Network embedding, aiming at learning the low-dimensional representations of nodes in a network, is a key to many network analysis tasks. All the current network embedding methods primarily explore the network topology or node attributes, while no effort has been made to analyze the edge content for network embedding. The edge content, such as the email content between two users in an email network, is often naturally associated with edges. They carry rich information to describe the interaction between nodes, and provide valuable supervision to learn the representations of nodes. In this paper, we propose a novel edge content enhanced network embedding model, which incorporates the edge content to guide the network representation learning process. We provide the efficient updating rules to infer the parameters in the model, along with theoretical analysis on correctness and convergence guarantees. Extensive experiments, in comparison with the state-of-the-arts, show the superior performance of our proposed new approach on different network analysis tasks.

Detection of overlapping communities has drawn much attention lately as they are essential properties of real complex networks. Despite its influence and popularity, the well studied and widely adopted stochastic model has not been made effective for finding overlapping communities. Here we extend the stochastic model method to detection of overlapping communities with the virtue of autonomous determination of the number of communities. Our approach hinges upon the idea of ranking node popularities within communities and using a Bayesian method to shrink communities to optimize an objective function based on the stochastic generative model. We evaluated the novel approach, showing its superior performance over five state-of-the-art methods, on large real networks and synthetic networks with ground-truths of overlapping communities.

In this paper, an automatic visual inspection scheme with phase identification of microdrill bits in printed circuit board (PCB) production is proposed. Different from conventional methods in which the geometric quantities of microdrill bits are measured to compare with the prior standards, the proposed method adopts a strategy of machine learning. Thus, it lowers the requirement for the enlargement of lens and the resolution of charge-coupled device; therefore, the cost of inspecting instrument can be relatively reduced. Our method mainly includes two procedures: First, the statistical shape models of microdrill bit are built to get the shape subspace, and then the phase identification is performed in the shape subspace using some pattern recognition techniques. In this paper, we compared the performance of two statistical model methods, principal component analysis (PCA) and linear discriminate analysis, together with three classifiers, support vector machines (SVMs), neural networks, andk-nearest neighbors, respectively, for phase identification of microdrill bits. The experimental results demonstrate that using low enlargement and resolution microdrill bit images the proposed method can measure up to high inspection accuracy, and provide a conclusion that the highest identification rates are obtained by PCA-SVMs, which are higher than that of the conventional method.

CALL (computer assisted language learning) systems using ASR (automatic speech recognition) for second language learning have received increasing interest recently. However, it still remains a challenge to achieve high speech recognition performance, including accurate detection of erroneous utterances by non-native speakers. Conventionally, possible error patterns, based on linguistic knowledge, are added to the ASR grammar network. However, this approach easily falls in the trade-off of coverage of errors and the increase of perplexity. To solve the problem, we propose a method based on a decision tree to learn effective prediction of errors made by non-native speakers. An experimental evaluation with a number of foreign students in our university shows that the proposed method can effectively generate an ASR grammar network, given a target sentence, to achieve both better coverage of errors and smaller perplexity, resulting in significant improvement in ASR accuracy.

We have developed a new CALL system to aid students learning Japanese as a second language. The system offers students the chance to practice the Japanese grammar and vocabulary, by creating their own sentences based on visual prompts, before receiving feedback on their mistakes. Questions are dynamically generated along with sentence patterns of the lesson point, to realize variety and flexibility of the lesson. Students can give their answers with either text input or speech input. To enhance speech recognition performance, a decision tree-based method is incorporated to predict possible errors made by non-native speakers for each generated sentence on the fly. Trials of the system have been conducted with a number of foreign students in our university, and positive feedbacks were obtained.

We are developing a new CALL system to aid students learning Japanese as a second language. This system is designed to allow students to create their own sentences based on visual prompts, receiving feedback based on their mistakes. The questions are dynamically generated, resulting in a large variety of challenges. The students may choose to receive guidance in order to complete each task, selecting the level of help that best suits their needs. A scoring system is also incorporated, which awards a grade to students based on the errors made and hints used. The trial of the system has been conducted with twenty one students, providing the statistics of actual errors and hint usages. With these data, we have trained the weights of the scoring system by taking into account the impact of each issue on the proficiency of the students. The validity of the estimated score is generally confirmed by predicting the proficiency of the students. Index Terms: CALL, second language learning, Japanese

In this paper, an agent-mediated service framework is proposed to ease the construction of virtual organizations of standalone applications. Using this framework, on one hand, service providers can control the usage of their own services, and on the other hand, an agent representing certain stakeholders can dynamically search for capable partners and negotiate with their agents to dynamically form a virtual organization. Incorporating business level service composition language - VINCA into the framework further promotes the participation of business level users in constructing virtual organizations