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Zero-shot fine-grained entity typing in information security based on ontology

KNOWLEDGE-BASED SYSTEMS 2021年 232卷

作者： Zhang, Han Zhu, Jiaxian Chen, Jicheng Liu, Junxiu Ji, Lixia Zheng Zhou Univ 100 Sci Ave Zhengzhou 450001 Peoples R China Informat Engn Univ 62 Sci Ave Zhengzhou 450000 Peoples R China Ulster Univ Intelligent Syst Res Ctr Sch Comp Engn & Intelligent Syst Magee Campus Derry BT48 7JL North Ireland

The field of information security suffers from the lack of labelled entities. This study proposes a zero shot hybrid approach, combining a clustering algorithm with a method for representing category labels, to classify fine-grained entity typing based on unified cybersecurity ontology (UCO) to address this issue. However, certain category labels in UCO do not have distinct domain features, while certain abbreviations cannot be obtained directly from word embedding using Word2vec. Thus, we propose a new method, referred to as mixed entities and hierarchy of UCO (MEHC), to represent the category labels. Moreover, to further improve the performance of fine-grained entity typing we propose the triclustering algorithm to re-cluster coarse-grained classification results or determine corresponding types for new entities, based on the theorem that the sum of two sides of a triangle is greater than the third. The experimental results prove that our triclustering algorithm can effectively shorten the computation time and that the proposed hybrid method is superior to other baselines for information security applications. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Fine-grained entity typing clustering algorithm Representation method for categories Information security Unified cybersecurity ontology

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Practical fuzzy model approach for asymmetrical V-shaped microshield line by CADMFILT software

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INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING 2021年第3期31卷

作者： Karpuz, Ceyhun Inan, Onur Urhan, Mehmet Pamukkale Univ Dept Elect & Elect Engn TR-20070 Denizli Turkey Mehmet Akif Ersoy Univ Bucak Emin Gulmez Tech Sci Vocat Sch Dept Biomed Device Technol Bucak Turkey

In this paper, practical fuzzy models depending on subtractive fuzzy clustering have been presented for asymmetrical V-shaped microshield line. In addition, a packet program has been introduced to run the proposed fuzzy models. In order to calculate the electrical parameters of each model, a linear equation system has been formed in each fuzzy model. Finally, the coefficients of each equation system have been found by means of fuzzy rules obtained by cluster extraction. The accuracy of each model has been confirmed by error analysis and the validity of the model results has been proven by comparing with the results of the quasi-static approach. At last, an object-oriented package program including theoretical solutions and fuzzy modeling has been presented. This computer-aided design program can be used for analysis, synthesis and modeling of transmission lines and microwave filters. It has been developed to calculate design processes with high speed and accuracy for researchers. It is expected that the introduced package program could be an alternative to commercial programs in the relevant field.

关键词： clustering algorithm fuzzy inference system fuzzy modeling software packages V-shaped microshield lines

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Prospects and obstacles analysis of applying blockchain technology to power trading using a deeply improved model based on the DEMATEL approach

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SUSTAINABLE CITIES AND SOCIETY 2021年 70卷

作者： Zhou, Jianli Wu, Yunna Liu, Fangtong Tao, Yao Gao, Jianwei North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China North China Elect Power Univ Beijing Key Lab New Energy & Low Carbon Dev Beijing 102206 Peoples R China

Blockchain technology is widely concerned, and its related applications can promote the process of smart cities and sustainable society. However, while mining the potential application scenarios in power trading, we must recognize the barriers, help it survive the hype stage, and promote its healthy development and technology landing. For the first time, hesitant fuzzy linguistic term set and K-mediods clustering algorithm are used to improve the decision-making trial and evaluation laboratory (DEMATEL) method, and the obstacle analysis model of the applied scene is constructed. Compared with the conventional DEMATEL method, the collection of evaluation information is more flexible and closer to reality. Besides, the classification of obstacle factors is more scientific and there can be more than two categories for effect degree. Firstly, thirteen barriers to its application in power trading are identified;at the same time, six specific application scenarios are summarized and analyzed. Then, a detailed discussion is conducted on each scenario: The quantification of the influence degree among obstacles, the classification and qualitative of the influence degree, and the causal mechanism analysis. The key obstacles identified can be used to guide practice. Finally, strategic solutions and policy recommendations are given to remove or alleviate these obstacles.

关键词： Power trading Sustainable consumption Obstacles analysis Blockchain technology DEMATEL method clustering algorithm

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Knowledge-Based Grasp Planning Using Dynamic Self-Organizing Network

Knowledge-Based Grasp Planning Using Dynamic Self-Organizing...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Yang, Shiyi Jeon, Soo Univ Waterloo Dept Mech & Mechatron Engn Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9781728162126

Category-based methods for task-specified grasp planning have recently been proposed in the literature. Such methods, however, are normally time consuming in both training and grasp determination process and lack capabilities to improve grasping skills due to the fixed training data set. This paper presents an improved approach for knowledge-based grasp planning by developing a multi-layer network using self-organizing map. A number of grasp candidates are learned in the experiments and the information that is associated with these grasp candidates is clustered based on different criteria on each network layer. A codebook which is composed of a small number of generalized models and the corresponding task-oriented grasps is generated from the network. In addition, the proposed network is capable of automatically adjusting its size so that the codebook can be continuously updated from each interaction with the novel objects. In order to increase the accuracy and convergence rate of the clustering process, a new initialization method is also proposed. Simulation results present the advantages of the proposed initialization method and the auto-growing algorithm in terms of accuracy and efficiency over some conventional methods. Experimental results demonstrate that novel objects can be successfully grasped in accordance with desired tasks using the proposed approach.

关键词： grasp planning clustering algorithm self-organizing map initialization method growing network

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Current Unbalance Cluster Analysis Based on Self-organizing Competitive Neural Network 4th

Current Unbalance Cluster Analysis Based on Self-organizing ...

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4th International Forum on Smart Grid Protection and Control (Purple Mountain Forum)

作者： Wu, Peng Liang, Ruiqi Zhou, Haocheng Wang, Kai Liu, Youchun Zhu, Hongming Jiangsu Elect Power Informat Technol Co Ltd Nanjing 210000 Peoples R China Southeast Univ Sch Informat Sci & Engn Nanjing 210000 Peoples R China

ISBN: (纸本)9789811397837;9789811397820

In power system, current unbalance is a kind of common fault that seriously affects the safety and efficiency of power system. There are many reasons that may cause three-phase unbalance, and now rely on manpower to judge according to specific conditions. This paper proposes a new algorithm for clustering analysis of unbalanced three-phase current data. We define a serials of feature parameters, and then use self-organizing competitive neural network for clustering analysis to subdivide current unbalance into five categories. In the experiment, a large amount of historical current data is analyzed by the proposed algorithm. We get five categories with obvious features and differences. The clustering results are reasonable and interpretable. The algorithm makes full use of the large amount of unmarked historical data produced by power system, and is helpful for the early warning of current unbalance and pre-judgment of causes.

关键词： Current unbalance Self-organizing competitive neural network clustering algorithm Feature extraction

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An Improved Neighborhood-Based Recommendation algorithm Optimization with clustering Analysis and Latent Factor Model

An Improved Neighborhood-Based Recommendation Algorithm Opti...

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第三十八届中国控制会议

作者： Zhenyu Yao Jinkuan Wang Yinghua Han College of Information Science and Engineering Northeastern University School of Computer and Communication Engineering Northeastern University at Qinhuangdao

With the development of Internet, information overload about products is pervasive. It is important for commercial platforms to predict users' preferences and recommend information. Neighborhood-based recommendation algorithm is one of the most popular methods, which are used to predict the rating of items that have not yet been rated. However, neighborhood-based algorithm suffers from data sparsity in practice, causing low accuracy. A recommendation algorithm is proposed in this paper, in which auxiliary information of users and items are utilized. Specifically, clustering method is introduced to divide users into categories, which reduces the time complexity of the algorithm, and latent factor model approach is applied to predict user-item matrix, which improves the accuracy of neighborhood-based algorithm. Experiments on real-world dataset demonstrate that the proposed KC-LFMCF approach is superior to the conventional neighborhood-based algorithm in terms of recommendation accuracy and time complexity.

关键词： KC-LFMCF Rating Predict clustering algorithm Latent Factor Model

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Density-Based Fuzzy C-Means Multi-Center Re-clustering Radar Signal Sorting algorithm 17

Density-Based Fuzzy C-Means Multi-Center Re-Clustering Radar...

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17th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA)

作者： Cao, Sheng Wang, Shucheng Zhang, Yan Harbin Inst Technol Dept Microelect Sci & Technol Harbin Heilongjiang Peoples R China Harbin Inst Technol Dept Informat Engn Harbin Heilongjiang Peoples R China Heilongjiang Univ Dept Informat Engn Harbin Heilongjiang Peoples R China

ISBN: (纸本)9781538668054

As the improving strategic position of electronic warfare in modern warfare, radar sorting detection becomes the eye of modern information warfare and plays an important role in it. This paper designs a new pulse radar sorting algorithm: a Density-Based Fuzzy C-Means Multi-Center Re-clustering (DFCMRC) radar signal sorting algorithm. This algorithm mainly combines the advantages of Density-Based Spatial clustering of Applications with Noise (DBSCAN) clustering algorithm and Fuzzy C-means (FCM) clustering algorithm. This paper also optimizes the structure of the DFCMRC algorithm, which changes the algorithm that randomly generated the initial center point to the clustering by Fast Search and Find of Density Peaks (CFSFDP) algorithm. After comparison tests, the DFCMRC algorithm sorting result is better than the K-means algorithm, the DBSCAN algorithm and the FCM algorithm. Also, the membership grade description of DFCMRC makes more sense than the FCM's. Accelerated optimized DFCMRC algorithm can reduce more than half iterations, which greatly shortens the algorithm calculation time.

关键词： radar signal sorting clustering algorithm algorithm acceleration

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A Cooperative Spectrum Sensing algorithm Based on Principal Component Analysis and K-medoids clustering 33

A Cooperative Spectrum Sensing Algorithm Based on Principal ...

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33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Sun, Chenhao Wang, Yonghua Wan, Pin Du, Yiqi Guangdong Univ Technol Sch Automat Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538672556

In order to decrease the effects of noise on the signal feature extraction and improve the spectrum sensing performance of cognitive radio system. This paper proposes an improved cooperative spectrum sensing (CSS) method based on the principal component analysis (PCA) and K-medoids clustering. Firstly, principal component analysis extracts signal principal components by applying multiple antenna systems, then the corresponding signal features are extracted through the signal principal component matrix. Finally, these features are classified using the K-medoids clustering algorithm. The experimental simulations are performed with different features and K-medoids clustering algorithms. The simulation results show that the proposed method has better detection performance than traditional spectrum sensing methods and can effectively improve the efficiency of spectrum sensing.

关键词： Spectrum sensing principal component analysis clustering algorithm feature extraction

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Improved Initial clustering Center Selection Method for k-means algorithm 8

Improved Initial Clustering Center Selection Method for k-me...

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8th International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC)

作者： Xie, Qingqing Jiang, He Han, Bing Wang, Dongyuan QiLu Univ Technol ShanDong Acad Sci Jinan Peoples R China

ISBN: (纸本)9781538682463

Aiming at the Randomness about selecting the initial center *** paper presents improvement scheme, in which one can select initial center point based on the Pearson correlation to avoid the randomness. After the experiment, the initial center point selected can effectively reduce the number of iterations of the clustering algorithm, and improve the efficiency of clustering algorithm. In the mean time, clustering results and the number of iterations has good stability.

关键词： clustering algorithm initial center point Pearson correlation Randomness

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Group decision making with hesitant fuzzy linguistic preference relations based on modified extent measurement

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 171卷

作者： Ren, Peijia Xu, Zeshui Wang, Xinxin Zeng, Xiao-Jun South China Univ Technol Sch Business Adm Guangzhou 510640 Peoples R China Sichuan Univ Business Sch Chengdu 610064 Peoples R China Univ Manchester Sch Comp Sci Manchester M13 9PL Lancs England

The paper presents a consensus model for group decision making (GDM) with hesitant fuzzy linguistic preference relations (HFLPRs), which is composed of two parts: (1) clustering HFLPRs by mapping them into a higher dimension space based on a kernel function;(2) building a consensus model based on measuring the modified extents of decision makers? HFLPRs for reducing the biased judgements existing in their less-familiar ways. The paper further makes comprehensive analyses for the proposed model on: (1) the influence of decision makers? different sensitive attitudes towards the distances between the individual HFLPRs and the overall HFLPRs on decision-making results;(2) the differences and complexities of another model with a different consensus perspective and the proposed model. The experimental analyses provide the support for the maximum modified extent determination in different decision scenarios, and show that the proposed consensus model makes sense. Finally, the proposed model is illustrated by the application in choosing an optimal flood discharge technique for a hydropower station.

关键词： Group decision making Hesitant fuzzy linguistic preference relation clustering algorithm Consensus index Modified extent

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