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2019中国智能自动化大会(CIAC 2019)

作者： Xiaoyuan Li Feng Chen Yaohui Jia Xinyu Liu School of Electrical Engineering Zhengzhou University Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou University School of Intelligent Manufacturing Huanghuai University China Shipping Heavy Industry 713 Research Institute

Non-invasive brain-computer interface(BCI) technology is becoming more and more popular in current BCI application study due to its safety,non-invasive and low *** main problem of non-invasive BCI technology is that the spatial resolution of signal is low and it is easy to be drowned by ***,this paper reviews the key technologies and the existing challenges of non-invasive BCI technology from the aspect of signal detection and processing,and discusses its future application ***,it is still a great challenge for non-invasive BCI technology to become the mainstream in the future.

关键词： brain-computer interface Non-invasive BCI technology Signal detection Pattern recognition

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Multi-objective Feature Attribution Explanation for Explainable Machine Learning

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ACM Transactions on Evolutionary Learning and Optimization 2024年第1期4卷 1-32页

作者： Wang, Ziming Huang, Changwu Li, Yun Yao, Xin Research Institute of Trustworthy Autonomous Systems Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Departmentof Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Advanced Cognitive Technology Lab Huawei Technologies Co. Ltd Shanghai201206 China School of Computer Science University of Birmingham BirminghamB15 2TT United Kingdom

The feature attribution-based explanation (FAE) methods, which indicate how much each input feature contributes to the model's output for a given data point, are one of the most popular categories of explainable machine learning techniques. Although various metrics have been proposed to evaluate the explanation quality, no single metric could capture different aspects of the explanations. Different conclusions might be drawn using different metrics. Moreover, during the processes of generating explanations, existing FAE methods either do not consider any evaluation metric or only consider the faithfulness of the explanation, failing to consider multiple metrics simultaneously. To address this issue, we formulate the problem of creating FAE explainable models as a multi-objective learning problem that considers multiple explanation quality metrics simultaneously. We first reveal conflicts between various explanation quality metrics, including faithfulness, sensitivity, and complexity. Then, we define the considered multi-objective explanation problem and propose a multi-objective feature attribution explanation (MOFAE) framework to address this newly defined problem. Subsequently, we instantiate the framework by simultaneously considering the explanation's faithfulness, sensitivity, and complexity. Experimental results comparing with six state-of-The-Art FAE methods on eight datasets demonstrate that our method can optimize multiple conflicting metrics simultaneously and can provide explanations with higher faithfulness, lower sensitivity, and lower complexity than the compared methods. Moreover, the results have shown that our method has better diversity, i.e., it provides various explanations that achieve different tradeoffs between multiple conflicting explanation quality metrics. Therefore, it can provide tailored explanations to different stakeholders based on their specific requirements. © 2024 Copyright held by the owner/author(s). Publication rights licensed

关键词： Machine learning

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Learning-Based Problem Reduction for Large-Scale Uncapacitated Facility Location Problems 13

Learning-Based Problem Reduction for Large-Scale Uncapacitat...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Zhang, Shuaixiang Yang, Yixuan Tong, Hao Yao, Xin Lingnan University Department of Computing and Decision Sciences Hong Kong Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen China

ISBN: (纸本)9798350308365

The uncapacitated facility location problem (UFLP) is a well-known combinational optimization problem, attracting numerous heuristic and meta-heuristic methods. However, these effective algorithms still encounter challenges when solving large-scale UFLP instances. To deal with the thousands of decision variables, one possible approach is to predict and remove unimportant facilities and thus decreases the dimensionality of problem instances. This paper explores the problem reduction of large-scale instances of UFLP. We propose suitable features of facilities to construct machine learning models for UFLP by learning from small instances. Based on this model, we introduce a novel problem reduction strategy-based optimization framework, and apply it to large instances. Through comprehen-sive experiments, we show that the proposed problem reduction strategy can effectively transform the UFLP instances to smaller ones. The performance of existing solution methods can be significantly enhanced, especially on large-scale instances. Under the proposed framework, we also show the generalization abilities of our models, which can be improved further. © 2024 IEEE.

关键词： Adversarial machine learning

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Demonstration of biophoton-driven DNA replication via gold nanoparticle-distance modulated yield oscillation

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Nano Research 2021年第1期14卷 40-45页

作者： Na Li Daoling Peng Xianjing Zhang Yousheng Shu Feng Zhang Lei Jiang Bo Song School of Optical-Electrical Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China State Key Laboratory of Respiratory Disease Key Laboratory of Oral MedicineGuangzhou Institute of Oral DiseaseStomatology HospitalDepartment of Biomedical EngineeringSchool of Basic Medical SciencesGuangzhou Medical UniversityGuangzhou 511436China School of Chemistry South China Normal UniversityGuangzhou 510006China State Key Laboratory of Medical Neurobiology Institute for Translational Brain ResearchMOE Frontiers Center for Brain ScienceDepartment of NeurologyHuashan HospitalFudan UniversityShanghai 200032China Biomedical Nanocenter School of Life SciencesInner Mongolia Agricultural UniversityHohhot 010011China Key Laboratory of Bio-inspired Materials and Interfacial Science Technical Institute of Physics and ChemistryChinese Academy of SciencesBeijing 100190China School of Future Technology University of Chinese Academy of SciencesBeijing 100049China Key Laboratory of Bio-inspired Smart Interfacial Science and Technology of Ministry of Education School of ChemistryBeihang UniversityBeijing 100191China

Biologically,there exist two kinds of syntheses:photosynthesis and ATP-driven *** light harvesting of photosynthesis is known to achieve an efficiency of〜95%by the quantum energy transfer of ***,how the ATP-driven biosynthesis reaches its high efficiency still remains *** triphosphates(dNTPs)in polymerase chain reaction(PCR)adopt the identical way of ATP to release their energy,and thus can be employed to explore the ATP energy ***,using a gold nanoparticle(AuNP)enhanced PCR(AuNP-PCR),we demonstrate that the energy released by phosphoanhydride-bond(PB)hydrolysis of dNTPs is in form of photons(PB-photons)to drive DNA replication,by modulating their resonance with the average inter-AuNP distance(D).The experimental results show that both the efficiency and yield of PCR periodically oscillate with D increasing,indicating a quantized process,but not simply a thermal *** PB-photon wavelength is further determined to 8.4 *** these results support that the release,transfer and utilization of bioenergy are in the form of *** findings of ATP-energy quantum conversion will open a new avenue to the studies of high-efficiency bioenergy utilization,biochemistry,biological quantum physics,and even brain sciences.

关键词： intrinsic biophoton DNA replication polymerase chain reaction(PCR) gold nanoparticle high-efficiency energy utilization biological quantum physics

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Overview of wireless implantable energy supply and communication technology

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Recent Patents on Engineering 2021年第4期15卷 12-22页

作者： Zhang, Shuang Qin, Yuping Kuang, Jiang-Ming Yang, Jining Xu, Jin Wang, Jiujiang Liu, Yihe Data Recovery Key Laboratory of Sichuan Province College of Computer Science & AI Neijiang Normal University Neijiang Sichuan China School of Life Science and Technology University of Electronic Science and Technology of China Chengdu Sichuan China High Field Magnetic Resonance Brain Imaging Laboratory of Sichuan Chengdu Sichuan China BeiDou & Wisdom Medical Doctor Workstation Neijiang Normal University Neijiang Sichuan China

With the development of integrated circuits and microelectronics, integrated and miniaturized implantable medical devices are increasingly used in modern medical technologies, e.g., cardiac pacemakers, vasodilators, and cochlear implants. However, the normal operation of these devices is inseparable from the availability of sufficient energy supply and the bidirectional transmission of internal and external signals. Due to the limitation of the working environment of sensors, there is only a small space for most implanted electronic devices, which is a challenge faced by existing technology. In this paper, current wireless implantable energy supply and communication technologies are reviewed to determine the best available technologies, thereby providing a reference for method selection in designing implantable medical systems. © 2021 Bentham science Publishers.

关键词： Microelectronics

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An entity-relationship model based conceptual framework for model driven development

An entity-relationship model based conceptual framework for ...

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IASTED International Conference on Software Engineering, as part of the 24th IASTED International Multi-Conference on APPLIED INFORMATICS

作者： Duan, Yucong Cheung, Shing-Chi Fu, Xiaolan Gu, Yuqing Institute of Software Chinese Academy of Sciences China Department of Computer Science Hong Kong University of Science and Technology Hong Kong State Key Laboratory of Brain and Cognitive Science Institute of Psychology Chinese Academy of Sciences China

ISBN: (纸本)0889865744

The Unified Modeling Language (UML) is a defacto notation for model driven software development (MDSD), emphasizing on model to model refinements and transformations. However UML may not be expressive enough to describe all aspects as required by recent MDSD models. A noticeable weakness is its inability to express the ontological concepts often encountered in MDSD. In this paper, we propose an extended Entity-Relationship (ER) model called Runtime Entity-Relationship model (RER) for ontological modeling of common MDSD concepts. Different layers of the UML metamodel hierarchy are analyzed in the context of modeling the MDSD using the RER model. Modifications of the UML architecture for more precise modeling of MDSD are suggested.

关键词： Unified Modeling Language

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A Framework based on Deep Neural Network for Ranking-oriented Software Defect Prediction 23

A Framework based on Deep Neural Network for Ranking-oriente...

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23rd IEEE International Conference on Software Quality, Reliability, and Security, QRS 2023

作者： Dai, Jiapeng Yang, Xiaoxing Huang, Bingding Lu, Xiaofen Shenzhen Technology University College of Big Data and Internet Shenzhen518118 China Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China

ISBN: (纸本)9798350319583

Software systems are getting larger and more complex than ever before. In order to improve software reliability, software defect prediction is applied to assist developers in bug discovery. The ranking-oriented software defect prediction aims to rank software modules according to the predicted defect counts. However, existing ranking-oriented defect prediction models are constructed based on traditional hand-crafted features, which might overlook the rich syntactic information buried inside the source codes. In this paper, we propose a universal deep learning-based framework called United Deep Network for ranking-oriented software defect prediction. This framework utilizes deep neural networks to automatically generate features from source code with the syntactic and structural information preserved, and it can combine extracted features with traditional hand-crafted features in order to take advantage of both kinds of features to construct prediction models. Experimental results over 29 sets of data show the good performance of the proposed framework for building ranking-oriented defect prediction models. © 2023 IEEE.

关键词： Deep neural networks

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A metamodel based model transformation approach

A metamodel based model transformation approach

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3rd ACIS International Conference on Software Engineering Research, Management and Applications, SERA 2005

作者： Duan, Yucong Cheung, S.C. Fu, Xiaolan Gu, Yuqing Institute of Software Chinese Academy of Sciences China Department of Computer Science Hong Kong University of Science and Technology Hong Kong State Key Laboratory of Brain and Cognitive Science Institute of Psychology Chinese Academy of Sciences China

ISBN: (纸本)0769522971

The information transformation problem of model driven software development (MDSD) processes is gaining increasing attentions. In this paper, we present an indepth analysis of the major causes leading to the ambiguities of existing transformation. A metamodel based approach is then proposed to address these causes, A mechanism to generate transformation rules is devised based on a classification of the UML metamodel elements to be used in subsequent transformation processes. Some other critical issues relating to the generated transformation system are also discussed. © 2005 IEEE.

关键词： Software engineering

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Bio-RFX: Refining Biomedical Extraction via Advanced Relation Classification and Structural Constraints

Bio-RFX: Refining Biomedical Extraction via Advanced Relatio...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Wang, Minjia Liu, Fangzhou Li, Xiuxing Dong, Bowen Li, Zhenyu Pan, Tengyu Wang, Jianyong Tsinghua University China Harvard University United States School of Computer Science & Technology Beijing Institute of Technology China Key Laboratory of Brain-Machine Intelligence Technology Ministry of Education Nanjing University of Aeronautics and Astronautics China

ISBN: (纸本)9798891761643

The ever-growing biomedical publications magnify the challenge of extracting structured data from unstructured texts. This task involves two components: biomedical entity identification (Named Entity Recognition, NER) and their interrelation determination (Relation Extraction, RE). However, existing methods often neglect unique features of the biomedical literature, such as ambiguous entities, nested proper nouns, and overlapping relation triplets, and underutilize prior knowledge, leading to an intolerable performance decline in the biomedical domain, especially with limited annotated training data. In this paper, we propose the Biomedical Relation-First eXtraction (Bio-RFX) model by leveraging sentence-level relation classification before entity extraction to tackle entity ambiguity. Moreover, we exploit structural constraints between entities and relations to guide the model's hypothesis space, enhancing extraction performance across different training scenarios. Comprehensive experimental results on biomedical datasets show that Bio-RFX achieves significant improvements on both NER and RE tasks. Even under the low-resource training scenarios, it outperforms all baselines in NER and has highly competitive performance compared to the state-of-the-art fine-tuned baselines in RE. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Rolling Horizon Co-evolution for Snake AI Competition 13th

Rolling Horizon Co-evolution for Snake AI Competition

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13th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2024

作者： Li, Hui Zhou, Jiayi Zhang, Qingquan Research Institute of Trustworthy Autonomous System Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9783031578076

The Snake game, a classic in the gaming world, gains new dimensions with the Snake AI competition, where two players controlled by AI algorithms can now compete simultaneously in the same game session. This competition holds significance in advancing our understanding of artificial intelligence (AI) algorithms. In the 2020 and 2021 Snake AI competitions, popular algorithms, using graph-based search or heuristic strategies, demonstrate competitive performance, such as the A* algorithm, Monte Carlo Tree Search (MCTS). Contrary to these heuristic approaches, the Rolling Horizon Co-evolution Algorithm (RHCA), characterised by its core principles of rolling horizon evaluation and co-evolution, maintains two populations, one for each player, to co-evolve with each other without reliance on heuristics. RHCA has been verified its effectiveness in a two-player spaceship game. In this paper, we extend the RHCA application to the two-player Snake AI game, comparing it with other state-of-the-art methods. Additionally, we introduce various obstacles to create different complex scenarios, ensuring a comprehensive analysis. Experimental results reveal RHCA’s superior and stable performance, especially in resource-constrained and complex scenarios. Furthermore, an analysis of RHCA’s behaviours across maps with diverse obstacle scenarios highlights its ability to make intelligent decisions in competing with state-of-the-art methods. © IFIP International Federation for Information Processing 2024.

关键词： Trees (mathematics)

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