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Joint of the ESWC 2024 Workshops and Tutorials, ESWC-JP 2024

作者： Ilievski, Filip De Berardinis, Jacopo Kim, Jongmo Jain, Nitisha Department of Computer Science Vrije Universiteit Amsterdam Netherlands Department of Computer Science University of Manchester United Kingdom Department of Informatics King's College London United Kingdom

The fields of generative and neuro-symbolic AI have recently gained significant traction in both academia and industry, owing to their profound impact on real-world applications and their potential to achieve human-level intelligence. While generative AI excels in producing human-like outputs across various tasks, neuro-symbolic AI aims to integrate cognitive and perceptive intelligence. Despite their apparent relevance to human-level AI, the relationship between these two paradigms remains largely unexplored. The GeNeSy 2024 workshop was conceived to address this gap, providing a platform for researchers to present and discuss novel ideas and approaches in the emerging field of generative neuro-symbolic AI. © 2024 Copyright for this paper by its authors.

关键词： Generative Model Large Language Models Neuro-symbolic AI Neuro-symbolic Representations

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A Systematic Literature Review of XAI-based Approaches on Brain Disease Detection using Brain MRI Images 4

A Systematic Literature Review of XAI-based Approaches on Br...

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4th International Conference on Advanced Research in computing, ICARC 2024

作者： Madapatha, Savin Fernando, Pumudu University of Westminster Department of Computer Science and Engineering London United Kingdom School of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (纸本)9798350384864

Brain Magnetic Resonance Imaging (MRI) analysis is a widely used medical procedure for the early diagnosis of various brain diseases. Accurate pathology identification during the brain MRI analysis procedure is crucial as misdiagnoses or missed findings can greatly affect a patient's treatment and long-term prediction. With the recent advancement of Artificial Intelligence (AI) in the medical field, researchers have approached various techniques to detect brain diseases using AI. Although AI models exhibit high accuracy, they suffer from a lack of transparency and interpretability, paving the way for the development of eXplainable Artificial Intelligence (XAI) methods in brain disease diagnosis. Image segmentation, machine learning, deep learning and XAI are important for assisting the diagnostic procedure. In this paper, a comprehensive overview of various existing techniques in brain disease detection using MRI is presented, starting with image segmentation techniques, followed by classification techniques, and finally, XAI techniques. In conclusion, the paper identifies a critical need for further research on XAI integration to advance brain disease detection. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Design and Implementation of an IoT-Based Smart Irrigation System for Optimized Water Management in Wheat Cultivation 12

Design and Implementation of an IoT-Based Smart Irrigation S...

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12th International Conference on System Modeling and Advancement in Research Trends, SMART 2023

作者： Shukla, Ravi Shankar George, Remya P. Hussain, Sajithunisa Ahmad, Nazia Babu, Prakash Jain, Vishal College of Computing and Informatics Saudi Electronic University Department of Computer Science Saudi Arabia The Applied College Imam Abdulrahman Bin Faisal University Computer Science Department Saudi Arabia Acharya Institute of Technology Dept of It Bangalore India

ISBN: (纸本)9798350369861

The current study presents the planning and implementation of a custom-built, Internet of Things (IoT)-based smart irrigation system for wheat cultivation that helps to combat problems such as water scarcity and inefficiency in traditional irrigation methods. The proposed device employs a network of environmental sensor that ensures optimal crop development with minimum water waste. These sensors monitor soil temperature and moisture levels and shows significant improvements in water management efficiency. When compared to standard irrigation approaches, the system's pilot installation results reveal considerable water usage reductions and benefits in the health of the wheat crop. These findings prove that the system is efficient at optimizing resource use and show how it can change the way wheat is farmed forever. © 2023 IEEE.

关键词： Internet of things

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Divide-and-train: A new approach to improve the predictive tasks of bike-sharing systems

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Mathematical Biosciences and Engineering 2024年第7期21卷 6471-6492页

作者： Ali, Ahmed Salah, Ahmad Bekhit, Mahmoud Fathalla, Ahmed Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Higher Future Institute for Specialized Technological Studies Cairo3044 Egypt College of Computing and Information Sciences University of Technology and Applied Sciences Ad-Dhahirah Ibri Oman Zagazig University Department of Computer Science Faculty of Computers and Informatics Sharkeya Zagazig Egypt Sydney Australia Kaplan Business School Sydney Australia Department of Mathematics Faculty of Science Suez Canal University Ismailia Egypt

Bike-sharing systems (BSSs) have become commonplace in most cities worldwide as an important part of many smart cities. These systems generate a continuous amount of large data volumes. The effectiveness of these BSS systems depends on making decisions at the proper time. Thus, there is a vital need to build predictive models on the BSS data for the sake of improving the process of decision-making. The overwhelming majority of BSS users register before utilizing the service. Thus, several BSSs have prior knowledge of the user’s data, such as age, gender, and other relevant details. Several machine learning and deep learning models, for instance, are used to predict urban flows, trip duration, and other factors. The standard practice for these models is to train on the entire dataset to build a predictive model, whereas the biking patterns of various users are intuitively distinct. For instance, the user’s age influences the duration of a trip. This endeavor was motivated by the existence of distinct user patterns. In this work, we proposed divide-and-train, a new method for training predictive models on station-based BSS datasets by dividing the original datasets on the values of a given dataset attribute. Then, the proposed method was validated on different machine learning and deep learning models. All employed models were trained on both the complete and split datasets. The enhancements made to the evaluation metric were then reported. Results demonstrated that the proposed method outperformed the conventional training approach. Specifically, the root mean squared error (RMSE) and mean absolute error (MAE) metrics have shown improvements in both trip duration and distance prediction, with an average accuracy of 85% across the divided sub-datasets for the best performing model, i.e., random forest. © 2024 the Author(s).

关键词： Forecasting

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Soft-GNN:towards robust graph neural networks via self-adaptive data utilization

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Frontiers of computer science 2025年第4期19卷 1-12页

作者： Yao WU Hong HUANG Yu SONG Hai JIN National Engineering Research Center for Big Data Technology and System Service Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China College of Information and Communication National University of Defense TechnologyWuhan 430019China Department of Computer Science and Operations Research Universitéde MontréalMontreal H3C 3J7Canada

Graph neural networks(GNNs)have gained traction and have been applied to various graph-based data analysis tasks due to their high ***,a major concern is their robustness,particularly when faced with graph data that has been deliberately or accidentally polluted with *** presents a challenge in learning robust GNNs under noisy *** address this issue,we propose a novel framework called Soft-GNN,which mitigates the influence of label noise by adapting the data utilized in *** approach employs a dynamic data utilization strategy that estimates adaptive weights based on prediction deviation,local deviation,and global *** better utilizing significant training samples and reducing the impact of label noise through dynamic data selection,GNNs are trained to be more *** evaluate the performance,robustness,generality,and complexity of our model on five real-world datasets,and our experimental results demonstrate the superiority of our approach over existing methods.

关键词： graph neural networks node classification label noise robustness

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A novel brain tumor segmentation and classification model using deep neural network over MRI-flair images

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Multimedia Tools and Applications 2024年第17期84卷 1-32页

作者： Rajendirane, Rajmohan Ananth Kumar, T. Sandhya, S.G. Hu, Yu-Chen Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Campus Tamil Nadu Kattankulathur India Department of Computer Science and Engineering IFET College of Engineering Tamil Nadu Villupuram India Department of Computer Science and Engineering Annamalai University Chidambaram India Department of Computer Science Tunghai University No. 1727 Sec. 4 Boulevard Xitun District Taichung City407224 Taiwan

Brain tumors pose a significant health concern globally, with their detection and diagnosis being crucial for timely intervention and treatment planning. These abnormal growths can develop within the brain or originate from other parts of the body, spreading to the brain. They can be benign or malignant, and their impact on cognitive and physical function can vary widely depending on their location, size, and type. Detecting brain tumors involves a combination of imaging techniques and clinical assessment. Magnetic Resonance Imaging (MRI) is one of the primary imaging modalities used for this purpose due to its ability to provide detailed images of the brain's anatomy and pathology. Advanced image processing techniques and machine learning algorithms play an increasingly important role in enhancing the accuracy and efficiency of brain tumor detection. This study presents a comprehensive approach for enhancing brain tumor detection in MRI images using a combination of advanced techniques. Firstly, an improved K-means clustering algorithm is introduced to segment tumor regions effectively. Following this, Median Filtering is applied to refine image quality and reduce noise interference. Subsequently, a deep neural network architecture is employed for precise tumor classification utilizing the BRATS dataset. The proposed methodology aims to improve the accuracy and efficiency of brain tumor detection, offering potential advancements in medical image analysis and diagnosis. Experimental results demonstrate promising outcomes, indicating the effectiveness of the proposed approach in accurately identifying tumor regions in MRI scans. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Magnetic resonance imaging

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Longitudinal Evaluation of Casual Puzzle Tablet Games by Older Adults

Longitudinal Evaluation of Casual Puzzle Tablet Games by Old...

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2024 ACM Designing Interactive Systems Conference, DIS 2024

作者： Chaudhry, Beenish Moalla Islam, Muhammad Usama Chawla, Nitesh Vinay School of Computing and Informatics University of Louisiana Lafayette United States Department of Computer Science and Engineering University of Notre Dame United States

ISBN: (纸本)9798400705830

Despite growing interest in mobile games for older adults, there is limited exploration of older adults’ gaming behaviors, perceptions, and experiences as they engage with casual puzzle games over a period of time. To address this, we conducted a 9-month study with 20 older adults, examining training needs, in-situ experiences, and preferences. Participants were trained on tablet PCs and ten selected games. During the study, participants documented their experiences and attended technology workshops. Gaming behaviors were logged and analyzed using descriptive and inferential statistics, revealing patterns and statistically signifcant diferences in play frequency and duration over the course of the study. Thematic analysis identifed facilitators and barriers to engagement such as customization, co-play experiences, and health issues. Based on these fndings, we recommend incorporating educational elements, enhancing user control, leveraging identity and nostalgia, supporting social interactions, designing for tangible interaction, and emphasizing the importance of learning aids. Future research should test the efectiveness of these recommendations in increasing older adults’ engagement with casual games. © 2024 Copyright held by the owner/author(s).

关键词： Personal computers

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Sharding:A Scalability Solutions for Blockchain Networks 3

Sharding:A Scalability Solutions for Blockchain Networks

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Kale, Dattatray Raghunath Jadhav, Amolkumar N. Kruishna Prasad, K. Salunkhe, Somanath Janardan Hirve, Sumit Goswami, Chandrashekhar Mit Art Design and Technology University Mit School of Computing Department of Computer Science & Engg Pune India Annasaheb Dange College of Engineering and Technology Department of Computer Science & Engg Ashta India Shrinivas University Department of Computer Science & Engg Mangalore India D Y Patil Agriculture and Technical University Department of Computer Science & Engg Talsande India Sir Padampat Singhania University Faculty of Computing and Informatics Rajasthan Udaipur India

ISBN: (纸本)9798350354348

Blockchain technology's decentralized and immutable data storage has changed a number of sectors. But typical blockchain networks scalability issues prevent them from being widely used for large-scale applications. By dividing the blockchain network into smaller, more controllable sections known as shards, sharding has become a viable remedy for scalability issues. This paper offers a thorough introduction to sharding as a blockchain network scalability solution. We explore the basic ideas of sharding as well as its advantages, drawbacks, and several sharding strategies. We also analyze experimental results and real-world implementations to assess how well sharding contributes to increased blockchain scalability. Lastly, we talk about possible developments in sharding approaches and future research paths to further improve the scalability of blockchain networks. © 2024 IEEE.

关键词： Distributed ledger

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Attention mechanisms in computer vision:A survey

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Computational Visual Media 2022年第3期8卷 331-368页

作者： Meng-Hao Guo Tian-Xing Xu Jiang-Jiang Liu Zheng-Ning Liu Peng-Tao Jiang Tai-Jiang Mu Song-Hai Zhang Ralph R.Martin Ming-Ming Cheng Shi-Min Hu BNRist Department of Computer Science and TechnologyTsinghua UniversityBeijing 100084China TKLNDST College of Computer ScienceNankai UniversityTianjin 300350China School of Computer Science and Informatics Cardiff UniversityCardiffUK

Humans can naturally and effectively find salient regions in complex *** by this observation,attention mechanisms were introduced into computer vision with the aim of imitating this aspect of the human visual *** an attention mechanism can be regarded as a dynamic weight adjustment process based on features of the input *** mechanisms have achieved great success in many visual tasks,including image classification,object detection,semantic segmentation,video understanding,image generation,3D vision,multimodal tasks,and self-supervised *** this survey,we provide a comprehensive review of various attention mechanisms in computer vision and categorize them according to approach,such as channel attention,spatial attention,temporal attention,and branch attention;a related repository https://***/MenghaoG uo/Awesome-Vision-Attentions is dedicated to collecting related *** also suggest future directions for attention mechanism research.

关键词： attention transformer computer vision deep learning salience

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Automated Autism Spectral Disorder Classification Using Optimal Machine Learning Model

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computers, Materials & Continua 2023年第3期74卷 5251-5265页

作者： Hanan Abdullah Mengash Hamed Alqahtani Mohammed Maray Mohamed K.Nour Radwa Marzouk Mohammed Abdullah Al-Hagery Heba Mohsen Mesfer Al Duhayyim Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial Intelligence and Unit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Autism Spectrum Disorder (ASD) refers to a neuro-disorder wherean individual has long-lasting effects on communication and interaction *** information technologywhich employs artificial intelligence(AI) model has assisted in early identify ASD by using pattern *** advances of AI models assist in the automated identification andclassification of ASD, which helps to reduce the severity of the *** study introduces an automated ASD classification using owl searchalgorithm with machine learning (ASDC-OSAML) model. The proposedASDC-OSAML model majorly focuses on the identification and classificationof ASD. To attain this, the presentedASDC-OSAML model follows minmaxnormalization approach as a pre-processing stage. Next, the owl searchalgorithm (OSA)-based feature selection (OSA-FS) model is used to derivefeature subsets. Then, beetle swarm antenna search (BSAS) algorithm withIterative Dichotomiser 3 (ID3) classification method was implied for ASDdetection and classification. The design of BSAS algorithm helps to determinethe parameter values of the ID3 classifier. The performance analysis of theASDC-OSAML model is performed using benchmark dataset. An extensivecomparison study highlighted the supremacy of the ASDC-OSAML modelover recent state of art approaches.

关键词： Autism spectral disorder machine learning owl search algorithm feature selection id3 classifier

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