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arXiv 2023年

作者： Frieder, Simon Berner, Julius Petersen, Philipp Lukasiewicz, Thomas Department of Computer Science University of Oxford Oxford United Kingdom Department of Computing and Mathematical Sciences Caltech Pasadena United States Faculty of Mathematics and Research Network Data Science University of Vienna Vienna Austria Institute of Logic and Computation Vienna University of Technology Vienna Austria

Large language models (LLMs) such as ChatGPT have received immense interest for their general-purpose language understanding and, in particular, their ability to generate high-quality text or computer code. For many professions, LLMs represent an invaluable tool that can speed up and improve the quality of work. In this note, we discuss to what extent they can aid professional mathematicians. We first provide a mathematical description of the transformer model used in all modern language models. Based on recent studies, we then outline best practices and potential issues and report on the mathematical abilities of language models. Finally, we shed light on the potential of LLMs to change how mathematicians work. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational linguistics

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An Ensemble Models for the Prediction of Sickle Cell Disease from Erythrocytes Smears

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EAI Endorsed Transactions on Pervasive Health and Technology 2023年 9卷

作者： Ayoade, Oluwafisayo Babatope Oladele, Tinuke Omolewa Imoize, Agbotiname Lucky Adeloye, Jerome Adetoye Awotunde, Joseph Bamidele Olorunyomi, Segun Omotayo Faboya, Olusola Theophilus Idowu, Ayorinde Oladele Department of Computing and Information Science School of Pure and Applied Sciences College of Science Bamidele Olumilua University of Education Science & Technology Ekiti State Ikere-Ekiti Nigeria Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Kwara State Nigeria Department of Electrical and Electronics Faculty of Engineering University of Lagos Lagos State Akoka 100213 Nigeria Ekiti State Data Center Office Old Governor’s Office Ekiti State Ado-Ekiti Nigeria

INTRODUCTION: The human blood as a collection of tissues containing Red Blood Cells (RBCs), circular in shape and acting as an oxygen carrier, are frequently deformed by multiple blood diseases inherited from parents. These hereditary diseases of blood involve abnormal haemoglobin (Hb) or anemia which are major public health issues. Sickle Cell Disease (SCD) is one of the common non-communicable disease and genetic disorder due to changes in hematological conditions of the RBCs which often causes the inheritance of mutant Hb genes by the patient.. OBJECTIVES: The process of manual valuation, predictions and diagnosis of SCD necessitate for a passionate time spending and if not done properly can lead to wrong predictions and diagnosis. Machine Learning (ML), a branch of AI which emphases on building systems that improve performance based on the data they consume is appropriate. Despite previous research efforts in predicting with single ML algorithm, the existing systems still suffer from high false and wrong predictions. METHODS: Thus, this paper aimed at performing comparative analysis of individual ML algorithms and their ensemble models for effective predictions of SCD (elongated shapes) in erythrocytes blood cells. Three ML algorithms were selected, and ensemble models were developed to perform the predictions and metrics were used to evaluate the performance of the model using accuracy, sensitivity, Receiver Operating Characteristics-Area under Curve (ROC-AUC) and F1 score metrics. The results were compared with existing literature for model(s) with the best prediction metrics performance.. RESULTS: The analysis was carried out using Python programming language. Individual ML algorithms reveals that their accuracies show MLR=87%, XGBoost=90%, and RF=93%, while hybridized RF-MLR=92% and RF-XGBoost=99%. The accuracy of RF-XGBoost of 99% outperformed other individual ML algorithms and Hybrid models. CONCLUSION: Thus, the study concluded that involving hybridized M

关键词： Forecasting

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Adapting Physics-Informed Neural Networks to Improve ODE Optimization in Mosquito Population Dynamics

arXiv

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arXiv 2024年

作者： Cuong, Dinh Viet Lalić, Branislava Petrić, Mina Nguyen, Thanh Binh Roantree, Mark School of Computing Dublin City University Dublin Ireland Faculty of Agriculture University of Novi Sad Serbia Avia-GIS NV Zoersel Belgium University of Science Ho Chi Minh City Viet Nam Insight Centre for Data Analytics Dublin City University Dublin Ireland

Physics informed neural networks have been gaining popularity due to their unique ability to incorporate physics laws into data-driven models, ensuring that the predictions are not only consistent with empirical data but also align with domain-specific knowledge in the form of physics equations. The integration of physics principles enables the method to require less data while maintaining the robustness of deep learning in modelling complex dynamical systems. However, current PINN frameworks are not sufficiently mature for real-world ODE systems, especially those with extreme multi-scale behavior such as mosquito population dynamical modelling. In this research, we propose a PINN framework with several improvements for forward and inverse problems for ODE systems with a case study application in modelling the dynamics of mosquito populations. The framework tackles the gradient imbalance and stiff problems posed by mosquito ordinary differential equations. The method offers a simple but effective way to resolve the time causality issue in PINNs by gradually expanding the training time domain until it covers entire domain of interest. As part of a robust evaluation, we conduct experiments using simulated data to evaluate the effectiveness of the approach. Preliminary results indicate that physics-informed machine learning holds significant potential for advancing the study of ecological systems. © 2024, CC BY.

关键词： Population dynamics

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Unveiling the Conceptual Analysis in the Direction of Developing a Fusion Virtual Reality Model for Hearing-Impaired Visual Interaction

Unveiling the Conceptual Analysis in the Direction of Develo...

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International Conference on Computer Applications and Industrial Electronics (ICCAIE)

作者： Ermiera Shafika Mokhtar Nurulnadwan Aziz Siti Salbiah Hamzah Siti Zulaiha Ahmad Ahmad Affandi Supli Azizah Che Omar College of Computing Informatics and Mathematics Universiti Teknologi MARA Cawangan Terengganu Dungun Terengganu Malaysia Faculty of Business and Management Universiti Teknologi MARA Cawangan Terengganu Dungun Terengganu Malaysia College of Computing Informatics and Mathematics Universiti Teknologi MARA Cawangan Perlis Arau Perlis Malaysia School of Computing and Data Science Xiamen University Malaysia Sepang Selangor Malysia School of Multimedia Technology and Communication Universiti Utara Malaysia Sintok Kedah Malysia

ISBN: (数字)9798350348798

ISBN: (纸本)9798350348804

Virtual Reality (VR) stands out as a rapidly evolving wearable technology. However, findings from previous studies reveal that existing VR models exhibit a significant deficiency in addressing the visual interaction requirements of the hearing-impaired (HI) population. While some studies focus on developing VR for HI individuals and incorporating assistive technology (AT) concepts, however, the integration of AT, empathic design (ED), and visual interaction (VI) principles remain underexplored. This study addresses the gap by proposing a fusion VR model that prioritizes the needs of HI individuals. The challenges faced by the HI, such as difficulties in visually utilizing sign language, gestures, body language, and lip sync, underscore the importance of adopting AT, ED, and VI design principles. Through a rapid review approach, this paper strengthens the research foundation and outlines a conceptual framework for the development of a fusion VR model. This study aligns with the 12th Malaysian Plan's Pillar 5, emphasizing the empowerment of specific target groups, and supports SDG 10 by promoting social equality for the HI. The proposed fusion VR model aims to enhance existing designs by integrating AT, ED, and VI principles, providing a valuable reference for researchers in the field.

关键词： Solid modeling Visualization Analytical models Systematics Sociology Virtual reality Auditory system

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RETRACTED ARTICLE: Obstacles Uncovering System for Slender Pathways Using Unmanned Aerial Vehicles with Automatic Image Localization Technique

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International Journal of Computational Intelligence Systems 2023年第1期16卷 1-14页

作者： Selvarajan, Shitharth Manoharan, Hariprasath Khadidos, Alaa O. Shankar, Achyut Khadidos, Adil O. Onyema, Edeh Michael Department of Computer Science Kebri Dehar University Kebri Dehar Ethiopia School of Built Environment Engineering and Computing Leeds Beckett University Leeds UK Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz University Jeddah Saudi Arabia Secure Cyber Systems Research Group (SCSRG) WMG University of Warwick Coventry UK School of Computer Science Engineering Lovely Professional University Phagwara India Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Mathematics and Computer Science Coal City University Enugu Nigeria

In this study, unidentified flying machines are built with real-time monitoring in mid-course settings for obstacle avoidance in mind. The majority of the currently available methods are implemented as comprehensive monitoring systems, with significant success in monitored applications like bridges, railways, etc. So, the predicted model is developed exclusively for specific monitoring settings, as opposed to the broad conditions that are used by the current approaches. Also, in the design model, the first steps are taken by limiting the procedure to specific heights, and the input thrust that is provided for take up operation is kept to a minimum. Due to the improved altitudes, the velocity and acceleration units have been cranked up on purpose, making it possible to sidestep intact objects. In addition, Advanced Image Mapping Localization (AIML) is used to carry out the implementation process, which identifies stable sites at the correct rotation angle. Besides, Cyphal protocol integration improves the security of the data-gathering process by transmitting information gathered from sensing devices. The suggested system is put to the test across five different case studies, where the designed Unmanned aerial vehicle can able to detect 25 obstacles in the narrow paths in considered routs but existing approach can able to identify only 14 obstacle in the same routes.

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Internet of Thing (IoT) and data Analytics with Challenges and Future Applications

Internet of Thing (IoT) and Data Analytics with Challenges a...

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Computer science and Emerging Technologies (CSET), International Conference on

作者： Mohammmadnour Mosbah Aljarrah Farah H. Zawaideh Murad Magableh Hassan Al Wahshat Rajina R. Mohamed Archana K V MIS BI and Data Analysis Department Faculty of Business and Financial Science Irbid National University Irbid Jordan Department of BI and Data Analysi Faculty of Financial Sciences and Business Irbid National University Irbid Jordan Department of Management Information System Faculty of Business Ajloun National University Ajloun Jordan College of Computing dan Informatics Universiti Tenaga Nasional Malaysia Jain Deemed To Be University Bangalore India

The Internet of Things (IoT) has emerged as a transformative technology, enabling seamless connectivity and communication between smart devices and the digital world. IoT's integration with data analytics has opened up a myriad of possibilities for extracting valuable insights from the vast volumes of data generated by connected devices. This research paper explores the convergence of IoT and data analytics, shedding light on the challenges encountered during their integration and envisioning future applications that can revolutionize various domains. The paper begins with a comprehensive review of the IoT ecosystem and its significance in shaping the modern era of interconnected devices. It highlights the diverse range of IoT applications across industries, including smart cities, healthcare, agriculture, manufacturing, transportation, and more. Moreover, it delves into the underlying technologies and communication protocols that enable IoT devices to gather, transmit, and process data efficiently. As data generated by IoT devices continues to escalate exponentially, effective data analytics becomes paramount to extract meaningful insights and support informed decision-making. The research delves into various data analytics techniques, such as machine learning, artificial intelligence, and big data analytics, illustrating their relevance in harnessing the potential of IoT-generated data. However, amidst the promises, several challenges impede the seamless amalgamation of IoT and data analytics. Issues concerning data privacy, security, interoperability, scalability, and data quality are thoroughly examined. The research emphasizes the need for robust frameworks and standards to address these challenges and ensure the secure and efficient functioning of IoT ecosystems. In the final section, this paper envisions the future applications of IoT and data analytics. It envisions intelligent and autonomous systems that adapt to dynamic environments, leading to advancement

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Implementation of Mining Frequent Patterns on big data Using new Version of Algorithm 1

Implementation of Mining Frequent Patterns on big data Using...

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Prashanth, M.V. Shambhubhardwaj Raju, Ramana Pramanik, Kundan Kumar Mohanty, Swaroop Barua, Kuntal Vidyavardhaka College of Engineering Information Science and Engineering India Teer Thanker Mahaveer University Faculty of Engineering Department of Computing Sciences & IT Uttar Pradesh Moradabad India Department of Data Science & Mathematics Bangalore India ARKA Jain University Department of School of Engg. &iT Jharkhand Jamshedpur India Management Cv Raman Global University Bhubaneswar India SAGE University Madhya Pradesh Indore India

ISBN: (纸本)9781665456876

Pattern analysis is one of the most important tasks for strong data and useful data from unstructured data. The item-sets for this work must include the aforementioned homogeneity, but rather frequency in the data. Despite the fact that numerous efficient algorithms have been developed in this field, the rising demand for information has led to deterioration in the effectiveness of present pattern mining techniques. The goal of this study is to provide fresh, efficient methods for mining huge data patterns. To accomplish this, a variety of methods based on the Map reduction algorithm as well as the Apache expansive implementation have been proposed. The recommended algorithms may be divided into three main groups. The Attribute Mapper (AprioriMR) and recurrent AprioriMR techniques, which eliminate any previous objects in data without employing a pruning methodology, are the first two described. Second, two recommended approaches (space cutting AprioriMR or top AprioriMR) to improve the search space leverage certain well-generalized pro property. The last approach for mining compressed versions of frequent patterns is the maximum AprioriMR algorithm. To evaluate the efficacy of the offered strategies, up to 3 1018 interactions and more than 5 million distinct ones have been examined in a range of big data datasets. Evaluations of well-known and efficient pattern mining algorithms are part of the testing process. The results show both the benefit of employing Dremel versions when taking into consideration intricate issues and the insufficiency of this strategy when dealing with little amounts of data. © 2022 IEEE.

关键词： Deterioration

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Anticipation of Parking Vacancy during Peak/Non-peak Hours using Convolutional Neural Network-YOLOv3 in University Campus

Anticipation of Parking Vacancy during Peak/Non-peak Hours u...

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2021 International Conference on Green Energy, computing and Sustainable Technology, GECOST 2021

作者： Davan, Jasvina Mae A.P. Ealn Koh, Tieng Wei Tong, Dong Ling Tseu, Kwan Lee First City University College Artificial Intelligence Lab Faculty of Computing and Engineering Petaling Jaya Malaysia University of Putra Malaysia Faculty of Computer Science and Information Technology Selangor Malaysia Universiti Tunku Abdul Rahman Centre of IoT Big Data Faculty of Information and Communication Technology Perak Malaysia

ISBN: (纸本)9781665438650

Searching for a publicly available parking space has become a nightmare to many drivers. With the constant development of global urbanization, human population has increased drastically in the past decades. Searching for a publicly available parking space in a highly populated area can be daunting and time consuming. No matter how much time is spent to find a vacant parking space, it always causes traffic congestion in the area. To alleviate these problems, it is of utmost importance to have a system that can detect and display the vacant parking spaces in real-time. This paper has conducted a study of anticipation of parking vacancy using convolutional neural network called YOLOv3 in a university campus. Image data is gathered from the video capture of the university's campus open space parking lot. The YOLOv3 algorithm is used to train and predict whether the space is vacant or occupied. Results showed that YOLOv3 has been able to correctly predict the vacant space. The result of the rendering video will then be transformed into an image and is sent to the students via a Telegram group. © 2021 IEEE.

关键词： Convolutional neural networks

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The Power of Bamboo: On the Post-Compromise Security for Searchable Symmetric Encryption

arXiv

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arXiv 2024年

作者： Chen, Tianyang Xu, Peng Picek, Stjepan Luo, Bo Susilo, Willy Jin, Hai Liang, Kaitai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Cluster and Grid Computing Lab School of Computer Science and Technology China Huazhong University of Science and Technology Wuhan430074 China Digital Security Group Radboud University Nijmegen Netherlands Department of EECS Institute of Information Sciences The University of Kansas LawrenceKS United States Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands

Dynamic searchable symmetric encryption (DSSE) enables users to delegate the keyword search over dynamically updated encrypted databases to an honest-but-curious server without losing keyword privacy. This paper studies a new and practical security risk to DSSE, namely, secret key compromise (e.g., a user’s secret key is leaked or stolen), which threatens all the security guarantees offered by existing DSSE schemes. To address this open problem, we introduce the notion of searchable encryption with key-update (SEKU) that provides users with the option of non-interactive key updates. We further define the notion of post-compromise secure with respect to leakage functions to study whether DSSE schemes can still provide data security after the client’s secret key is compromised. We demonstrate that post-compromise security is achievable with a proposed protocol called "Bamboo". Interestingly, the leakage functions of Bamboo satisfy the requirements for both forward and backward security. We conduct a performance evaluation of Bamboo using a real-world dataset and compare its runtime efficiency with the existing forward-and-backward secure DSSE schemes. The result shows that Bamboo provides strong security with better or comparable performance. © 2024, CC BY.

关键词： Bamboo

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Female sex hormones and the oral contraceptive pill modulate asthma severity through GLUT-1

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Mucosal Immunology 2025年第3期18卷 656-667页

作者： Brown, Alexandra C. Carroll, Olivia R. Mayall, Jemma R. Zounemat-Kermani, Nazanin Vinzenz, Samantha L.E. Gomez, Henry M. Mills, Ed F. Kim, Richard Y. Donovan, Chantal Baines, Katherine J. Williams, Evan J. Berthon, Bronwyn S. Wynne, Katie Scott, Hayley A. Pinkerton, James W. Guo, Yike Hansbro, Philip M. Foster, Paul S. Wark, Peter A.B. Dahlen, Sven-Erik Adcock, Ian M. Wood, Lisa G. Horvat, Jay C. The University of Newcastle and Immune Health Program Hunter Medical Research Institute Newcastle NSW Australia The Data Science Institute Department of Computing Imperial College London United Kingdom School of Life Sciences Faculty of Science University of Technology Sydney Sydney NSW Australia Respiratory Pharmacology & Toxicology Group National Heart & Lung Institute Imperial College London London United Kingdom Centre for Inflammation Centenary Institute and Faculty of Science University of Technology Sydney Sydney NSW Australia Clinical Lung and Allergy Research Unit Department of Medicine Huddinge Karolinska Institutet and Department of Respiratory Medicine and Allergy Karolinska University Hospital Huddinge and Unit of Integrative Metabolomics Institute of Environmental Medicine Karolinska Institutet Stockholm Sweden The Airway Disease Section National Heart & Lung Institute Imperial College London London United Kingdom Woolcock Institute of Medical Research Sydney NSW Australia School of Translational Medicine Monash University Melbourne VIC Australia Woolcock Institute of Medical Research and Faculty of Medicine Health and Human Sciences Macquarie University Sydney NSW Australia Hong Kong University of Science and Technology Hong Kong

Females are disproportionately affected by asthma. An increased understanding of how female sex hormones influence key pathophysiological processes that underpin asthma may identify new, more effective asthma therapies, particularly for females with severe, poorly controlled asthma. We assessed the effects of oral ethinylestradiol/levonorgestrel (representing OCP use) and depot-medroxyprogesterone acetate (DMPA) and estradiol injections on key features of experimental asthma, and determined their effects on glucose transporter-1 (GLUT-1). The effects of OCP use on clinical asthma outcomes, and the relationships between estrogen receptors and type 2 (T2), non-T2, and GLUT-1 responses, in clinical asthma were also determined. OCP and DMPA reduce T2 responses, disease features, and lung expression of GLUT-1, whereas estradiol increases lung expression of GLUT-1, and results in severe, corticosteroid-insensitive, neutrophil-enriched disease, in experimental asthma. OCP use is associated with reduced T2 cytokine and GLUT-1 responses in clinical asthma. GLUT-1 expression is increased in sputum of severe asthmatics, and positively correlates with estrogen receptor expression and both T2 and non-T2 inflammatory responses. Significantly, OCP or GLUT-1 inhibition protects against obesity-associated or estradiol-induced, severe, experimental asthma, respectively. Together, these data show how female sex hormones and the OCP likely modulate asthma severity by modifying GLUT-1 responses in the airways. © 2025 The Authors

关键词： Asthma Estrogen GLUT-1 oral contraceptive pill (OCP) Progesterone

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