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arXiv 2021年

作者： Yan, Xu Fan, Xiaoliang Yang, Peizhen Wu, Zonghan Pan, Shirui Chen, Longbiao Zang, Yu Wang, Cheng Fujian Key Laboratory of Sensing and Computing for Smart Cities School of Informatics Computer Science & Technology Department Xiamen University Xiamen China Centre for Artificial Intelligence FEIT University of Technology Sydney Australia Department of Data Science and AI Faculty of Information Technology Monash University Australia

—Representation learning on temporal interaction graphs (TIG) is to model complex networks with the dynamic evolution of interactions arising in a broad spectrum of problems. Existing dynamic embedding methods on TIG discretely update node embeddings merely when an interaction occurs. They fail to capture the continuous dynamic evolution of embedding trajectories of nodes. In this paper, we propose a two-module framework named ConTIG, a continuous representation method that captures the continuous dynamic evolution of node embedding trajectories. With two essential modules, our model exploit three-fold factors in dynamic networks which include latest interaction, neighbor features and inherent characteristics. In the first update module, we employ a continuous inference block to learn the nodes’ state trajectories by learning from time-adjacent interaction patterns between node pairs using ordinary differential equations. In the second transform module, we introduce a self-attention mechanism to predict future node embeddings by aggregating historical temporal interaction information. Experiments results demonstrate the superiority of ConTIG on temporal link prediction, temporal node recommendation and dynamic node classification tasks compared with a range of state-of-the-art baselines, especially for long-interval interactions prediction. © 2021, CC BY.

关键词： Ordinary differential equations

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CFAD: Coarse-to-Fine Action Detector for Spatiotemporal Action Localization 16th

CFAD: Coarse-to-Fine Action Detector for Spatiotemporal Acti...

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16th European Conference on Computer Vision, ECCV 2020

作者： Li, Yuxi Lin, Weiyao See, John Xu, Ning Xu, Shugong Yan, Ke Yang, Cong Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China Institute for Advanced Communication and Data Science Shanghai University Shanghai China Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia Adobe Research San Francisco United States Clobotics Shanghai China

ISBN: (纸本)9783030585167

Most current pipelines for spatio-temporal action localization connect frame-wise or clip-wise detection results to generate action proposals, where only local information is exploited and the efficiency is hindered by dense per-frame localization. In this paper, we propose Coarse-to-Fine Action Detector (CFAD), an original end-to-end trainable framework for efficient spatio-temporal action localization. The CFAD introduces a new paradigm that first estimates coarse spatio-temporal action tubes from video streams, and then refines the tubes location based on key timestamps. This concept is implemented by two key components, the Coarse and Refine Modules in our framework. The parameterized modeling of long temporal information in the Coarse Module helps obtain accurate initial tube estimation, while the Refine Module selectively adjusts the tube location under the guidance of key timestamps. Against other methods, the proposed CFAD achieves competitive results on action detection benchmarks of UCF101-24, UCFSports and JHMDB-21 with inference speed that is 3.3 faster than the nearest competitor. © 2020, Springer Nature Switzerland AG.

关键词： Tubes (components)

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Effect of copper nanoparticle volume fraction on flow in a 3D lid-driven cavity with phase change materials using molecular dynamics simulation

Case Studies in Chemical and Environmental Engineering

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Case Studies in Chemical and Environmental Engineering 2025年 11卷

作者： Sawaran Singh, Narinderjit Singh Hassan, Waqed H. Thiab, Rifqa F. Al-zahy, Younis Mohamed Atiah Salahshour, Soheil Hekmatifar, M. Faculty of Data Science and Information Technology INTI International University Persiaran Perdana BBN Putra Nilai Nilai 71800 Malaysia University of Warith Al-Anbiyaa Kerbala 56001 Iraq Department of Civil Engineering College of Engineering University of Kerbala Kerbala 56001 Iraq College of Health and Medical Techniques Al-Zahraa University for Women Karbala 56100 Iraq Department of Physics College of Education Misan University Maysan Iraq Faculty of Engineering and Natural Sciences Istanbul Okan University Istanbul Turkey Faculty of Engineering and Natural Sciences Bahcesehir University Istanbul Turkey Research Center of Applied Mathematics Khazar University Baku Azerbaijan Fast Computing Center Shabihsazan Ati Pars Tehran Iran

Background: Phase Change Materials are substances characterized by specific properties, including defined melting points and substantial latent heat of fusion. Effective heat transfer management is vital in modern industries, as it supports essential processes across various sectors. Methods: This study investigates the effect of copper nanoparticle volume fraction on flow behavior and thermal dynamics in a 3D lid-driven cavity. This enclosure was a controlled environment filled with phase change materials, designed to optimize thermal energy management. The system configuration comprised a wavy bottom wall and an adaptable upper wall, allowing for dynamic adjustments during the simulation. The results show that as the volume ratio of copper nanoparticles increased from 1 % to 3 %, the steady heat transfer process in the simulated nanoparticles also increased. Increasing the volume ratio from 1 % to 3 % resulted in a decrease in the maximum density of nanoparticles, which decreased from 0.0152 to 0.0146 atom/Å³. Additionally, this increase led to a rise in thermal conductivity from 1.26 to 1.45 W/m·K and in heat flux from 8.26 to 9.95 W/m². Significant Findings: The study demonstrates that optimizing the volume fraction of Cu-NPs in PCMs can significantly enhance thermal conductivity and heat flux, offering potential improvements in thermal energy storage systems. © 2025 The Authors

关键词： Density Heat flux Molecular dynamics simulation Phase change material Thermal conductivity

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Application of Kronecker convolutions in deep learning technique for automated detection of kidney stones with coronal CT images

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Information sciences 2023年 640卷

作者： Patro, Kiran Kumar Allam, Jaya Prakash Neelapu, Bala Chakravarthy Tadeusiewicz, Ryszard Acharya, U Rajendra Hammad, Mohamed Yildirim, Ozal Plawiak, Pawel Department of Electronics and Communication Engineering Aditya Institute of Technology and Management AP Tekkali532201 India Department of Electronics and Communication Engineering National Institute of Technology Rourkela Odisha Rourkela769008 India Department of Biotechnology and Medical Engineering National Institute of Technology Rourkela Odisha Rourkela769008 India AGH University of Science and Technology Department of Biocybernetics and Biomedical Engineering Krakow Poland School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia EIAS Data Science Lab College of Computer and Information Sciences Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology Faculty of Computers and Information Menoufia University Shibin El Kom32511 Egypt Department of Software Engineering Firat University Elazig Turkey Department of Computer Science Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 Krakow31-155 Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland

Kidney stone disease is a serious public health concern that is getting worse with changes in diet, obesity, medical conditions, certain supplements etc. A kidney stone also called a renal calculus, is a hard buildup of urine minerals that form in the kidneys. Computed tomography (CT) is one of the imaging models used to identify kidney stones by clinical experts. Due to the low resolution of these images, sometimes detecting kidney stones is tedious with the naked eye, which may lead to false alarms. In this work, a computer-based diagnosis system with a deep learning technique has been developed as a practical solution to aid clinicians in their diagnosis. The traditional convolutional neural network (CNN)-based deep learning technology can detect stones in the kidney. Still, it suffers from the performance and standard implementation of the convolution operations in convolution layers. A Kronecker product-based convolution technique is incorporated in the proposed deep learning architecture to reduce the redundancy in feature maps without convolution overlapping. Our proposed method helps to make the network more effective by extracting abstract and in-depth features from the input images. The publicly available GitHub kidney stone CT scans are utilized to develop the proposed architecture. Our automated model detected kidney stones with an accuracy of 98.56% utilizing CT images. Our system is more effective than the most recent and cutting-edge techniques developed for identifying kidney stones of any size, including the smallest ones. © 2023 The Author(s)

关键词： Convolution

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A review on image processing for fish disease detection

A review on image processing for fish disease detection

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2021 Asian Conference on Intelligent computing and data sciences, ACIDS 2021

作者： Pauzi, S.N. Hassan, M.G. Yusoff, N. Harun, N.H. Abu Bakar, A.H. Kua, B.C. School of Technology Management and Logistics Universiti Utara Malaysia SintokKedah06010 Malaysia Department of Data Science Universiti Malaysia Kelantan Kota BharuKelantan16100 Malaysia School of Computing Universiti Utara Malaysia SintokKedah06010 Malaysia Faculty of Electronic Engineering Technology Universiti Malaysia Perlis KangarPerlis01000 Malaysia National Fish Health Research Centre Fisheries Research Institute Department of Fisheries Malaysia Batu MaungPenang11960 Malaysia

Fish disease is considered the main cause for production and economic losses by fish farmers. Fish disease detection and health monitoring is a demanding task by manual method of human visualization. Therefore, any potential approach that is fast, reliable and possesses high automation supports an interest in this issue. Nowadays, with the current emergence in the technology revolution, image processing has been extensively used in disease detection field, especially in human and plant, aiding the human experts in providing the right treatment. Image processing technique offers opportunities to improve the traditional approach in achieving accurate results. Besides, several steps in image processing are adopted including image acquisition, image pre-processing, image segmentation, object detection, feature extraction and classification. The objective of this paper is to briefly review the work established in the fish disease detection field with the use of numerous classification techniques of image processing, including rule-based expert system, machine learning, deep learning, statistical method and hybrid method. The present review recognizes the need for improvement in these image processing approaches that would be valuable for further advancement in terms of performance. © Published under licence by IOP Publishing Ltd.

关键词： Object detection

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GauSS-MI: Gaussian Splatting Shannon Mutual Information for Active 3D Reconstruction

arXiv

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arXiv 2025年

作者： Xie, Yuhan Cai, Yixi Zhang, Yinqiang Yang, Lei Pan, Jia School of Computing and Data Science The University of Hong Kong Hong Kong Division of Robotics Perception and Learning KTH Royal Institute of Technology Stockholm Sweden Faculty of Engineering The University of Hong Kong Hong Kong Centre for Transformative Garment Production Hong Kong

This research tackles the challenge of real-time active view selection and uncertainty quantification on visual quality for active 3D reconstruction. Visual quality is a critical aspect of 3D reconstruction. Recent advancements such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have notably enhanced the image rendering quality of reconstruction models. Nonetheless, the efficient and effective acquisition of input images for reconstruction—specifically, the selection of the most informative viewpoint—remains an open challenge, which is crucial for active reconstruction. Existing studies have primarily focused on evaluating geometric completeness and exploring unobserved or unknown regions, without direct evaluation of the visual uncertainty within the reconstruction model. To address this gap, this paper introduces a probabilistic model that quantifies visual uncertainty for each Gaussian. Leveraging Shannon Mutual Information, we formulate a criterion, Gaussian Splatting Shannon Mutual Information (GauSS-MI), for real-time assessment of visual mutual information from novel viewpoints, facilitating the selection of next best view. GauSS-MI is implemented within an active reconstruction system integrated with a view and motion planner. Extensive experiments across various simulated and real-world scenes showcase the superior visual quality and reconstruction efficiency performance of the proposed system. Copyright © 2025, The Authors. All rights reserved.

关键词： Gaussian distribution

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Artificial Intelligence-based Service Chains Scheduling for Medical Emergency in Healthcare

Artificial Intelligence-based Service Chains Scheduling for ...

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International Conference on Contemporary computing and Informatics (IC3I)

作者： Mukesh Soni Maher Ali Rusho Haewon Byeon Azzah AlGhamdi Shaikh Abdul Hannan Parth Rameshchandra Dave Dr. D. Y. Patil Vidyapeeth Dr. D. Y. Patil School of Science & Technology Tathawade Pune Marashtra Department of Lockheed Martin Engineering Management University of Colorado Boulder Colorado Department of AI and Software & Medical Big Data Research Centre Inje University Gimhae Republic of Korea Computer Information Systems Department College of Computer Science and Information Technology Imam Abdalrhman Bin Faisal University Khobar Saudi Arabia Faculty of Computing and Information AlBaha University AlBaha Kingdom of Saudi Arabia Department of Computer Engineering L. D. College of Engineering Ahmedabad Gujarat India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

This study analyses the weekly scheduling challenge of health and medical information network service function chains to meet medical emergency categorization and grading needs. Emergency services are divided into four sorts by urgency and service area, with priorities and weights. The health and medical information network architecture, service model, and operation method are built accordingly. To reduce weighted completion time, a service function chain scheduling model is created. The function chain scheduling scheme is solved by matching game and Q -learning reinforcement learning algorithms for different issue scales. Finally, simulation studies demonstrate that this approach can accomplish graded medical emergency service, build health and medical information networks, and progress smart healthcare.

关键词： Schedules Job shop scheduling Service function chaining Hospitals Processor scheduling Reinforcement learning Games Emergency services Network function virtualization Standards

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Pattern-based Wordiness Reduction System for Thai Texts 23

Pattern-based Wordiness Reduction System for Thai Texts

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23rd International Computer science and Engineering Conference, ICSEC 2019

作者： Tongtep, Nattapong Charoenporn, Thatsanee Prince of Songkla University College of Computing Phuket Thailand Musashino University Asia AI Institute Faculty of Data Science Tokyo Japan

ISBN: (纸本)9781728125442

In this paper, we propose a method and web application to reduce wordiness in Thai by adapting pattern-based analysis with wordiness initial corpus. The small amount of Thai text is gathered to create the initial corpus for grammatically analyzing the patterns of Thai wordiness. 18 wordiness have been decided to intensely analyze the co-occurrence, patterns contexts and construction rules. And 99 wordiness patterns are discovered for the experiments. The experiment has been done with 100 documents from Thai National Corpus by 15 students. For the user satisfaction which is analyzed from the questionnaire, users are highly satisfied with the use of the proposed system. And the wordiness reduction result of the experiment shows the effectiveness of the wordiness identification and reduction which the accuracy is 86.51%. The 13.49% of errors come from word segmentation and unknown words. Wordiness automatically corpus will be developed for further experiment in the future. © 2019 IEEE.

关键词： Information extraction Thai language Web application Wordiness

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SemRel2024: A Collection of Semantic Textual Relatedness datasets for 13 Languages

arXiv

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arXiv 2024年

作者： Ousidhoum, Nedjma Muhammad, Shamsuddeen Hassan Abdalla, Mohamed Abdulmumin, Idris Ahmad, Ibrahim Said Ahuja, Sanchit Aji, Alham Fikri Araujo, Vladimir Ayele, Abinew Ali Baswani, Pavan Beloucif, Meriem Biemann, Chris Bourhim, Sofia De Kock, Christine Dekebo, Genet Shanko Hourrane, Oumaima Kanumolu, Gopichand Madasu, Lokesh Rutunda, Samuel Shrivastava, Manish Solorio, Thamar Surange, Nirmal Tilaye, Hailegnaw Getaneh Vishnubhotla, Krishnapriya Winata, Genta Yimam, Seid Muhie Mohammad, Saif M. Cardiff University United Kingdom Imperial College London United Kingdom Data Science for Social Impact Research Group University of Pretoria South Africa Institute For Experiential AI Northeastern University United States BITS Pilani India MBZUAI United Arab Emirates KU Leuven Belgium Universität Hamburg Language Technology Group Germany Bahir Dar University Faculty of Computing Ethiopia IIIT Hyderabad India Uppsala University Sweden The University of Melbourne Australia Adama Science and Technology University Ethiopia Digital Umuganda Rwanda Kotebe University of Education Ethiopia University of Toronto Canada HKUST Hong Kong National Research Council Canada

Exploring and quantifying semantic relatedness is central to representing language and holds significant implications across various NLP tasks. While earlier NLP research primarily focused on semantic similarity, often within the English language context, we instead investigate the broader phenomenon of semantic relatedness. In this paper, we present SemRel, a new semantic relatedness dataset collection annotated by native speakers across 13 languages: Afrikaans, Algerian Arabic, Amharic, English, Hausa, Hindi, Indonesian, Kinyarwanda, Marathi, Moroccan Arabic, Modern Standard Arabic, Spanish, and Telugu. These languages originate from five distinct language families and are predominantly spoken in Africa and Asia – regions characterised by a relatively limited availability of NLP resources. Each instance in the SemRel datasets is a sentence pair associated with a score that represents the degree of semantic textual relatedness between the two sentences. The scores are obtained using a comparative annotation framework. We describe the data collection and annotation processes, challenges when building the datasets, baseline experiments, and their impact and utility in NLP. Copyright © 2024, The Authors. All rights reserved.

关键词： Natural language processing systems

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An Effective Learning Management System for Revealing Student Performance Attributes

arXiv

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arXiv 2024年

作者： Zhang, Xinyu Lee, Vincent C.S. Xu, Duo Chen, Jun Obaidat, Mohammad S. The School of Electronics and Information Northwestern Polytechnical University Xi’an710072 China The Department of Data Science and Artificial Intelligence Faculty of IT Monash University Melbourne3800 Australia Guangyuan Foreign Language School Guangyuan628018 China Chongqing Institute for Brain and Intelligence Guangyang Bay Laboratory Chongqing400064 China The King Abdullah II School of Information Technology The University of Jordan Amman11942 Jordan School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Department of Computational Intelligence School of Computing SRM University SRM Nagar TN Kattankulathur603203 India School of Engineering The Amity University UP Noida201301 India

Contribution: A learning management system (LMS) incorporated with an advanced educational data mining module is proposed, as a means to mine efficiently from student performance records to provide valuable insights for educators in helping plan effective learning pedagogies, improve curriculum design, and guarantee quality of teaching. Background: An LMS streamlines the management of the teaching process in a centralized place, recording, tracking, and reporting the delivery of educational courses and student performance. Educational knowledge discovery from such an elearning system plays a crucial role in rule regulation, policy establishment, and system development. However, existing LMSs do not have embedded mining modules to directly extract knowledge. As educational modes become more complex, educational data mining efficiency from those heterogeneous student learning behaviours is gradually degraded. Intended outcomes: The design and application of the LMS enable educators to learn from past student performance experiences, empowering them to guide and intervene with students in time, and eventually improve their academic success. Application design: This study proposes an effective LMS which processes the stored data through an advanced educational data mining module. The mining module utilizes probability-based frequent pattern mining for generating candidate itemsets and updating LMS databases in each iteration for complexity reduction. In addition, the conditional probability increment ratio (CPIR) is deployed as an exceptionality measure to mine effective common and exception rules simultaneously. The iteratively generated rules can reveal academic performance patterns so that have merits in learning personal goal achievement. Findings: Through two illustrative case studies, experimental results demonstrate increased mining efficiency of the proposed mining module without information loss compared to classic educational mining algorithms. The mined knowl

关键词： data mining

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