In this work, an "analytical data model of mosquito vector" was developed to perform analytical computation to the character of the dengue vectors. Our goal is to investigate a way to understand how the temporal trend of collected dataset correlates with the incidence dengue as identified by national health authorities. Based upon the mosquito-vector big data collections, we investigate how changes in some specific variables such as rainfall, temperature, and humidity can dramatically affect the population of mosquito vectors, in order to provide early warnings of dengue outbreaks. Thus, our system will collectively collect online sensing data of the variables and store them in a database, in order to combine the historical big data as training datasets for analytical computation. Also, the developed model is able to merge the experimental datasets with current hot-topic information which is relevant to mosquito vectors obtained from data of social sensors (i.e. social messages). The experimental data show that our system is of great potentials in providing early warnings of dengue outbreaks.

In this paper, we describe our work on extracting entities from the online social messages regarding emergent events for ontology learning, which can contribute to a solution for quick response of emerging disastrous events. Our work started with the development of a real-time event detection system using a data-cluster slicing approach which combines social data analysis and early warning algorithms, allowing for quickly detecting emerging large-scale events from collected tweets. Subsequently, our system computes the energy of each collected event dataset, and then encapsulates ranked temporal, spatial and topical keywords into a structured node for event-entity extraction, in order to learn and update event ontologies for fast response of emergent events. The preliminary experimental results demonstrate that our developed system is workable, allowing for prediction of possible evolution for early warning of critical incidents with a dynamic ontology engineering.

Social networks have been regarded as a timely and cost-effective source of spatio-temporal information for many fields of application. However, while some research groups have successfully developed topic detection methods from the text streams for a while, and even some popular microblogging services such as Twitter did provide information of top trending topics for selection, it is still unable to fully support users pickup all of the real-time event topics with a comprehensive spatio-temporal viewpoint to satisfy their information needs. This paper aims to enhance the understanding on how social networks can be used as a reliable source of spatio-temporal information, by analyzing the temporal and spatial dynamics of Twitter activity. In this work, we developed several algorithms for mining microblogging text stream to obtain real-time and geospatial event information. The goal of our approach is to effectively detecting and grouping emerging topics by making use of real-time messages and geolocation data provided by social network services.

Social book marking Web sites have emerged recently for collecting and sharing of interesting Web sites among users. People can add Web pages to such sites as bookmarks and allow themselves as well as others to manipulate them. One of the key features of the social book marking sites is the ability of annotating a Web page when it is being bookmarked. The annotation usually contains a set of words or phrases, which are collectively known as tags, that could reveal the semantics of the annotated Web page. Efficient and effective search of Web pages can then be achieved via such tags. However, spam tags that are irrelevant to the content of Web pages often appear to deceive other users for malicious or commercial purposes. Various techniques have been devised to tackle such tag spam detection problem. Most of these techniques were able to detect a user that always annotate spam tags. However, finer levels of detection are seldom discussed. In this work, we will propose a method based on a text mining approach to discover the relations between Web pages and there tag posts. These relations are then used to compute the similarity between a Web page and its tag post to decide if it is a spam post. Preliminary experiments show that the accuracy of the post-level spam detection task is 83%.

Microblog is a social network service which is able to aggregate messages to explore new knowledge. Nowadays, more and more users contribute what they found and what they thought by posting short messages. This phenomenon makes people spend lots of time monitoring text streams of microblogging messages, in order for acquisition of information regarding real-time events and news. In this work, we adopted several algorithms for mining topics on microblogs, and developed a novel algorithm for ranking topics and messages on microblogs. The developed ranking solution is expected to assist people to quickly identify emerging topics and events which might yield the most recently trending topics, with the hope to effectively resolve the information overload.

Social tags are annotations for Web pages collaboratively added by users. It will be much easier to understand the meaning of Web pages and classify them according to their tags. The precision in retrieving Web pages may also increase using such tags. Nowadays social tags are mostly annotated manually by users via social bookmarking Web sites. Such manual annotation process may produce diverse, redundant, and inconsistent tags. Besides, many tags which are inconsistent with their annotated Web pages exist and deteriorate the feasibility of social tags. In this work we will develop an automatic scheme to discover the associations between Web pages and social tags and apply such associations on applications of social tag spam detection. We applied a text mining approach based on self-organizing maps to find the relationships between Web pages and social tags. The disadvantages of manual annotation will be remedied through such relationships. The discovered associations were then used to identify social tag spams. Preliminary experiments show that the quality and usability of social tags were improved through this method.

In this paper we describe our work on developing a novel technique for discovery of implicit knowledge about patents from multilingual patent information sources. In this work we developed a system platform to support locating similar and relevant multilingual patent documents. The platform was implemented using a multilingual vector space based on the latent semantic indexing (LSI) model, and utilizing collected professional Chinese-English parallel corpora for training the system model. These multilingual patent documents could then be mapped into the semantic vector space for evaluating their similarity by means of text clustering techniques. The preliminary results show that our platform framework has potential for retrieval and relatedness evaluation of multilingual patent documents.

One major approach for information finding in the WWW is to navigate through some web directories and browse them for the goal pages. However, such directories are generally constructed manually and have disadvantages of narrow coverage and inconsistency. In this work, we propose NaviSOM, a machine learning approach to automatically construct a navigational structure for the WWW. A self-organizing map is constructed to train the web pages and obtain two feature maps, which reveal the relationships among web pages and thematic keywords respectively. We then use these maps to develop a structure that may assist the users finding the information they need. We used a small set of web pages in the experiments and obtained promising result.

Game-based learning has been recognized as an effective way for learning and has emerged to be a promising approach in addition to traditional lecture-based learning. Many schemes, methodologies, as well as systems were developed and applied to training courses in both academic and industrial domains. However, the creation of games for learning purposes is not easy for common teachers since it requires lots of specific knowledge about game development such as computer animations, programming, human-computer interface design, etc. Customized games are thus used for specific types of courses. Such customized games are, unfortunately, hard to adapt to individual courses and require much time for development. In this work, we propose a general framework for creating games that can be applied in many domains without much effort of the teachers. Teachers may create games through writing scripts that define the objectives of courses and the steps required to complete the objectives. The system will automatically create a game based on the scripts.

There are enormous amount of Web pages in the world. Retrieval of required information from the WWW is thus an arduous task. Different models for retrieving Web pages have been used by the WWW community. One of the most widely used model is by traversing a predefined Web directory hierarchy to reach a user's goal. The Web directories are compiled or classified folders of Web pages and are usually organized into a hierarchical structure. The classification of Web pages into proper directories and the organization of directory hierarchies are generally performed by human experts. We provide a method to apply a kind of text mining techniques on a set of Web pages to automatically create Web directories and organize them into hierarchies. The method is based on the self-organizing map learning algorithm and requires no human intervention during the construction of Web directories and hierarchies. The experiments show that our method can produce comprehensible and reasonable Web directories and hierarchies.

The paper describes a new application of a text-mining algorithm to the text sources of bilingual parallel corpora. The ultimate task, being undertaken in the context of a Chinese-English machine translation project, will be to develop a language-neutral method to discover similar documents from multilingual text collections. Using a variation of automatic clustering techniques which apply a neural net approach, namely the self-organizing maps (SOM), we have conducted several experiments to uncover associated documents based on Chinese-English bilingual parallel corpora, and a hybrid Chinese-English corpus. The experiments show some interesting results and a couple of potential ways for future work towards the field of multilingual information discovery. In addition, for exploring the impacts on linguistic issues with the machine learning approach to mining sensible linguistics elements from multilingual texts, we have examined the resulting term associations and text associations from the view of cross-lingual text similarity. To evaluate semantic relatedness of the mined bilingual texts, we applied a measure technique of semantic similarity in the resulting bilingual document clusters and word clusters. The paper presents algorithms that enable multilingual text mining based on the self-organizing map (SOM) for automatically grouping similar multilingual texts (i.e. Chinese and English texts), along with a means of measuring their semantic similarity to resolve the difficulties of syntactic and semantic ambiguity in multilingual information access.

One important task for text data mining is automatic text categorization, which assigns a text document to some predefined category according to their correlations. Traditionally, these categories as well as the correlations among them are determined bp human experts. In this paper, we devised a novel approach to automatically generate categories. The self-organizing map model is used to generate two maps, namely the word cluster map and the document cluster map, in which a neuron represents a cluster of words and documents respectively. Our approach is to analyze the document cluster map to find centroids of some super-clusters. We also devised a method to select the category term from the word cluster map. The hierarchical structure of categories may be generated by recursively applying the same method. Text categorization is the natural consequence of such automatic category generation process.

A structural-feature-to-structural-feature configuration is naturally constructed using a set of sampled features from a cursive pattern. These features are sampled by maximally fitting bent ellipses in local strokes. This configuration is transformed into an undirected graph to resolve the asymmetric difficulty. The compatibility associated with the graph is further formulated into a devised Hopfield network, where both interfeature and interlink similarities are incorporated into the compatibility. We operate this network to recognize a radical as a whole in a handprinted pattern to accomplish the selective attention task.

In this work we present a self-organization matching approach to accomplish the recognition of handprinted characters drawn with thick strokes. This approach is used to flex the unknown handprinted character toward matching its object characters gradually. The extracted character features used in the self-organization matching are center loci, orientation, and major axes of ellipses which fit the inked area of the patterns. Simulations provide encouraging results using the proposed method.