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25th IEEE International Conferences on High Performance computing and Communications, 9th International Conference on Data Science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Chen, Tianhao Gao, Qian Fan, Jun Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Jinan250014 China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd Shandong Jinan250101 China

ISBN: (纸本)9798350330014

Group recommendation involves comprehensively considering various aspects, including members and items, to predict the overall interests of a group and recommend suitable items through a recommendation system. With the rapid evolution of the Internet, online group activities have become increasingly prevalent, making group recommendation a highly discussed topic within the realm of recommendation systems. However, current research in group recommendation still confronts the following challenges. Firstly, a predominant portion of group recommendation research focuses solely on aggregating group-level information, neglecting the valuable contribution of higher-order member-level insights to group recommendation. Moreover, some aggregation strategies overly prioritize fairness, thereby disregarding common real-world patterns. Secondly, previous studies have primarily relied on aggregating individual member interests to establish group preferences, lacking a holistic consideration of group dynamics. To tackle these challenges, this study introduces an innovative approach by implementing multi-channel hypergraph convolution within the member perspective. This approach aims to effectively extract higher-order insights from members by utilizing a member information enhancement module. By establishing group interconnections based on similarity and employing an adaptive fusion network to amalgamate multiple viewpoints, a final representation is derived. Experimental results demonstrate that our proposed model surpasses baseline models by 3% to 4% in terms of hit rate and accuracy, validating the efficacy of our approach. © 2023 IEEE.

关键词： Recommender systems

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Social-enhanced recommendation using graph-based contrastive learning 25

Social-enhanced recommendation using graph-based contrastive...

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作者： Xue, Peng Gao, Qian Fan, Jun Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Jinan250014 China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd Shandong Jinan250101 China

ISBN: (纸本)9798350330014

The social network-based recommendation model use social network information to mitigate data sparsity issues and improve the accuracy of recommendation models. However, In most social network-based recommendation algorithms, the social neighbors' contributions are difficult to distinguish from the central user's, neglecting hidden correlations in social infor-mation. In order to solve this problem, this paper presents a graph-based contrastive learning framework for social-enhanced recommendation (SoGCLR) that utilizes implicit social information captured by a new social relation attention mechanism, en-rich user representations, and improve model robustness through graph-based contrastive learning. Specifically, the paper captures the degree of contribution of each neighbor in the social graph to the central user through a social relation attention layer, thus obtaining hidden correlations in social information, and further integrates this with user information in the user-item interaction graph to enrich user representations. In addition, the paper incorporates graph-based contrastive learning into the recommendation task using cross-layer contrastive learning, calculating contrastive loss and mapping nodes with similar but different exposure rates to nearby regions to mitigate exposure bias issues. Results from Ciao and Epinions demonstrate that SoGCLR reduces RMSE and MAE by 1.33% to 1.84% compared with baseline models. © 2023 IEEE.

关键词： Recommender systems

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Sports Events Recognition Using Multi Features and Deep Belief Network

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Computers, Materials & Continua 2024年第10期81卷 309-326页

作者： Bayan Alabdullah Muhammad Tayyab Yahay AlQahtani Naif Al Mudawi Asaad Algarni Ahmad Jalal Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science Applied CollegeKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

In the modern era of a growing population,it is arduous for humans to monitor every aspect of sports,events occurring around us,and scenarios or *** recognition of different types of sports and events has increasingly incorporated the use of machine learning and artificial *** research focuses on detecting and recognizing events in sequential photos characterized by several factors,including the size,location,and position of people’s body parts in those pictures,and the influence around those *** approaches utilized,here are feature descriptors such as MSER(Maximally Stable Extremal Regions),SIFT(Scale-Invariant Feature Transform),and DOF(degree of freedom)between the joint points are applied to the skeleton ***,for the same purposes,other features such as BRISK(Binary Robust Invariant Scalable Keypoints),ORB(Oriented FAST and Rotated BRIEF),and HOG(Histogram of Oriented Gradients)are applied on full body or *** integration of these techniques increases the discriminative nature of characteristics retrieved in the identification process of the event,hence improving the efficiency and reliability of the entire *** extracted features are passed to the early fusion and DBscan for feature fusion and *** deep belief,network is employed for *** results demonstrate a separate experiment’s detection average recognition rate of 87%in the HMDB51 video database and 89%in the YouTube database,showing a better perspective than the current methods in sports and event identification.

关键词： Machine learning silhouettes extremal regions joint points scalable keypoints

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SHAPE CONSTRAINTS IN SYMBOLIC REGRESSION USING PENALIZED LEAST SQUARES

arXiv

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arXiv 2024年

作者： Martinek, Viktor Reuter, Julia Frotscher, Ophelia Mostaghim, Sanaz Richter, Markus Herzog, Roland Interdisciplinary Center for Scientific Computing Heidelberg University Heidelberg69120 Germany Otto von Guericke University Faculty of Computer Science Magdeburg39106 Germany Faculty of Mechanical Engineering University of Technology Chemnitz Applied Thermodynamics Chemnitz09107 Germany

We study the addition of shape constraints (SC) and their consideration during the parameter identification step of symbolic regression (SR). SC serve as a means to introduce prior knowledge about the shape of the otherwise unknown model function into SR. Unlike previous works that have explored SC in SR, we propose minimizing SC violations during parameter identification using gradient-based numerical optimization. We test three algorithm variants to evaluate their performance in identifying three symbolic expressions from synthetically generated data sets. This paper examines two benchmark scenarios: one with varying noise levels and another with reduced amounts of training data. The results indicate that incorporating SC into the expression search is particularly beneficial when data is scarce. Compared to using SC only in the selection process, our approach of minimizing violations during parameter identification shows a statistically significant benefit in some of our test cases, without being significantly worse in any instance. © 2024, CC BY-NC-SA.

关键词： Equations of state

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Optimizing Neighborhood Area Network Topology for Enhanced Communication in Smart Grid

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IEEE Transactions on Consumer Electronics 2025年

作者： Li, Jian-Hui Ding, Yuemin Yu, Zu-Guo Zhang, Weizhe Ren, Honglin Tian, Yu-Chu Xiangtan University National Center for Applied Mathematics in Hunan Key Laboratory of Intelligent Computing and Information Processing of the Ministry of Education of China Xiangtan University Hunan 411105 China Queensland University of Technology School of Computer Science BrisbaneQLD Australia University of Navarra Ikerbasque Foundation Tecnun School of Engineering San Sebastián Spain Bilbao Spain Harbin Institute of Technology School of Computer Science and Technology Shenzhen China

In modern power grids, smart meters, as a type of consumer electronics, act as metering and communication nodes, which form Neighborhood Area Networks (NANs) spanning electricity distribution networks from substations to energy producers and consumers. In smart meter communication, wireless technologies have been widely adopted due to their inherent advantages of easier installation, flexibility, and low cost. For achieving low-latency communications in NANs, it is crucial to optimize the existing topology to enhance communication efficiency. However, optimizing the NAN topology is a challenging task due to the computational infeasibility of obtaining the optimal solution. To tackle this challenge, topology optimization in NANs is first formulated into a combinatorial optimization problem. After that, a heuristic solution driven by complex network theory is presented to solve the formulated optimization problem. The proposed heuristic solution consists of an effective network rewiring scheme and a long-range edge connection strategy. The rewiring optimizes the topology of wireless NANs to reduce local network length, whilst the long-range edge connection best pairs a small number of NAN nodes to reduce global network length. Numerical studies are conducted on diverse case studies to demonstrate the presented solution to enhance communication efficiency in smart grid NANs, outperforming the benchmark schemes. © 1975-2011 IEEE.

关键词： Combinatorial optimization

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Machine Learning-Based Radio Access technology Selection in the Internet of Moving Things

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China Communications 2021年第7期18卷 13-24页

作者： Ramon Sanchez-Iborra Luis Bernal-Escobedo Jose Santa Dept.Engineering and Applied Techniques University Centre of Defence at the Spanish Air Force Academy30729 San JavierSpain Dept.Information and Communication Engineering University of Murcia30100 MurciaSpain Dept.of Electronics Computing Technology and ProjectsTechnical University of Cartagena30202 CartagenaSpain

The Internet of Moving Things(IoMT)takes a step further with respect to traditional static IoT *** this line,the integration of new eco-friendly mobility devices such as scooters or bicycles within the Cooperative-Intelligent Transportation Systems(C-ITS)and smart city ecosystems is crucial to provide novel *** this end,a range of communication technologies is available,such as cellular,vehicular WiFi or Low-Power Wide-Area Network(LPWAN);however,none of them can fully cover energy consumption and Quality of Service(QoS)***,we propose a Decision Support System(DSS),based on supervised Machine Learning(ML)classification,for selecting the most adequate transmission interface to send a certain message in a multi-Radio Access technology(RAT)set *** ML algorithms have been explored taking into account computing and energy constraints of IoMT enddevices and traffic ***,a real implementation of a decision tree-based DSS for micro-controller units is presented and *** attained results demonstrate the validity of the proposal,saving energy in communication tasks as well as satisfying QoS requirements of certain urgent *** footprint of the real implementation on an Arduino Uno is 444 bytes and it can be executed in around 50µs.

关键词： internet of moving things multi-RAT CITS classification personal mobility

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Fuzzy with Metaheuristics Based Routing for Clustered Wireless Sensor Networks

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Intelligent Automation & Soft computing 2023年第1期35卷 367-380页

作者： Ashit Kumar Dutta Yasser Albagory Majed Alsanea Abdul Rahaman Wahab Sait Hazim Saleh AlRawashdeh Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Computing University Name:Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia Department of Cyber Security Onaizah Private CollegesUnaizah51911Kingdom of Saudi Arabia

Wireless sensor network(WSN)plays a vital part in real time tracking and data collection *** incorporates a set of numerous sensor nodes(SNs)commonly utilized to observe the target *** SNs operate using an inbuilt battery and it is not easier to replace or charge ***,proper utilization of available energy in the SNs is essential to prolong the lifetime of the *** this study,an effective Type-II Fuzzy Logic with Butterfly Optimization Based Route Selection(TFL-BOARS)has been developed for clustered *** TFL-BOARS technique intends to optimally select the cluster heads(CHs)and routes in the clustered ***,the TFL-BOARS technique incorporates Type-II Fuzzy Logic(T2FL)technique with distinct input parameters namely residual energy(RE),link quality(LKQ),trust level(TRL),inter-cluster distance(ICD)and node degree(NDE)to select CHs and construct ***,the butterfly optimization algorithm based route selection(BOARS)technique is derived to select optimal set of routes in the *** addition,the BOARS technique has computed afitness function using three parameters such as communication cost,distance and *** order to demonstrate the improved energy effectiveness and prolonged lifetime of the WSN,a wide-ranging simulation analysis was implemented and the experimental results reported the supremacy of the TFL-BOARS technique.

关键词： Type II-fuzzy logic uncertainty WSN energy lifetime route selection

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Quantum Compilation Toolkit for Rydberg Atom Arrays with Implications for Problem Hardness and Quantum Speedups

arXiv

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arXiv 2024年

作者： Schuetz, Martin J.A. Andrist, Ruben S. Salton, Grant Yalovetzky, Romina Raymond, Rudy Sun, Yue Acharya, Atithi Chakrabarti, Shouvanik Pistoia, Marco Katzgraber, Helmut G. Amazon Advanced Solutions Lab SeattleWA98170 United States AWS Center for Quantum Computing PasadenaCA91125 United States Global Technology Applied Research JPMorgan Chase New YorkNY10017 United States

We propose and implement a comprehensive quantum compilation toolkit for solving the maximum independent set (MIS) problem on quantum hardware based on Rydberg atom arrays. Our end-to-end pipeline involves three core components to efficiently map generic MIS instances onto Rydberg arrays with unit-disk connectivity, with modules for graph reduction, hardware compatibility checks, and graph embedding. The first module (reducer) provides hardware-agnostic and deterministic reduction logic that iteratively reduces the problem size via lazy clique removals. We find that real-world networks can typically be reduced by orders of magnitude on sub-second time scales, thus significantly cutting down the eventual load for quantum devices. Moreover, we show that reduction techniques may be an important tool in the ongoing search for potential quantum speedups, given their ability to identify hard problem instances. In particular, for Rydberg-native MIS instances, we observe signatures of an easy-hard-easy transition and quantify a critical degree indicating the onset of a hard problem regime. The second module (compatibility checker) implements a hardware compatibility checker that quickly determines whether or not a given input graph may be compatible with the restrictions imposed by Rydberg quantum hardware. The third module (embedder) describes hardware-efficient graph embedding routines to generate (approximate) encodings with controllable overhead and optimized ancilla placements. We exemplify our pipeline with experiments run on the QuEra Aquila device available on Amazon Braket. In aggregate, our work provides a set of tools that extends the class of problems that can be tackled with near-term Rydberg atom arrays. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph embeddings

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Navigating the Newscape: Long Short-Term Memory Models for Misinformation Detection

Navigating the Newscape: Long Short-Term Memory Models for M...

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International Conference on Software Defined Systems (SDS)

作者： Dina Tahat Raghad Alfaisal Khalaf Tahat Said Salloum Applied Sociology Department Al Ain University UAE Faculty of Computing and Meta-Technology Universiti Pendidikan Tanjung Malim Malaysia Media & Creative Industries United Arab Emirates University and Yarmouk University Jordan University of Sharjah Sharjah UAE

ISBN: (数字)9798331518325

ISBN: (纸本)9798331518332

In an era where digital misinformation poses a severe challenge to societal trust, identifying fake news is imperative. This study employs a Long Short-Term Memory (LSTM) neural network to discern misinformation within a dataset of approximately 79,000 labeled news texts. Our LSTM model benefits from its aptitude for processing sequential data, crucial for understanding textual context. We evaluate our model's proficiency through accuracy, precision, recall, and F1 score, key metrics reflecting its classification capability. The model exhibits robust performance with a test accuracy of 94.09%, precision of 91.57%, recall of 95.45%, and an F1 score of 93.47%. These results underscore the potential of LSTM networks in automating fake news detection, which could significantly support the integrity of information consumption in the digital realm.

关键词： Measurement Analytical models Accuracy Navigation Neural networks Software Data models Fake news Long short term memory Context modeling

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Multi-fidelity Bayesian Optimisation of Syngas Fermentation Simulators

arXiv

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arXiv 2023年

作者： Eskandari, Mahdi Puiman, Lars Zeitler, Jakob Division of Computing Engineering and Mathematical Sciences University of Kent Canterbury United Kingdom Faculty of Applied Sciences Department of Biotechnology Delft University of Technology Delft Netherlands Matterhorn Studio Oxford United Kingdom

A Bayesian optimization approach for maximizing the gas conversion rate in an industrial-scale bioreactor for syngas fermentation is presented. We have access to a high-fidelity, computational fluid dynamic (CFD) reactor model and a low-fidelity ideal-mixing-based reactor model. The goal is to maximize the gas conversion rate, with respect to the input variables (e.g., pressure, biomass concentration, gas flow rate). Due to the high cost of the CFD reactor model, a multi-fidelity Bayesian optimization algorithm is adopted to solve the optimization problem using both high and low fidelities. We first describe the problem in the context of syngas fermentation followed by our approach to solving simulator optimization using multiple fidelities. We discuss concerns regarding significant differences in fidelity cost and their impact on fidelity sampling and conclude with a discussion on the integration of real-world fermentation data. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational fluid dynamics

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