Harvesting Data from Advanced Technologies

Data streams are emerging everywhere such as Web logs, Web page click streams, sensor data streams, and credit card transaction flows. Different from traditional data sets, data streams are sequentially generated and arrive one by one rather than being available for random access before learning begins, and they are potentially huge or even infinite that it is impractical to store the whole data. To study learning from data streams, we target online learning, which generates a best–so far model on the fly by sequentially feeding in the newly arrived data, updates the model as needed, and then applies the learned model for accurate real-time prediction or classification in real-world applications. Several challenges arise from this scenario: first, data is not available for random access or even multiple access; second, data imbalance is a common situation; third, the performance of the model should be reasonable even when the amount of data is limited; fourth, the model should be updated easily but not frequently; and finally, the model should always be ready for prediction and classification. To meet these challenges, we investigate streaming feature selection by taking advantage of mutual information and group structures among candidate features. Streaming feature selection reduces the number of features by removing noisy, irrelevant, or redundant features and selecting relevant features on the fly, and brings about palpable effects for applications: speeding up the learning process, improving learning accuracy, enhancing generalization capability, and improving model interpretation. Compared with traditional feature selection, which can only handle pre-given data sets without considering the potential group structures among candidate features, streaming feature selection is able to handle streaming data and select meaningful and valuable feature sets with or without group structures on the fly. In this research, we propose 1) a novel streaming feature selection algorithm (GFSSF, Group Feature Selection with Streaming Features) by exploring mutual information and group structures among candidate features for both group and individual levels of feature selection from streaming data, 2) a lazy online prediction model with data fusion, feature selection and weighting technologies for real-time traffic prediction from heterogeneous sensor data streams, 3) a lazy online learning model (LB, Live Bayes) with dynamic resampling technology to learn from imbalanced embedded mobile sensor data streams for real-time activity recognition and user recognition, and 4) a lazy update online learning model (CMLR, Cost-sensitive Multinomial Logistic Regression) with streaming feature selection for accurate real-time classification from imbalanced and small sensor data streams. Finally, by integrating traffic flow theory, advanced sensors, data gathering, data fusion, feature selection and weighting, online learning and visualization technologies to estimate and visualize the current and future traffic, a real-time transportation prediction system named VTraffic is built for the Vermont Agency of Transportation

AAANE: Attention-based Adversarial Autoencoder for Multi-scale Network Embedding

Network embedding represents nodes in a continuous vector space and preserves structure information from the Network. Existing methods usually adopt a "one-size-fits-all" approach when concerning multi-scale structure information, such as first- and second-order proximity of nodes, ignoring the fact that different scales play different roles in the embedding learning. In this paper, we propose an Attention-based Adversarial Autoencoder Network Embedding(AAANE) framework, which promotes the collaboration of different scales and lets them vote for robust representations. The proposed AAANE consists of two components: 1) Attention-based autoencoder effectively capture the highly non-linear network structure, which can de-emphasize irrelevant scales during training. 2) An adversarial regularization guides the autoencoder learn robust representations by matching the posterior distribution of the latent embeddings to given prior distribution. This is the first attempt to introduce attention mechanisms to multi-scale network embedding. Experimental results on real-world networks show that our learned attention parameters are different for every network and the proposed approach outperforms existing state-of-the-art approaches for network embedding.Comment: 8 pages, 5 figure

LDA-Based Topic Strength Analysis

Topic strength is an important hotspot in topic research. The evolution of topic strength not only indicates emerging new topics, but also helps us to determine whether a topic will produce some fluctuation of topic strength over time. Thus, topic strength analysis can provide significant findings in public opinion monitoring and user personalization. In this paper, we present an LDA-based topic strength analysis approach. We take topic quality into our topic strength consideration by combining local LDA and global LDA. For empirical studies, we use three data sets in real applications: film critic data of "A Chinese Odyssey" in Douban Movies, corruption news data in Sina News, and public paper data. Compared to existing approaches, experimental results show that our proposed approach can obtain better results of topic strength analysis in detecting the time of event topic occurrences and distinguishing different types of topics, and it can be used to monitor the occurrences of public opinions and the changes of public concerns

A Semantic Network for Modeling Biological Knowledge in Multiple Databases

We have developed a semantic network of biological terminology to aid in the retrieval and integration of biological information from a variety of disparate information sources. Our semantic network strives to provide a categorization of biological concepts and relationships among these concepts. The semantic network will impart a knowledge structure through which computers can reason and draw conclusions about biological data objects and will provide a federated view of the many disparate databases of interest to biologists. In the development of our system, we have included the concepts from several established controlled vocabularies, chief among them being the National Library of Medicine\u27s Unified Medical language System (UMLS). While the UMLS Metathesaurus provides an excellent controlled vocabulary, we have found their semantic network lacking in sufficient detail to be useful as a tool for categorization of biological concepts in databases. We would like to provide a categorization of concepts that provides finer detail than their semantic network without the considerable size and complexity of their Metathesaurus. Our complete semantic network consists of 183 semantic types and 69 relationships

Knowledge Acquisition from Data Bases

Centre for Intelligent Systems and their ApplicationsGrant No.6897502Knowledge acquisition from databases is a research frontier for both data base technology and machine learning (ML) techniques,and has seen sustained research over recent years.It also acts as a link between the two fields,thus offering a dual benefit. Firstly, since database technology has already found wide application in many fields ML research obviously stands to gain from this greater exposure and established technological foundation. Secondly, ML techniques can augment the ability of existing database systems to represent acquire,and process a collection of expertise such as those which form part of the semantics of many advanced applications (e.gCAD/CAM).The major contribution of this thesis is the introduction of an effcient induction algorithm to facilitate the acquisition of such knowledge from databases. There are three typical families of inductive algorithms: the generalisation- specialisation based AQ11-like family, the decision tree based ID3-like family,and the extension matrix based family. A heuristic induction algorithm, HCV based on the newly-developed extension matrix approach is described in this thesis. By dividing the positive examples (PE) of a specific class in a given example set into intersect in groups and adopting a set of strategies to find a heuristic conjunctive rule in each group which covers all the group's positiv examples and none of the negativ examples(NE),HCV can find rules in the form of variable-valued logic for PE against NE in low-order polynomial time. The rules generated in HCV are shown empirically to be more compact than the rules produced by AQ1-like algorithms and the decision trees produced by the ID3-like algorithms. KEshell2, an intelligent learning database system, which makes use of the HCV algorithm and couples ML techniques with database and knowledgebase technology, is also described