then extracting and creating a dialects lexicon for Arabic dialects, followed by walking the reader through detailed steps of cleaning and pre-processing a manually annotated dataset. Finally, we examined classification algorithms including Decision Tree (DT), and Support Vector Machine (SVM). We achieved a maximum accuracy of about 82% using the SVM classifier. © 2023 IEEE.

关键词： Sentiment analysis

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Psychological Stress Classification Using Extreme Gradient Boosting Algorithm 15

Psychological Stress Classification Using Extreme Gradient B...

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15th International Conference on Information and Communication technology Convergence, ICTC 2024

作者： Sakanti, Maria Mahardini Siniaev, Viacheslav Amaris, Aurelia Luo, Win-Jet Suhartono Kuncoro, C. Bambang Dwi Graduate Institute of Precision Manufacturing National Chin-Yi University of Technology Taichung41170 Taiwan Institute of Applied Computer Science Itmo University St. Petersburg19710 Russia Universitas Brawijaya Faculty of Computer Science Department of Informatics Engineering Malang65145 Indonesia National Chin-Yi University of Technology Taichung41170 Taiwan National Chin-Yi University of Technology Department of Refrigeration Air Conditioning and Energy Engineering Taichung41170 Taiwan

ISBN: (纸本)9798350364637

Stress, as a reaction to threatening situations, can raise heart rate and result in serious conditions that might cause significant damage or even be life-threatening. Traditional methods for evaluating stress, which rely on subjective self-reporting and clinical assessments, often suffer from biases and inconsistencies. Artificial intelligence models have been explored to predict stress levels more accurately. This paper investigates the application of Extreme Gradient Boosting in classifying psychological stress using the WESAD dataset, which includes parameters such as acceleration, electrocardiogram, electromyography, electrodermal activity, temperature, and respiration. The dataset was balanced and sampled to create a manageable subset for experimental. Extreme Gradient Boosting was chosen for its efficiency and scalability in handling complex datasets. The model was trained and validated, achieving a 95% accuracy in predicting stress levels. This study highlights the potential of integrating Extreme Gradient Boosting models into wearable devices for real-time stress monitoring. Future work involves optimizing the model to utilize fewer sensors without decreasing accuracy, ensuring it can be integrated into portable/wearable systems using tiny microcontrollers. © 2024 IEEE.

关键词： Electromyography

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Resource Exhaustion Attack Detection Scheme for WLAN Using Artificial Neural Network

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Computers, Materials & Continua 2023年第3期74卷 5607-5623页

作者： Abdallah Elhigazi Abdallah Mosab Hamdan Shukor Abd Razak Fuad A.Ghalib Muzaffar Hamzah Suleman Khan Siddiq Ahmed Babikir Ali Mutaz H.H.Khairi Sayeed Salih Faculty of Engineering Future UniversityKhartoum 10553Sudan Faculty of Computing and Informatics Universiti Malaysia SabahKota Kinabalu88400Malaysia Information Assurance and Security Research School of ComputingUniversiti Teknologi MalaysiaJohor81310Malaysia School of Psychology and Computer Science University of Central LancashirePreston PR12HEUK Deaptement of Management Information Systems College of Administrative ScienceApplied Science UniversityAl Eker623Bahrain Deaptement of Information Technology College of Computer and Information SciencesKing Saud UniversityRiyadh11461Saudi Arabia

IEEE 802.11 Wi-Fi networks are prone to many denial of service(DoS)attacks due to vulnerabilities at the media access control(MAC)layer of the 802.11 *** to the data transmission nature of the wireless local area network(WLAN)through radio waves,its communication is exposed to the possibility of being attacked by illegitimate ***,the security design of the wireless structure is vulnerable to versatile *** example,the attacker can imitate genuine features,rendering classificationbased methods inaccurate in differentiating between real and false *** security standards have been proposed over the last decades to overcome many wireless network attacks,effectively detecting such attacks is crucial in today’s real-world *** paper presents a novel resource exhaustion attack detection scheme(READS)to detect resource exhaustion attacks *** proposed scheme can differentiate between the genuine and fake management frames in the early stages of the attack such that access points can effectively mitigate the consequences of the *** scheme is built through learning from clustered samples using artificial neural networks to identify the genuine and rogue resource exhaustion management frames effectively and efficiently in *** proposed scheme consists of four modules whichmake it capable to alleviates the attack impact more effectively than the related *** experimental results show the effectiveness of the proposed technique by gaining an 89.11%improvement compared to the existing works in terms of detection.

关键词： 802.11 media access control(MAC) wireless local area network(WLAN) artificial neural network denial-of-service(DoS)

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A Novel Optimized Resource Allocation Algorithm using GWO Optimization Technique for WBAN 23

A Novel Optimized Resource Allocation Algorithm using GWO Op...

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Proceedings of the 2023 12th International Conference on Software and Information engineering

作者： Amira Olayan Albashir Adel Youssef Mohamed Samir Abou El-Seoud Faculty of Informatics and Computer Science The British University (BUE) Egypt Department of Electronics and Communication Engineering Arab Academy for Science Technology and Maritime Transport Egypt

ISBN: (纸本)9798400708107

Wireless Body Area Networks have severe challenges in energy management to enhance the longevity of the system. Specifically, for a WBAN system that operates by ambient energy sources. The system combines energy scavenging modules integrated into the sensors carried by patients, enabling data transmission to a personal device. Our approach does not rely on previous information as the characteristics of the scavenged and consumed energy are stochastic. To optimize user utility, a formulation of an optimization problem by employing the Grey Wolf Optimization technique (GWO) compared to previous works that used different optimization techniques, to decompose it into three sub-problems: battery management, collecting rate control, and transmission power allocation. To achieve our goals, we apply the GWO to the introduced online resource allocation algorithm that serves two primary purposes: (1) balancing energy scavenging and consumption of network nodes to ensure system stability, and (2) maximizing user utility. Through simulation results, we validate the effectiveness and optimization capabilities of the algorithm while applying a different optimization technique from the previous works maintained.

关键词： Energy management GWO WBAN

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Privacy-Preserving Information Fusion Technique for Device to Server-Enabled Communication in the Internet of Things:A Hybrid Approach

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Computers, Materials & Continua 2024年第7期80卷 1305-1323页

作者： Amal Al-Rasheed Rahim Khan Tahani Alsaed Mahwish Kundi Mohamad Hanif Md.Saad Mahidur R.Sarker Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science AbdulWali Khan University MardanMardan23200Pakistan Faculty of Computing and Informatics University Malaysia SabahKota KinabaluSabah88400Malaysia Applied College Taibah UniversityMadinah41477Saudi Arabia Maynooth International Engineering College Maynooth UniversityCo KildareW23 X021Irland Department of Mechanical Engineering Faculty of Engineering and Built EnvironmentUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia Institute of Visual Informatics Universiti Kebangsaan MalaysiaBangiSelangor43600Malaysia University of Design Innovation and Technology(UDIT)Av.Alfonso XIII97Madrid28016Spain

Due to the overwhelming characteristics of the Internet of Things(IoT)and its adoption in approximately every aspect of our lives,the concept of individual devices’privacy has gained prominent attention from both customers,i.e.,people,and industries as wearable devices collect sensitive information about patients(both admitted and outdoor)in smart healthcare *** addition to privacy,outliers or noise are among the crucial issues,which are directly correlated with IoT infrastructures,as most member devices are resource-limited and could generate or transmit false data that is required to be refined before processing,i.e.,***,the development of privacy-preserving information fusion techniques is highly encouraged,especially those designed for smart IoT-enabled *** this paper,we are going to present an effective hybrid approach that can refine raw data values captured by the respectivemember device before transmission while preserving its privacy through the utilization of the differential privacy technique in IoT *** window,i.e.,δi based dynamic programming methodology,is implemented at the device level to ensure precise and accurate detection of outliers or noisy data,and refine it prior to activation of the respective transmission ***,an appropriate privacy budget has been selected,which is enough to ensure the privacy of every individualmodule,i.e.,a wearable device such as a smartwatch attached to the patient’s *** contrast,the end module,i.e.,the server in this case,can extract important information with approximately the maximum level of ***,refined data has been processed by adding an appropriate nose through the Laplace mechanism to make it useless or meaningless for the adversary modules in the *** proposed hybrid approach is trusted from both the device’s privacy and the integrity of the transmitted information *** and analytical results ha

关键词： Internet of things information fusion differential privacy dynamic programming Laplace function

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Monte Carlo method for map area calculation in wildland fire map management 6

Monte Carlo method for map area calculation in wildland fire...

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6th International Conference on Information technology, Information Systems and Electrical engineering, ICITISEE 2022

作者： Yuana, Kumara Ari Kusrini Setyanto, Arief Laksito, Arif Dwi Maruf, Zauvik Rizaldi Johari, Muhammad Zuhdi Fikri Adninda, Gardyas Bidari Kartikakirana, Renindya Azizza Suprapto, Rhisa Aidilla Mimboro, Prasetyo Widayani, Wiwi Universitas Amikom Yogyakarta Faculty of Computer Science Yogyakarta Indonesia Universitas Amikom Yogyakarta Magister of Informatic Engineering Yogyakarta Indonesia Magister of Informatics Engineering Universitas Amikom Yogyakarta Yogyakarta Indonesia Universitas Amikom Yogyakarta Faculty of Science and Technology Yogyakarta Indonesia

ISBN: (纸本)9798350399615

Fire spreading or fire propagation is the transfer of heat from the fire to the fuel bed, and heating it to its ignition point. Wildland fire management needs information about the characteristics and areas on the map. The map is always available in complicated geometry and difficult to calculate the area. The Monte Carlo technique gives an easy but powerful and accurate method to handle that problem. This study prepares the methodology for complicated area calculation with a large area range ratio. Hypothetic map areas with different shapes and sizes ensure the applicability of the real map. For validation purposes, the hypothetic map is also ready for theoretical calculation due to its border involving ideal geometry. The results show that the Monte Carlo technique give 1 % error for small size area (1 % of total area), 0.2% - 0.05% error for mid-size area (20% - 30% of total area), and 0.025% error for big-size area (50% of the total area). The accuracy level of this Monte Carlo technique indicate the readiness for wild land map area that always involved as complicated shape maps. © 2022 IEEE.

关键词： accuracy disaster management error Monte Carlo wildland fire

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