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7th International Conference on Multimedia Analysis and Pattern Recognition, MAPR 2024

作者： Anantathanavit, Rungpilin Raswa, Farchan Hakim Thaipisutikul, Tipajin Wang, Jia-Ching Mahidol University Faculty of Information and Communication Technology Nakhon Pathom Thailand National Central University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350368437

Brain tumors pose a significant medical challenge, necessitating precise and rapid diagnosis for effective treatment and improved patient outcomes. This paper introduces knowledge distillation, which has the potential to revolutionize brain tumor diagnosis by enabling early identification from medical imaging data. Using a sophisticated teacher' model to capture intricate patterns, we distill this knowledge into a more efficient "student' model, aiming for comparable accuracy with reduced memory usage and improved inference times. Our method, based on a dataset of 357 MRI scans, demonstrated the potential of knowledge distillation in brain tumor diagnosis, offering a promising avenue for advancing patient care. The proposed model serves as a vital tool for healthcare practitioners, providing accurate and efficient support in detecting brain tumors and contributing to advancements in healthcare technology. The evaluation results indicate the effectiveness of our technique, achieving an impressive accuracy of 98.10 © 2024 IEEE.

关键词： Patient treatment

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Shortest Cover After Edit 35

Shortest Cover After Edit

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35th Annual Symposium on Combinatorial Pattern Matching, CPM 2024

作者： Mitani, Kazuki Mieno, Takuya Seto, Kazuhisa Horiyama, Takashi Graduate School of Information Science and Technology Hokkaido University Japan Department of Computer and Network Engineering University of Electro-Communications Tokyo Japan Faculty of Information Science and Technology Hokkaido University Japan

ISBN: (纸本)9783959773263

This paper investigates the (quasi-)periodicity of a string when the string is edited. A string C is called a cover (as known as a quasi-period) of a string T if each character of T lies within some occurrence of C. By definition, a cover of T must be a border of T;that is, it occurs both as a prefix and as a suffix of T. In this paper, we focus on the changes in the longest border and the shortest cover of a string when the string is edited only once. We propose a data structure of size O(n) that computes the longest border and the shortest cover of the string in O(log n) time after an edit operation (either insertion, deletion, or substitution of some string) is applied to the input string T of length n, where is the length of the string being inserted or substituted. The data structure can be constructed in O(n) time given string T. © Kazuki Mitani, Takuya Mieno, Kazuhisa Seto, and Takashi Horiyama.

关键词： Data structures

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Privacy preserving large language models: ChatGPT case study based vision and framework

IET Blockchain

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IET Blockchain 2024年第S1期4卷 706-724页

作者： Ullah, Imdad Hassan, Najm Gill, Sukhpal Singh Suleiman, Basem Ahanger, Tariq Ahamed Shah, Zawar Qadir, Junaid Kanhere, Salil S. School of Computer Science Faculty of Engineering The University of Sydney SydneyNSW Australia Higher Colleges of Technology Abu Dhabi United Arab Emirates School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Management Information Systems Department College of Business Administration Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabia Department of Information Technology Sydney International School of Technology and Commerce SydneyNSW Australia Department of Computer Science and Engineering College of Engineering Qatar University Doha Qatar Sydney Australia

The generative Artificial Intelligence (AI) tools based on Large Language Models (LLMs) use billions of parameters to extensively analyse large datasets and extract critical information such as context, specific details, identifying information, use this information in the training process, and generate responses for the requested queries. The extracted data also contain sensitive information, seriously threatening user privacy and reluctance to use such tools. This article proposes the conceptual model called PrivChatGPT, a privacy-preserving model for LLMs consisting of two main components, that is, preserving user privacy during the data curation/pre-processing and preserving private context and the private training process for large-scale data. To demonstrate the applicability of PrivChatGPT, it is shown how a private mechanism could be integrated into the existing model for training LLMs to protect user privacy;specifically, differential privacy and private training using Reinforcement Learning (RL) were employed. The privacy level probabilities are associated with the document contents, including the private contextual information, and with metadata, which is used to evaluate the disclosure probability loss for an individual's private information. The privacy loss is measured and the measure of uncertainty or randomness is evaluated using entropy once differential privacy is applied. It recursively evaluates the level of privacy guarantees and the uncertainty of public databases and resources during each update when new information is added for training purposes. To critically evaluate the use of differential privacy for private LLMs, other mechanisms were hypothetically compared such as Blockchain, private information retrieval, randomisation, obfuscation, anonymisation, and the use of Tor for various performance measures such as the model performance and accuracy, computational complexity, privacy vs. utility, training latency, vulnerability to attacks, and

关键词： Blockchain

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Self-Stabilizing Weakly Byzantine Perpetual Gathering of Mobile Agents 4

Self-Stabilizing Weakly Byzantine Perpetual Gathering of Mob...

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4th Symposium on Algorithmic Foundations of Dynamic Networks, SAND 2025

作者： Hirose, Jion Eguchi, Ryota Sudo, Yuichi Department of Electronics and Information Engineering National Institute of Technology Ishikawa College Ishikawa Japan Graduate School of Science and Technology Nara Institute of Science and Technology Nara Japan Faculty of Computer and Information Sciences Hosei University Tokyo Japan

ISBN: (纸本)9783959773683

We study the Byzantine gathering problem involving k mobile agents with unique identifiers (IDs), f of which are Byzantine. These agents start the execution of a common algorithm from (possibly different) nodes in an n-node network, potentially starting at different times. Once started, the agents operate in synchronous rounds. We focus on weakly Byzantine environments, where Byzantine agents can behave arbitrarily but cannot falsify their IDs. The goal is for all non-Byzantine agents to eventually terminate at a single node simultaneously. In this paper, we first prove two impossibility results: (1) for any number of non-Byzantine agents, no algorithm can solve this problem without global knowledge of the network size or the number of agents, and (2) no self-stabilizing algorithm exists if k ≤ 2f even with n, k, f, and the length Λg of the largest ID among IDs of non-Byzantine agents, where the self-stabilizing algorithm enables agents to gather starting from arbitrary (inconsistent) initial states. Next, based on these results, we introduce a perpetual gathering problem and propose a self-stabilizing algorithm for this problem. This problem requires that all non-Byzantine agents always be co-located from a certain time onwards. If the agents know Λg and upper bounds N, K, F on n, k, f, the proposed algorithm works in O(K · F ·Λg ·X(N)) rounds, where X(n) is the time required to visit all nodes in a n-nodes network. Our results indicate that while no algorithm can solve the original self-stabilizing gathering problem for any k and f even with exact global knowledge of the network size and the number of agents, the self-stabilizing perpetual gathering problem can always be solved with just upper bounds on this knowledge. © Jion Hirose, Ryota Eguchi, and Yuichi Sudo.

关键词： Clustering algorithms

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IoT-based Heartbeats Monitoring system 9

IoT-based Heartbeats Monitoring System

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9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Tamron, Mohamad Asyraf Noorza Bin Mohd Attarbashi, Zainab S. Abuzaraida, Mustafa Ali Atitallah, Nesrine Iftikhar, Saman Handayani, Dini Oktarina Dwi Basri, Atikah Balqis Binti International Islamic University Malaysia Kulliyyah of Information & Communication Technology Selangor Malaysia International Islamic University Malaysia Faculty of Information & Communication Technology Department of Computer Science Selangor Malaysia Misurata University Faculty of Information Technology Computer Science Department Libya Arab Open University Faculty of Computer Studies Saudi Arabia International Islamic University Malaysia Faculty of Information and Communication Technology Department of Information Systems Selangor Malaysia

ISBN: (纸本)9798350370126

The advent of the Internet of Things (loT) has opened up a realm of possibilities in developing globally accessible systems and devices. With the current state of internet speeds, these systems offer real-time control, making them more powerful than ever. Simultaneously, the healthcare sector has been witnessing notable advancements, but it also grapples with an increasing number of health issues, with heart problems standing out as a prominent concern in many countries. These problems manifest in various symptoms, some of which can lead to more serious conditions, such as irregular heartbeats. In response to these challenges and leveraging cutting-edge technology, researchers have embarked on endeavors to enhance healthcare devices and render them more readily available to medical professionals. This paper explores the merging of loT technology with healthcare, specifically focusing on the development of a system designed to monitor and record a patient's heartbeat. This is achieved through the deployment of a Heartbeat/SpO2 Sensor, and the data is seamlessly transmitted to a mobile application accessible remotely. The outcomes of this study demonstrate that the heartbeat data captured by Arduino is effectively transmitted to the cloud, meeting the system's predefined objectives. This represents a significant stride in harnessing loT's potential to enhance healthcare practices and patient monitoring. © 2023 IEEE.

关键词： Internet of things

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StressMLIoT: IoT Sensor Features Reduction and Machine Learning driven Stress Identification system 2

StressMLIoT: IoT Sensor Features Reduction and Machine Learn...

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2nd International Conference on Informatics, ICI 2023

作者： Vinayaka, Samarth Dhariwal, Harsh Aggarwal, Kirti Arora, Anuja Jaypee Institute of Information Technology Department of Computer Science and Engineering and Information Technology Noida India

ISBN: (纸本)9798350343830

Disruption and disturbance in activities in the work environment are the causes of stress in employees. A variety of IoT sensors, Kinect 3D sensors, health data sensors, log files, etc. assist in fetching human activity and interruption details. Sensors captured data is usually high volume and inputting it into the learning model is a challenging task. The research work aims to build an optimal stress identification system due to interruptions while doing office work. In order to build an optimal system, the dimensions of sensor data are reduced with a negotiable reduction in stress identification accuracy. Principal Component Analysis has been applied for feature reduction and three machine learning algorithms- K Nearest Neighbour, Random Forest, and Support vector machine are used to validate the outcome of reduced dimension and not reduced dimensions stress identification system. Random forest algorithm outperformed and achieved accuracy and F-measures are 99.9% and 99.9% respectively in 25 seconds with all dimensions and 92% and 92.4% with reduced dimensions in approximately one-fourth less time i.e. 6.9 seconds. Overall, After dimension reduction the execution time was brought down to 75% - 50% with a slight reduction in achieved accuracy in the 5%-7% range. © 2023 IEEE.

关键词： Principal component analysis

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A decision model for decentralized autonomous organization platform selection: Three industry case studies

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Blockchain(Research and Applications) 2023年第2期4卷 82-100页

作者： E.Baninemeh S.Farshidi S.Jansen Department of Information and Computer Science Utrecht UniversityUtrechtthe Netherlands Department of Software Engineering Lappeenranta University of TechnologyLappeenrantaFinland

Context:Decentralized autonomous organizations are a new form of smart contract-based *** autonomous organization platforms,which support the creation of such organizations,are becoming increasingly popular,such as Aragon and *** the best fitting platform is challenging for organizations,as a significant number of decision criteria,such as popularity,developer availability,governance issues and consistent documentation of such platforms,should be ***,decision-makers at the organizations are not experts in every domain,so they must continuously acquire volatile knowledge regarding such ***:Supporting decision-makers in selecting the right decentralized autonomous organization platforms by designing an effective decision model is the main objective of this *** aim to provide more insight into their selection process and reduce time and effort significantly by designing a decision ***:This study presents a decision model for the decentralized autonomous organization platform selection *** decision model captures knowledge regarding such platforms and concepts *** model is based on an existing theoretical framework that assists software engineers with a set of multi-criteria decision-making problems in software ***:We conducted three industry case studies in the context of three decentralized autonomous organizations to evaluate the effectiveness and efficiency of the decision model in assisting *** case study participants declared that the decision model provides significantly more insight into their selection process and reduces time and ***:We observe in the empirical evidence from the case studies that decision-makers can make more rational,efficient,and effective decisions with the decision ***,the reusable form of the captured knowledge regarding decentralized autonomous organization platforms can be

关键词： Decentralized autonomous organization Decision model Multi-criteria decision making Decision support system Decentralized autonomous organization platform Case study research

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Handling Imbalanced Datasets in Researcher Scientific Classification Using Oversampling and Undersampling Approaches 11

Handling Imbalanced Datasets in Researcher Scientific Classi...

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11th International Conference on Electrical Engineering, computer science and Informatics, EECSI 2024

作者： Zulius, Antoni Defit, Sarjon Stiawan, Deris Abdiansah, Abdiansah Samsuryadi, Samsuryadi Rahmi, Nadya Alinda Universitas Bina Insan Lubuklinggau Universitas Sriwijaya Faculty of Engineering Sciences Faculty of Engineering Palembang Indonesia Universitas Putra Indonesia YPTK Department of Information Technology Faculty of Computer Science Padang Indonesia Universitas Sriwijaya Department of Computer Engineering Faculty of Computer Science Palembang Indonesia Universitas Sriwijaya Department of Informatic Engineering Faculty of Computer Science Palembang Indonesia

ISBN: (纸本)9798350355314

The challenge of class imbalance in datasets is a prevalent issue in machine learning, often resulting in biased and inaccurate predictions. This study investigates the effectiveness of different data balancing techniques in enhancing classifier accuracy when applied to an imbalanced Indonesian National Scientific Manuscript dataset. Specifically, we examine how various classifiers respond to three widely used data balancing methods: Synthetic Minority Over-sampling Technique (SMOTE), Random Under-sampling, and Random Over-sampling. We assess the performance of six classifiers-Support Vector Machine (SVM), Logistic Regression (LR), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), Naïve Bayes (NB), and Gradient Boosting (GB)-when these techniques are applied. The analysis reveals diverse outcomes, with some classifiers showing substantial accuracy gains, while others experience moderate or even adverse effects. The findings underscore the importance of selecting an appropriate balancing method based on the dataset's characteristics and the specific classification algorithm employed in the knowledge classification process. © 2024 IEEE.

关键词： Adversarial machine learning

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Personalized Health Framework for Visually Impaired

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Informatica (Slovenia) 2022年第1期46卷 77-86页

作者： Rathi, Megha Sahu, Shruti Goel, Ankit Gupta, Pramit Department of Computer Science & IT Jaypee Institute of Information Technology Noida India

Vision is one of the most essential human sense. The life of a visually impaired person can be transformed from a dependent individual to a productive and functional member of the society with the help of modern assistive technologies that use the concepts of deep learning and computer vision, the science that aims to mimic and automate human vision to provide a similar, if not better, capability to a computer. However, the different solutions and technologies available today have limited outreach and end users cannot fully realize their benefits. This research work discusses an easily-operable and affordable android application designed to aid the visually impaired in healthcare management. It also aims to resolve the challenges faced due to visual impairment in daily life and uses the concepts of computer vision and deep learning. Broadly, the application consists of the following modules: object recognition in immediate surroundings using region-based convolutional neural networks, disease prediction with the help of symptoms, monitoring of health issues and voice assistant for in-app interaction and navigation. © 2022 Slovene Society Informatika. All rights reserved.

关键词： Android (operating system)

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Potato Disease Prediction system Using Deep Learning 2

Potato Disease Prediction System Using Deep Learning

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2nd IEEE International Conference on Advances in information technology, ICAIT 2024

作者： Zaidi, Zehra Zaidi, Mohd Akbar Department of Computer Science and Information Technology KIET Group of Institutions NCR Delhi India

ISBN: (纸本)9798350383867

This research presents a Potato Disease Prediction system powered by deep learning, specifically Convolutional Neural Networks (CNN). The study delves into efficient disease classification by utilizing TensorFlow Dataset and Data Augmentation for data preprocessing. TF Serving and FastAPI serve as the backend, with model optimization via quantization and TensorFlow Lite for resource-efficient deployment. The user-friendly interfaces are developed in ReactJS (website) and React Native (mobile app). Deployment on the Google Cloud Platform ensures accessibility and scalability. Data comes from Kaggle's PlantVillage dataset, showcasing deep learning's potential in practical disease prediction. This research highlights the potential of deep learning, along with associated technologies, in the field of potato disease prediction. By incorporating the development of a website and mobile application, this work showcases the practical implementation and usability of the system. The findings presented in this paper serve as a foundation for future advancements in disease prediction using deep learning techniques. © 2024 IEEE.

关键词： Convolutional neural networks

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