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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Ibrahim, Abdelhameed Gatzoulis, Christos Eid, Marwa M. Rizk, Faris H. Abualigah, Laith El-Kenawy, El-Sayed M. Isa Town Bahrain Delta University for Science and Technology Faculty of Artificial Intelligence Mansoura35111 Egypt Computer Science and Intelligent Systems Research Center BlacksburgVA24060 United States Al al-Bayt University Computer Science Department Mafraq25113 Jordan Middle East University MEU Research Unit Amman11831 Jordan Applied Science Private University Applied Science Research Center Amman11931 Jordan Delta Higher Institute of Engineering and Technology Department of Communications and Electronics Mansoura35111 Egypt Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan

ISBN: (纸本)9798350351408

This paper focuses on improving learning and involvement in studies by employing game elements and considering online learning platforms to deliver statistics curriculum. According to the problem statement, the research examines gamification methods to transform studying from a routine to having enjoyment while improving academic results. This area of investigation is being extended to predict academic outcomes by applying different machine learning models to student performance data received from gamified. The strategy entails using a pedagogy based on collecting data from students who participated in a gamified educational platform;the data comprises indicators like pre-and post-platform exam grades, grades on quizzes, and indicators of platform usage. Multiple regression algorithms, including Random Forest and Gradient Boosting, are applied to student performance. In the experiments, the output results indicate that the model of Random Forest Regressor outperforms other models with the least Mean Squared Error (MSE) of 0.052. This model performs the best in terms of prediction accuracy with competence level in predicting students' results in gamified environments. The findings can support decision-making in designing educational activities. The Random Forest Regressor model can be identified as the best predictive model and may help train gamified learning platforms, which can encourage student engagement that correlates to performance. © 2024 IEEE.

关键词： Prediction models

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Mqtt Routing Optimizing Based Intrusion Detection for Internet of Things Using Hybrid Machine Learning

Mqtt Routing Optimizing Based Intrusion Detection for Intern...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Hoomod, Haider K. Al-Hamami, Mohammad Alaa Kadhim, Aqeel Kamil Mohammed, Qutaiba Humadi Niaf, Jolan Rokan College of Education Mustansiriyah University Baghdad Iraq Applied Science University Manama Department of Management Information Systems Manam Bahrain Imam Al-Kadhim College Computer Techniques Engineering Department Baghdad Iraq College of Nursing University of Baghdad Baghdad Iraq Informatics Institute for Postgraduate Studies University of Information Technology and Communications Baghdad Iraq

ISBN: (纸本)9798350369106

With the advent of the Internet of Things, a world was born in which everything could be uniquely identified and monitored, tracked, and managed by computer programs. Items can self-configure using a predefined communication protocol. Other software programs can be linked to operate together using Message Queue Telemetry Transport Protocol (MQTT) or MQSeries components. Business integration software and middleware are common terms for this category of applications. With its suggested lightweight, easy-to-implement, and lowbandwidth system for billions of smart items, the MQTT protocol offers a wide range of potential applications, including home automation. In order to enhance the power savings in MQservice routing, we presented an intelligent assistant for optimizing routing model. For Internet of Things (IoT) purposes, it introduces an optimal routing model based on optimized hybrid machine learning-based anomaly detection (OHMLA). In order to extend the reach of a network, the offered approach allows Internet of Things devices to make efficient use of energy. To further enhance Intrusion Detection identification and get optimum network selection routes, the moth Salp Swarm technique (SSA) technique is used for the optimized hyperparameters. The model is capable of accurately detecting attacks that result in network congestion, causing delays in connection response and data transfer in IoT network. © 2024 IEEE.

关键词： Middleware

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CASCADING BANDITS ROBUST TO ADVERSARIAL CORRUPTIONS

arXiv

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arXiv 2025年

作者： Xie, Jize Chen, Cheng Wang, Zhiyong Li, Shuai Department of Industrial Engineering Hong Kong University of Science and Technology Hong Kong Software Engineering Institute East China Normal University China Department of Computer Science The Chinese University of Hong Kong Hong Kong John Hopcroft Center Shanghai Jiao Tong University China

Online learning to rank sequentially recommends a small list of items to users from a large candidate set and receives the users’ click feedback. In many real-world scenarios, users browse the recommended list in order and click the first attractive item without checking the rest. Such behaviors are usually formulated as the cascade model. Many recent works study algorithms for cascading bandits, an online learning to rank framework in the cascade model. However, the performance of existing methods may drop significantly if part of the user feedback is adversarially corrupted (e.g., click fraud). In this work, we study how to resist adversarial corruptions in cascading bandits. We first formulate the "Cascading Bandits with Adversarial Corruptions" (CBAC) problem, which assumes that there is an adaptive adversary that may manipulate the user feedback. Then we propose two robust algorithms for this problem, which assume the corruption level is known and agnostic, respectively. We show that both algorithms can achieve logarithmic regret when the algorithm is not under attack, and the regret increases linearly with the corruption level. The experimental results also verify the robustness of our methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Adversarial machine learning

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A Mathematical Model for the Selection of software Requirements Elicitation Techniques

A Mathematical Model for the Selection of Software Requireme...

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International Conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Virat Raj Saxena Rajeev Yadav Azra Parveen Mohd. Sadiq Department of Computer Science and Engineering Mangalayatan University Aligarh Uttar Pradesh India Indraprastha Research Laboratory Indraprastha Institute of Information Sciences Private Limited New Delhi India Software Engineering Laboratory Computer Engineering Section UPFET Jamia Millia Islamia A Central University New Delhi India

Various techniques have been developed for the identification of different types of requirements like interview, questionnaire, group elicitation techniques, attributed goal-oriented requirements analysis, fuzzy based goal-oriented requirements analysis method, non-functional requirements framework, etc. Based on the critical analysis of requirements elicitation methods, we found that single elicitation technique is not suitable to understand the need of the stakeholders. In real life applications, multiple elicitation techniques are required to elicit and select the requirements of an information system because each technique has its strength and limitations. Therefore, to address this issue, this paper presents a mathematical model for the selection of requirements elicitation techniques so that complete set of requirements along with its priority can be identified before the development of an information system. The institute examination system is considered to show the applicability of the proposed mathematical model.

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Enactment of Adaptation in Data Stream Processing with Latency Implications-A Systematic Literature Review

Enactment of Adaptation in Data Stream Processing with Laten...

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Fachtagung des GI-Fachbereichs softwaretechnik, software engineering 2020 - Conference of the GI Special Interest Group on software engineering, software engineering 2020

作者： Qin, Cui Eichelberger, Holger Schmid, Klaus University of Hildesheim Institute of Computer Science Software Systems Engineering Universitätsplatz 1 Hildesheim31141 Germany

ISBN: (纸本)9783885796947

This summary refers to the paper Enactment of adaptation in data stream processing with latency implications - A systematic literature review [QES19]. This paper is a journal paper published in Information and software Technology (IST) in July 2019. Runtime adaptation in stream processing plays a signiĄcant role in supporting the optimization of data processing tasks. In recent years, runtime adaptation, particularly its enactment, has received signiĄcant interest in scientiĄc literature. However, so far no categorization of the enactment approaches for runtime adaptation in stream processing has been established. This paper presents a systematic literature review (SLR), where we identify and characterize different approaches towards the enactment of runtime adaptation in stream processing with a main focus on latency as quality dimension. We discovered 75 relevant papers out of 244 papers from the search. We identiĄed 17 different enactment categories and developed a taxonomy to characterize all possible enactment approaches. We extracted the realization techniques of each identiĄed enactment approach and classiĄed them into categories. Furthermore, we identiĄed 9 categories of processing problems, 6 adaptation goals, 9 evaluation metrics and 12 evaluation parameters from the identiĄed enactment approaches. The research interest on enactment approaches has signiĄcantly increased in recent years. The most commonly applied enactment approaches are parameter adaptation to tune parameters or settings of the processing, load balancing used to re-distribute workloads, and processing scaling to dynamically scale up and down the processing. © 2020 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Big data

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Class Attention to Regions of Lesion for Imbalanced Medical Image Recognition

arXiv

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arXiv 2023年

作者： Zhuang, Jia-Xin Cai, Jiabin Zhang, Jianguo Zheng, Wei-Shi Wang, Ruixuan Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Department of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineerin Southern University of Science and Technology China Peng Cheng Laboratory China

Automated medical image classification is the key component in intelligent diagnosis systems. However, most medical image datasets contain plenty of samples of common diseases and just a handful of rare ones, leading to major class imbalances. Currently, it is an open problem in intelligent diagnosis to effectively learn from imbalanced training data. In this paper, we propose a simple yet effective framework, named Class Attention to REgions of the lesion (CARE), to handle data imbalance issues by embedding attention into the training process of Convolutional Neural Networks (CNNs). The proposed attention module helps CNNs attend to lesion regions of rare diseases, therefore helping CNNs to learn their characteristics more effectively. In addition, this attention module works only during the training phase and does not change the architecture of the original network, so it can be directly combined with any existing CNN architecture. The CARE framework needs bounding boxes to represent the lesion regions of rare diseases. To alleviate the need for manual annotation, we further developed variants of CARE by leveraging the traditional saliency methods or a pretrained segmentation model for bounding box generation. Results show that the CARE variants with automated bounding box generation are comparable to the original CARE framework with manual bounding box annotations. A series of experiments on an imbalanced skin image dataset and a pneumonia dataset indicates that our method can effectively help the network focus on the lesion regions of rare diseases and remarkably improves the classification performance of rare diseases. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Edge-Intelligence based Federated Learning in the Internet of Medical Things

Edge-Intelligence based Federated Learning in the Internet o...

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International Multidisciplinary Conference on computer and Energy science (SpliTech)

作者： Suresh Chavhan Beerelly Srinitha Sikander Kathat Ashit Kumar Dutta Joel J. P. C. Rodrigues Department of Computer Science and Engineering Indian Institute of Information and Technology Raichur Karnataka India Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Riyadh Kingdom of Saudi Arabia Amazonas State University Manaus - AM Brazil

ISBN: (数字)9789532901351

ISBN: (纸本)9798350390797

In the evolving healthcare landscape, the Internet of Medical Things (IoMT) enables real-time data collection through connected devices. Concerns about data privacy in electronic health records are driving changes in health assessment. Blended learning is emerging as a solution, allowing simulation training without sharing critical information centrally. The proposed techniques emphasize privacy and efficiency and use federated averaging to analyze edge computation. Smart healthcare addresses the benefits and challenges of integrating AI and edge tech. In this revolutionary approach, federated learning uses server-side federated averaging, combining local client model parameters while reducing edge computation latency and energy consumption The integration of AI and edge technologies not only increases efficiency but also provides forward-looking approaches for personalized and responsive healthcare. Experimental validation with the Covid-pneumonia dataset highlights the effectiveness of the integrated learning approach, confirming the important contribution to privacy protection and efficient machine learning applications in computing in healthcare of the policies established in countries.

关键词： Training Data privacy Privacy Federated learning Internet of Medical Things Vectors Real-time systems

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Audio Adversarial Example Detection Using the Audio Style Transfer Learning Method

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IEEE Access 2023年 1-1页

作者： Kwon, Hyun Lee, Kangjun Ryu, Junyeol Lee, Jun Department of Artificial Intelligence and Data Science Korea Military Academy Seoul Republic of Korea Department of Software Science and Engineering Kunsan National University South Korea Department of systems & Mechanical Engineering Korea Military Academy Seoul Korea Division of Computer Information and Science Hoseo University Asan-si South Korea

In this paper, we propose a method for defending against audio adversarial examples that operates by applying audio style transfer learning. The proposed method has the effect of maintaining the classification result produced by the target model and removing the adversarial noise by changing only the style while maintaining the content of the input audio sample. In an experimental evaluation using the Mozilla Common Voice dataset as the test data source and TensorFlow as the machine learning library, the proposed method improved the target model’s accuracy on the adversarial examples from 2.1% to 79.2% while maintaining its accuracy on the original samples at 81.4%. Author

关键词： Speech recognition

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ConZone: A Zoned Flash Storage Emulator for Consumer Devices

ConZone: A Zoned Flash Storage Emulator for Consumer Devices

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2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Yu, Dingcui Liu, Jialin Zhao, Yumiao Li, Wentong Huang, Ziang Yan, Zonghuan Ma, Mengyang Shi, Liang Software Engineering Institute East China Normal University Shanghai China MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application East China Normal University Shanghai China College of Computer Science and Technology East China Normal University Shanghai China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9783982674100

Considering the potential benefits to lifespan and performance, zoned flash storage is expected to be incorporated into the next generation of consumer devices. However, due to the limited volatile cache and heterogeneous flash cells of consumer-grade flash storage, adopting a zone abstraction requires additional internal hardware design to maximize its benefits. To understand and efficiently improve the hardware design on consumer-grade zoned flash storage, we present ConZone - the first emulator tailored to the characteristics of consumer-grade zoned flash storage. Users can explore the internal architecture and management strategies of consumer-grade zoned flash storage and integrate the optimization with software. We validate the accuracy of ConZone by realizing a hardware architecture for consumer-grade zoned flash storage and comparing it with the state-of-the-art. We also make a case study for read performance research with ConZone to explore the design of mapping mechanisms and cache management strategies. © 2025 EDAA.

关键词： Strategic planning

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AutoDebloater: Automated Android App Debloating 23

AutoDebloater: Automated Android App Debloating

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Proceedings of the 38th IEEE/ACM International Conference on Automated software engineering

作者： Jiakun Liu Xing Hu Ferdian Thung Shahar Maoz Eran Toch Debin Gao David Lo School of Computing and Information Systems Singapore Management University Singapore School of Software Technology Zhejiang University China School of Computer Science Tel Aviv University Israel Department of Industrial Engineering Tel Aviv University Israel

ISBN: (纸本)9798350329964

Android applications are getting bigger with an increasing number of features. However, not all the features are needed by a specific user. The unnecessary features can increase the attack surface and cost additional resources (e.g., storage and memory). Therefore, it is important to remove unnecessary features from Android applications. However, it is difficult for the end users to fully explore the apps to identify the unnecessary features, and there is no off-the-shelf tool available to assist users to debloat the apps by themselves. In this work, we propose AutoDebloater to debloat Android applications automatically for end users. AutoDebloater is a web application that can be accessed by end-users through a web browser. In particular, AutoDebloater can automatically explore an app and identify the transitions between activities. Then, AutoDebloater will present the Activity Transition Graph to users and ask them to select the activities they do not want to keep. Finally, AutoDebloater will remove the activities that are selected by users from the app. We conducted a user study on five Android apps downloaded from three categories (i.e., Finance, Tools, and Navigation) in Google Play and F-Droid. The results show that users are satisfied with AutoDebloater in terms of the stability of the debloated apps and the ability of AutoDebloater to identify features that are never noticed before. The tool is available at http://***. The code is available at https://***/jiakun-liu/autodebloater/ and the demonstration video can be found at https://***/Gmz0-p2n9D4.

关键词： android

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