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A NONPARAMETRIC ROBUST OPTIMIZATION APPROACH FOR CHANCE-CONSTRAINED KNAPSACK PROBLEM

SIAM Journal on Optimization 2025年第2期35卷 739-766页

作者： Xu, Liang Zhang, Chao Xu, Zhou Long, Daniel Zhuoyu School of Business Administration Southwestern University of Finance and Economics Chengdu China Big Data Laboratory on Financial Security and Behavior SWUEF Laboratory of Philosophy and Social Sciences Ministry of Education Chengdu China School of Medical Informatics Chongqing Medical University Chongqing China Department of Logistics Management and Maritime Studies Hong Kong Polytechnic University Hong Kong Department of System Engineering and Engineering Management Chinese University of Hong Kong Hong Kong

A chance-constrained knapsack problem (CCKP) is a knapsack problem restricted by a chance constraint, which ensures that the total capacity constraint under uncertain volume can be violated only up to a given probability threshold. CCKP is challenging to solve due to its combinatorial nature and the involvement of its chance constraint. Existing solution methods for CCKP with tractability guarantees mainly focus on two approaches: (1) a full-information approach (stochastic programming) that assumes the uncertain volume follows certain distributions, such as normal or empirical distribution;(2) a partial-information approach (robust optimization) that adopts specific statistics of the unknown distribution, such as the mean and variance. The existing full-information approach lacks robustness under limited samples due to its strong assumption;the existing partial-information approach can be further improved, as the uncertainty set or distributional ambiguity set can be ameliorated. With these concerns in mind, we propose a nonparametric robust approach for CCKP by involving a novel nonparametric statistic to form a new distributional ambiguity set. Furthermore, we develop an upper bound on the violation probability of the chance constraint under the distributional ambiguity set to approximate CCKP by a deterministic robust counterpart. In terms of solution methodology, we decompose the deterministic robust counterpart into cardinality-constrained knapsack problems, which can be efficiently solved by the proposed dynamic programming algorithm. Computational results show that our proposed solution methods produce better solutions to CCKP compared with existing approaches. © 2025 Society for Industrial and Applied Mathematics.

关键词： Stochastic programming

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A Comparative Analysis of Artificial Intelligence and Business Intelligence Using Big data Analytics 1

A Comparative Analysis of Artificial Intelligence and Busine...

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1st International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Marella, Bhagath Chandra Chowdari Vegineni, Gopi Chand Addanki, Sireesha Ellahi, Ehsan Arun Kumar, K. Mandal, Rishabh Insights & Data Department of Financial Services Capgemini India Enrollment and Eligibility Team India System soft Technologies LLC India Cleveland State University Department of Information Systems OH United States Tula's Institute Department of Computer Science and Engineering UK Dehradun India India

ISBN: (纸本)9798331518578

Business intelligence (BI) encompasses the tools and uses to gather, combine, examine, and display data about a specific firm. Using historical and current efficiency comparisons, these structures assist in formulating plans and choices to achieve better outcomes in the future. Additionally, it helps firms determine which business issues and industry trends require attention. It has several benefits, such as increasing responsibility and efficiency, but it carries some disadvantages, such as being expensive, complicated, and time-consuming to adopt. Using big data-driven approaches to optimize talent recruitment is essential for improved decisions in the cutthroat world of BI. The efficacy of many machine learning algorithms to increase recruiting performance and accuracy is compared in this investigation. For optimizing, cross-validating, and hyperparameter modification were employed. The Random Forest (RF) algorithm has the greatest accuracy (92.9%), trailed by the Gradient Boosting Classifying system (92.6%) and the neural network (NNs) models (92.7%). These findings imply that sophisticated ML algorithms, in particular RF and NN, can greatly improve BI platforms' hiring procedures. Recruiters can benefit from this research's insightful recommendations for incorporating advanced ML methods into talent recruitment plans. © 2025 IEEE.

关键词： data accuracy

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OHMiner: An Overlap-centric system for Efficient Hypergraph Pattern Mining 25

OHMiner: An Overlap-centric System for Efficient Hypergraph ...

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20th European Conference on Computer systems, EuroSys 2025, co-located 30th ACM International Conference on Architectural Support for Programming Languages and Operating systems, ASPLOS 2025

作者： Qi, Hao Luo, Kang He, Ligang Zhang, Yu Cai, Minzhi Dai, Jingxin He, Bingsheng Jin, Hai Zhang, Zhan Zhao, Jin Yue, Hengshan Yu, Hui Liao, Xiaofei National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computer Science University of Warwick United Kingdom National University of Singapore Singapore Zhejiang Lab China Jilin University China

ISBN: (纸本)9798400711961

Hypergraph Pattern Mining (HPM) aims to identify all the instances of user-interested subhypergraphs (patterns) in hypergraphs, which has been widely used in various applications. However, existing solutions either need significant enumeration overhead because they extend subhypergraphs at the granularity of vertices, or suffer from massive redundant computations because they often need to repeatedly fetch and process the same incident hyperedges for different vertices. This paper presents an overlap-centric system named OHMiner to efficiently support HPM. OHMiner proposes an overlap-centric execution model to determine the subhypergraphs isomorphism through computing and comparing overlaps among hyperedges using set operations. This model aims to efficiently handle the vertices that collectively share the same incident hyperedges. To automatically and precisely retrieve an arbitrary pattern’s overlapping semantics without performing redundant set computations, OHMiner further proposes a redundancy-free compiler, which constructs an Overlap Intersection Graph (OIG) for the pattern, optimizes the OIG, and generates an overlap-centric execution plan to guide the procedure of HPM. Moreover, OHMiner designs an overlap-centric parallel execution engine, which adopts an incremental overlap-pruned approach to fast validate candidates for HPM. Additionally, it proposes a degree-aware data store to support efficient generation of candidates. Through evaluating OHMiner on a broad range of real-world hypergraphs with various patterns, our experimental results show that OHMiner outperforms the state-of-the-art HPM system by 5.4×-22.2×. © 2025 Copyright held by the owner/author(s).

关键词： Compiler

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Constructing a Biomass-data Center Nexus for Circular Economy-Based Energy systems Integration

IEEE Open Access Journal of Power and Energy

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IEEE Open Access Journal of Power and Energy 2025年 12卷 270-283页

作者： Wang, Pengya Pan, Li He, Guannan Li, Gengyin Song, Jie Zhou, Ming Wang, Jianxiao State Grid Dezhou Power Supply Company Shandong Dezhou253000 China The Chinese University of Hon g Kong School of Science and Engineering Guangdong Shenzhen518000 China Peking University College of Engineering Department of Industrial Engineering and Management Beijing100871 China North China Electric Power University State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources Beijing102206 China Peking University National Engineering Laboratory for Big Data Analysis and Applications Beijing100871 China

The dual challenges of energy crises and waste management have spurred interest in a circular economy, where biomass, valued for its carbon neutrality, is crucial. Remote areas, rich in biomass, are also becoming hubs for data center (DC) construction. Despite DCs’ low energy efficiency and production of low-grade waste heat, recovering this heat offers a promising path toward a circular economy. There are few systematic studies on the relationships among DC energy supply, waste heat generation, and renewable energy resources. In this study, we formulate day-ahead, real-time optimal scheduling strategies to provide electricity, heat, and gas using a complementary photovoltaic-biomass system. The biogas production process requires heat to promote the anaerobic reaction, and the adaptation temperature of anaerobic bacteria matches the low-quality waste heat generated from of the DC. We realize a circular economy by coupling a DC and a renewable energy system by directly using the waste heat from the DC and by using the queuing theory of the DC’s delay tolerance workload for partial load response. Moreover, the nonlinearity in the process is linearized by the least squares method. Actual data calculations show that the introduction of biogas can result in a fully renewable energy supply. Based on specific configurations—including a 350 kW PV system, 450 kWh BSS, 250 kW CHP unit, and an AD operating at 20°C to 45°C—the system is tested under 0% and 50% carbon emission scenarios. The DC workload combines delay-tolerant and delay-sensitive tasks, enabling flexible scheduling for energy and heat optimization, the power usage effectiveness (PUE) of the DC decreases from 1.73 to 1.24, operating expenses decrease by 36.06%, and system energy consumption decreases by 44.6%. © 2020 IEEE.

关键词： Waste heat utilization

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Performance Investigation of a Water-Based Photovoltaic Collector system: Velocity and PV Temperature Impact 4th

Performance Investigation of a Water-Based Photovoltaic Coll...

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4th International Conference on Electronic engineering and Renewable Energy systems, ICEERE 2024

作者： El Manssouri, Oussama Abdulmajeed, Omayma M. Hajji, Bekkay Silva, João Hajji, Mohamed Teixeira, José Teixeira, Senhorinha Renewable Energy Embedded System and Data Processing Laboratory National School of Applied Sciences Mohamed First University BP. 669 Oujda60000 Morocco Department of Electromechanical Engineering University of Technology-Iraq Baghdad10066 Iraq University of Minho Guimarães Portugal

ISBN: (纸本)9789819799749

Photovoltaic-Thermal (PV-T) systems are used to optimize the performance of PV modules in addition to the utilization of the heating fluids for various usages. In the present work, a PV-T system is designed with a serpentine-shaped tube to cool the PV module with water. 3D steady-state analysis was developed, and numerical simulation was performed using ANSYS-Fluent 2020-R2 software. The impact of passing water with different velocities under the PV module is studied. Moreover, the collected results showed that 0.5–0.4 m/s are the optimal values that achieve performance improvement. Finally, the effect of the length of the collector on the electrical efficiency as a design parameter was investigated in this paper. Other parameters can be studied as future contributions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Solar energy

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Learning to Price with Resource Constraints: From Full Information to Machine-Learned Prices

arXiv

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arXiv 2025年

作者： Ao, Ruicheng Jiang, Jiashuo Simchi-Levi, David Institute of Data System and Society Massachusetts Institute of Technology United States Department of Industrial Engineering and Decision Analytics Hong Kong University of Science and Technology Hong Kong

We study the dynamic pricing problem with knapsack, addressing the challenge of balancing exploration and exploitation under resource constraints. We introduce three algorithms tailored to different informational settings: a Boundary Attracted Re-solve Method for full information, an online learning algorithm for scenarios with no prior information, and an estimate-then-select re-solve algorithm that leverages machine-learned informed prices with known upper bound of estimation errors. The Boundary Attracted Re-solve Method achieves logarithmic regret without requiring the non-degeneracy condition, while the online learning algorithm attains an optimal O(√T) regret. Our estimate-then-select approach bridges the gap between these settings, providing improved regret bounds when reliable offline data is available. Numerical experiments validate the effectiveness and robustness of our algorithms across various scenarios. This work advances the understanding of online resource allocation and dynamic pricing, offering practical solutions adaptable to different informational structures. © 2025, CC BY-NC-ND.

关键词： Adversarial machine learning

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A Machine Learning Model for Foreign Exchange Prediction 2nd

A Machine Learning Model for Foreign Exchange Prediction

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： San, Tan Jun Ajibade, Samuel-Soma M. Ajibade, Temiloluwa Iyanuoluwa Jasser, Muhammed Basheer Ayodele, Olamide Emmanuel Oyewopo, Abdulgafar Olorunleke Adediran, Anthonia Oluwatosin Majeed, Anwar P. P. Abdul Luo, Yang Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Petaling Jaya Malaysia Department of Computer Engineering Istanbul Ticaret University Istanbul Turkey Department of Business Management Scholar School System Birmingham United Kingdom School of Engineering and Technology Sunway University Selangor Darul Ehsan Petaling Jaya47500 Malaysia Department of Banking and Finance Ekiti State University Ekiti State Ado Ekiti Nigeria Department of Business Administration Kwara State University Malete Nigeria Department of Real Estate Faculty of Built Environment Universiti Malaya Kuala Lumpur Malaysia Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

The rise of deep learning algorithms has sparked interest in the financial domain. The foreign exchange market (FOREX) is the globally leading financial market with millions of active stakeholders. Research in deep learning for forecasting price movements has shown its efficacy in financial markets. Notably, LSTM have empirically proven the best performance among other machine learning techniques. However, studies have shown the lack of deep learning hybrid comparisons to fortify empirical claims, especially in the FX market characterized for its high volatility and variance among currency pairs. This study develops a generalized LSTM model to forecast the major pairs and conducts a comparative analysis with a secondary hybrid deep learning model to assess and solidify the robustness of deep learning methods in forex forecasting. The empirical scenario simulates a classification problem employing cross-validation techniques to optimize the models’ generalization rigorously. Evaluation of the model utilized the accuracy, precision, recall, and f1-score metrics. Results indicated the performance of the models from best to worst were LSTM, SRNN and followed by CNN indicating the importance of testing deep learning approaches in different empirical setups. Conclusively, this study highlights the need challenges in forex forecasting and raises the need for more robust assessments on machine learning models forecasting capabilities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Small Signal Stability-Guaranteed Moving Target Defense Against Load Redistribution Attack On IoT-based Smart Grid

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IEEE Internet of Things Journal 2025年第11期12卷 17413-17429页

作者： Wang, Bingdong Zhang, Zhenyong Wang, Mufeng Liu, Mengxiang Ye, Rui Deng, Ruilong Zhang, Xin Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China China Industrial Control Systems Cyber Emergency Response Team Beijing100040 China University of Sheffield Department of Automatic Control and System Engineering Sheffield United Kingdom Guizhou Power Grid Co. Ltd. Intelligent Operation Center China Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China Huzhou Institute of Industrial Control Technology Huzhou313000 China

Moving target defense (MTD) is a promising approach to defend against load redistribution attacks on the internet-of-things (IoT)-based smart grid networks by probing the distorted state estimates with the distributed flexible AC transmission system. However, existing studies mainly focus on optimizing the performance of MTD and ignore the safety effect of it on the system’s operation. In this paper, we fill this gap by deeply analyzing the effect of MTD on the small signal stability and aim to alleviate the negative impact and guarantee its defending performance simultaneously. Firstly, the stability is formally described using the eigenvalue sensitivity. The relationship between the MTD-induced perturbation (MTDper) and the stability criteria is derived. Secondly, a new indicator is proposed to measure the effectiveness of MTDper. Thirdly, a constrained optimization problem is formulated to compute the bound of MTDper for guaranteeing the small signal stability. In addition, a surprising finding is that the stability margin can be improved and enhanced by optimizing the value of MTDper without losing the MTD’s effectiveness. Finally, we evaluate the performance of MTDper and its impact on the small signal stability with extensive simulations on the IEEE 30-bus, 39-bus, and 68-bus test power systems. © 2014 IEEE.

关键词： Constrained optimization

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ACGraph: Accelerating Streaming Graph Processing via Dependence Hierarchy 23

ACGraph: Accelerating Streaming Graph Processing via Depende...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Zihan Jiang Fubing Mao Yapu Guo Xu Liu Haikun Liu Xiaofei Liao Hai Jin Wei Zhang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Electronic and Computer Engineering HKUST Hong Kong

ISBN: (纸本)9798350323481

Streaming graph processing needs to timely evaluate continuous queries. Prior systems suffer from massive redundant computations due to the irregular order of processing vertices influenced by updates. To address this issue, we propose ACGraph, a novel streaming graph processing approach for monotonic graph algorithms. It maintains dependence trees during runtime, and makes affected vertices processed in a top-to-bottom order in the hierarchy of the dependence trees, thus normalizing the state propagation order and coalescing of multiple propagation to the same vertices. Experimental results show that ACGraph reduces the number of updates by 50% on average, and achieves the speedup of 1.75~7.43× over state-of-the-art systems.

关键词： graph processing

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Automatic Repeat Request Design in AoI-Aware Broadcast With Heterogeneous Direct and Relay-Assisted Users

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IEEE Internet of Things Journal 2025年

作者： Wang, Xueer Pan, Haoyuan Chan, Tse-Tin Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering Shenzhen China The Education University of Hong Kong Department of Mathematics and Information Technology Hong Kong Shenzhen University National Engineering Laboratory for Big Data System Computing Technology The College of Computer Science and Software Engineering Shenzhen China

This paper investigates the design of automatic repeat request (ARQ) protocols in age of information (AoI)-aware broadcast systems with heterogeneous users, including both direct and relay-assisted users. In this setup, a direct user receives status updates directly via a single-hop link, while a relay-assisted user can receive status updates through either a direct single-hop link or a two-hop relay-assisted link. While ARQ is commonly used to ensure reliable transmission in error-prone wireless networks, previous studies suggest that ARQ does not improve the average AoI in single-hop networks. However, its impact on AoI in systems with relays, particularly those involving heterogeneous users, remains unclear. We address this gap by analyzing the average AoI under different ARQ strategies, introducing a transmission limit k ≥ 0 at the relay. Here, k = 0, k = 1, and k > 1 correspond to non-relay, non-ARQ-at-relay, and truncated-ARQ-at-relay strategies, respectively. Utilizing a unified Markov chain framework that models the transmission processes for each user type, we derive the theoretical average AoI. Our results show that the direct user benefits most from non-relay and non-ARQ strategies, similar to single-hop systems. In contrast, the relay-assisted user achieves optimal performance with truncated-ARQ-at-relay, leveraging both direct and relay-assisted links. For the overall system, applying a non-ARQ-at-relay approach strikes a balance in AoI between the direct and relay-assisted users, leading to a more stable and lower system-wide average AoI. © 2014 IEEE.

关键词： Mobile telecommunication systems

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