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IEEE International Conference on Big knowledge (ICBK)

作者： Ze Yang Changyang Tai Gongqing Wu Zan Zhang Xianyu Bao Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shenzhen Academy of Inspection and Quarantine Shenzhen China

ISBN: (纸本)9781665438599

Few clustering methods show good performance on multivariate time series (MTS) data. Traditional methods rely too much on similarity measures and perform poorly on the MTS data with complex structures. This paper proposes an MTS clustering algorithm based on graph embedding called MTSC-GE to improve the performance of MTS clustering. MTSC-GE can map MTS samples to the feature representations in a low-dimensional space and then cluster them. While mining the information of the samples themselves, MTSC-GE builds the whole time series data into a graph, paying attention to the connections between samples from an overall perspective and discovering the local structural feature of MTS data. The proposed MTSC-G E consists of three stages. The first stage builds a graph using the original dataset, where each of the MTS samples is regarded as a node in the graph. The second stage uses the graph embedding technique to obtain a new representation of each node. Finally, MTSC-G E uses the K - Means algorithm to cluster based on the newly obtained representation. We compare MTSC-GE with six state-of-the-art benchmark methods on five public datasets, experimental results show that MTSC-GE has achieved good performance.

关键词： Deep learning Conferences Clustering methods Time series analysis Clustering algorithms Benchmark testing Stability analysis

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Load pattern window aware power supply device clustering

International Journal of Database Theory and Application

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International Journal of database Theory and Application 2016年第8期9卷 269-280页

作者： Sheng, Wanxing Liu, Ke-Yan Yu, Yixi An, Rungong Zhou, Ningnan Zhang, Xiao China Electric Power Research Institute Beijing100192 China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Renmin University of China Beijing100872 China School of Information Renmin University of China 100872 China

data-driven decision in big data era is becoming ubiquitous in electronic grid. In particular, daily collected power consumption records enable workload aware device clustering, which is crucial for critical domain applications such as device functionality identification. In this paper, we propose a load pattern window aware method for clustering power supply devices. Our approach overcomes the drawbacks in existing works, such as fuzzy based clustering, K-means based clustering and neutral network based clustering. After investigating the large scale records from power supply devices, our approach partitions device records into disjoint time intervals with parameterized window size, which indicate the load pattern feature for a period of time given a specific device. Devices are then decomposed into a mixture of these features, and those devices with similar dominating features are grouped together. The experimental results demonstrate the effectiveness and efficiency of our solution based on the real data collected from power grid in China. © 2016 SERSC.

关键词： Big data

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Semi-supervised Short Text Classification Based On Dual-channel data Augmentation

Semi-supervised Short Text Classification Based On Dual-chan...

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International Joint Conference on Neural Networks (IJCNN)

作者： Jiajun Li Peipei Li Xuegang Hu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei City Anhui Province China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei City Anhui Province China

In practical applications, high-quality labeled data is critical for short text classification. But in many cases, it is expensive and time-consuming to obtain labeled information. Semi-supervised short text classification is hence attracting more attention. However, due to the sparsity of short texts, the performance of existing short text classification models always needs to be improved. Therefore, in this paper, we propose a semi-supervised Short text classification method based on Dual-Channel data Augmentation called SDCA. More specifically, in order to solve the sparsity of short texts, this model first adopts the multi-stage word-level TCN (Temporal Convolutional Network)-based attention to enhanced semantic features and an one-dimensional convolution-based attention mechanism to augment the relevance of surrounding short texts. Secondly, the unlabeled data are augmented by word embedding weighted augmentation and word replacement augmentation, so that the model can make full use of the unlabeled short texts and further enhance the network training. Finally, extensive experiments conducted on four benchmark datasets demonstrate the effectiveness of the proposed model on semi-supervised short text classification.

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Web page malicious code detection based on embedded fingerprints

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Qinghua Daxue Xuebao/Journal of Tsinghua University 2009年第SUPPL. 2期49卷 2208-2214页

作者： Huang, Jianjun Liang, Bin Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education School of Information Renmin University of China Beijing 100872 China

Web sites have become the main targets of many attackers. Signature-based detection needs to maintain a large signature database and Honeypot based methods are not efficient. Since attackers always make the malicious codes in Web pages difficult to detect by the browser users, their methods can be classified into various fingerprints. Various malicious codes were analyzed to identify 6 types of fingerprints. The system utilizes a spider integrated with script interpretation to fetch target Web pages and extract specific tags for detection by HTML parsing for matching with the fingerprints to detect malicious codes. This method needs fewer fingerprints than traditional detection methods and is more efficient. Results for 60 websites show that the system has a false negative rate of 2.63% and a false positive rate of 1.99%.

关键词： Websites

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Pathological Brain Detection Based on Online Sequential Extreme Learning Machine

Pathological Brain Detection Based on Online Sequential Extr...

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2016 IEEE International Conference on Progress in Informatics and Computing

作者： Siyuan Lu Hainan Wang Xueyan Wu Shuihua Wang School of Computer Science and Technology Nanjing Normal University State Key Lab of CAD & CG Zhejiang University Key laboratory of symbolic computation and knowledge engineering of ministry of education Jilin University Key Laboratory of Statistical information technology and data mining State Statistics Bureau Department of Electrical Engineering The City College of New YorkCUNY

Magnetic resonance imaging(MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography(CT). It is especially suitable for brain disease detection. It is beneficial to detect diseases automatically and accurately. We proposed a pathological brain detection method based on brain MR images and online sequential extreme learning machine. First, seven wavelet entropies(WE) were extracted from each brain MR image to form the feature vector. Then, an online sequential extreme learning machine(OS-ELM) was trained to differentiate pathological brains from the healthy *** experiment results over 132 brain MRIs showed that the proposed approach achieved a sensitivity of 93.51%, a specificity of 92.22%, and an overall accuracy of 93.33%,which suggested that our method is effective.

关键词： magnetic resonance imaging wavelet entropy online sequential extreme learning machine Classification pattern recognition

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Speech emotion recognition using emotion perception spectral feature

Speech emotion recognition using emotion perception spectral...

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作者： Jiang, Lin Tan, Ping Yang, Junfeng Liu, Xingbao Wang, Chao Institute of Big Data and Internet Innovation College of Computer and Information Engineering Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China College of Software East China University of Technology Nanchang China Collaborative Innovation Center for Economics Crime Investigation and Prevention Technology Jiangxi Police College Nanchang China National Engineering Research Center for Multimedia Software Wuhan University Wuhan China

Speech emotion recognition is an important technique for human-computer interface applications. Due to contain rich information of emotion, the spectral feature is widely used for emotion recognition. However, the recognition performance is limited because of imprecise extracted rule and uncertain size of resolution of spectral feature. To address this issue, motivated by speech coding, we introduced psychoacoustics model, provided a perception spectral subband partition method for obtaining more precise frequency resolution. Moreover, we also provided a new spectral feature on the divided subband frequency signals. The proposed feature includes emotional perception entropy, spectral inclination, and spectral flatness. Then, a Support Vector Machine classifier is used to recognize emotion categories. The experiment results show that the proposed spectral feature is superior to the traditional MFCC feature, and also better than the state-of-the-art Fourier feature and multi-resolution amplitude feature. © 2019 John Wiley & Sons, Ltd.

关键词： Emotion Recognition

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Causal structure learning of nonlinear additive noise model based on streaming feature

Causal structure learning of nonlinear additive noise model ...

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IEEE International Conference on data Mining Workshops (ICDM Workshops)

作者： Jing Yang Gaojin Fan Anbo Shen Aiguo Wang Ministry of Education Key Laboratory of Knowledge Engineering With Big Data(Hefei University of Technology) School of Computer Science and Information Engineering Hefei University of Technology Hefei P. R. China School of Electronic and Information Engineering Foshan University China

ISBN: (纸本)9781665424288

The current streaming feature structure learning needs to be improved in the processing of nonlinear continuous data and the dynamic acquisition of causal structures. In this paper, we propose a causal structure learning algorithm, CANSF, based on the streaming feature of additive noise models. We have made three contributions. First, by using the information carried by the noise of nonlinear continuous data, we propose a real correlation identification method based on logarithmic likelihood, which can identify the real correlation and redundant features of target features, and dynamically select parent and child nodes for each feature. Second, based on regression analysis, a method to determine the causal direction is proposed, which can be used for dynamic orientation. Third, a learning method of causal structure based on streaming features is proposed, which can obtain the Causal structure diagram directly and dynamically.

关键词： Additive noise Learning systems Correlation Heuristic algorithms Conferences data models Regression analysis

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Analyzing the Spillover Effects of the Binance Incident on Financial Markets: A Machine Learning Approach

SSRN

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SSRN 2024年

作者： Feng, Lingbing Qi, Jiajun Liu, Ye Wang, Wei School of Statistics and Data Science Jiangxi University of Finance and Economics Jiangxi Nanchang330013 China Key Laboratory of Data Science in Finance and Economics Jiangxi University of Finance and Economics Jiangxi Nanchang330013 China Research Centre for Financial Development and Risk Prevention Jiangxi University of Finance and Economics Jiangxi Nanchang330013 China School of Applied Economics Jiangxi University of Finance and Economics Jiangxi Nanchang330013 China School of Management and Engineering Nanjing University Jiangsu Nanjing210093 China School of Economics and Business Administration Chongqing University Chongqing400044 China

This paper integrates event study with machine learning to analyze the spillover effects of the "Binance Incident" in the cryptocurrency market on financial markets. Utilizing the Lasso model, this paper predicts normal returns during the event window, outperforming traditional market models. Findings reveal positive effects on precious metal markets due to their safe-haven nature, while energy markets exhibit negative effects due to reduced investor confidence and weaker safe-haven attributes. Fiat currencies show negligible reactions. Summarizing across assets, this paper finds a negative overall impact of this adverse event on the financial markets. © 2024, The Authors. All rights reserved.

关键词： Machine learning

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NEW BOOLEAN SATISFIABILITY PROBLEM HEURISTIC STRATEGY: MINIMAL POSITIVE NEGATIVE PRODUCT STRATEGY

arXiv

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arXiv 2023年

作者： Qun, Zhao Xintao, Wang Menghui, Yang School of Information Resource Management Renmin University of China Beijing100872 China Institute of Economics Chinese Academy of Social Sciences Beijing100872 China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing100872 China

This study presents a novel heuristic algorithm called the "Minimal Positive Negative Product Strategy" to guide the CDCL algorithm in solving the Boolean satisfiability problem. It provides a mathematical explanation for the superiority of this algorithm over widely used heuristics such as the Dynamic Largest Individual Sum (DLIS) and the Variable State Independent Decaying Sum (VSIDS). Experimental results further confirm the effectiveness of this heuristic strategy in *** Codes 03 Copyright © 2023, The Authors. All rights reserved.

关键词： Heuristic algorithms

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An improved elastic net with heuristic cloning strategy for traveling salesman problem

引用

ICIC Express Letters 2011年第11期5卷 3995-4000页

作者： Yang, Gang Yang, Nan Yi, Junyan Key Laboratory of Data Engineering and Knowledge Engineering MOE School of Information Renmin University of China No. 59 Zhongguancun Street Haidian District Beijing 100872 China Department of Computer Science and Technology Zhejiang University of Technology No. 18 Chaowang Road Hangzhou 310014 China

Based on analysis of relationships between solution qualities and number of initial dynamic points in elastic net algorithm, we propose an improved elastic network algorithm (IENA) introduced in a heuristic cloning strategy for traveling salesman problem (TSP). The improved elastic net consists of four evolution processes with less dynamic points and gradually reducing cities, and it accelerates the convergent process without compromising the equality of solutions. IENA is conducted on some random TSP instances to verify its performance by comparison with other algorithms. © 2011 ICIC International.

关键词： Traveling salesman problem

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