Examining the Part-of-speech Features in Assessing the Readability of Vietnamese Texts

The readability of the text plays a very important role in selecting appropriate materials for the level of the reader. Text readability in Vietnamese language has received a lot of attention in recent years, however, studies have mainly been limited to simple statistics at the level of a sentence length, word length, etc. In this article, we investigate the role of word-level grammatical characteristics in assessing the difficulty of texts in Vietnamese textbooks. We have used machine learning models (for instance, Decision Tree, K-nearest neighbor, Support Vector Machines, etc.) to evaluate the accuracy of classifying texts according to readability, using grammatical features in word level along with other statistical characteristics. Empirical results show that the presence of POS-level characteristics increases the accuracy of the classification by 2-4%

Multimodal Personal Verification Using Likelihood Ratio for the Match Score Fusion

In this paper, the authors present a novel personal verification system based on the likelihood ratio test for fusion of match scores from multiple biometric matchers (face, fingerprint, hand shape, and palm print). In the proposed system, multimodal features are extracted by Zernike Moment (ZM). After matching, the match scores from multiple biometric matchers are fused based on the likelihood ratio test. A finite Gaussian mixture model (GMM) is used for estimating the genuine and impostor densities of match scores for personal verification. Our approach is also compared to some different famous approaches such as the support vector machine and the sum rule with min-max. The experimental results have confirmed that the proposed system can achieve excellent identification performance for its higher level in accuracy than different famous approaches and thus can be utilized for more application related to person verification

Bittm: A core biterms-based topic model for targeted analysis

While most of the existing topic models perform a full analysis on a set of documents to discover all topics, it is noticed recently that in many situations users are interested in fine-grained topics related to some specific aspects only. As a result, targeted analysis (or focused analysis) has been proposed to address this problem. Given a corpus of documents from a broad area, targeted analysis discovers only topics related with user-interested aspects that are expressed by a set of user-provided query keywords. Existing approaches for targeted analysis suffer from problems such as topic loss and topic suppression because of their inherent assumptions and strategies. Moreover, existing approaches are not designed to address computation efficiency, while targeted analysis is supposed to provide responses to user queries as soon as possible. In this paper, we propose a core BiTerms-based Topic Model (BiTTM). By modelling topics from core biterms that are potentially relevant to the target query, on one hand, BiTTM captures the context information across documents to alleviate the problem of topic loss or suppression; on the other hand, our proposed model enables the efficient modelling of topics related to specific aspects. Our experiments on nine real-world datasets demonstrate BiTTM outperforms existing approaches in terms of both effectiveness and efficiency

A Multilingual Study of Multi-Sentence Compression using Word Vertex-Labeled Graphs and Integer Linear Programming

Multi-Sentence Compression (MSC) aims to generate a short sentence with the key information from a cluster of similar sentences. MSC enables summarization and question-answering systems to generate outputs combining fully formed sentences from one or several documents. This paper describes an Integer Linear Programming method for MSC using a vertex-labeled graph to select different keywords, with the goal of generating more informative sentences while maintaining their grammaticality. Our system is of good quality and outperforms the state of the art for evaluations led on news datasets in three languages: French, Portuguese and Spanish. We led both automatic and manual evaluations to determine the informativeness and the grammaticality of compressions for each dataset. In additional tests, which take advantage of the fact that the length of compressions can be modulated, we still improve ROUGE scores with shorter output sentences.Comment: Preprint versio

Automatic extraction and detection of characteristic movement patterns in children with ADHD Based on a convolutional neural network (CNN) and acceleration images

Attention deficit and hyperactivity disorder (ADHD) is a neurodevelopmental disorder, which is characterized by inattention, hyperactivity and impulsive behaviors. In particular, children have difficulty keeping still exhibiting increased fine and gross motor activity. This paper focuses on analyzing the data obtained from two tri-axial accelerometers (one on the wrist of the dominant arm and the other on the ankle of the dominant leg) worn during school hours by a group of 22 children (11 children with ADHD and 11 paired controls). Five of the 11 ADHD diagnosed children were not on medication during the study. The children were not explicitly instructed to perform any particular activity but followed a normal session at school alternating classes of little or moderate physical activity with intermediate breaks of more prominent physical activity. The tri-axial acceleration signals were converted into 2D acceleration images and a Convolutional Neural Network (CNN) was trained to recognize the differences between non-medicated ADHD children and their paired controls. The results show that there were statistically significant differences in the way the two groups moved for the wrist accelerometer (t-test p-value <0.05). For the ankle accelerometer statistical significance was only achieved between data from the non-medicated children in the experimental group and the control group. Using a Convolutional Neural Network (CNN) to automatically extract embedded acceleration patterns and provide an objective measure to help in the diagnosis of ADHD, an accuracy of 0.875 for the wrist sensor and an accuracy of 0.9375 for the ankle sensor was achieved

Kangkong (Ipomoea, Convolvulaceae) and the geographies of interstitial urban spaces in Southeast Asia

This is a study of the life and heritage of a plant and the people involved in the prod~ction of kangkong, Ipomoea aquatica Forskal within dynamic peri-urban spaces m mamland Southeast Asia. Kangkong has a distinction of being both a food in much ofAsia and a weed in other parts of the world. It has become an important vegetable in Cambodia, Thailand, and Viet Nam. The production of this vegetable largely occurs around cities. In Hanoi and Phnom Penh, the use of wastewater is an important aspect of its production while in Bangkok, though wastewater is not used, kangkong has become a commercial vegetable replacing rice production in 'some areas. Such disparate trajectories offer insights into the households involved in its production and the spaces upon which it thrives so that opportunities for understanding the desakota characteristics ofspatial change in mainland Southeast Asia can be made.In understanding desakota geographies, this study looks at the key factors that explain livelihood dependence through the use ofsurvey data and sequential regression. Then their geographical underpinnings are fleshed out The results showed that, in Bangkok, the occupational multiplicity of the wife explains dependence while it is the performance of kangkong production by both the husband and the wife in Hanoi. In Phnom Penh, it was shown that it is the occupational multiplicity of the husband that explains a household's dependence on kangkong production.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

3rd ASIA International Conference (AIC 2017) Conference Program and Abstract Book

Rural tourism hasbeen shown to benefit local communities from the economic perspective. Digitalmarketing allows marketing information to be transmitted directly to potentialvisitors without the need for an intermediary, in a low-cost but effective way.Rural tourism destinations in Sarawak now have an opportunity to benefit from the Sarawak state government’sinitiative, the Digital Sarawak Centre of Excellence, in terms of digitalcontent creation and website maintenance. However, the current level of adoption is zero to minimal in ruraltourism destinations. This study examines the barriers towards digital marketingadoption from the perspective of rural tourism providers. Fieldwork was performed at two sites,Ba’kelalan and Long Lamai, in July 2016 and February 2017 respectively. A total of 19 respondents were interviewedin-depth. The study revealed thattourism providers currently depended on word-of-mouth or direct contact forbookings, but were willing to adopt digital marketing with the assistance ofknowledgeable parties. However, certainphysical, logistical and social constraints may have a detrimental effect onthe community’s readiness level to entertain tourists on a larger scale and mayfurther impede the overall progress of digital marketing adoption, at both theindividual and destination levels

Designing behavior change support systems in the context of knowledge documentation: development of theory and practical implementation

Although innovation and operating efficiently require creating, transferring, and applying knowledge, successful knowledge documentation remains a challenge for organizations. While knowledge management systems support knowledge management activities, the missing link to applying knowledge management relies on human actions and their behaviors. This dissertation extends prior design knowledge about designing Behavior Change Support Systems in the context of knowledge documentation by developing theory and showing practical implementation. Combining technical and psychological models within information systems frameworks based on the principles of abstraction, originality, justification, and benefit, this dissertation draws on design science to propose prescriptive knowledge, for example, in the form of design principles and a specific artifact