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21st International Symposium on distributed computing and Artificial Intelligence, DCAI 2024

作者： Martins, Rafael Pereira, Hugo Corrente, Gustavo Xavier, William Conceição, Luís Freitas, Alberto Marreiros, Goreti GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Institute of Engineering – Polytechnic of Porto Porto Portugal LASI – Intelligent Systems Associate Laboratory Guimarães Portugal Wiseware Solutions Gafanha da Encarnação Portugal MEDCIDS – Department of Community Medicine Information and Health Decision Sciences Faculty of Medicine University of Porto Porto Portugal CINTESIS@RISE – Center for Health Technology and Services Faculty of Medicine University of Porto Porto Portugal

ISBN: (纸本)9783031809453

An increase in the elderly population has led to a growing demand for constant health care. Remote monitoring of seniors aims to provide more effective care and promote patient independence, whether they live in residential homes, or their own homes supported by a domiciliary care service. It encompasses several issues present in their lives that may benefit from being assisted and monitored remotely. There are many successful studies and products focused on one of these issues, but there are no systems, to the best of our knowledge, that serve as a broad remote monitoring solution for multiple of these topics simultaneously. This paper proposes an architecture for an intelligent health monitoring ecosystem to interact with these various aspects of the elderly’s daily life, including monitoring of vital signs, movement and medication intake, detection of falls, sleep quality analysis, access control, and patient–caregiver communication. This solution will make use of wearables and medication dispensers, and it will securely store all patient-related information into an HL7 FHIR standardized database, from where machine learning models can import data for predicting important information and generating alerts when necessary. Next steps involve the development of this proposed system and testing it with elderly volunteers living in a residential home or subscribed to its domiciliary care services. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Remote patient monitoring

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Efficient Algorithms for Minimizing the Kirchhoff Index via Adding Edges

arXiv

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arXiv 2025年

作者： Zhou, Xiaotian Zehmakan, Ahad N. Zhang, Zhongzhi Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China Research Institute of Intelligent Complex Systems Fudan University Shanghai200433 China School of Computing Australian National University Canberra Australia

The Kirchhoff index, which is the sum of the resistance distance between every pair of nodes in a network, is a key metric for gauging network performance, where lower values signify enhanced performance. In this paper, we study the problem of minimizing the Kirchhoff index by adding edges. We first provide a greedy algorithm for solving this problem and give an analysis of its quality based on the bounds of the submodularity ratio and the curvature. Then, we introduce a gradient-based greedy algorithm as a new paradigm to solve this problem. To accelerate the computation cost, we leverage geometric properties, convex hull approximation, and approximation of the projected coordinate of each point. To further improve this algorithm, we use pre-pruning and fast update techniques, making it particularly suitable for large networks. Our proposed algorithms have nearly-linear time complexity. We provide extensive experiments on ten real networks to evaluate the quality of our algorithms. The results demonstrate that our proposed algorithms outperform the state-of-the-art methods in terms of efficiency and effectiveness. Moreover, our algorithms are scalable to large graphs with over 5 million nodes and 12 million edges. Copyright © 2025, The Authors. All rights reserved.

关键词： Consensus algorithm

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Dynamic QoS-Driven Framework for Co-Scheduling of distributed Long-Running Applications on Shared Clusters

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IEEE Transactions on Cloud computing 2025年

作者： Zhu, Jianyong Wang, Hongtao Su, Pan Wang, Yang Pan, Weihua North China Electric Power University Department of Computer China Hebei Key Laboratory of Knowledge Computing for Energy & Power China Ministry of Education Engineering Research Center of Intelligent Computing for Complex Energy Systems China

Cloud service providers typically co-locate various workloads within the same production cluster to improve resource utilization and reduce operational costs. These workloads primarily consist of batch analysis jobs composed of multiple parallel short-running tasks and long-running applications (LRAs) that continuously reside in the system. The adoption of microservice architecture has led to the emergence of distributed long-running applications (DLRAs), which enhance deployment flexibility but pose challenges in detecting and investigating QoS violations due to workload variability and performance propagation across microservices. State-of-the-art resource managers are only responsible for resource allocation among applications/jobs and do not prioritize runtime QoS aspects, such as applicationlevel latency. To address this, we introduce Prank, a QoSdriven resource management framework for co-located workloads. Prank incorporates a non-intrusive performance anomaly detection mechanism for DLRAs and proposes a root cause localization algorithm based on PageRank-weighted analysis of performance anomalies. Moreover, it dynamically balances resource allocation between DLRAs and co-located batch jobs on nodes hosting critical microservices, optimizing for both DLRA performance and overall cluster efficiency. Experimental results demonstrate that Prank outperforms state-of-the-art baselines, reducing DLRA tail latency by over 38% while increasing batch job completion time by no more than 21% on average © 2013 IEEE.

关键词： anomaly detection colocation distributed long-running application root cause investigation tail latency

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An adaptive language model-based intelligent medication assistant for the decision support of antidepressant prescriptions

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Computers in Biology and Medicine 2025年 190卷 110065-110065页

作者： Silva, Regina Gomes, Luis Centro Hospitalar Universitário de Santo António Unidade Local de Saúde de Santo António Porto Portugal GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI – Intelligent Systems Associate Laboratory ISEP Polytechnic of Porto Porto Portugal

Background: Depression, a pervasive mental disorder affecting millions worldwide, requires a holistic and personalized approach to treatment. Combining therapy and antidepressant medication is crucial, but selecting the right medication remains a complex task due to the variety of options available, each with unique, contraindications and drug interactions. While intelligent clinical decision support systems exist, there is a gap in medication-specific solutions. This paper proposes an innovative intelligent medication assistant, leveraging retrieval-augmented generation and large language models to provide personalized recommendations considering patient records and current health situation. Method: The proposed assistant tailors recommendations based on individual context by injecting patient-specific documents into large language models, enhancing efficiency and minimizing adverse effects. Our approach adapts to various query formats and dynamically incorporates relevant sources, empowering healthcare providers to make informed decisions. The analysis covers 40 clinical cases of patients requiring psychiatric monitoring. The patient's basic biography, clinical context, and ongoing situation were presented for each model, followed by questions about antidepressant pharmacological treatment. Results: The case study used 40 mental health patient clinical cases, and the results showed that the proposed assistant represents a significant advancement for the health provider, enabling the support, fast search, and consultation of medication information leaflets in almost real-time. A total of ten large language models with two configurations each were evaluated. Two commercial models and eight open local models were used. Conclusions: The commercial model gpt4o had a total score of 4.10/5.00, while the open model llama3 had a total score of 2.92/5.00. The results showed that commercial models present better results than open models, providing more accurate answers, cons

关键词： Recommender systems

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PEAK: Python-based framework for heterogeneous agent communities

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SoftwareX 2025年 30卷

作者： Ribeiro, Bruno Dias, David Gomes, Luis Vale, Zita GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate Laboratory Polytechnic of Porto Rua Dr. António Bernardino de Almeida 431 Porto4249-015 Portugal

Python-based framework for heterogeneous agent communities (PEAK) is a multi-agent system development framework aiming at facilitating the interoperability of agents from different environments and their interactions and exchange of knowledge. With PEAK, it is possible to develop from simple agent systems to more complex ones where hundreds of agents, or even thousands, interact with each other to fulfil their purpose. PEAK has contributed to the validation of solutions, published articles and advanced training sessions in different research fields such as energy systems and federated learning. The idea is that, in the future, it will continue to contribute to the enrichment of scientific knowledge in more fields of application. © 2025

关键词： Federated learning

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Safe Reinforcement Learning for Social Motor Coordination in Two Dimensional Space 26

Safe Reinforcement Learning for Social Motor Coordination in...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Yang, Fan Huang, Yuening Zhai, Chao Zhang, Hehong China University of Geosciences School of Automation Wuhan Campus China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Rice University George R. Brown School of Engineering and Computing HoustonTX United States Fuzhou University School of Computer and Data Science Fuzhou China

ISBN: (纸本)9798331521950

This paper presents an interactive motion control method based on reinforcement learning, designed to assist children with autism who have social motor impairments through a mirror game intervention. The virtual teacher uses the Deep Deterministic Policy Gradient (DDPG) algorithm to optimize its actions, guiding the participant to follow a Lissajous trajectory. To ensure safety, a motion-correction mechanism was developed, which automatically adjusts actions when the predicted trajectory surpasses predefined safety boundaries. The reward function considers both the distance between the virtual teacher and the target trajectory, as well as the distance between the virtual teacher and the participant, with dynamic adjustments applied by the motion-correction mechanism. Experimental results demonstrate that the virtual teacher effectively guides the participant towards the target trajectory while adhering to safety constraints. © 2025 IEEE.

关键词： Reinforcement learning

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Semantic Pre-Extraction for Energy-Efficient AoI Minimization in UAV-Assisted Wireless Networks

Semantic Pre-Extraction for Energy-Efficient AoI Minimizatio...

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IEEE Conference on Wireless Communications and Networking

作者： Yusi Long Gary C.F. Lee Lanhua Li Shimin Gong Sumei Sun Dusit Niyato School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University China Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology China Institute for Infocomm Research (IR) A*STAR Singapore College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

This paper investigates an unmanned aerial vehicle (UAV)-assisted semantic communication network. The energy-limited ground users (GUs) provide semantic services to periodically generated raw data and a UAV relays the extracted semantic information to a base station (BS). Semantic extraction enhances data responsiveness and reduces the age-of-information (AoI) by transmitting only the most essential information. However, more complex semantic extraction increases energy consumption, making it easier for the GUs to deplete their energy. Therefore, we introduce a novel energy-efficient AoI (EAoI) metric to capture both information freshness and energy consumption of the GUs. We formulate a time-averaged EAoI minimization problem by jointly optimizing the GUs' scheduling, pre-extraction strategy, semantic control, computing resource allocation, and the UAV's trajectory. We further propose a semantic-aware joint pre-extraction and trajectory planning (Sem-JPT) algorithm to decompose the complex optimization problem into three subproblems, which are solved by a series of approximation methods. Simulation results demonstrate that semantic communication can reduce the overall EAoI by more than 18% compared with conventional bit-based communication. Moreover, the proposed Sem-JPT algorithm can maintain information freshness and prolong the GUs' lifetimes, outperforming existing baselines.

关键词： Energy consumption Trajectory planning Processor scheduling Wireless networks Semantic communication Minimization Information age Energy efficiency Trajectory Data mining

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Nested Named Entity Recognition: A Survey of Latest research

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Expert systems 2025年第7期42卷

作者： Ji, Lixia Dang, Yiping Du, Yunlong Gao, Wenzhao Zhang, Han School of Cyber Science and Engineering Zhengzhou University Zhengzhou China College of Computer Science Sichuan University Chengdu China Intelligent Systems Research Centre School of Computing Engineering & Intelligent Systems Ulster University Belfast United Kingdom Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College Luzhou China

The research on nested named entity recognition (NER) is conducive to providing richer semantic representations and capturing the nested structure among entities, which is crucial for the execution of downstream tasks. This paper aims to summarise the nested NER methods that have been combined with emerging technologies in recent years. We summarise the nested NER methods that are integrated with emerging technologies from three dimensions: model, framework, and data. Additionally, we explore the research progress of nested NER in two scenarios: cross-lingual modality and multi-modal in different modalities. Furthermore, we discuss the practical applications of NER technology in five fields: biomedicine, justice, finance, media, and e-commerce. Through this review, we can clearly see the development trends of nested NER technology under emerging technologies and different modalities, as well as its broad application prospects in various fields. This provides a reference for future exploration directions in nested NER. © 2025 John Wiley & Sons Ltd.

关键词： application fields modal nested named entity recognition new technologies

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PDRL-CM: An efficient cooperative caching management method for vehicular networks based on deep reinforcement learning

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Ad Hoc Networks 2025年 176卷

作者： Ou, Pingjie Chen, Ningjiang Yang, Long School of Computer and Electronic Information Guangxi University Nanning Guangxi530004 China Key Laboratory of Parallel Distributed and Intelligent Computing Education Department of Guangxi Zhuang Autonomous Region Nanning Guangxi530004 China The Guangxi Intelligent Digital Services Research Center of Engineering Technology Nanning Guangxi530004 China

In vehicular networks, onboard devices face the challenge of limited storage, and computational resources constrain their processing and storage capabilities. This limitation is particularly significant for applications that require complex computations and real-time responses. Additionally, limited storage capacity reduces the range of cacheable data, which can impact the immediate availability of data and the continuity of services. Therefore, improving cache utilization and meeting vehicles’ real-time data demands pose significant challenges. Deep reinforcement learning can optimize the issues arising from agents’ continuously changing state and action spaces due to increasing request demands. However, training the network may encounter instability and convergence difficulties in dynamic and complex environments or situations with sparse rewards. In response to these issues, this paper proposes a Priority-based Deep Reinforcement Learning Collaborative Cache Management method (PDRL-CM). PDRL-CM first designs a lightweight cache admission strategy that leverages data's inherent and combined attributes. It then makes cache admission decisions Using Monte Carlo sampling and a max-value search strategy combined with a feedforward neural network. Secondly, the method considers minimizing system latency and reducing vehicle energy consumption as joint optimization problems. An improved deep reinforcement learning algorithm solves this problem and makes cache-sharding decisions. A prioritized experience replay mechanism is incorporated to adjust the network prediction model quickly and accelerate the convergence process. Experimental results indicate that, compared to existing DRL-based caching methods, PDRL-CM offers faster data processing efficiency and higher cache hit rates under varying vehicle density, storage capacity, and content volume conditions. © 2025

关键词： Cache memory

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Probabilistic bootstrap-based evolutionary algorithm for three-objective wind farm turbine position optimization

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Swarm and Evolutionary Computation 2025年 96卷

作者： Zhang, Chen Li, Haotian Li, Xiuxian Cheng, Jiujun Lei, Zhenyu Gao, Shangce Faculty of Engineering University of Toyama Toyama-shi 930-8555 Japan College of Electronics and Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China Ministry of Education Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

Amid the worsening energy crisis, wind farm layout optimization (WFLO) to increase power generation, reduce costs, and mitigate potential environmental impacts is of great significance. This paper formulates three-objective wind farm layout optimization (TWFLO) which is rarely considered, aiming to effectively utilize existing information to optimize power output, land usage, and costs. We propose a new algorithm (MOEA/D-P) based on probability distributions to guide turbine placement and improve the performance of a Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D). This algorithm addresses the issue of neglecting valuable information during layout optimization. Additionally, we make improvements to the traditional land-use function to avoid situations where non-convex layouts result in an area calculation of zero. The MOEA/D-P is tested on six different initial layouts and compared with five algorithms under two wind conditions. Results are evaluated using inverted generational distance, hypervolume, and scatter plot distributions. The impact of initial probability distribution on algorithm performance is discussed under four simple wind conditions. The results show that MOEA/D-P outperforms the other five algorithms in terms of performance. © 2025 Elsevier B.V.

关键词： Distribution functions

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