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arXiv 2023年

作者： Chen, Zhaoliang Wu, Zhihao Zhong, Luying Plant, Claudia Wang, Shiping Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China Faculty of Computer Science University of Vienna Vienna1090 Austria ds:UniVie Vienna1090 Austria

Heterogeneous graph neural networks aim to discover discriminative node embeddings and relations from multi-relational networks. One challenge of heterogeneous graph learning is the design of learnable meta-paths, which significantly influences the quality of learned embeddings. Thus, in this paper, we propose an Attributed Multi-Order Graph Convolutional Network (AMOGCN), which automatically studies meta-paths containing multi-hop neighbors from an adaptive aggregation of multi-order adjacency matrices. The proposed model first builds different orders of adjacency matrices from manually designed node connections. After that, an intact multi-order adjacency matrix is attached from the automatic fusion of various orders of adjacency matrices. This process is supervised by the node semantic information, which is extracted from the node homophily evaluated by attributes. Eventually, we utilize a one-layer simplifying graph convolutional network with the learned multi-order adjacency matrix, which is equivalent to the cross-hop node information propagation with multi-layer graph neural networks. Substantial experiments reveal that AMOGCN gains superior semi-supervised classification performance compared with state-of-the-art competitors. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Streamlining Eye-Tracking and Observational data for Field Study Visual Analysis 25

Streamlining Eye-Tracking and Observational Data for Field S...

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Proceedings of the 2025 Symposium on Eye Tracking Research and Applications

作者： Yidan Zhang Nethara Athukorala Ziying Liang Yidan Qiao Simran - Yu Xuan Yio Lee Lawrence Benjamin Tag Mor Vered Michael Wybrow Sarah Goodwin Department of Human-Centred Computing Monash University Melbourne Australia Monash University Melbourne Victoria Australia Faculty of Information Technology Monash University Melbourne Victoria Australia Monash University Melboure Victoria Australia Faculty of Information Technology Monash University Melboure Victoria Australia Monash Business School Monash University Melbourne Australia School of Computer Science and Engineering University of New South Wales Sydney New South Wales Australia Dept of Data Science & AI Monash University Clayton VIC Australia Department of Human-Centred Computing Monash University Melbourne VIC Australia Monash University Melbourne Australia

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Blockchain-based data Sharing Framework for Malaysia Government Aid Management System

Blockchain-based Data Sharing Framework for Malaysia Governm...

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Digital Applications, Transformation & Economy (ICDATE), International Conference on

作者： Azman Azmi Farashazillah Yahya Ervin Gubin Moung Hasimi Sallehudin Rio Guntur Utomo Nur Afrina Azman Advanced Machine Intelligence Research Lab Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia Data Technologies and Application Research Lab Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia Centre for Software Technology and Management Faculty of Information Science and Technology Universiti Kebangsaan Malaysia School of Computing Telkom University Bandung Indonesia Foundation Study and Language Institute University of Reading Malaysia Iskandar Puteri Johor Malaysia

The Covid-19 pandemic has impacted the world economy, and Malaysia is no exception. Due to the closure of businesses, the rate of unemployment has been recorded at an all-time high. The Malaysian Government through its agencies is assisting citizens through an online platform for incentives and an aid-giving platform. The current architecture of data sharing is using centralized, organization-oriented authentication and is not immutable for audit capability. In addition, the blockchain network is still a proof of concept in the digital government ecosystem. This paper studies the current aid data-sharing ecosystem and analyzes the potential of having a state government consortium blockchain framework that will be used in the Government Network Infrastructure for decentralized, transparent data sharing with audit capability. Several works involving Blockchain adoption based on domain, Blockchain consortium architecture and Government applications using blockchain are being presented. The need for a framework in implementing decentralized data sharing using consortium blockchain architecture for multi-agencies data sharing focusing on Malaysia Government Aid Management is discussed in this paper to provide the opportunity for the research domain. The findings in this paper will be used for future work on the requirement of a proposed government multi-layer consortium blockchain framework for data sharing in the Malaysian Government specifically for the Johore State Government ecosystem.

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GPU-based Parallel Technique for Solving the N-Similarity Problem in Textual data Mining

GPU-based Parallel Technique for Solving the N-Similarity Pr...

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Distributed computing and High Performance computing (DCHPC), International Conference on

作者： Mahmood Fazlali Mina Mirhosseini Mahyar Shahsavari Alex Shafarenko Mashallah Mashinchi Department of Computer Science Cyber Security and Computing Research Group University of Hertfordshire Hertfordshire UK Department of Computer and Data Sciences Faculty of Mathematical Sciences Shahid Beheshti University Tehran Iran AI Department Donders Institute for Brain Cognition and Behaviour Radboud University Nijmegen The Netherlands Department of Statistics Faculty of Mathematics and Computer Shahid Bahonar University of Kerman Kerman Iran

An important issue in data mining and information retrieval is the problem of multiple similarity or n-similarity. This problem entails finding a group of n data points with the highest similarity within a large dataset. Exact methods to solve this problem exist but come with high time and space complexities. Additionally, various metaheuristic algorithms have been proposed, including genetic algorithms, gravitational search algorithms, particle swarm optimization, imperialist competitive algorithms, and fuzzy imperialist competitive algorithms. These metaheuristics are capable of finding near-optimal solutions within a reasonable timeframe, although there is no guarantee of achieving exact results. In this paper, we employ a parallelization technique using CUDA to expedite the exact method. We conduct experiments on textual datasets to identify a group of n textual documents with the highest similarity to each other. The experimental results demonstrate that the proposed parallel exact method significantly reduces execution time compared to the best sequential approach and CPU multi-core implementation. Furthermore, it is evident that the proposed method requires less memory space than the exact method.

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Node Localization Method in Wireless Sensor Networks Using Combined Crow Search and Weighted Centroid Method

SSRN

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SSRN 2023年

作者： Sankaranarayanan, Suresh Rajaram, V. Srividhya, S. Lorenz, Pascal Rodrigues, Joel J.P.C. Department of Computer Science College of Computer Science and Information Technology King Faisal University Al Hofuf Saudi Arabia Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603203 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603203 India University of Haute Alsace Colmar France Qingdao266555 China Instituto de Telecomunicações Covilhã6201-001 Portugal

Localization of nodes is critical for acquiring access to diverse nodes that would provide services in remote places. Single-anchor localization techniques suffer from a co-linearity problem, resulting in poor performance. The reliable multiple anchor selection method can be computationally intensive and requires a lot of processing power and time to identify suitable anchor nodes. There has been work where researchers have employed nature-inspired algorithms over conventional optimization method towards localization of unknown nodes in WSN. These methods have challenges like longer time and lot of processing power to solve. In addition, these methods are sensitive to parameter selection impacting the ***, we in this research work have employed the nature-inspired crow search algorithm for selecting the suitable anchors from the population thereby reducing errors in identifying the location of unknown nodes. In addition, the weighted centroid method is proposed for the identification of the exact location of an unknown node. This makes the weighted centroid method and the crow search algorithm a more trustworthy and efficient way for node localization in wireless sensor networks. The Crow Search-based Weighted Centroid Localization (CS-WCL) has outperformed WCL and DV-Hop with the use of the best anchor node in terms of Localization Error, Total data packets, and Energy Consumption. This resulted in CSWCL outperforming other nature-inspired algorithms for node localization. © 2023, The Authors. All rights reserved.

关键词： Energy utilization

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RAPID MIXING OF THE FLIP CHAIN OVER NON-CROSSING SPANNING TREES

arXiv

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arXiv 2024年

作者： Anand, Konrad Feng, Weiming Freifeld, Graham Guo, Heng Jerrum, Mark Wang, Jiaheng School of Mathematical Sciences Queen Mary University of London Mile End Road LondonE1 4NS United Kingdom School of Computing and Data Science The University of Hong Kong Pokfulam Road Hong Kong School of Informatics University of Edinburgh Informatics Forum EdinburghEH8 9AB United Kingdom Faculty of Informatics and Data Science University of Regensburg Bajuwarenstrasse 4 Regensburg93053 Germany

We show that the flip chain for non-crossing spanning trees of n + 1 points in convex position mixes in time O(n8 log n). We use connections between Fuss-Catalan structures to construct a comparison argument with a ch... 详细信息

关键词： Trees (mathematics)

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A Novel Hybrid Optimizer Based on Coati Optimization Algorithm and Differential Evolution for Global Optimization and Constrained Engineering Problems

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International Journal of Computational Intelligence Systems 2025年第1期18卷 1-70页

作者： Biswas, Saptadeep Maiti, Binanda Singh, Gyan Ezugwu, Absalom E. Saleem, Kashif Abualigah, Laith Smerat, Aseel Bera, Uttam Kumar Department of Mathematics National Institute of Technology Agartala Tripura India Unit for Data Science and Computing North-West University Potchefstroom South Africa Department of Computer Science & Engineering College of Applied Studies & Community Service King Saud University Riyadh Saudi Arabia Computer Science Department Al al-Bayt University Mafraq Jordan Faculty of Educational Sciences Al-Ahliyya Amman University Amman Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura India Computer Technologies Engineering Mazaya University College Nasiriyah Iraq

This paper presents a novel hybrid metaheuristic, the Hybrid Coati Optimization Algorithm with Differential Evolution (HCOADE), developed to address complex global optimization tasks and constrained engineering design problems. HCOADE integrates the exploration-driven behaviour of the Coati Optimization Algorithm (COA)-inspired by the social foraging and predation strategies of coatis-with the powerful mutation and crossover mechanisms of Differential Evolution (DE), thereby achieving a balanced and adaptive search process. The hybridization enhances global exploration and local exploitation, enabling the algorithm to efficiently navigate diverse and challenging optimization landscapes. To rigorously evaluate its performance, HCOADE is tested on benchmark suites from CEC 2014, 2017, 2020, and 2022, which encompass unimodal, multimodal, hybrid, and composition functions. It is also applied to real-world constrained engineering problems, such as pressure vessel design, cantilever beam optimization, and reinforced concrete beam design. Comparative experiments against state-of-the-art algorithms—including COA, DE, RSA, PSO, SSA, BBO, QIO, DMOA, and others—demonstrate that HCOADE consistently delivers superior solution quality, faster convergence, and higher robustness. Quantitative results show that HCOADE achieved the 1st place average rank across all four benchmark suites. It obtained top performance on 80% of the functions in CEC 2014, 66.7% in CEC 2017, 70% in CEC 2020, and 66.7% in CEC 2022. Furthermore, HCOADE outperformed or matched CEC competition-winning algorithms such as LSHADE-cnEpSin, LSHADE-SPACMA, and CMA-ES on numerous challenging functions of CEC 2017. Statistical analyses, including Wilcoxon Rank Sum Tests and ranking evaluations, confirm the significance and reliability of HCOADE’s performance. Furthermore, convergence behaviour, measurement of exploration and exploitation, and sensitivity analysis highlight the algorithm’s adaptability and stability

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National Telecommunications and Information Administration: Comments from Researchers at Boston university and the university of Chicago

SSRN

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SSRN 2023年

作者： Canetti, Ran Cohen, Aloni Conley, Chris Crovella, Mark Dogan, Stacey Gaboardi, Marco Hartzog, Woodrow Van Loo, Rory Robertson, Christopher T. Silbaugh, Katharine B. Department of Computer Science United States Technology Law Clinic Boston University School of Law United States Boston University School of Law United States Boston University United States Affiliated Faculty Boston University Faculty of Computing and Data Sciences United States Boston University School of Public Health United States

These comments were composed by an interdisciplinary group of legal, computer science, and data science faculty and researchers at Boston university and the university of Chicago. This group collaborates on research projects that grapple with the legal, policy, and ethical implications of the use of algorithms and digital innovation in general, and more specifically regarding the use of online platforms, machine learning algorithms for classification, prediction, and decision making, and generative AI. Specific areas of expertise include the functionality and impact of recommendation systems;the development of Privacy Enhancing Technologies (PETs) and their relationship to privacy and data security laws;legal regulation of platforms under privacy, intellectual property, and antitrust laws;the science of monitoring and measuring the behavior of large deployed systems and networks;and programming languages and the science of rigorously specifying and verifying properties of algorithms and information systems. © 2023, The Authors. All rights reserved.

关键词： Decision making

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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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Physics-Based Dynamic Models Hybridisation Using Physics-Informed Neural Networks

arXiv

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arXiv 2024年

作者： Lalic, Branislava Cuong, Dinh Viet Petric, Mina Pavlovic, Vladimir Sremac, Ana Firanj Roantree, Mark Faculty of Agriculture University of Novi Sad Novi Sad Serbia School of Computing Dublin City University Dublin 9 Ireland AVIA GIS Zoersel Belgium Department of Computer Science Rutgers University New BrunswickNJ United States Insight Centre for Data Analytics Dublin City University Dublin 9 Ireland

Physics-based dynamic models (PBDMs) are simplified representations of complex dynamical systems. PBDMs take specific processes within a complex system and assign a fragment of variables and an accompanying set of parameters to depict the processes. As this often leads to suboptimal parameterisation of the system, a key challenge requires refining the empirical parameters and variables to reduce uncertainties while maintaining the model’s explainability and enhancing its predictive accuracy. We demonstrate that a hybrid mosquito population dynamics model, which integrates a PBDM with Physics-Informed Neural Networks (PINN), retains the explainability of the PBDM by incorporating the PINN-learned model parameters in place of its empirical counterparts. Specifically, we address the limitations of traditional PBDMs by modelling the parameters of larva and pupa development rates using a PINN that encodes complex, learned interactions of air temperature, precipitation and humidity. Our results demonstrate improved mosquito population simulations including the difficult-to-predict mosquito population peaks. This opens the possibility of hybridisation concept application on other complex systems based on PBDMs such as cancer growth to address the challenges posed by scarce and noisy data, and to numerical weather prediction and climate modelling to overcome the gap between physics-based and data-driven weather prediction models. Copyright © 2024, The Authors. All rights reserved.

关键词： Large scale systems

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