Trường Đại học Công nghệ Thông tin, Đại học Quốc gia Thành phố Hồ Chí Minh

Contraction property of dynamical systems, especially difference systems, is one of the qualitative properties which have attracted much attention from many researchers for recent decades. Contraction of dynamical systems has many practical applications which means that two trajectories of the system convergence to each other when the time reaches to positive infinity. In this paper, by improving some existing approaches, we present a new approach to contraction problem of a class of nonlinear time-varying delay difference system with continuous time. We generalize the definition of contraction to -contraction. Then, we give some new explicit sufficient criteria for -contraction and global exponential stability of the mentioned system. Furthermore, we investigate-contraction of perturbed difference systems with continuous time under nonlinear perturbations in which perturbations are general time-varying functions. Then we obtain a new explicit-contraction bound for such systems subject to nonlinear time-varying perturbations. The obtained theorems generalize some existing results in the literature as particular cases. An example is given to illustrate the obtained results.

Trong bài báo này chúng tôi đề xuất khái niệm dưới vi phân parabolic thông qua dưới đạo hàm parabolic. Bên cạnh đó, chúng tôi trình bày một số tính chất của dưới vi phân parabolic cũng như các áp dụng của dưới vi phân parabolic vào nghiên cứu điều kiện tối ưu. Hơn nữa, trong bài báo này chúng tôi cũng xây dựng ví dụ minh họa cho các kết quả đạt được.

Large-scale image classification is a fundamental problem in computer vision due to many real applications in various domains. A label tree-based classification is one of effective approaches for reducing the testing complexity with a large number of class labels. However, how to build a label tree structure with cost efficiency and high accuracy classification is a challenge. The popular building tree method is to apply a clustering algorithm to a similarity matrix which is obtained by training and evaluating one-versus-all classifiers on validation set. So, this method quickly become impracticable because the cost of training OvA classifiers is too high for large-scale classification problem. In this paper, we introduce a new method to obtain a similarity matrix without using one-versus-all classifiers. To measure the similarity among classes, we used the sum-match kernel that is able to be calculated simply basing on the explicit feature map. Furthermore, to gain computational efficiency in classification, we also propose an algorithm for learning balanced label tree by balancing a number of class labels in each node. The experimental results on standard benchmark datasets ImageNet-1K, SUN-397 and Caltech-256 show that the performance of the proposed method outperforms significantly other methods.

In recent years, Online Meeting Technology has been investigated intensively for possible use in various application areas e.g. Distance E-learning, Video Conferencing. The benefits of using Online Meeting Technology are to easily transfer the meeting information to peers around the world. For a company, using online meeting Technology can significantly reduce the company's costs associated with traveling expenses and lost working time. In this paper, we introduce a solution of setting online meeting room which is approaching the ordinary meeting room by using the online system “Easy Online” for helping companies to improve their management processes and training programs for their employees.

OLAP (Online Analytical Processing) is a technology that enables the user to easily and selectively extract and view data from different points of view. It is also an important part of the decision system. This study proposes the decisions in fulfillment process which could be supported by the OLAP technology, including the quality of sales, the main product of the company, the salary and the bonus for sales staff, the credit limit and the price policy for the customer. Furthermore, this research also demonstrates the application of the OLAP technology in the decision making for the main product of A manufacturing and distributing company.

In this paper, a computational semantic method is proposed to estimate the phrasal semantic distance used in our model of a Vietnamese document retrieval system. The semantic distances between phrases are defined in terms of semantic classes and semantic relations to ensure that it can reflect how different two certain phrases are. To estimate the semantic distance, the semantic classes of a phase are identified by using the n-gram model. After identification of the semantic classes, their semantic relations are also identified by using a Vietnamese Lexicon Ontology. This handcrafted ontology contains defined semantic classes and their potential relations in Vietnamese language explicitly. For the evaluation purpose, a phrasal semantic retrieval system has been built to test with a data set of 720 phrases and 30 queries. The evaluation shows the precision of 96.6% and the recall of 78.4% on experimental results.

The trend of technological development and increasing varieties of social media lead to the changes in people’s behaviors in society and forming online communities. Changes of human’s behaviors make many models of business, marketing, services and even the field of education, security, politicsl change from approaches to user management. Community of users on social networks influence behaviors, habits of each user involved in the community. Therefore, exploring community on social networks from many different data sources via analyzing exchanged contents will help know the user community’s behaviors which are reflected in the content and topics that users are interested in discussing in messages. In this paper, we propose a new model of discovering communities of users on social networks based on the topic model combined with Kohonen network. In the proposed model, we focus on discovering communities of users on social networks and analyzing the interested topics change of online community in each period of time. The proposed model is experimented with a set of vectors in interested topics of online users in higher education field.

Khuyến nghị cộng tác trong nghiên cứu khoa học là bài toán tự động liệt kê những người, nhóm nghiên cứu cộng tác tiềm năng ứng với đầu vào là một người, nhóm nghiên cứu nào đó. Đây là bài toán được quan tâm bởi những chuyên gia trong lĩnh vực này trong thời gian gần đây. Tiếp cận phổ biến đã và đang được các nghiên cứu này áp dụng là dựa vào phân tích mạng xã hội, cụ thể là phân tích các mối quan hệ trong mạng đồng tác giả, cũng như ảnh hưởng của nó đối với việc tìm ra những người, nhóm nghiên cứu cộng tác tiềm năng. Tuy nhiên, các phương pháp hiện nay đều chưa quan tâm đến thông tin về xu hướng cộng tác, một yếu tố quan trọng trong việc hình thành các mối quan hệ cộng tác mới. Bài báo này đề xuất ba phương pháp mới để tính tương tự đỉnh trong mạng đồng tác giả: (1) Maximum Path based Relation Strength (MPRS); (2) Maximum Path based Relation Strength Plus (MPRS+); và (3) Relation Strength Similarity Plus (RSS+). Hai phương pháp MPRS+ và RSS+ có sử dụng thông tin về xu hướng cộng tác để cải tiến việc tính toán mức độ quan hệ của những người nghiên cứu trong mạng đồng tác giả. Các phương pháp đề xuất ứng dụng vào bài toán khuyến nghị cộng tác ở mức cá nhân người nghiên cứu. Thực nghiệm được tiến hành trên hai tập dữ liệu khoa học: i) cơ sở dữ liệu khoa học mở ‘Digital Bibliography & Library Project’ (DBLP); ii) tập dữ liệu do chúng tôi rút trích từ trang web của hệ thống Microsoft Academic Search (gọi tắt là MAS). Kết quả thực nghiệm cho thấy các phương pháp đề xuất cho kết quả tốt hơn các phương pháp tương tự đỉnh truyền thống và phổ biến hiện nay.

In business, most of companies focus on growing their profits. Besides considering profit from each product, they also focus on the relationship among products in order to support effective decision making, gain more profits and attract their customers, e.g. shelf arrangement, product displays, or product marketing, etc. Some high utility association rules have been proposed, however, they consume much memory and require long time processing. This paper proposes LHAR (Lattice-based for mining High utility Association Rules) algorithm to mine high utility association rules based on a lattice of high utility itemsets. The LHAR algorithm aims to generates high utility association rules during the process of building lattice of high utility itemsets, and thus it needs less memory and runtime

In this article, we focus on researching models in social networks analysis and use them to develop the system which enables users to exactly find topics as well as necessary research of authors. In this research, we study Author – Conference – Topic (ACT) model to find out the topic relevant to users’ requirements. With each of these topics, the system will create a graph of Topical Affinity Propagation (TAP) in purpose of analyzing the topic based influence of authors. Experimental results on the corpus extracted from Microsoft Academic Research include 34,330 authors, 19,921 articles, 1,335 conference and 424,501 co-author relations.