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arXiv 2025年

作者： Zhang, Yuwei Jin, Zhi Xing, Ying Li, Ge Liu, Fang Zhu, Jiaxin Dou, Wensheng Wei, Jun Affiliated with Nanjing Institute of Software Technology University of Chinese Academy of Sciences Nanjing China Key Laboratory of System Software Chinese Academy of Sciences Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Key Laboratory of High Confidence Software Technologies Peking University Ministry of Education School of Computer Science Peking University Beijing China School of Computer Science Wuhan University Wuhan China School of Intelligent Engineering and Automation Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Complex & Critical Software Environment School of Computer Science and Engineering Beihang University Beijing China

Bug fixing holds significant importance in software development and maintenance. Recent research has made substantial strides in exploring the potential of large language models (LLMs) for automatically resolving software bugs. However, a noticeable gap in existing approaches lies in the oversight of collaborative facets intrinsic to bug resolution, treating the process as a single-stage endeavor. Moreover, most approaches solely take the buggy code snippet as input for LLMs during the patch generation stage. To mitigate the aforementioned limitations, we introduce a novel stage-wise framework named PATCH. Specifically, we first augment the buggy code snippet with corresponding dependence context and intent information to better guide LLMs in generating the correct candidate patches. Additionally, by taking inspiration from bug management practices, we decompose the bug-fixing task into four distinct stages: bug reporting, bug diagnosis, patch generation, and patch verification. These stages are performed interactively by LLMs, aiming to simulate the collaborative behavior of programmers during the resolution of software bugs. By harnessing these collective contributions, PATCH effectively enhances the bug-fixing capability of LLMs. We implement PATCH by employing the powerful dialogue-based LLM ChatGPT. Our evaluation on the widely used bug-fixing benchmark BFP demonstrates that PATCH has achieved better performance than state-of-the-art LLMs. Copyright © 2025, The Authors. All rights reserved.

关键词： computer software maintenance

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Linear Distribution system State Estimation with Synchrophasor Measurements and Voltage-Dependent Load Model

Linear Distribution System State Estimation with Synchrophas...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Behzad Zargar Mohsen Ferdowsi Jinshun Su Yifu Li Payman Dehghanian Richard Hibberts-Caswell Electrical System Studies Department Fichtner GmbH & Co. KG Stuttgart Germany Siemens Grid Software Nurnberg Germany Department of Electrical & Computer Engineering The George Washington University Washington DC USA

ISBN: (数字)9798331541125

ISBN: (纸本)9798331541132

Distribution system state estimation (DSSE) is a critical enabler for distribution system operators (DSOs) to manage active distribution grids efficiently. Timely detection of state changes requires fast-executing state estimation techniques, yet the prevalent nonlinear weighted least squares approach, though effective, presents computational challenges. Linearizing inherent nonlinearities is a way to expedite DSSE solutions. This paper proposes a linear DSSE method that leverages synchrophasor measurements and a linear voltage-dependent load model for pseudo-measurements. This approach provides a more accurate representation of load response to voltage variations, yielding more realistic estimation results than methods using conventional constant active and reactive load models.

关键词： Voltage measurement Accuracy Power measurement Sensitivity Power distribution Phasor measurement units Real-time systems Loss measurement State estimation Load modeling

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Multi-Timescale Optimal Operation Framework for Integrated Economic and Reliability Analysis of Hybrid Power Plants

Multi-Timescale Optimal Operation Framework for Integrated E...

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IEEE General Meeting Power& Energy Society

作者： Yuxin Deng Xin Fang Ningchao Gao Jin Tan Department of Electrical & Computer Engineering Mississippi State University Starkville MS USA Power System Engineering Center National Renewable Energy Laboratory Golden CO USA

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

This paper introduces a hierarchical modeling framework for hybrid power plants (HPP) to facilitate the operation of HPP in power systems similar to conventional generators (Congens) in the integrated multi-timescale optimal operation framework. To consider the uncertainties of HPP renewable power in the day-ahead scheduling, distributionally robust optimization (DRO) is used. To ensure that the state-of-charge (SOC) of energy storage systems in HPPs aligns closely with the planned value for long-term reliability, real-time SOC management is incorporated. In addition, an adjustable real-time control is designed for the robust delivery of HPP real-time services. Case studies performed on a revised IEEE 39-bus system demonstrate the effectiveness of the proposed framework for HPP operation. Simulation results highlight that the proposed framework not only can help operators schedule HPP similar to Congens in varying weather conditions but can also maintain the frequency reliability of the system.

关键词： Economics Uncertainty Real-time systems Hybrid power systems Stability analysis Power system reliability Reliability Power generation Meteorology Load modeling

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Lax-net: Freezing of Gait Detection in Parkinson's Disease Using LSTM with Attention and XGBoost

Lax-net: Freezing of Gait Detection in Parkinson's Disease U...

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Decision Aid Sciences and Application (DASA), International Conference on

作者： Bouslah Ayoub Khaoucha Aicha Taleb Nora Messaoud Bouthaina Computer Science Department LRI Laboratory Badji Mokhtar University Annaba Algeria Computer Science Department Complex System Engineering Laboratory (LISCO) Badji Mokhtar University Annaba Algeria

Parkinson's disease (PD) is a neurodegenerative disorder marked by motor symptoms like gait freezing (FoG), which notably affects patients' mobility and overall quality of life. Early detection of FOG episodes is crucial for timely intervention and reducing the risk of falls and injuries. In this paper, we present Lax-net, a deep learning-based approach for the early detection of FOG using wearable sensor data with time series information. These data often exhibit complex temporal dependencies, making it challenging to extract meaningful features and accurately classify them. To address this challenge, our proposed method combines the strengths of LSTM with attention mechanisms to capture temporal relationships and identify important features in the data. The derived features are then fed into the XGBoost model, a robust classifier with the ability to grasp non-linear relationships. We evaluate the performance of our proposed method on a benchmark dataset, comparing it with other state-of-the-art methods. The results demonstrate that our combined approach achieves superior accuracy and outperforms individual models such as LSTM and XGBoost.

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Identify the potential pathways and candidate biomarkers of stroke associated with bipolar disorder: Bioinformatics and system biology approach

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Journal of Proteins and Proteomics 2025年第2期16卷 141-159页

作者： Hosen, Md. Faruk Basar, Md. Abul Yasmin, Mst. Farjana Hasan, Md. Rakibul Uddin, Muhammad Shahin Department of Information and Communication Technology (ICT) Mawlana Bhashani Science and Technology University (MBSTU) Tangail Bangladesh Department of Computing and Information System (CIS) Daffodil International University (DIU) Dhaka Bangladesh Department of Computer Science and Engineering (CSE) Uttara University Dhaka Bangladesh Department of Software Engineering (SWE) Daffodil International University (DIU) Dhaka Bangladesh

The field of computational bioinformatics and systems biology analysis is rapidly expanding due to advancements in bioinformatics tools. Bipolar disorder (BD) is a severe psychiatric condition affecting both adults and adolescents. In recent years, the risk of stroke has increased among individuals with BD. Background studies indicate that BD and stroke share numerous biochemical and genetic characteristics. Due to traditional endocrinological approaches, no collaborative research exists, and no treatment options have been established for BD and stroke patients. The aim of this study is to identify common molecular pathways and potential therapeutic targets in BD and stroke that could be utilized to predict disease progression. To achieve this, shared genes were identified to determine common pathways. Based on the biochemical, molecular, and genetic interactions among these shared genes, this study identifies the most significant hub genes. To further explore these interactions, analysis such as the protein–protein interaction (PPI) network, topological properties, enrichment analysis, co-expression network, gene regulatory network (GRN), and physical interaction network are conducted. The GO annotation indicates that most of the genes are linked to MAP kinase activity and tumor necrosis factor receptor superfamily binding. The KEGG pathway analysis revealed that the predominant genes are associated with apoptosis and the IL-18 signaling pathway. This comparison helps understand the biochemical and genetic characteristics shared between BD and stroke. Predictive drug analysis has identified consistent potential biomarkers associated with both conditions, providing a scientific foundation for investigating their diagnosis, treatment, and prognosis. Finally, chemical experiments could be conducted to further validate the efficacy of these drugs.

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Navigating the Future: Artificial Intelligence and Sustainable Transportation in Morocco 9th

Navigating the Future: Artificial Intelligence and Sustainab...

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9th International Conference on Smart City Applications, SCA 2024

作者： Chegri, B.E. Rhajbal, Z. El Mettiti, A. Berhich, A. Saadane, R. Charif, M. Chehri, A. Mohammed V University in Rabat Rabat Morocco LRIT-GSCM Faculty of Sciences University Mohamed V Rabat Rabat Morocco AMIPS Research Team Computer Science Department EMI Mohammed V University in Rabat Rabat Morocco Laboratory Engineering System SIRC/LAGeS EHTP Casablanca Morocco Department of Mathematics and Computer Science Royal Military College Kingston Canada

ISBN: (纸本)9783031886522

The integration of Artificial Intelligence (AI) into sustainable mobility represents a transformative paradigm for transportation systems, particularly within the unique socioeconomic landscape of Morocco. This study delves into the dynamic intersection of AI and sustainable mobility, examining its implications for enhancing efficiency, reducing environmental impact, and improving overall accessibility in Moroccan transportation. Against the backdrop of Morocco’s commitment to sustainable development, the research navigates through the intricate web of challenges and opportunities posed by the fusion of AI technologies and mobility solutions. Through a comprehensive literature review, case studies, and recommendations, this article provides a roadmap for policymakers, researchers, and stakeholders to harness the potential of AI in shaping a more resilient and technologically advanced transportation infrastructure. With a focus on both global best practices and Morocco’s specific needs, the study not only underscores the benefits of AI integration but also addresses ethical considerations and potential socio-cultural implications. As Morocco advances towards a sustainable future, the synergies between AI and mobility emerge as a pivotal force in steering transportation towards efficiency, accessibility, and environmental responsibility. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Autonomous vehicles

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Preserving Location Privacy of IoT Devices in Heterogeneous Edge Computing Architecture Through Deniability-Based Authentication

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IEEE Transactions on Consumer Electronics 2025年

作者： Ali, Ikram Li, Jianqiang Chen, Jie Chen, Yong Ullah, Shamsher Wakeel, Abdul Shenzhen University National Engineering Laboratory of Big Data System Computing Technology Shenzhen518060 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China National University of Sciences and Technology Military College of Signals Department of Electrical Engineering Islamabad Pakistan

Edge computing moves cloud services closer to consumer Internet of Things (IoT) devices, reducing latency and bandwidth usage. This setup enables faster responses but also introduces new security challenges, particularly concerning location privacy when sender and receiver use different security techniques. The authentication process in such architectures can expose the location of IoT devices. To address this issue, researchers have proposed various privacy-preserving schemes. However, these are computationally heavy for IoT devices. To tackle this issue, we propose two novel heterogeneous deniable authentication schemes: HDA-IoT-I and HDA-IoT-II. These schemes use public-key infrastructure and identity-based cryptography to protect the location privacy of IoT devices in heterogeneous edge computing environments. Our schemes enable the designated edge device to verify the source of a message without being able to prove its origin to a third party, thus preserving privacy. Additionally, they incorporate a batch verification method, which speeds up the verification of multiple deniable authenticators. The proposed schemes are formally proven secure in the random oracle model based on the hardness assumption of elliptic curve inverse computational Diffie-Hellman problem. The performance evaluation demonstrate that our schemes significantly enhance computational and communication efficiency, making them suitable for IoT devices. © 2025 IEEE.

关键词： Differential privacy

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A Contribution to the Spatial Analysis of Territorial systems Based on Graph-Structured Data

A Contribution to the Spatial Analysis of Territorial System...

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Metrology for Living Environment (MetroLivEn), IEEE International Workshop on

作者： Simone Corrado Federico Romaniello Francesco Scorza School of Engineering Laboratory of Urban and Regional System Engineering (LISUT) University of Basilicata Potenza Italy Department of Mathematics Computer Science and Economics University of Basilicata Potenza Italy

ISBN: (数字)9798350385014

ISBN: (纸本)9798350385021

This work aims to present the methodological framework within which the T4Y project is developing as a contribution to the spatial analysis of complex systems related to tourism. The mapping of tourist ecosystems from a territorial perspective is a complex phenomenon due to the difficulty in understanding the interactions between tourists and the territory. Through the integration of AI-based techniques, the study aims to propose an approach based on graph-structured data to manage the complexity of geospatial information and improve planners' ability to abstract the possible relationships between elements of the territory. The approach utilizes graph-based data modeling while preserving spatial connections. It has been demonstrated how, through a bipartite graph approach, it is possible to decode the territorial components while taking into account the spatial connections between different pieces of information. Furthermore, the implementation of Graph Neural Networks (GNN) has been discussed as a way to facilitate a more integrated and context-aware analysis. This work aims to contribute to the ongoing debate on complex systems in spatial planning and provide valuable tools for policymakers and planners to experiment with scenarios for spatial development.

关键词： Ecosystems Metrology Graph neural networks Data models Planning Geospatial analysis Complexity theory

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Targeted Pareto Optimization for Subset Selection With Monotone Objective Function and Cardinality Constraint

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Shang, Ke Wu, Guotong Pang, Lie Meng Ishibuchi, Hisao National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

Subset selection, a fundamental problem in various domains, is to choose a subset of elements from a large candidate set under a given objective or multiple objectives. Pareto optimization for subset selection (POSS) has emerged as a powerful paradigm for addressing subset selection problems. Recently, some POSS variants have been proposed to further improve its performance. In this paper, we propose a new POSS variant, named Targeted Pareto Optimization for Subset Selection (TPOSS). TPOSS differs from POSS in four aspects: problem formulation, population initialization, mutation, and environmental selection. The main idea of TPOSS is to focus the search on the target region of subset selection with respect to the subset cardinality in order to improve the search efficiency. We conduct comprehensive experiments to compare TPOSS with six state-of-the-art algorithms on three subset selection tasks (i.e., sparse regression, unsupervised feature selection, and hypervolume subset selection) where the size of the candidate sets ranges from 20 to 400. Experimental results show that with respect to the objective value of the best feasible subset, TPOSS outperforms the other algorithms on all the three tasks, which suggests the potential of TPOSS to enhance subset selection in various domains. IEEE

关键词： Linear programming

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Probabilistic-Search and Neighbor-Density Based Doppler-Shift Acquisition in Space Communications

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IEEE Transactions on Communications 2025年

作者： Zhang, Zhaowei Du, Shuai Liu, Lin Liu, Hui Zuo, Jiakuo Miao, Wang Zhao, Haitao Nanjing University of Posts and Telecommunications School of Internet of Things China Nanjing University State Key Laboratory for Novel Software Technology China Jinling Institute of Technology School of Software Engineering China University of Exeter Department of Computer Science United Kingdom

In space communications, the signal's long-distance transmission between a flying transmitter-receiver pair encounters two obstacles: a huge path-loss and a high-speed movement. The huge path-loss results in a low signal-to-noise ratio (SNR) and the high-speed movement brings a dynamic Doppler-shift, thus posing a great challenge for Doppler-shift acquisition. Under the low SNR, the Doppler-shift acquisition has to accumulate many symbols in a long-time period. However, during this period, the dynamic Doppler-shift disperses all these symbols' total energy over a wide range, thus causing the energy dispersion problem. To address this problem, we propose a probabilistic-search and neighbor-density (PSND) scheme, where the probabilistic-search considers the element's amplitude and sacrifices some redundant signal elements for a narrower search-range, while the neighbordensity sums the signal element's all neighbors for a larger signal energy. The PSND scheme includes two algorithms: the Generic PSND and Iterative PSND. The Generic PSND algorithm first selects some search-elements with their probabilities to construct a probabilistic-search-range, then selects some density-elements with their neighbor-elements to derive the neighbor-densities, and finally searches the largest neighbor-density to obtain the acquisition result. Built upon the Generic PSND algorithm above, the Iterative PSND algorithm iteratively updates the searchelements to further strengthen the neighbor-density in a narrower probabilistic-search-range. Moreover, the simulations results have demonstrated our PSND scheme's higher acquisition probability, as compared with the existing schemes. © 1972-2012 IEEE.

关键词： Signal to noise ratio

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