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IEEE Transactions on artificial intelligence 2024年第3期5卷 1411-1424页

作者： Zhang, Yuhong Shi, Congmei Li, Xinzheng Zhang, Zan Hu, Xuegang School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei230009 China

Cross-network node classification aims to train a classifier for an unlabeled target network using a source network with rich labels. In applications, the degree of nodes mostly conforms to the long-tail distribution, i.e., most nodes in the network are tail nodes with sparse neighborhoods. The established methods focus on either the discrepancy cross network or the long tail in a single network. As for the cross-network node classification under long tail, the coexistence of sparsity of tail nodes and the discrepancy cross-network challenges existing methods for long tail or methods for the cross-network node classification. To this end, a multicomponent similarity graphs for cross-network node classification (MS-CNC) is proposed in this article. Specifically, in order to address the sparsity of the tail nodes, multiple component similarity graphs, including attribute and structure similarity graphs, are constructed for each network to enrich the neighborhoods of the tail nodes and alleviate the long-tail phenomenon. Then, multiple representations are learned from the multiple similarity graphs separately. Based on the multicomponent representations, a two-level adversarial model is designed to address the distribution difference across networks. One level is used to learn the invariant representations cross network in view of structure and attribute components separately, and the other level is used to learn the invariant representations in view of the fused structure and attribute graphs. Extensive experimental results show that the MS-CNC outperforms the state-of-the-art methods. Impact Statement-Node classification is an important task in graph mining. With the unavailability of labels, some researchers propose cross-network node classification, using one labeled network to assist the node classification of another unlabeled network. However, the long-tail of nodes leads to unsatisfactory performance and challenges the recent cross-network node classification m

关键词： Graphic methods

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Two Different Approaches of Applying ANFIS Based MPPT for a PV Water Pumping System with a SEPIC Converter

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Applied Solar Energy (English translation of Geliotekhnika) 2024年第3期60卷 383-399页

作者： Miqoi, S. Tidhaf, B. El Ougli, A. Team of Embedded Systems Renewable Energy and Artificial Intelligence National School of Applied Sciences Mohammed First University Oujda Oujda Morocco Computer Science Signal Automation and Cognitivism Laboratory Faculty of Sciences Dhar El Mahraz Fez Fez30050 Morocco

Abstract: The main objective of this work is to enhance the performance of the Photovoltaic water pumping system to cover the water requirement in rural areas. To do so, it is important to make sure that the PV array produces its maximum power at all times, which can be influenced by external condition (mainly the temperature and irradiation). Hence, we are employing the Adaptive Neuro-Fuzzy Inference System based MPPT in two ways. The ANFIS controller is considered more accurate and efficient as it uses an artificial neural network to learn from training data and generate fuzzy rules based on that data. Both approaches of ANFIS are used to control the duty cycle of the SEPIC converter, which connects the PV panel to the DC motor feeding the water pump. The system combining the PV panel, the SEPIC converter, the controller and the DC motor, is designed and simulated under MATLAB/Simulink. The performance of the proposed methods is tested under various meteorological conditions. © Allerton Press, Inc. 2024. ISSN 0003-701X, Applied Solar Energy, 2024, Vol. 60, No. 3, pp. 383–399. Allerton Press, Inc., 2024. ISSN 0003-701X, Applied Solar Energy, 2024. Allerton Press, Inc., 2024.

关键词： DC motors

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Knowledge-guided Network Pruning for EEG-based Emotion Recognition

Knowledge-guided Network Pruning for EEG-based Emotion Recog...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Rao, Wenjie Zhong, Sheng-Hua Zhang, Zhi Liu, Yan Shenzhen University Guangdong Laboratory of Artificial Intelligence and Digital Economy College of Computer Science and Software Shenzhen China Hong Kong Polytechnic University Department of Computer Science Hong Kong

ISBN: (纸本)9798350337488

With the development of deep learning in EEG-related tasks, the complexity of learning models has gradually increased. These complex models often result in long inference times, high energy consumption, and an increased risk of overfitting. Therefore, model compression has become an important consideration. Although some EEG models have used lightweight techniques, such as separable convolution, no existing work has directly attempted to compress EEG models to reduce their complexity. In this paper, we integrate neuroscience knowledge into EEG model pruning recovery, and innovatively propose two loss functions in the learning process, the knowledge-guided region-wise loss that enforces the classification evidence consistent with the importance of the prefrontal lobe, and the knowledge-guided sample-wise loss that constrains the learning process by distinguishing the importance of different samples. © 2023 IEEE.

关键词： EEG-based emotion recognition knowledge-guided network pruning model compression

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Hyperautomation for Air Quality Evaluations: A Perspective of Evidential Three-way Decision-making

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Cognitive Computation 2024年第5期16卷 2437-2453页

作者： Ding, Juanjuan Zhang, Chao Li, Deyu Sangaiah, Arun Kumar School of Computer and Information Technology Shanxi University Shanxi Taiyuan030006 China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Shanxi Taiyuan030006 China International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Douliou64002 Taiwan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

Hyperautomation acts as a real digital transformation with the support of several cutting-edge cognitive computation methods that include robotic process automation, natural language processing, artificial intelligence, and other emerging ones, which is conducive to processing complex industrial processes via extending the range of various data-driven cognitive decision-making algorithms. The study of air quality evaluations (AQE) plays a significant role in ensuring healthy atmospheric environments. In view of the objective existence of uncertainties, AQE can be modeled and addressed by a typical data-driven automated decision-making problem, and hyperautomation can provide a reasonable solution via associating with a variety of techniques. This article explores hyperautomation for AQE via evidential three-way large-scale group decision-making (LSGDM) in an intuitionistic fuzzy (IF) setting. First, the notion of intuitionistic fuzzy sets (IFSs) is incorporated into the paradigm of multi-granularity (MG) three-way decisions (TWD), and the model of adjustable MG IF probabilistic rough sets (PRSs) is developed. Second, an IF clustering analysis with the improved technique for order preference by similarity to ideal solution (TOPSIS) method is conducted to affirm representative members within a decision group. Third, a novel IF LSGDM method is built via adjustable MG IF PRSs and the evidence reasoning (ER) method. Finally, a case study in the setting of AQE is studied by using the presented evidential three-way LSGDM method. Corresponding experimental analyses are carried out for illustrating the efficiency of hyperautomation for AQE. In general, the proposed method improves the performance of information fusion by virtue of adjustable MG IF PRSs, and the TOPSIS method avoids the influence of subjective factors on decision results. Meanwhile, the evaluation information of decision-makers (DMs) is fully analyzed by means of the ER method, which can provide more explaina

关键词： Decision making

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Multi-Metric Based Client Selection for Backdoor Defense in Federated Learning 1

Multi-Metric Based Client Selection for Backdoor Defense in ...

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1st International Conference on Intelligent Manufacturing and Cloud Computing, ICIMCC 2024

作者： Yuan, Changjiang Li, Yidong Chen, Naiyue Zhang, Zikai Key Laboratory of Big Data & Artificial Intelligence in Transportation Beijing Jiaotong University Ministry of Education China School of Computer Science and Technology Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9781643685779

The distributed nature of federated learning makes it highly susceptible to backdoor attacks, which aim to induce the model to produce incorrect results when specific data is input. Existing defense methods based on distance calculations mostly use one single distance, which can be easily circumvented by backdoor attacks. Therefore, we conducted research on the combination of multiple distances. Based on this, we propose a backdoor defense algorithm based on multi metrics, which evaluates models through distance calculation and normalization to select benign models for aggregation. To verify the effectiveness of our method, we conducted experiments on three datasets, and the results prove that our method can provide good defense without reducing the model's main task accuracy. © 2025 The Authors.

关键词： Federated learning

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Partially Observable Task and Motion Planning with Uncertainty and Risk Awareness

arXiv

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arXiv 2024年

作者： Curtis, Aidan Matheos, George Gothoskar, Nishad Mansinghka, Vikash Tenenbaum, Joshua Lozano-Pérez, Tomás Kaelbling, Leslie Pack MIT Computer Science and Artificial Intelligence Laboratory United States

Integrated task and motion planning (TAMP) has proven to be a valuable approach to generalizable long-horizon robotic manipulation and navigation problems. However, the typical TAMP problem formulation assumes full observability and deterministic action effects. These assumptions limit the ability of the planner to gather information and make decisions that are risk-aware. We propose a strategy for TAMP with Uncertainty and Risk Awareness (TAMPURA) that is capable of efficiently solving long-horizon planning problems with initial-state and action outcome uncertainty, including problems that require information gathering and avoiding undesirable and irreversible outcomes. Our planner reasons under uncertainty at both the abstract task level and continuous controller level. Given a set of closed-loop goal-conditioned controllers operating in the primitive action space and a description of their preconditions and potential capabilities, we learn a high-level abstraction that can be solved efficiently and then refined to continuous actions for execution. We demonstrate our approach on several robotics problems where uncertainty is a crucial factor and show that reasoning under uncertainty in these problems outperforms previously proposed determinized planning, direct search, and reinforcement learning strategies. Lastly, we demonstrate our planner on two real-world robotics problems using recent advancements in probabilistic perception. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion planning

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Multi-Blockchain Based Data Trading Markets With Novel Pricing Mechanisms

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IEEE/CAA Journal of Automatica Sinica 2023年第12期10卷 2222-2232页

作者： Juanjuan Li Junqing Li Xiao Wang Rui Qin Yong Yuan Fei-Yue Wang IEEE the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao 266590China the School of Artificial Intelligence Anhui UniversityHefei 266114 the Qingdao Academy of Intelligent Industries Qingdao 230031China the School of Mathematics Renmin University of ChinaBeijing 100872China the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 the Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China

In the era of big data,there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive *** security and data pricing,however,are still widely regarded as major challenges in this respect,which motivate this research on the novel multi-blockchain based framework for data trading markets and their associated pricing *** this context,data recording and trading are conducted separately within two separate blockchains:the data blockchain(DChain) and the value blockchain(VChain).This enables the establishment of two-layer data trading markets to manage initial data trading in the primary market and subsequent data resales in the secondary ***,pricing mechanisms are then proposed to protect these markets against strategic trading behaviors and balance the payoffs of both suppliers and ***,in regular data trading on VChain-S2D,two auction models are employed according to the demand scale,for dealing with users’ strategic *** incentive-compatible Vickrey-Clarke-Groves(VCG)model is deployed to the low-demand trading scenario,while the nearly incentive-compatible monopolistic price(MP) model is utilized for the high-demand trading *** temporary data trading on VChain-D2S,a reverse auction mechanism namely two-stage obscure selection(TSOS) is designed to regulate both suppliers’ quoting and users’ valuation ***,experiments are carried out to demonstrate the strength of this research in enhancing data security and trading efficiency.

关键词： Auction data trading markets multi-blockchain pricing mechanisms

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Predict Malware Using Machine Learning 11

Predict Malware Using Machine Learning

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11th International Symposium on Digital Forensics and Security, ISDFS 2023

作者： Araujo, Domingos Silva, Joaquim Leite, Patricia Ribeiro, Oscar Teixeira, Paulo Polythecnic Institute of Cavado and Ave School of Technology Barcelos Portugal 2Ai-School of Technology Ipca Barcelos Portugal Lasi-Associate Laboratory of Intelligent Systems Portugal Liacc-Artificial Intelligence and Computer Science Laboratory Porto Portugal Polythecnic Institute of Cavado and Ave Barcelos Portugal

ISBN: (纸本)9798350336986

This paper has as main goal to demonstrate how artificial intelligence models can be used to detect malicious files, executables, etc. The detection of malware allows preventing the loss of information, software, and hardware. Throughout this paper the models that were chosen and the results obtained by those models will be presented. The pipeline (application) that was created to help in the flow of information extraction, dataset creation, dataset processing, dataset training and testing for several models and the results will be presented. It will be shown how the pipeline helps in "fine-tuning"the models and datasets. It will be presented how the user can reproduce these results or obtain new results. In summary, the main objective of this paper is to demonstrate in a simple way which are the best Malware prediction models. © 2023 IEEE.

关键词： Machine learning

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Factor Graph Neural Network Meets Max-Sum: A Real-Time Route Planning Algorithm for Massive-Scale Trips 23

Factor Graph Neural Network Meets Max-Sum: A Real-Time Route...

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23rd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2024

作者： Li, Yixuan Wang, Wanyuan Xu, Weiyi Deng, Yanchen Wu, Weiwei Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications School of Computer Science and Engineering Southeast University Nanjing China School of Computer Science and Engineering Nanyang Technological University Singapore School of Computer Science and Engineering Southeast University Nanjing China

Global route planning (GRP) is a typical combinatorial optimization problem that has been solved for a variety of industrial purposes, such as traffic flow management, network routing, and conflict prevention. The goal of the GRP is to find a route for each trip query such that all queries have a minimum global travel time. The GRP problem is NP-hard and computationally challenging, even for medium-sized instances. However, in real-world GRP applications, such as Google Maps-based vehicle route guidance systems, there are always massive-scale trips issued simultaneously, and real-time response is required. Existing mathematical programming-based exact methods and heuristics struggle to balance the extremes of optimality and scalability. Considering that many closed-related GRP instances must be solved repeatedly, this paper explores a deep learning approach to learn real-time and efficient solutions for GRP. This paper first proposes a novel route-query factor graph (RQ-FG) to model the GRP problem, where the message-passing damped Max-sum (DMS) algorithm can be exploited to generate high-quality approximate solutions. A hybrid pruning method is proposed to accelerate solving the DMS. We further devise a route-query factor graph neural network (RQ-FGNN) based on the RQ-FG, which has the ability to return solutions in milliseconds. Experiments demonstrate that our method can generate high-quality solutions in massive-scale GRP instances in real-time. © 2024 International Foundation for Autonomous Agents and Multiagent Systems.

关键词： Message passing

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LLaVA-Endo:a large language-and-vision assistant for gastrointestinal endoscopy

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Frontiers of computer science 2025年第4期19卷 121-123页

作者： Jieru YAO Xueran LI Qiang XIE Longfei HAN Yiwen JIA Nian LIU Dingwen ZHANG Junwei HAN School of Automation Northwestern Polytechnical UniversityXi’an 710072China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230088China AHU-IAI AI Joint Laboratory Anhui UniversityHefei 230039China Institute of Advanced Technology University of Science and Technology of ChinaHefei 230026China School of Computer and Artificial Intelligence Beijing Technology and Business UniversityBeijing 100048China Department of Gastroenterology The Third Affiliated Hospital of Anhui Medical University(Hefei First People’s Hospital)Hefei 230061China The Computer Vision Department Mohamed Bin Zayed University of Artificial IntelligenceMasdarAbu Dhabi 200120United Arab Emirates Xijing Hospital The Fourth Military Medical UniversityXi’an 710032China

1 Introduction Endoscopy plays a crucial role in the diagnoses and treatment of gastrointestinal(GI)diseases[1],as it helps to identify abnormalities,classify lesion,and determine treatment *** GI endoscopic examinations,physicians may encounter practical hindrances,i.e.,fatigue,stress,or limited experience,which can lead to erroneous *** intelligence(AI)-assisted GI endoscopy technology has emerged to address these limitations[2].

关键词： stress giendoscopy fatigue identify abnormalitiesclassify lesionidentification artificialintelligence

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