Jason C. Hung

A survey of distance education challenges and technologies

Distance education, e-learning, and virtual university are similar terms for a trend of modern education. It is an integration of information technologies, computer hardware systems, and communication tools to support educational professionals in remote teaching. This chapter presents an overview of distance education from the perspective of policy, people, and technology. A number of questions frequently asked in distance learning panel discussions are presented, with the suggested answers from the authors. The survey presented in this chapter includes communication, intelligent, and educational technologies of distance education. Readers of this 2 Shih, Hung, Ma, and Jin Copyright

Applying word sense disambiguation to question answering system for e-learning

Interaction between the student and the instructor is important for the student to gain knowledge. Also, one of the major tasks on the instructor in e-learning is to reply student e-mails and posted messages. Students usually raise their questions by these two methods in an e-learning environment. In this paper, we introduce a semantic-based automated question answering system that can act like a virtual teacher to respond to student questions online. With the system, not only the instructor can be relieved from the load of answering lots of questions, but also the student can mostly get answers promptly without waiting for the instructor to get online and provide an answer. This would be a big help for both the instructor and the student in e-learning environment. Through the process of raising questions and getting answers, the knowledge base will be enriched for future questions answering. Further, not only the students can get answers for their questions, but also the instructors could know what problems students encounter in learning. These would be big aids to both the teaching and the learning.

Feature selection based on an improved cat swarm optimization algorithm for big data classification

Feature selection, which is a type of optimization problem, is generally achieved by combining an optimization algorithm with a classifier. Genetic algorithms and particle swarm optimization (PSO) are two commonly used optimal algorithms. Recently, cat swarm optimization (CSO) has been proposed and demonstrated to outperform PSO. However, CSO is limited by long computation times. In this paper, we modify CSO to present an improved algorithm, ICSO. We then apply the ICSO algorithm to select features in a text classification experiment for big data. Results show that the proposed ICSO outperforms traditional CSO. For big data classification, the results show that using term frequency-inverse document frequency (TF-IDF) with ICSO for feature selection is more accurate than using TF-IDF alone.

Real-time Hand Gesture Recognition by Shape Context Based Matching and Cost Matrix

How to recognize the shape gesture for new human-computer interface without controller required and bring entertainment, games industries and information appliances in new ways. In this paper, we would illustrate a real-time hand gesture recognition system by using shape context matching and cost matrix. The shape context is taken as a basis description for shape matching. It can be regarded as a global characterization descriptor to represent the distribution of points in a set with scale and rotation invariance. In this paper, we developed a perceptual interface for human-computer-interaction based on real-time hand gesture recognition. User could interact with computer program by performing body gesture instead of physical contact. The image of hand gesture was captured from CCD. The hand gesture image was transformed into proper instruction according to the shape information respectively. The instruction was transferred to an appropriate program to execute. The experience of our preliminary results shown the precision rates was up to 70% ~ 90%.

Feature selection and parameter optimization of support vector machines based on modified cat swarm optimization

Recently, applications of Internet of Things create enormous volumes of data, which are available for classification and prediction. Classification of big data needs an effective and efficient metaheuristic search algorithm to find the optimal feature subset. Cat swarm optimization (CSO) is a novel metaheuristic for evolutionary optimization algorithms based on swarm intelligence. CSO imitates the behavior of cats through two submodes: seeking and tracing. Previous studies have indicated that CSO algorithms outperform other well-known metaheuristics, such as genetic algorithms and particle swarm optimization. This study presents a modified version of cat swarm optimization (MCSO), capable of improving search efficiency within the problem space. The basic CSO algorithm was integrated with a local search procedure as well as the feature selection and parameter optimization of support vector machines (SVMs). Experiment results demonstrate the superiority of MCSO in classification accuracy using subsets with fewer features for given UCI datasets, compared to the original CSO algorithm. Moreover, experiment results show the fittest CSO parameters and MCSO take less training time to obtain results of higher accuracy than original CSO. Therefore, MCSO is suitable for real-world applications.

The smart-travel system: utilising cloud services to aid traveller with personalised requirement

Recent advancements in cloud computing allow smart phone provide a variety of movable services. This paper proposed a design of service, including human-centred recommendation service and travel-gaming service. The Smart-Travel System (STS) aids traveller with personal requirements to tour by smart phone. STS provides personalised travelling real-time information, and automatically tells traveller when they show up around the task, which providing by government or local store, to make the trip like a game mission for user. No matter if the traveller deviated from the planned route or not, the STS could provide the new trip itineraries and allows the share on social network in real-time from any computer. According to the cloud-based service, STS is a new way to look up the travel information. The STS provides a whole new experience in travelling for mobile phone users via smart phone, GPS, Google map and Augmented Realty.

Effects of blended e-Learning: a case study in higher education tax learning setting

This article has two main objectives. First, we describe the design of an e-learning system for a University Income Tax Law course. Second, we analyze and explore learning results in terms of students’ learning satisfaction and learning achievement. Learning achievement was examined by questions derived from the course content while learning satisfaction was analyzed based on an adaptation of the Technology Acceptance Model (TAM).Results indicate that neither gender nor the school system affect students’ e-learning system satisfaction. Since students’ knowledge and exposure to computers are equal regardless of gender or educational background this reduces the significance of both these variables. Participating samples are divided into three groups: traditional, fully on-line and blended learning. We find, however, a statistically significant difference existed in learning achievement among groups. The blended learning group, combining on- line learning with paper-and-pencil testing, has the best learning achievement among the three groups.

Facial emotion recognition towards affective computing‐based learning

Purpose – This study aims to introduce an affective computing‐based method of identifying student understanding throughout a distance learning course.Design/methodology/approach – The study proposed a learning emotion recognition model that included three phases: feature extraction and generation, feature subset selection and emotion recognition. Features are extracted from facial images and transform a given measument of facial expressions to a new set of features defining and computing by eigenvectors. Feature subset selection uses the immune memory clone algorithms to optimize the feature selection. Emotion recognition uses a classifier to build the connection between facial expression and learning emotion.Findings – Experimental results using the basic expression of facial expression recognition research database, JAFFE, show that the proposed facial expression recognition method has high classification performance. The experiment results also show that the recognition of spontaneous facial expression...

Feature Selection and Parameter Optimization of Support Vector Machines Based on Modified Artificial Fish Swarm Algorithms

Rapid advances in information and communication technology have made ubiquitous computing and the Internet of Things popular and practicable. These applications create enormous volumes of data, which are available for analysis and classification as an aid to decision-making. Among the classification methods used to deal with big data, feature selection has proven particularly effective. One common approach involves searching through a subset of the features that are the most relevant to the topic or represent the most accurate description of the dataset. Unfortunately, searching through this kind of subset is a combinatorial problem that can be very time consuming. Meaheuristic algorithms are commonly used to facilitate the selection of features. The artificial fish swarm algorithm (AFSA) employs the intelligence underlying fish swarming behavior as a means to overcome optimization of combinatorial problems. AFSA has proven highly successful in a diversity of applications; however, there remain shortcomings, such as the likelihood of falling into a local optimum and a lack of multiplicity. This study proposes a modified AFSA (MAFSA) to improve feature selection and parameter optimization for support vector machine classifiers. Experiment results demonstrate the superiority of MAFSA in classification accuracy using subsets with fewer features for given UCI datasets, compared to the original FASA.

A Smart-Travel System Based on Social Network Service for Cloud Environment

The rising of services and applications, are delivered from the cloud over to the next-generation networks now. Smart Travel design to help traveler capture the moment emotion memory and process all this data around user, and turn it into not just helpful information, or even personalized knowledge. Our system is also designed to develop the tourism industries could appealing to different customer segments, a new opportunity of business model, and provide of these innovative and desirable communications, information and entertainment applications and services. In this paper, we introduce a new ubiquitous tourism system based on SNS, IoT, and UGC, which we call it as Smart-Travel system. According to the cloud-based service¡¦s needs, we design a new way to look up the travel information. By thronging smart-phone, GPS, Google map, and AR, we provide a whole new experience in traveling for mobile phone users.