Heuiseok Lim

An MLP-based feature subset selection for HIV-1 protease cleavage site analysis

OBJECTIVE In recent years, several machine learning approaches have been applied to modeling the specificity of the human immunodeficiency virus type 1 (HIV-1) protease cleavage domain. However, the high dimensional domain dataset contains a small number of samples, which could misguide classification modeling and its interpretation. Appropriate feature selection can alleviate the problem by eliminating irrelevant and redundant features, and thus improve prediction performance. METHODS We introduce a new feature subset selection method, FS-MLP, that selects relevant features using multi-layered perceptron (MLP) learning. The method includes MLP learning with a training dataset and then feature subset selection using decompositional approach to analyze the trained MLP. Our method is able to select a subset of relevant features in high dimensional, multi-variate and non-linear domains. RESULTS Using five artificial datasets that represent four data types, we verified the FS-MLP performance with seven other feature selection methods. Experimental results showed that the FS-MLP is superior at high dimensional, multi-variate and non-linear domains. In experiments with HIV-1 protease cleavage dataset, the FS-MLP selected a set of 14 highly relevant features among 160 original features. On a validation set of 131 test instances, classifiers that used the 14 features showed about 95% accuracy which outperformed other seven methods in terms of accuracy and the number of features. CONCLUSIONS Our experimental results indicate that the FS-MLP is effective in analyzing multi-variate, non-linear and high dimensional datasets such as HIV-1 protease cleavage dataset. The 14 relevant features which were selected by the FS-MLP provide us with useful insights into the HIV-1 cleavage site domain as well. The FS-MLP is a useful method for computational sequence analysis in general.

Mining biometric data to predict programmer expertise and task difficulty

Programming mistakes frequently waste software developers’ time and may lead to the introduction of bugs into their software, causing serious risks for their customers. Using the correlation between various software process metrics and defects, earlier work has traditionally attempted to spot such bug risks. However, this study departs from previous works in examining a more direct method of using psycho-physiological sensors data to detect the difficulty of program comprehension tasks and programmer level of expertise. By conducting a study with 38 expert and novice programmers, we investigated how well an electroencephalography and an eye-tracker can be utilized in predicting programmer expertise (novice/expert) and task difficulty (easy/difficult). Using data from both sensors, we could predict task difficulty and programmer level of expertise with 64.9 and 97.7% precision and 68.6 and 96.4% recall, respectively. The result shows it is possible to predict the perceived difficulty of a task and expertise level for developers using psycho-physiological sensors data. In addition, we found that while using single biometric sensor shows good results, the composition of both sensors lead to the best overall performance.

Real-time vehicle tracking mechanism with license plate recognition from road images

Abstract Information about vehicles on the road is very important for the maintenance of traffic control in current complex traffic condition. Images of vehicles are captured by vehicle-directed cameras. This paper proposes a new vehicle tracking mechanism using license plate recognition technology, which is essential to having information about vehicles on the roads. The proposed method is a real-time processing system using multistep image processing, as well as recognition and tracking processes from 2D and 3D images. The experimental results of real environmental images in recognition and tracking using the proposed method are shown.

Multiple categorizations of products: cognitive modeling of customers through social media data mining

As various forms of social media are spreading, we often witness that an idea of an individual user drives macroscopic changes. From the perspectives of product development and marketing, the opinions left by potential consumers in online social network can generate big ripple effects. This study analyzes the user opinions in online space to grasp preferences toward various products psychologically categorized by users. We also suggest an aspect of the market mentally configured by users using network modeling while following the framework of economic sociology. Existing analyses on online market place are mainly dealing with structural issues such as inter-actor relationships and status measurement. This study, however, analyzes complex preferences regarding diverse products and brands and derives a new model for inter-market connections. We expect that our study will provide important consequences on digital marketing and community design of corporations planning word of mouth effect in online space.

Natural language-based user interface for mobile devices with limited resources

In this paper, we propose a natural language-based interface model to enable a user to articulate a request without having any specific knowledge about a mobile device. In consideration of the very limited computing and memory capacity of the mobile device and to keep the development cost low, the proposed model does not depend on typical natural language techniques, but on a ranking technique, that is simplified based on the mathematical derivation process with the following assumptions. One assumption is that a device control command consists of a function and its parameters. The other assumption is that the parameter is represented as few predictable patterns, whereas the function can be represented as various sentence patterns. To deal with these various sentence patterns, the proposed model selects the top ranked command candidate with the highest score after generating all possible candidates with their scores. Furthermore, the ranking score function is designed to achieve a high discriminative capability by the simulation of the process of generating every candidate. Experimental results show that the proposed model with 2.9 megabytes performs at 96.27% accuracy, which is slightly lower than 97.06% of the baseline model with 135.2 megabytes.

Comparing Programming Language Comprehension between Novice and Expert Programmers Using EEG Analysis

For programming language comprehension, high cognitive skills (e.g., reading, writing, working memory, etc.) and information processing are required. However, there are few papers that approach this from a neuroscientific perspective. In this paper, we examine program comprehension neuroscientifically and also observe the differences between novice and expert programmers. We designed an EEG (electroencephalogram) experiment and observed 18 participants during a series of program comprehension tasks. We found clear differences in program comprehension ability between novice and expert programmers. Experts exhibited higher brainwave activation than novices in electrodes F3 and P8. These results indicate that experts have outstanding program comprehension-associated abilities such as digit encoding, coarse coding, short-term memory, and subsequent memory effect. Our findings can serve as a foundation for future research in this pioneering field.

Automatic extraction of user's search intention from web search logs

Web search users complain of the inaccurate results produced by current search engines. Most of these inaccurate results are due to a failure to understand the user’s search goal. This paper proposes a method to extract users’ intentions and to build an intention map representing these extracted intentions. The proposed method makes intention vectors from clicked pages from previous search logs obtained on a given query. The components of the intention vector are weights of the keywords in a document. It extracts user’s intentions by using clustering the intention vectors and extracting intention keywords from each cluster. The extracted the intentions on a query are represented in an intention map. For the efficiency analysis of intention map, we extracted user’s intentions using 2,600 search log data a current domestic commercial search engine. The experimental results with a search engine using the intention maps show statistically significant improvements in user satisfaction scores.

Developing a hybrid collaborative filtering recommendation system with opinion mining on purchase review

The most commonly used algorithm in recommendation systems is collaborative filtering. However, despite its wide use, the prediction accuracy of this algorithm is unexceptional. Furthermore, whether quantitative data such as product rating or purchase history reflect users’ actual taste is questionable. In this article, we propose a method to utilise user review data extracted with opinion mining for product recommendation systems. To evaluate the proposed method, we perform product recommendation test on Amazon product data, with and without the additional opinion mining result on Amazon purchase review data. The performances of these two variants are compared by means of precision, recall, true positive recommendation (TPR) and false positive recommendation (FPR). In this comparison, a large improvement in prediction accuracy was observed when the opinion mining data were taken into account. Based on these results, we answer two main questions: ‘Why is collaborative filtering algorithm not effective?’ and ‘Do quantitative data such as product rating or purchase history reflect users’ actual tastes?’

Data-Driven Approaches to Game Player Modeling: A Systematic Literature Review

Modeling and predicting player behavior is of the utmost importance in developing games. Experience has proven that, while theory-driven approaches are able to comprehend and justify a models choices, such models frequently fail to encompass necessary features because of a lack of insight of the model builders. In contrast, data-driven approaches rely much less on expertise, and thus offer certain potential advantages. Hence, this study conducts a systematic review of the extant research on data-driven approaches to game player modeling. To this end, we have assessed experimental studies of such approaches over a nine-year period, from 2008 to 2016; this survey yielded 46 research studies of significance. We found that these studies pertained to three main areas of focus concerning the uses of data-driven approaches in game player modeling. One research area involved the objectives of data-driven approaches in game player modeling: behavior modeling and goal recognition. Another concerned methods: classification, clustering, regression, and evolutionary algorithm. The third was comprised of the current challenges and promising research directions for data-driven approaches in game player modeling.

A Method for Measuring Cooperative Activities in a Social Network Supported Learning Environment

Cooperative learning, which can foster an active learner-oriented learning environment and induce active interaction among students, is an important element that can enhance the effects of learning in the online learning environment as well. As most existing studies on cooperative learning are based only on a qualitative evaluation in the offline environment, however, it is difficult to measure cooperative learning in the online learning environment. Thus, for this study a group cooperative activity (GCA) was proposed to quantitatively measure the cooperative learning of learners in the online learning environment, and a social network analysis (SNA) method was used to visualize the cooperative activities among the learners. The result of the experiment shows that the GCA among teachers was higher than the GCA among learners in the Formal Learning Group, whereas many high-density network models were observed in the Non-Formal Learning Group and the GCA among learners was higher. The proposed GCA uses the interaction data generated among the learner group to measure its cooperative learning. Also, this study verified the effectiveness of the GCA by using an SNA for visualization purposes.