Nipon Charoenkitkarn

A Sieving ANN for Emotion-Based Movie Clip Classification

Effective classification and analysis of semantic contents are very important for the content-based indexing and retrieval of video database. Our research attempts to classify movie clips into three groups of commonly elicited emotions, namely excitement, joy and sadness, based on a set of abstract-level semantic features extracted from the film sequence. In particular, these features consist of six visual and audio measures grounded on the artistic film theories. A unique sieving-structured neural network is proposed to be the classifying model due to its robustness. The performance of the proposed model is tested with 101 movie clips excerpted from 24 award-winning and well-known Hollywood feature films. The experimental result of 97.8% correct classification rate, measured against the collected human-judges, indicates the great potential of using abstract-level semantic features as an engineered tool for the application of video-content retrieval/indexing.

Formal experiments in casual attire: case studies in information exploration

Abstract This paper addresses the issue of how research methodology can be developed for the specific needs of research into information exploration behaviour, based on a four year program of research on individual strategies in information exploration. We propose a meta-experimental framework where research is carried out through a dynamic interaction between what and why questions, and between confirmatory and exploratory analyses. This approach preserves many of the advantages of formal experimentation, while permitting a more holistic examination of phenomena that is characteristic of ethnography. The application of the meta-theoretical framework is illustrated in three case studies that examined new information exploration functionalities and interfaces and their relationship to expertise and exploration strategy

The impact of text browsing on text retrieval performance

The results from a series of three experiments that used Text Retrieval Conference (TREC) data and TREC search topics are compared. These experiments each involved three novel user interfaces (one per experiment). User interfaces that made it easier for users to view text were found to improve recall in all three experiments. A distinction was found between a cluster of subjects (a majority of whom were search experts) who tended to read fewer documents more carefully (readers, or exclusives) and subjects who skimmed through more documents without reading them as carefully (skimmers, or inclusives). Skimmers were found to have significantly better recall overall. A major outcome from our experiments at TREC and with the TREC data, is that hypertext interfaces to information retrieval (IR) tasks tend to increase recall. Our interpretation of this pattern of results across the three experiments is that increased interaction with the text (more pages viewed) generally improves recall. Findings from one of the experiments indicated that viewing a greater diversity of text on a single screen (i.e., not just more text per se, but more articles available at once) may also improve recall. In an experiment where a traditional (type-in) query interface was contrasted with a condition where queries were marked up on the text, the improvement in recall due to viewing more text was more pronounced with search novices. Our results demonstrate that markup and hypertext interfaces to text retrieval systems can benefit recall and can also benefit novices. The challenge now will be to find modified versions of hypertext interfaces that can improve precision, as well as recall and that can work with users who prefer to use different types of search strategy or have different types of training and experience.

A Framework of Multi-Stage Classifier for Identifying Criminal Law Sentences

Abstract This paper proposes a framework to identify the relevant law articles consisting of sentences and range of punishments, given facts discovered in the criminal case of interest. The model is formulated as a two-stage classifier according to the concept of machine learning. The first stage is to determine a set of case diagnostic issues, using a modular Artificial Neural Network (mANN), and the second stage is to determine the relevant legal elements which lead to legal charges identification, using SVM-equipped C4.5. The integrated multi-stage model aims at achieving high accuracy of classification while reserving “arguability”. Hypothetically, mANN handles well for digesting complexity in case-level issues analysis with acceptable explanatory power and C4.5 addresses the lesser extent of contingency and provides human-interpretable logic concerning the high-level context of legal codes.

Improving Sense of Well-Being by Managing Memories of Experience

Memories of experience are influenced by a peak-end effect [13]. Memories are modified to emphasize the final portions of an experience, and the peak positive, or negative, portion of that experience. We examine peak-end effects on judged Technical Quality (TQ) of online video. In two studies, sequences of different types of video disruption were varied so as to manipulate the peak-end effect of the experiences. The first experiment demonstrated an end effect, plus a possible peak effect involving negative, but not positive, experience. The second study manipulated payment conditions so that some sessions were structured as requiring payment to watch the video. The second study also distinguished between a peak effect and a possible sequence effect. Evidence was again found for an end effect, with a secondary effect of sequence, but no evidence was found for a peak effect independent of sequencing.

Influence of ERP Employment on Work Skills

The objective of this research is to investigate the potential of enterprise resource planning (ERP) in enhancing work skills of employees after being used for a while. The study is based on the hypothetical model that ERP vision and/or ERP quality could lever the work skills in the dimensions of conceptual skills, technical skills or interpersonal skills. The multivariate analysis of covariance (MANCOVA) was utilized to control the state covariation of before and after use of ERP. The questionnaire-based survey was conducted in two well developed and successful organizations in Thailand; One is a leading firm in the petrochemical industry and the other is in the construction material industry. The results show that quality of ERP system can significantly affect the enhancement of work skills in many dimensions. Using t-test and regression analysis, further validation and insights are also provided to envision enterprises in planning and managing ERP for enhancing not only the traditional functional success but also individual work skill and intercourse.