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Partially Shared Semi-supervised Deep Matrix Factorization with Multi-view data

Partially Shared Semi-supervised Deep Matrix Factorization w...

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IEEE international conference on data Mining Workshops (ICDM Workshops)

作者： Haonan Huang Naiyao Liang Wei Yan Zuyuan Yang Zhenni Lit Weijun Sun Guangdong Key Laboratory of IoT Information Technology Guangdong University of Technology Guangzhou China Ministry of Education Key Laboratory of iDetection and Manufacturing-IoT Guangzhou China Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou China

ISBN: (数字)9781728190129

ISBN: (纸本)9781728190136

Since many real-world data can be described from multiple views, multi-view learning has attracted considerable attention. Various methods have been proposed and successfully applied to multi-view learning, typically based on matrix factorization models. Recently, it is extended to the deep structure to exploit the hierarchical information of multi-view data, but the view-specific features and the label information are seldom considered. To address these concerns, we present a partially shared semi-supervised deep matrix factorization model (PSDMF). By integrating the partially shared deep decomposition structure, graph regularization and the semi-supervised regression model, PSDMF can learn a compact and discriminative representation through eliminating the effects of uncorrelated information. In addition, we develop an efficient iterative updating algorithm for PSDMF. Extensive experiments on five benchmark datasets demonstrate that PSDMF can achieve better performance than the state-of-the-art multi-view learning approaches. The MATLAB source code is available at https://***/libertyhhn/PartiallySharedDMF.

关键词： conferences Iterative algorithms data models Matrix decomposition data mining Optimization Matlab

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Short-Term Traffic Forecasting Using High-Resolution Traffic data

Short-Term Traffic Forecasting Using High-Resolution Traffic...

引用

international conference on Intelligent Transportation

作者： Wenqing Li Chuhan Yang Saif Eddin Jabari Division of Engineering New York University Abu Dhabi Abu Dhabi UAE Tandon school of Engineering New York University Brooklyn NY USA Tandon School of Engineering New York University Brooklyn NY USA

ISBN: (数字)9781728141497

ISBN: (纸本)9781728141503

This paper develops a data-driven toolkit for traffic forecasting using high-resolution (a.k. a. event-based) traffic data. This is the raw data obtained from fixed sensors in urban roads. Time series of such raw data exhibit heavy fluctuations from one time step to the next (typically on the order of 0.1-1 second). Short-term forecasts (10-30 seconds into the future) of traffic conditions are critical for traffic operations applications (e.g., adaptive signal control). But traffic forecasting tools in the literature deal predominantly with 3-5 minute aggregated data, where the typical signal cycle is on the order of 2 minutes. This renders such forecasts useless at the operations level. To this end, we model the traffic forecasting problem as a matrix completion problem, where the forecasting inputs are mapped to a higher dimensional space using kernels. The formulation allows us to capture both nonlinear dependencies between forecasting inputs and outputs but also allows us to capture dependencies among the inputs. These dependencies correspond to correlations between different locations in the network. We further employ adaptive boosting to enhance the training accuracy and capture historical patterns in the data. The performance of the proposed methods is verified using high-resolution data obtained from a real-world traffic network in Abu Dhabi, UAE. Our experimental results show that the proposed method outperforms other state-of-the-art algorithms.

关键词： Prediction algorithms Forecasting data models Testing Kernel Training Sensors

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Low-Cost Software and Hardware Architecture of a Supervisory System for Monitoring and Control of Dynamic Systems

Low-Cost Software and Hardware Architecture of a Supervisory...

引用

international conference on Semantic Computing

作者： Franco Damian Brocca Caballero Luciano Anacker Leston Bruno Ramos Vahl Sigmar de Lima Elmer Alexis Gamboa Penaloza Federal University of Pelotas Pelotas Brazil

ISBN: (数字)9781728163321

ISBN: (纸本)9781728163338

The application of dynamic systems is increasing due to the versatility of application in different areas of science and engineering. Therefore, the development of supervision and monitoring systems that allow the application of control actions, as well as the measurement of the variables involved in a real-time system and with low cost of implementation are increasingly necessary. The paper presents the development and implementation of a low cost supervisory system of control and monitoring for complex dynamic systems. Thus, hardware and software architectures, as well as communication and synchronization algorithms are presented. As a test and application system, a cable-guided parallel robot prototype is used. Consequently, results of the application of the proposed supervision architecture, on the trajectory tracking problem, are presented. The results show the communication efficiency and processing speed of the proposed architecture.

关键词： Computer architecture Monitoring Actuators Trajectory data acquisition Manipulators

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An Analysis of Natural Language Text Relating to Thai Criminal Law

An Analysis of Natural Language Text Relating to Thai Crimin...

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international conference on Electronics, Computers and Artificial Intelligence (ECAI)

作者： Weerayut Krungklang Sukree Sinthupinyo Department of Computer Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand

ISBN: (数字)9781728168432

ISBN: (纸本)9781728168449

This paper analyses Thailand's criminal law enforcement in chapter 1, Offenses causing death section category section 288 and 289 of title 10 offenses affecting life and body under the Thai Criminal Code. The first part of this paper is using criminal law domain knowledge and supreme court judgment results, to be the initial domain information and result is the rules that humans can understand. The second part of this paper is bringing training data set from the final judgment to train with deep learning methods. Due to the training set which have severe imbalances, the Synthetic Minority Over-Sampling TEchnique (SMOTE) [1] is used to solve this problem. models are trained on the training set using unidirectional Long Short-Term Memory (LSTM) [2] networks and bidirectional Long Short-Term Memory (BiLSTM) [3] are type of Recurrent Neural Networks (RNN) [2]. The word embeddings of the dataset can be learned while training a deep neural network. BiLSTM average F1 score is higher than LSTM. Pre-trained word embeddings are then used to make the average F1 score higher than before. Finally, using models to predict online crime news, the highest average probability of each model is selected by using Soft Voting as input to the rules. The test results compared with the predictions of our methods with the opinion of the lawyer, corresponding 76%.

关键词： Training Criminal law Decision trees data models Feature extraction Ontologies Classification algorithms

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Heavy Landing Detection and Prediction Based on QAR data

Heavy Landing Detection and Prediction Based on QAR Data

引用

IEEE international conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Yu You Kun Qin Yang Yu School of Remote Sensing and Information Engineering Wuhan University Wuhan China

ISBN: (数字)9781728199481

ISBN: (纸本)9781728199498

The approach and landing phase is the high accident phase throughout the flight, and the most typical accident is heavy landing. Heavy landing will not cause too many casualties, but it will cause damage to the aircraft structure and increase flight safety risks. Therefore, this paper uses a clustering algorithm to filter the key parameters in the landing phase, and then combines the Autoregressive Integrated Moving Average Model (ARIMA) with the Support Vector Machine Model (SVM) in series. The former carries out parameter prediction, the latter carries out heavy landing detection to jointly realize the real-time warning of heavy landing. It is of great practical significance to avoid heavy landing events by operation in time in the actual flight process.

关键词： Support vector machines Atmospheric modeling Predictive models Prediction algorithms Real-time systems Safety Accidents

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PoisonRec: An Adaptive data Poisoning Framework for Attacking Black-box Recommender Systems

PoisonRec: An Adaptive Data Poisoning Framework for Attackin...

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international conference on data Engineering

作者： Junshuai Song Zhao Li Zehong Hu Yucheng Wu Zhenpeng Li Jian Li Jun Gao The Key Laboratory of High Confidence Software Technologies Ministry of Education Peking University China Alibaba Group China

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

data-driven recommender systems that can help to predict users' preferences are deployed in many real online service platforms. Several studies show that they are vulnerable to data poisoning attacks, and attackers have the ability to mislead the system to perform as their desires. Considering the realistic scenario, where the recommender system is usually a black-box for attackers and complex algorithms may be deployed in them, how to learn effective attack strategies on such recommender systems is still an under-explored problem. In this paper, we propose an adaptive data poisoning framework, PoisonRec, which can automatically learn effective attack strategies on various recommender systems with very limited knowledge. PoisonRec leverages the reinforcement learning architecture, in which an attack agent actively injects fake data (user behaviors) into the recommender system, and then can improve its attack strategies through reward signals that are available under the strict black-box setting. Specifically, we model the attack behavior trajectory as the Markov Decision Process (MDP) in reinforcement learning. We also design a Biased Complete Binary Tree (BCBT) to reformulate the action space for better attack performance. We adopt 8 widely-used representative recommendation algorithms as our testbeds, and make extensive experiments on 4 different real-world datasets. The results show that PoisonRec has the ability to achieve good attack performance on various recommender systems with limited knowledge.

关键词： Recommender systems Reinforcement learning Learning (artificial intelligence) Adaptive systems data models Markov processes

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Sentiment Analysis of Emoticon Based Neuro Fuzzy System

Sentiment Analysis of Emoticon Based Neuro Fuzzy System

引用

Innovation in Technology (INOCON), IEEE international conference for

作者： Ritu Agarwal Sourabh Shreshth Delhi Technological University Delhi India

ISBN: (数字)9781728197449

ISBN: (纸本)9781728197456

The widespread Virtual societal space has become an integral part of human life, in which opinion generated by others plays a significant role in one's actions. Tweets as a point of the source have emerged as a way of expressing users' views on a particular subject. Historically sentiment analysis of tweets focused on polarized views, but in the real world, the opinion on a topic usually quite diverse. Therefore, using the classical classification methods cannot adequately fit the human sentiment, and we require a robust way that can match the broad scale of opinion-making. Present work describes an approach that can fit the opinion-making scale of human sentiment by using a fuzzy neuro system to train the model for opinion-making. Emoticon used in tweets provides an additional layer with classical textual analysis to incorporate complex emotions that can be easily conveyed by emoticons. The scored sentiment is then classified using various machine learning algorithms to measure its accuracy.

关键词： Fuzzy systems Social networking (online) Fuzzy logic data models Sentiment analysis Neural networks Blogs

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Multi-objective Clustering Algorithm with Parallel Games

Multi-objective Clustering Algorithm with Parallel Games

引用

“Organization of Knowledge and Advanced Technologies” (OCTA), international Multi-conference on:

作者： Dalila Kessira Mohand-Tahar Kechadi Laboratory of Medical Informatics (LIMED) University of Bejaia Bejaia Algeria Insight Center for Data Analytics University College of Dublin Dublin Ireland

ISBN: (数字)9781728164038

ISBN: (纸本)9781728164045

data mining and knowledge discovery are two important growing research fields in the last few decades due to the abundance of data collected from various sources. The exponentially growing volumes of generated data urge the development of several mining techniques to feed the needs for automatically derived knowledge. Clustering analysis (finding similar groups of data) is a well-established and widely used approach in data mining and knowledge discovery. In this paper, we introduce a clustering technique that uses game theory models to tackle multi-objective application problems. The main idea is to exploit a specific type of simultaneous move games, called congestion games. Congestion games offer numerous advantages ranging from being succinctly represented to possessing a Nash equilibrium that is reachable in a polynomial-time. The proposed algorithm has three main steps: 1) it starts by identifying the initial players (or the cluster-heads); 2) then, it establishes the initial clusters' composition by constructing the game to play and try to find the equilibrium of the game. The third step consists of merging close clusters to obtain the final clusters. The experiment results show that the proposed clustering approach obtains good results and it is very promising in terms of scalability, and performance.

关键词： Games Clustering algorithms Nash equilibrium Silicon Cost function data mining

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IEEE international conference on Big data

作者： Huaxiong Yao Yang Huang Jiabei Hu Wenqi Xie School of Computer Central China Normal University Wu Han China

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

In recent years, graph neural network has been widely used. Attention mechanism is introduced into the graph neural network to make it more applicable. Both GAT and AGNN prove that attention mechanism plays an important role in graph neural network. Attention mechanism algorithms such as gat and AGNN directly use a self-learning variable to do the point product after calculating the connection (or similarity calculation) of node and neighbor features (without further processing of the calculation results). Finally, we get an aggregation of neighbor information. A cosine similarity distance pruning algorithm based on graph attention mechanism (CDP-GA) is proposed to optimize the attention matrix of nodes and their adjacent nodes. By calculating the cosine similarity between node features and neighbor features (the feature here is obtained by linear transformation), the similarity of nodes is regarded as the distance between nodes (or the weight of edges). And we think that the aggregation degree of node information is inversely proportional to the distance between nodes (similar to the heat conduction formula). In the method, we prune the neighborhood of the node according to the cosine similarity to get the final attention coefficient matrix. In this way, the attention mechanism in the graph neural network is further refined, and the loss of aggregation neighbor information is reduced. In the experiments of three datasets, our model is compared with the experimental classification of GAT and AGNN and the experiment of correlation graph neural network algorithm. Finally, it is proved that the algorithm is better than three known datasets.

关键词： Heating systems Correlation Memory management Big data Graph neural networks Classification algorithms Standards

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Video Anomaly Detection Using Open data Filter and Domain Adaptation

Video Anomaly Detection Using Open Data Filter and Domain Ad...

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IEEE Visual Communications and Image processing (VCIP)

作者： Chen Zhang Guorong Li Li Su Weigang Zhang Qingming Huang School of Computer Science and Technology UCAS Beijing China Key Lab of Big Data Mining and Knowledge Management UCAS Beijing China Harbin Institute of Technology Weihai China Key Lab of Intelligent Information Processing ICT CAS Beijing China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Video anomaly detection is a very challenging task because of the rarity, openness, and the definition of the anomalies. Researchers pay more attention to the characteristics of anomalies and have proposed a variety of anomaly detection models. However, most existing methods only use normal events to construct anomaly detection models and ignore the diversity and openness of normal events. Actually, because real-world video data often have an open-ended distribution, some normal patterns hardly ever appeared in the training data. In addition, analogous to human experience in identifying anomalies, rare abnormal events can play a certain role in the detection of similar abnormal events in the dataset. Therefore, assuming that a small number of abnormal events are known, we propose a novel supervised anomaly detection model which explicitly detects open normal events and open abnormal events in the dataset and treats open data and seen data with different classifiers. First, we use the training video to train an imbalanced classifier as the seen data classifier. Then, during the testing phase, an open data filter module isused to divide the test data into seen data and open data. Finally, we directly use the seen data classifier to generate anomaly scores for the seen test data. For the open test data, we adopt a domain adaptation method to reduce the distribution difference between it and the training data and train a new classifier to score for it. Extensive experimental results prove the effectiveness of our model.

关键词： Training Training data Anomaly detection Feature extraction data models Testing Adaptation models

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