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12th International Conference on Learning Representations, ICLR 2024

作者： Dong, Xiangyu Zhang, Xingyi Wang, Sibo Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong

Graph-level anomaly detection has gained significant attention as it finds applications in various domains, such as cancer diagnosis and enzyme prediction. However, existing methods fail to capture the spectral properties of graph anomalies, resulting in unexplainable framework design and unsatisfying performance. In this paper, we re-investigate the spectral differences between anomalous and normal graphs. Our main observation shows a significant disparity in the accumulated spectral energy between these two classes. Moreover, we prove that the accumulated spectral energy of the graph signal can be represented by its Rayleigh Quotient, indicating that the Rayleigh Quotient is a driving factor behind the anomalous properties of graphs. Motivated by this, we propose Rayleigh Quotient Graph Neural Network (RQGNN), the first spectral GNN that explores the inherent spectral features of anomalous graphs for graph-level anomaly detection. Specifically, we introduce a novel framework with two components: the Rayleigh Quotient learning component (RQL) and Chebyshev Wavelet GNN with RQ-pooling (CWGNN-RQ). RQL explicitly captures the Rayleigh Quotient of graphs and CWGNN-RQ implicitly explores the spectral space of graphs. Extensive experiments on 10 real-world datasets show that RQGNN outperforms the best rival by 6.74% in Macro-F1 score and 1.44% in AUC, demonstrating the effectiveness of our framework. Our code is available at https://***/xydong127/RQGNN. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Anomaly detection

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LLMEdgeRefine: Enhancing Text Clustering with LLM-Based Boundary Point Refinement

LLMEdgeRefine: Enhancing Text Clustering with LLM-Based Boun...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Feng, Zijin Lin, Luyang Wang, Lingzhi Cheng, Hong Wong, Kam-Fai Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9798891761643

Text clustering is a fundamental task in natural language processing with numerous ***, traditional clustering methods often struggle with domain-specific fine-tuning and the presence of *** address these challenges, we introduce LLMEdgeRefine, an iterative clustering method enhanced by large language models (LLMs), focusing on edge points *** enhances currrent clustering methods by creating super-points to mitigate outliers and iteratively refining clusters using LLMs for improved semantic *** method demonstrates superior performance across multiple datasets, outperforming state-of-the-art techniques, and offering robustness, adaptability, and cost-efficiency for diverse text clustering applications. © 2024 Association for Computational Linguistics.

关键词： Semantics

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An Intellectual Capital Risk Measurement Model Using Bayesian Network

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Journal of the Knowledge Economy 2025年 1-33页

作者： Shabankhah, Sanaz Afrazeh, Mohammadhossein Afrazeh, Abbas Esfahanipour, Akbar Department of Industrial Engineering and Management Systems Amirkabir University of Technology Tehran Iran Department of Industrial Engineering University of Tehran Tehran Iran

This research introduces a hybrid problem-solving method to enhance the understanding and management of intellectual capital risks in organizations. It aims to assess the current situation, identify the underlying causes, and propose suitable strategies. The study offers a fresh perspective on measuring and exploring intellectual capital risks using the Bayesian network methodology, consisting of four comprehensive steps. First risks are identified and classified based on relevant literature, and hypothesizing that person, organization, and environment are key root causes, using a validated questionnaire. Second, a Bayesian network model is constructed to evaluate the probability of risks occurring across different situations. Third, data analysis techniques are used to prioritize risks and identify internal strengths and weaknesses, as well as external opportunities and threats. Finally, a strategic planning method is proposed, including three stages of input, matching, and decision making. Through the application of this methodology to a case study, all risk factors within the organization are identified and the probability of intellectual capital risk in various conditions is determined. The results highlighted the significant impact of human risks in the case studied. Based on the strategic formulation method, the study provides effective strategies for mitigating intellectual capital risks, prioritizing and proposing the best approach, which includes enhancing the company's knowledge through training programs, attracting educated individuals, and establishing connections with successful organizations. This method enables organizations to proactively monitor and manage intellectual capital risks, enhancing their competitive advantage in the market. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Bayesian network Intellectual capital risks Organizational strategy Risk management

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Auto-tuning PVT data using multi-objective optimization:Application of NSGA-II algorithm

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Petroleum 2024年第1期10卷 135-149页

作者： Abdolhadi Zarifi Mohammad Madani Mohammad Jafarzadegan Reservoir Engineering Systems Petroleum Engineering DepartmentMain Office Building of National Iranian South Oil Company(NISOC)AhvazIran Reservoir Engineering Division Petroleum Engineering DepartmentPetroleum Engineering and Development Company(PEDEC)TehranIran Combined Planning Management National Iranian Oil Company(NIOC)TehranIran

Reservoir simulation is known as perhaps the most widely used,accurate,and reliable method for field development in the petroleum *** integral part of a reliable reservoir simulation process is to consider robust and rigorous tuned EOS ***,EOS models are tuned iteratively through arduous workflows against experimental PVT ***,this comes with a number of drawbacks such as forcingly using weight factors,which upon alteration adversely affects the optimization *** objective of the current work is thus to introduce an auto-tune PVT matching tool using NSGA-II multi-objective *** order to illustrate the robustness of the presented technique,three different PVT samples are used,including two black-oil and one gas condensate *** utilize PengRobinson EOS during all the manual and auto-tuning *** of auto-tuned EOS-generated results with those of experimental and computed statistical error values for these samples clearly show that the proposed method is *** addition,the proposed method,contrary to the manual matching process,provides the engineer with several matched solutions,which allows them to select a match based on the engineering background to be best amenable to the problem at *** addition,the proposed technique is fast,and can output several solutions within less time compared to the traditional manual matching method.

关键词： Auto-tuning PVT Equation of state NSGA-II Multi-objective optimization

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System dynamics costing model for the capital cost estimation of electric vehicle batteries refurbishing facility

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International Journal of Industrial and systems engineering 2024年第3期48卷 377-404页

作者： Kalwar, Muhammad Ahmed Wassan, Asif Nawaz Sheikh, Waleed Ahmed Khan, Muhammad Ali Mari, Hussain Bux Industrial Engineering and Management Faculty of Mechanical Process and Earth Engineering Mehran University of Engineering and Technology Jamshoro Sindh76062 Pakistan Department of Mechanical and Energy Systems Engineering University of Bradford United Kingdom Khairpur Mirs Sindh Pakistan

Electric vehicles (EVs) are considered sustainable vehicles due to low carbon emissions. Lithium-ion batteries (LIBs) are expensive electrical vehicle batteries (EVBs) due to their expensive rare raw materials, i.e., lithium, nickel and cobalt. With increased production of EVs, authors warned of the expected huge quantity of dismantled EVBs shortly and so explored their reusability. An in-depth review is conducted, and system dynamics (SD) model was developed in Anylogic whereas origin was used for graphs. The capital costs dynamics of the refurbishing facility were evaluated and the model was validated and initialised. The analysis was conducted in three sales scenarios:1) locally;2) regionally;3) nationally. This paper provides a framework for entrepreneurs. The results indicated scenario-2 as more suitable with a maximum return on investment (ROI) (80.26%) and minimum payback period (626 weeks). Therefore, it is concluded that it would be feasible for the refurbishing facility to operate regionally rather than locally/nationally. © 2024 Inderscience Enterprises Ltd.

关键词： batteries capital cost carbon carbon emissions electric vehicle batteries electric vehicles environment EVs infrastructural cost LIBs lithium-ion batteries recycling refurbished EVBs refurbishment retired EVs sustainability system dynamics

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Enhancing connectivity and coverage in wireless sensor networks: a hybrid comprehensive learning-Fick’s algorithm with particle swarm optimization for router node placement

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Neural Computing and Applications 2024年第34期36卷 21671-21702页

作者： Amer, Dina A. Soliman, Sarah A. Hassan, Asmaa F. Zamel, Amr A. Computer Science Department Higher Technological Institute Tenth of Ramadan44228 Egypt Management Information Systems Department Faculty of Management Economics and Business Technology Egyptian Russian University Badr11829 Egypt Computer and Systems Engineering Department Faculty of Engineering Zagazig University Zagazig44519 Egypt

Wireless Sensor Networks (WSNs) are essential for collecting and transmitting data in modern applications that rely on data, where effective network connectivity and coverage are crucial. The optimal placement of router nodes within WSNs is a fundamental challenge that significantly impacts network performance and reliability. Researchers have explored various approaches using metaheuristic algorithms to address these challenges and optimize WSN performance. This paper introduces a new hybrid algorithm, CFL-PSO, based on combining an enhanced Fick’s Law algorithm with comprehensive learning and Particle Swarm Optimization (PSO). CFL-PSO exploits the strengths of these techniques to strike a balance between network connectivity and coverage, ultimately enhancing the overall performance of WSNs. We evaluate the performance of CFL-PSO by benchmarking it against nine established algorithms, including the conventional Fick’s law algorithm (FLA), Sine Cosine Algorithm (SCA), Multi-Verse Optimizer (MVO), Salp Swarm Optimization (SSO), War Strategy Optimization (WSO), Harris Hawk Optimization (HHO), African Vultures Optimization Algorithm (AVOA), Capuchin Search Algorithm (CapSA), Tunicate Swarm Algorithm (TSA), and PSO. The algorithm’s performance is extensively evaluated using 23 benchmark functions to assess its effectiveness in handling various optimization scenarios. Additionally, its performance on WSN router node placement is compared against the other methods, demonstrating its competitiveness in achieving optimal solutions. These analyses reveal that CFL-PSO outperforms the other algorithms in terms of network connectivity, client coverage, and convergence speed. To further validate CFL-PSO’s effectiveness, experimental studies were conducted using different numbers of clients, routers, deployment areas, and transmission ranges. The findings affirm the effectiveness of CFL-PSO as it consistently delivers favorable optimization results when compared to existing meth

关键词： Particle swarm optimization (PSO)

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Multivariate modelling of AA6082-T6 drilling performance using RSM, ANN and response optimization

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International Journal of Lightweight Materials and Manufacture 2024年第4期7卷 531-545页

作者： Tzotzis, Anastasios Antoniadis, Aristomenis Kyratsis, Panagiotis Department of Product and Systems Design Engineering University of Western Macedonia Kila Kozani50100 Greece Department of Production Engineering and Management Technical University of Crete Kounoupidiana Chania73100 Greece

The AA6082-T6 was experimentally studied in the present research with respect to the drilling performance. Drill diameter, cutting speed and feed rate were examined, using a full factorial design. Mathematical modelling of the process was carried out using the Response Surface Methodology (RSM) as well as the Artificial Neural Network (ANN) techniques. The output results in terms of cutting force, torque and surface roughness, revealed high levels of correlation between the experimental and the predicted data. Specifically, the Mean Absolute Percentage Error (MAPE) values using RSM compared to the ones of the experiments, were equal to 2.14%, 3.49% and 6.16% for Fz, Mz and Ra respectively. The equivalent MAPE between the ANN and the experiments were found to be 2.19%, 1.82% and 2.85% accordingly. Moreover, the most significant terms were revealed, being the interaction D × f for the thrust force and the torque with contribution percentages equal to approximately 44% and 42% respectively, and the term D2 for the surface roughness with 51%. The evaluation of the machining parameters, identified their significance, enabling the selection of the optimal cutting parameters, which were obtained by the desirability function, taking into account the importance of the generated surface quality and the reduction of cost. The solutions given by this approach, pointed out the Ø9 tool, coupled with Vc = 50 m/min and f = 0.15mm/rev as a well-balanced combination, whereas the Ø9.9 tool used under the same conditions, yielded the best possible surface quality (appr. 0.2 μm). © 2024 The Authors

关键词： Neural networks

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Texture development and surface reconstruction of BiVO_(4)photoanode via one-pot hydrothermal reaction for enhanced photoelectrochemical water splitting

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Journal of Advanced Ceramics 2025年第3期14卷 133-141页

作者： Sung Won Hwang Yoo Jae Jeong Runfa Tan Indhujasri Saravanan Hyun Soo Han Dong Hoe Kim In Sun Cho Department of Energy Systems Research Ajou UniversitySuwon 16499Republic of Korea Department of Material Science&Engineering Ajou UniversitySuwon 16499Republic of Korea Department of Mechanical Engineering Stanford UniversityStanford 94305USA Department of Material Science&Engineering Korea UniversitySeoul 02841Republic of Korea Materials Science and Engineering Program University of Colorado BoulderBoulder 80303USA

The simultaneous optimization of the bulk and surface characteristics of photoelectrodes is essential to maximize their photoelectrochemical(PEC)*** report a novel one-pot hydrothermal synthesis of textured and surface-reconstructed BiVO_(4)photoanodes(ts-BVO),achieving significant improvements in PEC water *** controlling precursor molarity and ethylene glycol(EG)addition,we developed a stepwise dual reaction(SDR)mechanism,which enables simultaneous bulk texture development and surface *** optimized CoBi/ts-BVO photoanode exhibited a photocurrent density of 4.3 mA∙cm^(−2)at 1.23 V *** hydrogen electrode(RHE)with a high Faradaic efficiency of 98%under one sun *** with nontextured BiVO_(4),the charge transport efficiency increased from 8%to 70%,whereas the surface charge transfer efficiency improved from 9%to 85%.These results underscore the critical role of both bulk and surface engineering in enhancing PEC *** findings offer a streamlined approach for improving the intrinsic properties of photoanodes in solar water splitting.

关键词： BiVO_(4) hydrothermal texture surface reconstruction ethylene glycol(EG) photoelectrochemical water splitting

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A Framework for Designing Zonal Architectures for In-Vehicle Networks: Balancing Communication Load and Wiring Length

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 7940-7952页

作者： Lim, Jongwook Lee, Jongheon Hong, Yoo Suk Kang, Changmuk Seoul National University Department of Industrial Engineering Seoul08826 Korea Republic of Incheon National University Department of Industrial and Management Engineering Incheon22012 Korea Republic of Soongsil University Department of Industrial and Information Systems Engineering Seoul06978 Korea Republic of

The electrical and electronic (E/E) architectures are a growing concern in the automotive industry, driven by the increasing prevalence of software-oriented features and technologies. As system complexity rises, many automotive companies are exploring new architectural strategies to manage this complexity effectively. In this research, we focused on the zonal E/E architectures as a future solution and proposed a framework for their design. The framework integrates the software and physical components that form the zonal architectures and analyzes the decisions that map the relationships between these elements. Its goal is to create zonal architectures that balance key performance metrics, such as communication load and wiring length. To achieve this, we formulated the zonal architecting problem (ZAP) based on the proposed framework and developed a column generation method to solve it. The framework and its mathematical model were applied to a use case involving advanced driver assistance systems (ADAS), demonstrating that our approach significantly improves both communication load and wiring length, thereby validating the effectiveness of the proposed framework. © 1967-2012 IEEE.

关键词： Automotive industry

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Analysis of construction supply chain critical success factors: a multi-criteria decision-making approach

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International Journal of Industrial and systems engineering 2023年第4期45卷 545-561页

作者： Kher Sarayji, Kenan M.H.D. Khan, Sharfuddin Ahmed Mubarik, Muhammad Shujaat Industrial Engineering and Engineering Management Department College of Engineering University of Sharjah Sharjah United Arab Emirates Industrial Systems Engineering University of Regina ReginaSK Canada Karachi Pakistan

This study analyses the critical success factors (CSFs) of construction supply chain management (CSCM) using a mix-method approach to identify and prioritise the CSFs for CSCM performance. An extensive literature review was conducted to identify the potential CSFs and with the help of experts, 21 relevant CSFs were selected. The analytical hierarchy process (AHP) was employed to prioritise the selected CSFs. Results showed that the top management commitment, information sharing and flow and supply chain finance are highly ranked among all the factors. Research findings will help to minimise resource wastage on less essential things and increase efficiency. This research is unique from several perspectives. Firstly, this study is one of the first studies, which identify CSF of construction supply chain in the context of UAE. Secondly, this study provides several managerial and practical implications to managers to minimise resource wastage on less essential things and increase productivity and efficiency. Copy;2023 Inderscience Enterprises Ltd. © 2023 Inderscience Enterprises Ltd.. All rights reserved.

关键词： Efficiency

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