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IEEE Conference on Wireless Communications and Networking

作者： Eftychia Datsika Nikolaos D. Kallimanis Laboratory of Knowledge & Intelligent Computing University of Ioannina and Municipality of Ioannina Greece Laboratory of Knowledge & Intelligent Computing University of Ioannina and ISI/Athena RC Greece

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Multi-access edge computing (MEC) is an important enabler of computationally demanding and delay sensitive intelligent applications, placing significant strain on traditional cloud computing systems. As massive multiple access and high offloading data rates are envisioned for the 6th generation (6G) networks, new paradigms that can support next generation multiple access systems have appeared, e.g., the reconfigurable intelligent surfaces (RIS). Particularly, a time-division multiple access (TDMA) MEC system can leverage the integration of multiple RISs. By regulating the interplay between multiple access control and RIS phase configuration, the inclusion of multiple RISs can improve the signal quality providing better channel conditions for uplink (UL) offloading. To that end, aiming to address the offloading sum-rate maximization problem in a multi-RIS assisted TDMA MEC network, we employ deep reinforcement learning (DRL) and propose a deep deterministic policy gradient (DDPG) based algorithm, i.e., UL-RIS-DDPG. DDPG is a DRL variant designed for high-dimensional continuous action and state spaces induced by the considered problem. The UL-RIS-DDPG algorithm jointly optimizes key parameters, i.e., the users' transmission powers, the RISs phase shift design, the user-RIS association, and the UL transmission scheduling and improves significantly the network's offloading sum-rate. The simulation results demonstrate that the algorithm adapts to the network environment offering flexible RIS selection and efficient time slot allocation, outperforming the benchmarks.

关键词： Time division multiple access Multi-access edge computing Simulation Reconfigurable intelligent surfaces Deep reinforcement learning Resource management Synchronization Servers Uplink Strain

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An Evolutionary Multitasking Algorithm for Efficient Multiobjective Recommendations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 518-532页

作者： Tian, Ye Ji, Luke Hu, Yiwei Ma, Haiping Wu, Le Zhang, Xingyi Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230601 China Anhui University Institutes of Physical Science and Information Technology Hefei230601 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Hefei230029 China

Represented by evolutionary algorithms and swarm intelligence algorithms, nature-inspired metaheuristics have been successfully applied to recommender systems and amply demonstrated effectiveness, in particular, for multiobjective recommendation. Owing to the population-based search paradigm, these algorithms can produce a number of recommendation lists, making diverse tradeoffs between multiple metrics and meeting the requirements of accuracy, novelty, diversity, and other user preferences. However, these algorithms are criticized for the low efficiency of the optimization process, especially when the number of users is large. To address this issue, this article proposes an evolutionary multitasking-based recommendation method, where each task corresponds to a user and all the tasks are optimized simultaneously, thus highly improving the efficiency of recommendation. To enhance the convergence speed, all the users are divided into multiple populations according to the similarity between their preferences, where each population evolves with internal knowledge transfer between users, and all the populations evolve with external knowledge transfer between populations. Experimental results on various datasets verify that the proposed method can better balance between multiple metrics than classical and deep neural network-based recommendation methods and exhibits significantly higher efficiency than evolutionary multiobjective optimization-based recommendation methods. © 2024 IEEE. All rights reserved.

关键词： Multiobjective optimization

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The superalignment of superhuman intelligence with large language models

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Science China(Information Sciences) 2025年第06期 101-111页

作者： Minlie HUANG Yingkang WANG Shiyao CUI Pei KE Jie TANG The CoAI Group Department of Computer Science and Technology Tsinghua University Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of China Knowledge Engineering Group Department of Computer Science and Technology Tsinghua University

We have witnessed the emergence of superhuman intelligence thanks to the fast development of large language models(LLMs) and multimodal language models. As the application of such superhuman models becomes increasingly popular, a critical question arises: how can we ensure they still remain safe, reliable, and aligned well with human values encompassing moral values, Schwartz's Values, ethics, and many more? In this position paper, we discuss the concept of superalignment from a learning perspective to answer this question by outlining the learning paradigm shift from large-scale pretraining and supervised fine-tuning, to alignment training. We define superalignment as designing effective and efficient alignment algorithms to learn from noisy-labeled data(point-wise samples or pair-wise preference data) in a scalable way when the task is very complex for human experts to annotate and when the model is stronger than human experts. We highlight some key research problems in superalignment, namely, weak-to-strong generalization, scalable oversight, and evaluation. We then present a conceptual framework for superalignment, which comprises three modules: an attacker which generates the adversary queries trying to expose the weaknesses of a learner model, a learner which refines itself by learning from scalable feedbacks generated by a critic model with minimal human experts, and a critic which generates critics or explanations for a given query-response pair, with a target of improving the learner by criticizing. We discuss some important research problems in each component of this framework and highlight some interesting research ideas that are closely related to our proposed framework, for instance, self-alignment, self-play, self-refinement, and more. Last, we highlight some future research directions for superalignment, including the identification of new emergent risks and multi-dimensional alignment.

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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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K-ON: Stacking knowledge On the Head Layer of Large Language Model

arXiv

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arXiv 2025年

作者： Guo, Lingbing Zhang, Yichi Bo, Zhongpu Chen, Zhuo Sun, Mengshu Zhang, Zhiqiang Zhang, Wen Chen, Huajun College of Computer Science and Technology Zhejiang University China ZJU-Ant Group Joint Lab of Knowledge Graph China Ant Group China School of Software Technology Zhejiang University China Zhejiang Key Laboratory of Big Data Intelligent Computing China

Recent advancements in large language models (LLMs) have significantly improved various natural language processing (NLP) tasks. Typically, LLMs are trained to predict the next token, aligning well with many NLP tasks. However, in knowledge graph (KG) scenarios, entities are the fundamental units and identifying an entity requires at least several tokens. This leads to a granularity mismatch between KGs and natural languages. To address this issue, we propose K-ON, which integrates KG knowledge into the LLM by employing multiple head layers for next k-step prediction. K-ON can not only generate entity-level results in one step, but also enables contrastive loss against entities, which is the most powerful tool in KG representation learning. Experimental results show that K-ON outperforms state-of-the-art methods that incorporate text and even the other modalities. Copyright © 2025, The Authors. All rights reserved.

关键词： knowledge graph

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MFE-Net: A Novel Multi-scale Feature Enhancement Network for SAR Ship Instance Segmentation

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IEEE Geoscience and Remote Sensing Letters 2025年

作者： Liu, Li Zhang, Shuo Hu, Mingtao North China Electric Power University Hebei Key Laboratory of Knowledge Computing for Energy and Power Department of Computer Baoding 071000 China Ministry of Education Engineering Research Center of Intelligent Computing for Complex Energy Systems Department of Computer North China Electric Power University Baoding 071000 China University of Chinese Academy of Sciences Aerospace Information Research Institute Chinese Academy of Sciences Beijing 100190 China

Deep learning has achieved significant progress in ship instance segmentation for synthetic aperture radar (SAR) im-ages. However, due to the challenges posed by inshore scenes, such as dense clustering, arbitrary arrangement orientation, and multi-scale targets, the performance of existing methods still falls short of expectations. To overcome the dilemma, we propose MFE-Net, a one-stage instance segmentation network based on oriented bounding box (OBB). First, we design an Orientation-aware Seg-mentation Head (OSeg Head) to enhance the network's perception of directional features while mitigating interference from complex background. Additionally, a Feature Aggregation Diffusion Pyra-mid (FADP) is proposed, consisting of the Multi-level Feature Alignment Module (MAM) and the Multi-scale Feature Enhance-ment Module (MFEM). The FADP aggregates global semantic and detailed information, and effectively enhances the feature repre-sentations of both deep and shallow layers through multi-scale fea-ture extraction. Experimental results on two public benchmark datasets (HRSID, SSDD) and a self-built dataset (HRSID_IS) demonstrate that MFE-Net achieves an ideal trade-off between ac-curacy and efficiency, respectively reaching 58.9%, 63.0%, and 46.9% AP with parameters of 12.36M. © 2004-2012 IEEE.

关键词： multi-scale information fusion oriented bounding box (OBB) ship instance segmentation Synthetic aperture radar (SAR)

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A Web-Geographical Information System for Real Time Monitoring of Arachthos River, Greece

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IFAC-PapersOnLine 2018年第30期51卷 384-389页

作者： Athanasiou, Thomas Salmas, Dimitrios Karvelis, Petros Angelis, Ioannis Andrea, Veronika Schismenos, Paraskeuas Styliou, Maria Stylios, Chrysostomos Laboratory of Knowledge and Intelligent Computing Department of Computer Engineering Technological Educational Institute of Epirus Arta Greece

The scope of this work is to present a Web-GIS that will monitor the broader area of Arachthos River in order to serve as an online mechanism for early warning and mapping of flood events. It describes briefly the main technologies for developing a web-based GIS system i.e. mapping technologies, developing databases and web applications. The advent of client-side web mapping has provided all the necessary tools to develop platforms for creating Web based Geographical Information Systems (Web-GIS). The information system combines data deriving from different sources such meteorological, geographical and water level data that are visualizing within the Web-GIS platform. The design and operation of the system aims to enhance the local government and relative stakeholders’ knowledge in order to improve decision making processes and efficient crisis management, in case of a flood event; while, another scope is also to keep the general public informed for the case of emergency situation. © 2016

关键词： Geographic information systems Artificial intelligence Decision making Decision support systems Floods Hazards Information systems Information use Mapping Monitoring Rivers Water levels Websites Crisis management Decision making process Design and operations Emergency situation Real time monitoring Web based geographical information systems Web geographical information system Web GIS

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LarTap: A Luminance-aware Framework with Text-correlation Priors for Multi-Exposure Image Fusion

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Wang, Enlong Li, Jiawei Yan, Tiantian Lei, Jia Zhou, Shihua Wang, Bin Liu, Jinyuan Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of the Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Xi’an Jiaotong University School of Software Engineering Shaanxi Xi’an710049 China Dalian Univer sity of Technology School of Mechanical Engineering Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1061 New Zealand

Conventional imaging devices often struggle to produce high-dynamic-range (HDR) images that accurately represent natural scenes. To overcome this limitation, multi-exposure image fusion (MEF) techniques have been introduced as a viable solution. Existing MEF approaches aim to enhance performance by optimizing or searching architectures. However, they face challenges in precise feature extraction and scene reconstruction, leading to distortion in the fused images. Additionally, most methods do not adequately address luminance variations across different image regions, which may result in the loss of essential details. To address these challenges, we present a novel luminance-aware MEF framework that integrates text-correlation priors (LarTap). By embedding textual information into fusion process, the proposed framework enhances content extraction and comprehension. Specifically, it consist of two key components: the text-image correlation network (N1) and the multi-exposure fusion network (N2). First, N1 performs correlation training to achieve a holistic alignment between text and image pairs. Its iterative vision encoders (VEs) generate text-correlated prior knowledge to facilitate the fusion process in N2. Second, N2 leverages these priors for scene reconstruction and dynamically adjusts luminance based on comparative perception. Extensive experiments on multiple datasets demonstrate that LarTap outperforms state-of-the-art methods. © 1991-2012 IEEE.

关键词： Image correlation

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MLFuse: Multi-Scenario Feature Joint Learning for Multi-Modality Image Fusion

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IEEE Transactions on Multimedia 2025年

作者： Lei, Jia Li, Jiawei Liu, Jinyuan Wang, Bin Zhou, Shihua Zhang, Qiang Wei, Xiaopeng Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1010 New Zealand Ulster University Intelligent Systems Research Center LondonderryBT52 1SA United Kingdom

Multi-modality image fusion (MMIF) entails synthesizing images with detailed textures and prominent objects. Existing methods tend to use general feature extraction to handle different fusion tasks. However, these methods have difficulty breaking fusion barriers across various modalities owing to the lack of targeted learning routes. In this work, we propose a multi-scenario feature joint learning architecture, MLFuse, that employs the commonalities of multi-modality images to deconstruct the fusion progress. Specifically, we construct a cross-modal knowledge reinforcing network that adopts a multipath calibration strategy to promote information communication between different images. In addition, two professional networks are developed to maintain the salient and textural information of fusion results. The spatial-spectral domain optimizing network can learn the vital relationship of the source image context with the help of spatial attention and spectral attention. The edge-guided learning network utilizes the convolution operations of various receptive fields to capture image texture information. The desired fusion results are obtained by aggregating the outputs from the three networks. Extensive experiments demonstrate the superiority of MLFuse for infrared-visible image fusion and medical image fusion. The excellent results of downstream tasks (i.e., object detection and semantic segmentation) further verify the high-quality fusion performance of our method. The code is publicly available at https://***/jialei-sc/MLFuse. © 1999-2012 IEEE.

关键词： Semantic Segmentation

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Progress in the Development of National knowledge Infrastructure

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Journal of Computer Science & Technology 2002年第5期17卷 523-534页

作者：曹存根丰强泽高颖顾芳司晋新眭跃飞田雯王海涛王丽丽曾庆田张春霞郑宇飞周肖彬 Knowledge Acquisition and Sharing Group Key Laboratory of Intelligent Information Processing Institute of Computing Technology The Chinese Academy of Sciences Beijing 100080 P.R. China

This paper presents the recent process in a long-term research project, calledNational knowledge Infrastructure (or NKI). Initiated in the early 2000, the project aims todevelop a multi-domain shareable knowledge base for knowledge-intensive applications. Todevelop NKI, we have used domain-specific ontologies as a solid basis, and have built morethan 600 ontologies. Using these ontologies and our knowledge acquisition methods, we haveextracted about 1.1 millions of domain assertions. For users to access our NKI knowledge,we have developed a uniform multi-modal human-knowledge interface. We have also imple-mented a knowledge application programming interface for various applications to share theNKI knowledge.

关键词： national knowledge infrastructure knowledge acquisition domain-specificontology human-knowledge interface knowledge application programming interface

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