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第32届中国控制与决策会议

作者： Ailiang Xing Biao Song Yixin Yin School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education Demonstration and Research Department of Network Information System Beijing Institute of Computer Application

This paper presents an adaptive dynamic surface control for uncertain nonlinear strict feedback systems with constraints on both input and *** technology is employed in the controller *** performance method with asymmetric barrier Lyapunov function(BLF) is used to deal with the output *** input saturation is solved by using the Nussbaum *** dynamic surface control(DSC) technique is employed to reduce the complexity of the virtual *** is illustrated that all closed-loop signals are semi-global uniformly ultimately bounded(SGUUB).Finally,a simulation example proves the proposed results.

关键词： Input saturation Output constraints Nussbaum function Dynamic surface control

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Utilizing bidirectional encoder representations from transformers for answer selection

arXiv

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arXiv 2020年

作者： Laskar, Md Tahmid Rahman Hoque, Enamul Huang, Jimmy Xiangji Department of Electrical Engineering and Computer Science York University Toronto Canada Information Retrieval & Knowledge Management Research Lab York University Toronto Canada School of Information Technology York University Toronto Canada

Pre-training a transformer-based model for the language modeling task in a large dataset and then fine-tuning it for downstream tasks has been found very useful in recent years. One major advantage of such pre-trained language models is that they can effectively absorb the context of each word in a sentence. However, for tasks such as the answer selection task, the pre-trained language models have not been extensively used yet. To investigate their effectiveness in such tasks, in this paper, we adopt the pre-trained Bidirectional Encoder Representations from Transformer (BERT) language model and fine-tune it on two Question Answering (QA) datasets and three Community Question Answering (CQA) datasets for the answer selection task. We find that fine-tuning the BERT model for the answer selection task is very effective and observe a maximum improvement of 13.1% in the QA datasets and 18.7% in the CQA datasets compared to the previous state-of-the-art. Copyright © 2020, The Authors. All rights reserved.

关键词： Signal encoding

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Prior-guided dual-stage diabetic retinopathy grading model based on feature collaboration of lesion and vascular structure

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Expert Systems with Applications 2025年 285卷

作者： Guo, Xiaoxin Yang, Guangqi Xu, Chenfangqian Dong, Hongliang Hu, Xiaoying Che, Songtian Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University 2699 Qianjin Street Changchun130012 China College of Computer Science and Technology Jilin University 2699 Qianjin Street Changchun130012 China Ophthalmology Department Bethune First Hospital of Jilin University Changchun Jilin130021 China Ophthalmology Department Bethune Second Hospital of Jilin University Changchun Jilin130041 China

In order to fully utilize lesion features and vascular structure and solve the problem of class imbalance, diabetes retinopathy (DR) grading is modeled as a dual-stage task, and the prior-guided dual-stage diabetes retinopathy grading model is proposed by designing an end-to-end hierarchical framework, consisting of XE-Net and MFC-Net. The former uses vascular structure from color fundus images as prior knowledge to guide the classification of proliferative diabetic retinopathy (PDR) and non-PDR;the latter further classifies non-PDR images using color fundus images with four types of segmented lesions. The core of MFC-Net is the feature collaboration block (FCB) for multi-classification tasks, which is proposed to facilitate effective collaboration between global and local features. In addition, a class-balanced (CB) loss weight is also introduced to address the common class imbalance problem in DR datasets. The experimental results on two publicly available datasets, DDR and IDRiD show that the proposed model has significant advantages. © 2025 Elsevier Ltd

关键词： Image segmentation

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Distributed Representations of Entities in Open-World knowledge Graphs

arXiv

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arXiv 2020年

作者： Guo, Lingbing Chen, Zhuo Chen, Jiaoyan Zhang, Yichi Sun, Zequn Bo, Zhongpu Fang, Yin Liu, Xiaoze Chen, Huajun Zhang, Wen College of Computer Science and Technology Zhejiang University China Zhejiang University Ant Group Joint Laboratory of Knowledge Graph China Donghai Laboratory China Department of Computer Science The University of Manchester United Kingdom Department of Computer Science and Technology Nanjing University China Ant Group China Elmore Family School of Electrical and Computer Engineering Purdue University United States School of Software Technology Zhejiang University China

Graph neural network (GNN)-based methods have demonstrated remarkable performance in various knowledge graph (KG) tasks. However, most existing approaches rely on observing all entities during training, posing a challenge in real-world knowledge graphs where new entities emerge frequently. To address this limitation, we introduce Decentralized Attention Network (DAN). DAN leverages neighbor context as the query vector to score the neighbors of an entity, thereby distributing the entity semantics only among its neighbor embeddings. To effectively train a DAN, we introduce self-distillation, a technique that guides the network in generating desired representations. Theoretical analysis validates the effectiveness of our approach. We implement an end-to-end framework and conduct extensive experiments to evaluate our method, showcasing competitive performance on conventional entity alignment and entity prediction tasks. Furthermore, our method significantly outperforms existing methods in open-world settings. © 2020, CC0.

关键词： knowledge graph

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Correction to: Multilayered-quality education ecosystem (MQEE): an intelligent education modal for sustainable quality education

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Journal of Computing in Higher Education 2021年第3期33卷 580-580页

作者： Verma, Anil Singh, Aman Lughofer, Edwin Cheng, Xiaochun Abualsaud, Khalid Department of Computer Science and Engineering Lovely Professional University Phagwara India Department of Knowledge-Based Mathematical Systems Johannes Kepler University Linz Linz Austria Department of Computer Science Middlesex University London UK Department of Computer Science Qatar University Doha Qatar

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A 2-opt guided discrete antlion optimizationalgorithm for multi-depotvehicle routing problem

Decision Making: Applications in Management and Engineering

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Decision Making: Applications in Management and engineering 2019年第2期2卷 112-125页

作者： Barma, Partha Sarathi Dutta, Joydeep Mukherjee, Anupam Department of Computer Science and Engineering NSHM Knowledge Campus Durgapur India Department of Mathematics National Institute of Technology Durgapur India

The Multi-depot vehicle routing problem (MDVRP) is a real-world variant of the vehicle routing problem (VRP) where the customers are getting service from some depots. The main target of MDVRP is to find the route plan of each vehicle for all the depots to fulfill the demands of all the customers, as well as that, needs the least distance to travel. Here all the vehicles start from different depots and return to the same after serving the customers in its route. In MDVRP each customer node must be served by only one vehicle which starts from any of the depots. In this paper, we have considered a homogeneous fleet of vehicles. Here a bio-inspired meta-heuristic method named Discrete Antli-on Optimization algorithm (DALO) followed by the 2-opt algorithm for local searching is used to minimize the total routing distance of the MDVRP. The comparison with the Genetic Algorithm, Ant colony optimization, and known best solutions is also discussed and analyzed. © 2019 Regional Association for Security and crisis management. All rights reserved.

关键词： Combinatorial optimization

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Educational website ranking using fuzzy logic and k-means clustering based hybrid method

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Ingenierie des Systemes d'Information 2019年第5期24卷 497-506页

作者： Deb, Kaustuv Banerjee, Sonali Chatterjee, Rudra Prasad Das, Atanu Bag, Rajib Department of Computer Science and Engineering Supreme Knowledge Foundation Group of Institutions Mankundu HooghlyWest Bengal712139 India Department of Computer Science and Engineering Netaji Subhash Engineering College KolkataWest Bengal700152 India

In the Internet era, it is a huge challenge for users to find suitable and pertinent information out of the huge amount of online data. The challenge is particularly arduous for students searching for education information in a specific domain. To solve the problem, this paper puts forward an educational website ranking method, which applies fuzzy logic and k-means clustering in sequence. First, a fuzzy inference system (FIS) was established based on the fuzzy logic, and used to find the utility value (UV) of an educational website according to the feedback marks of each student. Then, the general utility value (GUV) of each educational website was determined through k-means clustering of all the UVs of that website. Then, the educational websites were ranked by their GUVs. The experimental results show that the proposed method ranks the educational websites clearly and correctly, enabling students to find the desired education information. © 2019 International Information and engineering Technology Association. All rights reserved.

关键词： Students

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Dynamic anomaly detection based on recursive independent component analysis of multi-variate residual signals 33

Dynamic anomaly detection based on recursive independent com...

引用

33rd Annual European Simulation and Modelling Conference, ESM 2019

作者： Lughofer, Edwin Pratama, Mahardhika Eitzinger, Christian Radauer, Thomas Department of Knowledge-Based Mathematical Systems Johannes Kepler University Linz Austria School of Computer Science and Engineering Nanyang Technological University Singapore Profactor GmbH Steyr-Gleink Austria Stratec Consumables Anif Austria

ISBN: (纸本)9789492859099

We address the problem of anomaly detection in industrial facilities based on multi-sensor measurement systems. Any anomaly may point to an (early) indication of a fault in the system, which may turn latter into a severe failure and thus should be detected as early as possible. Our approach relies on causal relation networks gained from the measurements of process variables, whose influence weights between causes and effects are modelled by a specific non-linear fuzzy systems architecture comprising generalized rules. Residuals are extracted online from a bunch of (high-quality) causal relations and are analyzed over time through an advanced independent component analysis (ICA), which employs a specific strategy on dominant parts for automatically suppressing noise content in the data/residuals. A further important novelty aspect is that the mixing and demixing matrices which characterize the independent components are recursively updated in incremental and robust (converging) manner (thus, our approach is termed as RICA). The control signal extracted from the components is therefore also dynamically updated. The second dynamic aspect concerns the update of the causal relation models themselves through the usage of an evolving fuzzy systems approach. This integrates the possibility of structural changes by decreasing or increasing the models' non-linearity degree on demand and on the fly. RICA is compared with static ICA and with several (15) related state-of-the-art (SoA) methods based on data sets from a chip manufacturing system embedding two production stages. They comprise OK process phases with different machining parameters and NOT-OK (abnormal) phases. Results show improved detection capabilities with RICA while achieving lower false alarms rates than static ICA and than the SoA methods. Copyright © 2019 EUROSIS-ETI.

关键词： Anomaly detection

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Sure: Screening Unlabeled Samples for Reliable Negative Samples Based on Reinforcement Learning

SSRN

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SSRN 2022年

作者： Li, Ying Sun, Hang Fang, Wensi Ma, Qin Han, Siyu Wang-Sattler, Rui Du, Wei Yu, Qiong Key Laboratory of Symbolic Computation and Knowledge Engineering College of Computer Science and Technology Jilin University Qianjin Street Jilin Changchun130012 China Department of Biomedical Informatics Ohio State University Lane Ave ColumbusOH43210 United States TUM School of Medicine Technical University of Munich Ismaninger Straße 22 Bavaria Munich81675 Germany Helmholtz Center Munich German Research Center for Environmental Health Ingolstadter Landstraße 1 Bavaria Neuherberg85764 Germany Department of Epidemiology and Biostatistics School of public health Jilin University Qianjin Street Jilin Changchun130012 China

For many classification tasks, only experimentally validated positive samples are available, and experimentally validated negative samples are not recorded. The lack of negative samples poses a great challenge for using supervised machine learning. To address this problem, we propose a novel deep reinforcement learning based model to screen reliable negative samples from unlabeled samples, named SURE. SURE has two modules: sample selector and sample inspector. The sample selector screens reliable negative samples from unlabeled samples by two reinforcement strategies. The sample inspector classifies samples and provides rewards to the sample selector. In this paper, we focus on one popular issue in the field of bioinformatics: the ncRNA-protein interaction (NPI) prediction task, which lacks reliable negative samples. Thirty datasets for NPI prediction are used to test the screening effect of SURE. The Experimental results show that our model has a robust negative sample screening capability and is superior to all outstanding sample screening methods used in the NPI prediction task. In addition, we refine 5 NPI datasets containing reliable negative samples screened by SURE, and a webserver (***/sure) is available offering the NPI prediction refined by SURE. © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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A Malware Identification and Detection Method Using Mixture Correntropy-Based Deep Neural Network 3rd

A Malware Identification and Detection Method Using Mixture ...

引用

3rd International Conference on Cyberspace Data and Intelligence, Cyber DI 2019, and the International Conference on Cyber-Living, Cyber-Syndrome, and Cyber-Health, CyberLife 2019

作者： Luo, Xiong Li, Jianyuan Wang, Weiping Gao, Yang Zhao, Wenbing School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083 China Beijing Intelligent Logistics System Collaborative Innovation Center Beijing101149 China Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15261 United States China Information Technology Security Evaluation Center Beijing100085 China Department of Electrical Engineering and Computer Science Cleveland State University ClevelandOH44115 United States

ISBN: (纸本)9789811519215

With the rapid development of CPS technology, the identification and detection of malware has become a matter of concern in the industrial application of CPS. Currently, advanced machine learning methods such as deep learning are popular in the research of malware identification and detection, and some progress has been made so far. However, there are also some problems. For example, considering the existing noise or outliers in the datasets of malware, some methods are not robust enough. Therefore, the accuracy of classification of malware still needs to be improved. Aiming at it, we propose a novel method thought the combination of correntropy and deep neural network (DNN). In our proposed method for malware identification and detection, given the success of mixture correntropy as an effective similarity measure in addressing complex dataset with noise, it is therefore incorporated into a popular DNN, i.e., convolutional neural network (CNN), to reconstruct its loss function, with the purpose of further detecting the features of outliers. We present the detailed design process of our proposed method. Furthermore, the proposed method is tested both on a popular benchmark dataset and a real-world malware classification dataset, to verify its learning performance. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Mixtures

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