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Recently, approaches of applying personality on practical applications have attracted attention from multidisciplinary areas beyond psychology. An example is the personality-based recommender systems that have been successfully applied to music, movie, and game recommendation tasks. However, finding the personality traits of a person is not a trivial task. In this work, we will propose an automatic scheme for identifying the Big Five personality traits using text mining techniques. We analyzed the social messages posted by Facebook users to find the correlations among keywords and personality traits. The personality traits of a person can then be identified by examining his social messages according to such correlations. We performed the experiments using the myPersonality dataset and obtained promising results.

Recommender systems have emergedrecently in many aspects due to enormous amount of information or items existed in e-commerce or social network services. Users find it convenient if the service can recommend interesting items to them automatically. Past systems adopted users' preference or social connections for such recommendation. In this work, we will try to identify a game player's personality traits and use them to recommend games that may be interested by the player. The personality traits of a player is identified by applying a text mining process on textual contents related to the player. The same process is also applied on the comments of the games to identified the personality traits of games. Recommendation is then performed based on the similarity between the traits of the user and the game. We performed experiments on 63 players and 2050 games and obtained promising results which may demonstrate the plausibility of personality-based recommender systems.

Open election data is an emerged domain in open data for it reflects the maturity of governance and democracy. On this regard, a standardized approach for building open election data will significantly reduce the time and effort for publishing election data to the public for easy access. In this work, we will develop an initial step for building open election data, namely building election data model. The procedure and preliminary result of such task on Taiwan's election data will be described. We believe the process on building election data model could be beneficial to other authorities which intend to publicize their election data.

Issues on open data have attracted lots of attention from researchers and practitioners of various areas. Besides simply publicizing the data, linked open data (LOD) provide excessive information on relations among data items. Such relations can be used to furniture various tasks for possible performance improvement. In this work, we try to suggest an approach which incorporates semantic information provided through the links in LOD for resource recommendation. We performed experiments on DBpedia and obtained promising results.

In the manufacturing of semiconductor encapsulation, the production yield is one of critical issues concerned by all foundries. It is because that yield rate can directly affects the quality of the final product and the profitability. In this work we take the defect-products as samples and use machine learning techniques to classify the samples and verify the accuracy and feasibility of the experiment. We use Support Vector Machines (SVM) model to perform classification and compare the resulting accuracy with the results of Back Propagation Neural Network (BPN) model. Furthermore, we employ a statistical method namely Pearson product-moment correlation coefficient to identify the influential factors for production quality. The experimental result demonstrates that our hybrid method has great potentials for yield improvement in semiconductor manufacturing.

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.

Shaped-based image retrieval is achieved by measuring the spatial topology distance between the input image and each template image in the database. The contour of an object in the input image is regularly sampled as feature points which are matched to those of a template image by a pseudo elastic matching process. The elastic matching is achieved by performing the self-organizing map algorithm on the network which is constructed by distributing neurons to feature locations of the template image. The spatial topology distance is measured by the degree of distortion of the template pattern before and after elastic matching. We tested the method on the Columbia Object Image Library database. Preliminary experiments suggested promising result by our approach.