Browsing by Author "Lee, Chia-Hoang"

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A fall detection system using k-nearest neighbor classifier
(2010-10-01)
Liu, Chien-Liang
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Lee, Chia-Hoang
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Lin, Ping-Min
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資訊工程學系
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Department of Computer Science
The main purpose of this paper is to use off-the-shelf devices to develop a fall detection system. In human body identification, human body silhouette is adopted to improve privacy protection, and vertical projection histograms of the silhouette image and statistical scheme are used to reduce the effect of human body upper limb activities. The kNN classification algorithm is used to classify the postures using the ratio and difference of human body silhouette bounding box height and width. Meanwhile, since time difference is a vital factor to differentiate fall incident event and lying down event, the critical time difference is obtained from the experiment and verified by statistical hypothesis testing. With the help of the kNN classifier and the critical time difference, a fall incident detection system is developed to detect fall incident events. The experiment shows that it could reduce the effect of upper limb activities and the system has a correct rate of 84.44% on fall detection and lying down event detection. (C) 2010 Elsevier Ltd. All rights reserved.
A fall detection system using k-nearest neighbor classifier
(2010-10-01)
Liu, Chien-Liang
;
Lee, Chia-Hoang
;
Lin, Ping-Min
;
資訊工程學系
;
Department of Computer Science
The main purpose of this paper is to use off-the-shelf devices to develop a fall detection system. In human body identification, human body silhouette is adopted to improve privacy protection, and vertical projection histograms of the silhouette image and statistical scheme are used to reduce the effect of human body upper limb activities. The kNN classification algorithm is used to classify the postures using the ratio and difference of human body silhouette bounding box height and width. Meanwhile, since time difference is a vital factor to differentiate fall incident event and lying down event, the critical time difference is obtained from the experiment and verified by statistical hypothesis testing. With the help of the kNN classifier and the critical time difference, a fall incident detection system is developed to detect fall incident events. The experiment shows that it could reduce the effect of upper limb activities and the system has a correct rate of 84.44% on fall detection and lying down event detection. (C) 2010 Elsevier Ltd. All rights reserved.
AIMED - A personalized TV recommendation system
(2007)
Hsu, Shang H.
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Wen, Ming-Hui
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Lin, Hsin-Chieh
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Lee, Chun-Chia
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Lee, Chia-Hoang
Previous personalized DTV recommendation systems focus only on viewers' historical viewing records or demographic data. This study proposes a new recommending mechanism from a user oriented perspective. The recommending mechanism is based on user properties such as Activities, Interests, Moods, Experiences, and Demographic information-AIMED. The AIMED data is fed into a neural network model to predict TV viewers' program preferences. Evaluation results indicate that the AIMED model significantly increases recommendation accuracy and decreases prediction errors compared to the conventional model.
An HMM-Based Algorithm for Content Ranking and Coherence-Feature Extraction
(2013-03-01)
Liu, Chien-Liang
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Hsaio, Wen-Hoar
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Lee, Chia-Hoang
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Chi, Hsiao-Cheng
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資訊工程學系
In this paper, we propose an algorithm called coherence hidden Markov model (HMM) to extract coherence features and rank content. Coherence HMM is a variant of HMM and is used to model the stochastic process of essay writing and identify topics as hidden states, given sequenced clauses as observations. This study uses probabilistic latent semantic analysis for parameter estimation of coherence HMM. In coherence-feature extraction, support vector regression (SVR) with surface features and coherence features is used for essay grading. The experimental results indicate that SVR can benefit from coherence features. The adjacent agreement rate and the exact agreement rate are 95.24% and 59.80%, respectively. Moreover, this study submits high-scoring essays to the same experiment and finds that the adjacent agreement rate and exact agreement rate are 98.33% and 64.50%, respectively. In content ranking, we design and implement an intelligent assisted blog writing system based on the coherence-HMM ranking model. Several corpora are employed to help users efficiently compose blog articles. When users finish composing a clause or sentence, the system provides candidate texts for their reference based on current clause or sentence content. The experimental results demonstrate that all participants can benefit from the system and save considerable time on writing articles.
An Unsupervised Automated Essay-Scoring System
(2010-09-01)
Chen, Yen-Yu
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Liu, Chien-Liang
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Lee, Chia-Hoang
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Chang, Tao-Hsing
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資訊工程學系
Automated Essay Scoring Using Set of Literary Sememes
(2009-03-01)
Chang, Tao-Hsing
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Tsai, Pei-Yen
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Lee, Chia-Hoang
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Tam, Hak-Ping
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資訊工程學系
Automatic essay scoring system is a very important research tool for many educational studies. Many researches indicate that AES systems should be able to analyze semantic characteristics of an essay and include more such features to score essays. This paper makes an assumption: some concepts that can be regarded as literary concepts would only be utilized by skillful writers. However, it is a difficult task to extract literary concepts due to small size of training corpora. This work uses a semantic network tool to overcome the problem. The concepts in essays can be transformed into sememes using the tool and literary concepts are also transformed into literary sememes. This work introduces a method which makes use of the literary sememes in an essay to score the essay. Experimental results show that the accuracy of the proposed method for Chinese essays is comparable to those as achieved by several current English AES systems.
Automated Essay Scoring Using Set of Literary Sememes
(2008)
Chang, Tao-Hsing
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Lee, Chia-Hoang
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Tsai, Pei-Yen
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Tam, Hak-Ping
;
資訊工程學系
Automatic essay scoring system is a very important research tool for many educational studies. Many researches indicate that AES systems should be able to analyze semantic characteristics of an essay and include more such features to score essays. This paper makes an assumption: some concepts that can be regarded as literary concepts would only be utilized by skillful writers. However, it is a difficult task to extract literal concepts due to small size of training corpora. This work uses a semantic network tool to overcome the problem. The concepts in essays can be transformed into sememes using the tool and literary concepts are also transformed into literary sememes. This work introduces a method which makes use of the literary sememes in an essay to score the essay. Experimental results show that the accuracy of the proposed method for Chinese essays is comparable to those as achieved by several current English AES systems.
Automatic Chinese Essay Scoring Using Connections between Concepts in Paragraphs
(2009)
Chang, Tao-Hsing
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Lee, Chia-Hoang
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資訊工程學系
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Department of Computer Science
Automatic essay scoring (AES) system is a very important research tool for educational studies. Many studies indicate that current AES systems should be able to analyze semantic characteristics of an essay and include more such features to score essays. This study proposes a novel method which uses the similarity between the paragraphic conceptual connections in different essays to predict the scores of essays. Preliminary experiments show that the paragraphic conceptual structure in an essay can be an efficient feature for scoring the essay.
Corpus-based Subtopic Segmentation Using Concept Segment Method
(2010-05-01)
Chang, Tao-Hsing
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Lee, Chia-Hoang
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Tam, Hak-Ping
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資訊工程學系
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Department of Computer Science
Many studies on topic segmentation have been launched in the past, but these previous methods performed poorly on domain-specific short texts. This paper will propose a method which employs high-level concepts on concept hierarchy to predict the number and location of subtopics in a short text, and then determine the boundary between subtopics using similarity measures. Experimental results show that the proposed method can greatly increase the accuracy of subtopic segmentation for domain-specific short texts.
Database-driven Framework for RDF Statements Generation
(2011)
Liu, Chien-Liang
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Lee, Chia-Hoang
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資訊工程學系
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Department of Computer Science
The web has become the biggest database in the world. However, most web content today is designed for humans to read, not for computer programs to manipulate meaningfully. The first step to realize semantic web is putting data on the Web in a form that machines can naturally understand. The main objective of the work reported in this paper is to provide a database-driven framework to automatically extract relational database schema and transform the data into RDF statements representation. Moreover, we employ the Wikipedia to obtain meta-information about the concept. Then, the NGD score is used to measure the relatedness of the terms and the hyponym patterns are further used to discover "is_a" relationship. As our experience with a first simple prototype has shown that this approach could be applied to the environment where the web page content is stored in the database.
Enhancing automatic Chinese essay scoring system from figures-of-speech
(2006-01-01)
Chang, Tao-Hsing
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Lee, Chia-Hoang
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Chang, Yu-Ming
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資訊工程學系
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Department of Computer Science
Chinese automated essay scoring (CAES) is a very important tool for many educational researches. However, none of the methods for retrieving features in English essays is applicable to Chinese writings because Chinese grammar parsers cannot produce reliable and useful syntactic features. CAES systems must explore other distinct features in Chinese writing. This paper proposes a method for retrieving two Chinese figures-of-speech from essays and to predict the scores of essays based on the two skills used in the essays. A scoring model based on ID3 decision tree algorithm will validate and show that the proposed method increases the performance of CAES effectively.
Movie Rating and Review Summarization in Mobile Environment
(2012-05-01)
Liu, Chien-Liang
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Hsaio, Wen-Hoar
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Lee, Chia-Hoang
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Lu, Gen-Chi
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Jou, Emery
In this paper, we design and develop a movie-rating and review-summarization system in a mobile environment. The movie-rating information is based on the sentiment-classification result. The condensed descriptions of movie reviews are generated from the feature-based summarization. We propose a novel approach based on latent semantic analysis (LSA) to identify product features. Furthermore, we find away to reduce the size of summary based on the product features obtained from LSA. We consider both sentiment-classification accuracy and system response time to design the system. The rating and review-summarization system can be extended to other product-review domains easily.
Semi-supervised Linear Discriminant Clustering
(2014-07-01)
Liu, Chien-Liang
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Hsaio, Wen-Hoar
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Lee, Chia-Hoang
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Gou, Fu-Sheng
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交大名義發表
This paper devises a semi-supervised learning method called semi-supervised linear discriminant clustering (Semi-LDC). The proposed algorithm considers clustering and dimensionality reduction simultaneously by connecting K-means and linear discriminant analysis (LDA). The goal is to find a feature space where the K-means can perform well in the new space. To exploit the information brought by unlabeled examples, this paper proposes to use soft labels to denote the labels of unlabeled examples. The Semi-LDC uses the proposed algorithm, called constrained-PLSA, to estimate the soft labels of unlabeled examples. We use soft LDA with hard labels of labeled examples and soft labels of unlabeled examples to find a projection matrix. The clustering is then performed in the new feature space. We conduct experiments on three data sets. The experimental results indicate that the proposed method can generally outperform other semi-supervised methods. We further discuss and analyze the influence of soft labels on classification performance by conducting experiments with different percentages of labeled examples. The finding shows that using soft labels can improve performance particularly when the number of available labeled examples is insufficient to train a robust and accurate model. Additionally, the proposed method can be viewed as a framework, since different soft label estimation methods can be used in the proposed method according to application requirements.
Semi-Supervised Text Classification With Universum Learning
(2016-02)
Liu, Chien-Liang
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Hsaio, Wen-Hoar
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Lee, Chia-Hoang
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Chang, Tao-Hsing
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Kuo, Tsung-Hsun
Universum, a collection of nonexamples that do not belong to any class of interest, has become a new research topic in machine learning. This paper devises a semi-supervised learning with Universum algorithm based on boosting technique, and focuses on situations where only a few labeled examples are available. We also show that the training error of AdaBoost with Universum is bounded by the product of normalization factor, and the training error drops exponentially fast when each weak classifier is slightly better than random guessing. Finally, the experiments use four data sets with several combinations. Experimental results indicate that the proposed algorithm can benefit from Universum examples and outperform several alternative methods, particularly when insufficient labeled examples are available. When the number of labeled examples is insufficient to estimate the parameters of classification functions, the Universum can be used to approximate the prior distribution of the classification functions. The experimental results can be explained using the concept of Universum introduced by Vapnik, that is, Universum examples implicitly specify a prior distribution on the set of classification functions.
Simplify Multi-valued Decision Trees
(2008)
Liu, Chien-Liang
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Lee, Chia-Hoang
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資訊工程學系
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Department of Computer Science
Decision tree is one of the popular data, mining algorithms and it has been applied oil many classification application areas. lit many applications, the number of attribute values may be over hundreds and that will be difficult, to analyze the result. The purpose Of this paper will focus on the construction of categorical decision trees. A binary splitting decision tree algorithm is proposed to simplify the classification outcomes. It adopts the complement operation to simplify the split of interior nodes and it is suitable to apply on the decision trees where the number of outcomes is numerous. lit addition, meta-attribute could be applied oil some applications where the number of outcomes is numerous and the meta-attribute is meaningful. The benefit of meta-attribute representation is that it could transfer the original attributes into higher level concepts and that could reduce the number of outcomes.
Subtopic segmentation for small corpus using a novel fuzzy model
(2007-08-01)
Chang, Tao-Hsing
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Lee, Chia-Hoang
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資訊工程學系
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Department of Computer Science
Subtopic segmentation is a critical task in numerous applications, including information retrieval, automatic summarization, essay scoring, and others. Although several approaches have been developed, many are ineffective for specific domains with a small corpus because of the fuzziness of the semantics of words and sentences in the corpus. This paper explores the problem of subtopic segmentation by proposing a fuzzy model for the semantics of both words and sentences. The model has three characteristics. First, it can deal with the uncertainty in the semantics of words and sentences. Secondly, it can measure the fuzzy similarity between the fuzzy semantics of sentences. Thirdly, it can develop a fuzzy algorithm for segmenting a text into several subtopic segments. The experiments, especially for a short text with a small corpus in a specific domain, indicate that the method can efficiently increase the accuracy of subtopic segmentation over previous methods.