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arXiv 2022年

作者： Cinar, Ece Haseki, Sinem Elif Bessadok, Alaa Rekik, Islem BASIRA Lab Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey

The connectional brain template (CBT) captures the shared traits across all individuals of a given population of brain connectomes, thereby acting as a fingerprint. Estimating a CBT from a population where brain graphs are derived from diverse neuroimaging modalities (e.g., functional and structural) and at different resolutions (i.e., number of nodes) remains a formidable challenge to solve. Such network integration task allows for learning a rich and universal representation of the brain connectivity across varying modalities and resolutions. The resulting CBT can be substantially used to generate entirely new multimodal brain connectomes, which can boost the learning of the downs-stream tasks such as brain state classification. Here, we propose the Multimodal Multiresolution Brain Graph Integrator Network (i.e., M2GraphIntegrator), the first multimodal multiresolution graph integration framework that maps a given connectomic population into a well-centered CBT. M2GraphIntegrator first unifies brain graph resolutions by utilizing resolution-specific graph autoencoders. Next, it integrates the resulting fixed-size brain graphs into a universal CBT lying at the center of its population. To preserve the population diversity, we further design a novel clustering-based training sample selection strategy which leverages the most heterogeneous training samples. To ensure the biological soundness of the learned CBT, we propose a topological loss that minimizes the topological gap between the ground-truth brain graphs and the learned CBT. Our experiments show that from a single CBT, one can generate realistic connectomic datasets including brain graphs of varying resolutions and modalities. We further demonstrate that our framework significantly outperforms benchmarks in reconstruction quality, augmentation task, centeredness and topological soundness. © 2022, CC BY-NC-ND.

关键词： Integration

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可信联邦学习进化优化算法综述

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engineering 2024年第3期34卷 23-42页

作者： Qiqi Liu Yuping Yan Yaochu Jin Xilu Wang Peter Ligeti Guo Yu Xueming Yan School of Engineering Westlake UniversityHangzhou 310030China Faculty of Informatics Department of Computer AlgebraEötvös Loránd UniversityBudapest 1053Hungary Faculty of Technology Bielefeld UniversityBielefeld 33619Germany Institute of Intelligent Manufacturing Nanjing Tech UniversityNanjing 211816China School of Information Science and Technology Guangdong University of Foreign StudiesGuangzhou 510006China

With the development of edge devices and cloud computing,the question of how to accomplish machine learning and optimization tasks in a privacy-preserving and secure way has attracted increased attention over the past *** a privacy-preserving distributed machine learning method,federated learning(FL)has become popular in the last few ***,the data privacy issue also occurs when solving optimization problems,which has received little attention so *** survey paper is concerned with privacy-preserving optimization,with a focus on privacy-preserving data-driven evolutionary *** aims to provide a roadmap from secure privacy-preserving learning to secure privacy-preserving optimization by summarizing security mechanisms and privacy-preserving approaches that can be employed in machine learning and *** provide a formal definition of security and privacy in learning,followed by a comprehensive review of FL schemes and cryptographic privacy-preserving ***,we present ideas on the emerging area of privacy-preserving optimization,ranging from privacy-preserving distributed optimization to privacy-preserving evolutionary optimization and privacy-preserving Bayesian optimization(BO).We further provide a thorough security analysis of BO and evolutionary optimization methods from the perspective of inferring attacks and active *** the basis of the above,an in-depth discussion is given to analyze what FL and distributed optimization strategies can be used for the design of federated optimization and what additional requirements are needed for achieving these ***,we conclude the survey by outlining open questions and remaining challenges in federated data-driven *** hope this survey can provide insights into the relationship between FL and federated optimization and will promote research interest in secure federated optimization.

关键词： Federated learning Privacy-preservation Security Evolutionary optimization Data-driven optimization Bayesian optimization

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Clustering Approach for Modeling Course Difficulty Level in Adaptive Learning

Clustering Approach for Modeling Course Difficulty Level in ...

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IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications

作者： Imamah Umi Laili Yuhana Arif Djunaidy Eric Pardede Mauridhi Hery Purnomo Departement of Electrical Engineering Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Departement of Informatics Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Departement of Information Systems Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Departement of Computer Science and Information Technology La Trobe University Bundoora VIC Australia

ISBN: (数字)9798350322996

ISBN: (纸本)9798350323009

The variations in students’ abilities present challenges in achieving desired learning outcomes. Students exhibit different levels of comprehension regarding the complexity of learning materials. In this study, we aim to model the difficulty levels of learning materials using a clustering approach to offer personalized learning material recommendations within an adaptive learning framework. Data were collected from student scores of Data Structure course obtained between 2020 and 2022 at the University of Trunojoyo Madura. The dataset modeling process adheres to the CRISP-DM Methodology, employing the K-Means clustering method and Euclidean Distance. We employed the Within-Cluster Sum of Squares (WCSS) metric and silhouette score to identify the optimal K-value, which resulted in a value of 5, accompanied by a silhouette score of 0.629. Our analysis pinpointed Stack and Deque as the easiest, while Linked List emerged as the most challenging course material. Accordingly, we will assign a label of 1 to the easiest materials, 2 to those of medium difficulty, and 3 to the most challenging ones. These labels will serve as criteria for recommending suitable learning materials to students based on their knowledge level. We will assess the ratio between the overall knowledge level score and the difficulty level of the material. The ratio value will serve as a reference for determining material recommendations for students. Materials categorized with the highest difficulty levels are exclusively suggested to students demonstrating faster learning abilities with a high value of ratio, and vice versa. This approach is feasible for implementation in adaptive learning.

关键词： Measurement Adaptive learning Adaptation models Computational modeling Clustering methods Virtual environments Euclidean distance

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Multi Sentence Description of Complex Manipulation Action Videos

arXiv

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arXiv 2023年

作者： Ziaeetabar, Fatemeh Safabakhsh, Reza Momtazi, Saeedeh Tamosiunaite, Minija Wörgötter, Florentin III. Physics Institute University of Göttingen Germany Faculty of Computer Engineering Amirkabir University of Technology Iran Faculty of Informatics Vytautas Magnus University Lithuania

Automatic video description requires the generation of natural language statements about the actions, events, and objects in the video. An important human trait, when we describe a video, is that we are able to do this with variable levels of detail. Different from this, existing approaches for automatic video descriptions are mostly focused on single sentence generation at a fixed level of detail. Instead, here we address video description of manipulation actions where different levels of detail are required for being able to convey information about the hierarchical structure of these actions relevant also for modern approaches of robot learning. We propose one hybrid statistical and one end-to-end framework to address this problem. The hybrid method needs much less data for training, because it models statistically uncertainties within the video clips, while in the end-to-end method, which is more data-heavy, we are directly connecting the visual encoder to the language decoder without any intermediate (statistical) processing step. Both frameworks use LSTM stacks to allow for different levels of description granularity and videos can be described by simple single-sentences or complex multiple-sentence descriptions. In addition, quantitative results demonstrate that these methods produce more realistic descriptions than other competing approaches. © 2023, CC BY.

关键词： Visual languages

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Performance Evaluation of YOLO-Based Deep Learning Models for Real-Time Armour Unit Detection with Image Pre-processing Method

Performance Evaluation of YOLO-Based Deep Learning Models fo...

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International Electronics Symposium (IES)

作者： Firmansyah Putra Pratama Alfan Rizaldy Pratama Dewi Mutiara Sari Bayu Sandi Marta R. Haryo Dwito Armono Department of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia Data Science Department Faculty of Computer Science Universitas Pembangunan Nasional Veteran Jawa Timur Surabaya Indonesia Department of Ocean Engineering Faculty of Marine Technology Institut Teknologi Sepuluh Nopember Surabaya Surabaya Indonesia

ISBN: (数字)9798350391992

ISBN: (纸本)9798350392005

Breakwater construction in Indonesia still relies on divers to direct the placement of rock armour units, which is risky and time-constrained. This research aims to replace the diver's task with a deep learning-based vision system using YOLO-based deep learning models. The system utilizes image pre-processing technology by applying histogram equalization (HE) techniques to improve image quality before the detection process. This research evaluates the performance of the YOLO-based deep learning models in detecting armour units in real-time with a focus on various environmental conditions, which are clear and murky water. The analysis reveals clear water consistently supports higher average frame rates (FPS) compared to murky water, maintaining efficient frame processing across all models. In murky water, histogram equalization significantly enhances detection accuracy from 60% to 80% for YOLOv4-tiny and YOLOv7-tiny, demonstrating its effectiveness in challenging conditions. Notably, accuracy remains at 100% for all models in clear water, underscoring their robust performance under optimal visibility conditions.

关键词： Deep learning Performance evaluation Image quality Histograms Analytical models Accuracy Machine vision

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The future of E-commerce in the publishing industry 2

The future of E-commerce in the publishing industry

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2nd International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2021

作者： Aseh, Khairi Al-Qudah, Omar Mohammed Ali Abdullah Yusoff, M. Hafiz Pathmanathan, P. Ravindran Latiff, Nurul Adilah Abdul Jusoh, Julaily Aida Abu-Ulbeh, Waheeb El-Ebiary, Yousef A. Baker Al Moaiad, Yazeed International Institute of Applied Science Swiss School of Management Netherlands Irbid National University Faculty of Financial and Business Science Head of E-Marketing Department Jordan Albukhary International University School of Computing and Informatic Kedah Malaysia Faculty of Informatics and Computing UniSZA Malaysia UMT Faculty of Ocean Engineering Technology and Informatics Malaysia UniSZA Faculty of Informatics and Computing Malaysia Faculty of Computer Science and Informatics Cybersecurity Department Jordan MEDIU Faculty of Computer and Information Technology Malaysia

ISBN: (纸本)9781665432221

In comparison to conventional book-printed content, electronic "publishing"has allowed users to import files to either a personal computer or an e-book reader to read text in a digital format. Reduced printing costs and reduced time to market by streamlined drafting, proofing, and printing methods are the benefits of the publishing industry over the conventional printed book (p-book). It prevents binding, distribution and shipping. The intent of this paper is to explore the strategic use of e-commerce in the transformation of the publishing industry. The first part of the paper discusses the strategic use of e-commerce/Web-Based Business in the transition from a single distribution channel mass producer of printed books to a multiple distribution channel that will include e-books and print on demand technology in the publishing industry. Emphasis is put on management techniques in the global marketplace for the use of emerging information technologies. The second section of this paper addresses the transfer in competitive influence between the different levels of the supply chain of the publishing industry, including the latest trends in electronic publishing from the stakeholders' point of view. Expected to serve as the base for development within the digitized print industry. As e-publishing becomes popular and moves towards maturity, to allow a "mainstream"status, common standards must emerge and combine. © 2021 IEEE.

关键词： Electronic publishing

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EfficientNetB0-based Automated Diabetic Retinopathy Classification in Fundus Images

EfficientNetB0-based Automated Diabetic Retinopathy Classifi...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： Ezekiel Alaric Majaw G. Naveen Sundar D. Narmadha Senthil Kumar Thangavel Venkatesan Adeyemi Abel Ajibesin Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering Amrita School of Computing Coimbatore India Department of Information Technology Faculty of Informatics and Design Cape Peninsula University of Technology South Africa

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

The most common cause of blindness among diabetics is diabetic retinopathy. This disease must be detected in its early stages as delaying treatment can result in permanent blindness. In today’s modern world with the development of technologies, automated techniques have improved the accuracy and efficiency of detecting and classifying this disease. This study suggests a deep learning model utilizing EfficientNetB0 that focuses on designing a neural network structure that requires fewer parameters and computations compared to traditional architectures. Further to help in increasing the accuracy, the image is being processed with methods like CLAHE which is an image processing algorithm that helps in enhancing the contrast while keeping the noise of the images as low as possible. Also, augmentation is being done on the images and SMOTE is applied to handle class imbalance. We collected the APTOS Blindness Detection dataset consisting of retinal fundus images. The accuracy that is being obtained from the proposed methodology is 98.78% indicating the model's effectiveness in detection and classifying between the various stages of diabetic retinopathy.

关键词： Diabetic retinopathy Renewable energy sources Accuracy Computational modeling Blindness computer architecture Transformers Retina Real-time systems Diseases

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An E-Business Event Stream Mechanism for Improving User Tracing Processes

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computers, Materials & Continua 2021年第10期69卷 767-784页

作者： Ayman Mohamed Mostafa Saleh N.Almuayqil Wael Said College of Computers and Information Sciences Jouf UniversitySakaka72314Saudi Arabia Faculty of Computers and Informatics Zagazig UniversityZagazig44519Egypt College of Computer Science and Engineering Taibah UniversityMedina42353Saudi Arabia

With the rapid development in business transactions,especially in recent years,it has become necessary to develop different mechanisms to trace business user records in web server log in an efficient *** business transactions have increased,especially when the user or customer cannot obtain the required *** example,with the spread of the epidemic Coronavirus(COVID-19)throughout the world,there is a dire need to rely more on online business *** order to improve the efficiency and performance of E-business structure,a web server log must be well utilized to have the ability to trace and record infinite user *** paper proposes an event stream mechanism based on formula patterns to enhance business processes and record all user activities in a structured log *** user activity is recorded with a set of tracing parameters that can predict the behavior of the user in business *** experimental results are conducted by applying clustering-based classification algorithms on two different datasets;namely,Online Shoppers Purchasing Intention and Instacart Market Basket *** clustering process is used to group related objects into the same cluster,then the classification process measures the predicted classes of clustered *** experimental results record provable accuracy in predicting user preferences on both datasets.

关键词： Business transactions event stream log file tracing parameters clustering-based classification

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Prediction of Heat Energy Release Rate for Ammonia Combustion in a Constant Volume Combustion Chamber: A Machine Learning Approach 15

Prediction of Heat Energy Release Rate for Ammonia Combustio...

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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Fang, Lijia Singh, Hardeep Ichiyanagi, Mitsuhisa Suzuki, Takashi Raboaca, Maria Simona Filote, Constantin Verma, Chaman Sophia University Department of Engineering and Applied Sciences Tokyo Japan University of Windsor Turbulence and Energy Laboratory Windsor Canada Institute for Cryogenics and Isotopic Technologies Icsi Energy Department National Research and Development Ramnicu Valcea Romania Stefan Cel Mare University of Suceava Faculty of Electrical Engineering and Computer Science Suceava Romania Eötvös Loránd University Faculty of Informatics Budapest Hungary

ISBN: (纸本)9798350321388

Addressing global warming requires immediate and concerted action. Transitioning to non-conventional energy sources, such as solar and Aeolian power, reducing emissions from transportation and industry, and implementing sustainable land-use practices are crucial steps. Therefore, Ammonia attracts researchers when used as a fuel, and offers several potential benefits such as higher energy density. However, due to poor combustion speed of Ammonia offers slower energy release. To enhance the average flame velocity of Ammonia, the current investigation employed the exhaust gas expelled from the secondary chamber to compress the mixture within the primary chamber and facilitate its HCCI combustion. The main goal of research is the optimization on air-fuel ER to contribute to the development of more efficient and sustainable constant volume combustion. However, the time required for constructing physical prototypes and conducting experiments can be considerable, thus, to mitigate these challenges, the authors turned to machine learning tools as an alternative approach. In the proposed research, results were evaluated through Support Vector Regression. However, due to poor prediction accuracy, authors tried KNeighborsRegressor model for predicting and validating the results. Finally, it is revealed from the KNN model that data sets are highly accurate. © 2023 IEEE.

关键词： Ammonia

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Decision Support System in Determining the Achievement of Widyaiswara Value Using WASPAS and TOPSIS Algorithm 1

Decision Support System in Determining the Achievement of Wi...

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1st International Conference on Technology Innovation and Its Applications, ICTIIA 2022

作者： Pratama, Yudhistira Adhitya Iswara, Bagus Ajie Nababan, Adli Abdillah Information System Studies Program Pelita Harapan University Medan Campus Faculty Of Computer Science Medan Indonesia Master's Study Program Informatics Engineering Universitas Sumatera Utara Faculty Of Computer Science And Information Technology Medan Indonesia Digital Business Study Program Stmik Pelita Nusantara Medan Indonesia

ISBN: (数字)9781665488266

ISBN: (纸本)9781665488266

Widyaiswara is required to show the best performance to fulfill his duties and obligations. Therefore, it is very important to measure the performance of the Widyaiswara, so that it can be used as evaluation material by the BPSDM PROVSU. However, the assessment process that occurs is still not precise and time-consuming. Therefore, to minimize this, a decision support system is needed that can minimize time errors and data processing in decision-making so that the assessment runs well and accurately. This system was built by applying the WASPAS method and the TOPSIS method. Based on the results of tests carried out five times, the results of the WASPAS method processing time resulted in an average processing time of 0.00029791 ms and an average memory usage value of 2.836.44 kb, while the results of the TOPSIS method processing time test resulted in an average processing time - an average of 0.000296428 ms and an average memory usage value of 2.862.34 kb. © 2022 IEEE.

关键词： Decision support systems

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