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arXiv 2022年

作者： Rayhan, Md. Abdullah-Al-Wadud, M. Ahmed, M. Helal Uddin Department Of Computer Science And Engineering Brac University Dhaka Bangladesh Department Of Software Engineering College Of Computer And Information Sciences King Saud University Riyadh Saudi Arabia Department Of Management Information Systems University Of Dhaka Dhaka1000 Bangladesh

This study aims to statistically assess the effectiveness of vaccination against SARS-CoV-2. It is indispensable to investigate the relationship between Covid-19 deadliness and vaccination in order to study the impact of vaccine in real-world. We studied rates of infection and death due to Covid-19 in different countries with respect to their levels of vaccination. People who received the required dose of vaccination were considered as fully vaccinated in this study. Based on the percentage of fully vaccinated population, countries were categorized into several groups. Though a high-level study on the vaccine effectiveness may not provide much insight for individual level differences, a global analysis is imperative to infer the influence of vaccination as a controlling measure of the pandemic. © 2022, CC BY-NC-ND.

关键词： Vaccines

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The Minimum Covariance Determinant Technique for Insider Threat Detection in Communications and Network Security

SSRN

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SSRN 2024年

作者： Al-Shehari, Taher Kadri, Mohammed Al-Mhiqani, Mohammed Nasser Al-Razgan, Muna Alfakih, Taha Computer Skills Department of Self-Development Skill Common First Year Deanship King Saud University Riyadh11362 Saudi Arabia Department of Computer Science University of Huddersfield HuddersfieldHD1 3DH United Kingdom Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11345 Saudi Arabia Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia

Insider threats present a critical concern for network security of organizations, as malicious activities (data theft, hacking, security breaching, etc.) perpetrated by insiders with privileged access systems can result in substantial damage. Many approaches are proposed to detect insider threats, but still there are many challenges on the field such as the imbalanced nature of insider threat datasets, where instances of malicious behavior are often limited in comparison to benign activities. This makes insider threat detection an understudied research area. Existing approaches tried to address the imbalance issue of the insider threat dataset by manipulating the data level utilizing the duplication (over-sampling) and removal (under-sampling) of the dataset’s observations which is not reflecting the real-world data stream within organizations’ networks. To tackle this challenge, Artificial Intelligent (AI) algorithms like the Minimum Covariance Determinant (MCD) algorithm is adopted due to its robustness against outliers and its potential to handle imbalanced data effectively. The proposed approach is evaluated using the CERT insider threat dataset, which encompasses diverse real-world scenarios. Through comprehensive experimentation, the MCD algorithm demonstrates promising results, achieving a remarkable detection rate of 98%. This study underscores the significance of leveraging advanced anomaly detection techniques to address imbalanced insider threat datasets and highlights the practical applicability of the MCD algorithm in enhancing detection accuracy of threats within organizations’ networks. © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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Multimodal Fusion for Enhanced Parkinson's Disease Screening: Integrating Brain MRI, Hand Drawing, Facial Expressions, and Voice Analysis

Multimodal Fusion for Enhanced Parkinson's Disease Screening...

引用

Advancements in Computing (ICAC), International Conference on

作者： Naveen Warnakulasuriya Shalinika De Silva Janani Madushika Sadali Gamage Sanath Jayawardena Anuradha Karunasena Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

Parkinson's disease (PD) profoundly impacts millions in Sri Lanka, emphasizing the importance of early detection for better patient outcomes. We introduce “NeuraTrace PD,” an innovative application for early PD screening, combining brain MRI, hand-drawn image analysis, facial expression assessment, and voice analysis. For brain MRI analysis, we employed a Graph Convolutional Network (GCN), achieving an impressive accuracy of 91% and identifying distinct graph connectivity patterns using graph theory. We also use a deep Convolutional Neural Network (CNN) to reach 90% accuracy in analyzing hand-drawn images, while optimizing image resolution for efficiency and information retention. Facial expression analysis is a vital component, leveraging CNN and Constrained Local Models (CLM) to accurately extract Facial Action Units (FAUs), which contribute to a remarkable 95% accuracy using the XGBoost machine learning model. We introduce a Machine Learning (ML) algorithm and advanced signal processing to enhance voice feature analysis, offering a thorough understanding and classification of PD patients. It reaches 99% accuracy. In summary, our approach holds significant promise for advancing early PD diagnosis, ultimately improving the quality of life for those affected by this challenging condition.

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F2A2: flexible fully-decentralized approximate actor-critic for cooperative multi-agent reinforcement learning

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 8447-8521页

作者： Wenhao Li Bo Jin Xiangfeng Wang Junchi Yan Hongyuan Zha School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of Software Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China School of Computer Science and Technology Key Laboratory of Mathematics and Engineering Applications Ministry of Education East China Normal University Shanghai China Department of Computer Science and Engineering Key Laboratory of Artificial Intelligence Ministry of Education Shanghai Jiao Tong University Shanghai China

Traditional centralized multi-agent reinforcement learning (MARL) algorithms are sometimes unpractical in complicated applications due to non-interactivity between agents, the curse of dimensionality, and computation complexity. Hence, several decentralized MARL algorithms are motivated. However, existing decentralized methods only handle the fully cooperative setting where massive information needs to be transmitted in training. The block coordinate gradient descent scheme they used for successive independent actor and critic steps can simplify the calculation, but it causes serious bias. This paper proposes a exible fully decentralized actor-critic MARL framework, which can combine most of the actor-critic methods and handle large-scale general cooperative multi-agent settings. A primal-dual hybrid gradient descent type algorithm framework is designed to learn individual agents separately for decentralization. From the perspective of each agent, policy improvement and value evaluation are jointly optimized, which can stabilize multi-agent policy learning. Furthermore, the proposed framework can achieve scalability and stability for the large-scale environment. This framework also reduces information transmission by the parameter sharing mechanism and novel modeling-other-agents methods based on theory-of-mind and online supervised learning. Sufficient experiments in cooperative Multi-agent Particle Environment and StarCraft II show that the proposed decentralized MARL instantiation algorithms perform competitively against conventional centralized and decentralized methods.

关键词： cooperative MARL decentralized actor-critic primal-dual method

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AutoDebloater: Automated Android App Debloating

AutoDebloater: Automated Android App Debloating

引用

IEEE International Conference on Automated software engineering (ASE)

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

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.

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Student Perceptions of AI-Enhanced Adaptive Learning systems: A Pilot Survey

Student Perceptions of AI-Enhanced Adaptive Learning Systems...

引用

International IEEE Conference and Workshop: Óbuda on Electrical and Power engineering (CANDO-EPE)

作者： Klara Ida Katonane Gyonyoru Jozsef Katona Department of Organizational Development and Communication Science Institute of Social Sciences University of Dunaujvaros Dunaujvaros Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

Artificial Intelligence (AI) significantly enhances adaptive learning by personalizing and tailoring instruction to individual student needs. AI analyzes data in real-time to create personalized learning paths based on students' strengths, weaknesses, and preferences, which keeps students engaged and motivated. A major benefit of AI in adaptive learning is the provision of real-time feedback and assessment, allowing students to correct mistakes promptly and understand concepts more thoroughly. AI-based intelligent tutoring systems are primarily intended to simulate personalized tutoring processes that guide students in complex problem-solving and answering questions. It is convenient in teaching mathematics, sciences, and languages. AI also supports inclusive education, dealing with diversified learning requirements and styles, such as those of learners with disabilities. For the teacher, AI acts as a reflector of student performance so that one can intervene early and make adjustments in the method of instruction by creating effective learning environments. AI technology is a field in constant development and harbors the potential to change the face of adaptive learning, bringing an upswing in educational outcomes. This article will summarize the advantages and features that merit improvement of the AI-embedded adaptive learning systems with student feedback.

关键词： Surveys Adaptive learning Power engineering Data security Real-time systems Mathematics Problem-solving Artificial intelligence Faces

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NER- RoBERTa: Fine-Tuning RoBERTa for Named Entity Recognition (NER) within low-resource languages

arXiv

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arXiv 2024年

作者： Abdullah, Abdulhady Abas Abdulla, Srwa Hasan Toufiq, Dalia Mohammad Maghdid, Halgurd S. Rashid, Tarik A. Farho, Pakshan F. Sabr, Shadan Sh. Taher, Akar H. Hamad, Darya S. Veisi, Hadi Asaad, Aras T. Artificial Intelligence and Innovation Centre University of Kurdistan Hewler Erbil Iraq Computer Science and Engineering Department University of Kurdistan Hewler KR Erbil Iraq Department of Horticulture College of Agricultural Engineering Sciences University of Sulaimani- Kurdistan Region Iraq Branch of Basic Medical Science College of medicine University of Sulaimani- Kurdistan Region Iraq Department of Engineering Research Center Deanship of R&D Centers Koya University Kurdistan Region Iraq Department of Kurdish Language Faculty of Education Koya University Kurdistan Region Iraq Department of Kurdish Faculty of Education Koya University Kurdistan Region Iraq Department of Software Engineering Faculty of Engineering Koya University Kurdistan Region Iraq School of Intelligent Systems College of Interdisciplinary Science and Technologies University of Tehran Iran

Nowadays natural language Processing (NLP) is an important tool for most people’s daily life routines, ranging from understating speech, translation, name entity recognition (ENR), and text categorizing, to the generative text models such as ChatGPT. Due to existing big data and consequently big corpora for the most usable languages including English, Spanish, Turkish, Persia, and many more, these applications have been utilized accurately. However, the Kurdish language still needs more corpora and big datasets to be included in the NLP applications. This is because the Kurdish language has a rich linguistic structure, varied dialects, and a poor dataset, which poses unique challenges for Kurdish NLP (KNLP) application development. While several types of research have been conducted in KNLP for various applications, the Kurdish ENR (KNER) remained a challenge for most of the KNLP applications including text analysis and text classification. In this work, we address this limitation by proposing a methodology for fine-tuning the pre-trained RoBERTa model for KNER1. To this end, first, a Kurdish corpus is created, followed by designing a modified model architecture, and the training procedures are implemented. To evaluate the trained model, a set of experiments is conducted to show the performance of the KNER model using different tokenization methods and trained models. The experimental results showed that fine-tuned RoBERTa with sentence-piece tokenization method substantially improves KNER performance by 12.8% (F1-score improvement) in comparison to traditional models and consequently establishes a new benchmark for KNLP. © 2024, CC BY.

关键词： Natural language processing systems

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Generalized Coverage Criteria for Combinatorial Sequence Testing

arXiv

引用

arXiv 2022年

作者： Elyasaf, Achiya Farchi, Eitan Margalit, Oded Weiss, Gera Weiss, Yeshayahu Software and Information Systems Engineering Department Ben-Gurion University of the Negev Israel Computer Science Department Ben-Gurion University of the Negev Israel IBM Haifa Research Lab

We present a new model-based approach for testing systems that use sequences of actions and assertions as test vectors. Our solution includes a method for quantifying testing quality, a tool for generating high-quality test suites based on the coverage criteria we propose, and a framework for assessing risks. For testing quality, we propose a method that specifies generalized coverage criteria over sequences of actions, which extends previous approaches. Our publicly available tool demonstrates how to extract effective test suites from test plans based on these criteria. We also present a Bayesian approach for measuring the probabilities of bugs or risks, and show how this quantification can help achieve an informed balance between exploitation and exploration in testing. Finally, we provide an empirical evaluation demonstrating the effectiveness of our tool in finding bugs, assessing risks, and achieving coverage. © 2022, CC BY.

关键词： Risk assessment

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Fractional discrete neural networks with variable order: solvability, finite time stability and synchronization

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The European Physical Journal Special Topics 2024年 1-14页

作者： Hioual, Amel Alomari, Saleh Al-Tarawneh, Hassan Ouannas, Adel Grassi, Giuseppe Laboratory of Dynamical Systems and Control University of Oum El-Bouaghi Oum El Bouaghi Algeria Faculty of Science and Information Technology Software Engineering Jadara University Irbid Jordan Department of Data Sciences and Artificial Intelligence Al-Ahliyya Amman University Amman Jordan Department of Mathematics and Computer Science University of Oum EL-Bouaghi Oum El Bouaghi Algeria Dipartimento Ingegneria Innovazione Universita del Salento Lecce Italy

Research in the field of dynamic behaviors in neural networks with variable-order differences is currently a thriving area, marked by various significant discoveries. However, when it comes to discrete-time neural networks featuring fractional variable-order nonlocal and nonsingular kernels, there has been limited exploration. This paper stands as one of the initial contributions to this subject, focusing primarily on the topics of stability and synchronization in finite-time within discrete neural networks. The research employs the nabla ABC variable-order difference operator, with a primary approach involving the investigation of a novel Gronwall inequality using the Atangana-Baleanu difference variable-order sum operator. This analysis leads to the development of a uniqueness theorem and a criterion for the stability in finite-time of variable-order discrete neural networks. Furthermore, the requirements stemming from this type of stability and the novel Gronwall inequality serve as the foundation for establishing the conditions necessary for achieving finite-time synchronization in these networks, employing a specific control using state feedback method. Finally, the study utilizes numerical solutions to validate the obtained results.

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S2KAN-SLAM: Elastic Neural LiDAR SLAM with SDF Submaps and Kolmogorov-Arnold Networks

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Wang, Zhong Zhang, Lin Wang, Hesheng Shanghai Jiao Tong University State Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China Tongji University School of Computer Science and Technology National Pilot Software Engineering School with Chinese Characteristics Shanghai201804 China

Traditional LiDAR SLAM approaches prioritize localization over mapping, yet high-precision dense maps are essential for numerous applications involving intelligent agents. Recent advancements have introduced methods leveraging neural fields to enhance mapping capabilities;however, these approaches still face several limitations. Firstly, concerning scene representation, they typically employ neural fields with high-dimensional features and multi-layer perceptron decoders utilizing non-continuous activation functions. This results in low learning efficiency and challenges in capturing high-frequency signals. Secondly, in terms of scene organization, these methods often treat the entire scene as a singular neural field, leading to inefficiencies, inflexibility, and difficulties in rectifying accumulated errors when mapping large-scale environments over extended periods. To tackle the first issue, we propose a lightweight continuous SDF regression approach by encoding the scene in single-valued embeddings and decoding SDF values from a Kolmogorov-Arnold Network. By minimizing discrepancies in measuring range, sampling distance, and decoded SDF values, we facilitate iterative frame-to-model tracking and bundle adjustment neural mapping. To mitigate the second challenge, we propose structuring the whole scene into multiple neural SDF submaps. By establishing node-node, node-submap, and loop closure constraints into a global pose graph, the system can create dense neural maps with global consistency across large-scale scenes. Experimental evaluations in both real-world and simulated settings indicate that our system achieves superior mapping completeness and accuracy, enhanced learning efficiency, reduced memory consumption, and greater flexibility compared to its counterparts. © 2025 IEEE.

关键词： Mapping

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