Impact of Knowledge Representations on Problem-Oriented Learning in Online Environments

The objective of this study was to investigate the impact of knowledge representations on problem-oriented learning in online learning environments. The study compared the impact of knowledge map representation with traditional hierarchical representation with regard to learning memory and problem-solving performance. Twenty-nine students participated in an experiment in which they studied online materials with the goal of solving two programming problems (simple and complex). It was found that participants who used the hierarchical representation read in the depth-first sequence, whereas participants who used the knowledge map representation read in a sequence reflecting the system running mechanism implied by the graphical representation. In addition, participants who used the knowledge map representation had better memory of the learning content, especially about relations between knowledge nodes. When solving the complex problem, participants who used the knowledge map representation made a deeper analysis of the problem and had better problem-solving performance. These results were not significant in the simple problem-solving task.     

A novel reversible robust watermarking algorithm based on chaotic system

Reversible watermarking can be applied to the protection for important digital media, such as medical and military image, it allows the watermark to be extracted and the original image to be restored completely, but reversible watermarking with stronger robustness is seldom discussed in existing literature. In this paper, a novel reversible watermarking algorithm based on chaotic system is proposed; chaotic system is not only used to search space of reversibility of the scheme, but also used to randomly select the position of watermarking embedding. Consequently, the proposed scheme achieves larger threshold space of reversibility and better performance of security. For some specific thresholds, the proposed algorithm is not only reversible, but also has stronger robustness against image compression. The experimental results show that the ergodicity and sensitivity to initial values of chaotic system play an important role in reversible watermarking algorithm, and the proposed scheme can achieve balance between reversibility and robustness with the help of chaotic system.     

A distributed collaborative product design environment based on semantic norm model and role-based access control

With the boom of Internet Technology, it becomes possible to combine designers from different disciplines into one team to support product design globally. In this paper, a distributed collaborative product design environment is presented to support top-down process oriented product design. In conceptual design stage, the artifact is managed by semantic norm model (SNM). In SNM, the designers can define virtual components at early design stage and instantiate those components at later detailed design stage. By role-based access control (RBAC), different roles with corresponding permissions could be assigned to distributed designers, and the designers could concurrently modify different components of the product relevant to his or her roles. Based on the SNM and RBAC system, a distributed collaborative product design environment is developed and the top-down oriented product design process is demonstrated.     

Improving image tags by exploiting web search results

Automatic image tagging automatically assigns image with semantic keywords called tags, which significantly facilitates image search and organization. Most of present image tagging approaches are constrained by the training model learned from the training dataset, and moreover they have no exploitation on other type of web resource (e.g., web text documents). In this paper, we proposed a search based image tagging algorithm (CTSTag), in which the result tags are derived from web search result. Specifically, it assigns the query image with a more comprehensive tag set derived from both web images and web text documents. First, a content-based image search technology is used to retrieve a set of visually similar images which are ranked by the semantic consistency values. Then, a set of relevant tags are derived from these top ranked images as the initial tag set. Second, a text-based search is used to retrieve other relevant web resources by using the initial tag set as the query. After the denoising process, the initial tag set is expanded with other tags mined from the text-based search result. Then, an probability flow measure method is proposed to estimate the probabilities of the expanded tags. Finally, all the tags are refined using the Random Walk with Restart (RWR) method and the top ones are assigned to the query images. Experiments on NUS-WIDE dataset show not only the performance of the proposed algorithm but also the advantage of image retrieval and organization based on the result tags.     

Video archaeology: understanding video manipulation history

Facing the explosive growth of near-duplicate videos, video archaeology is quite desired to investigate the history of the manipulations on these videos. With the determination of derived videos according to the manipulations, a video migration map can be constructed with the pair-wise relationships in a set of near-duplicate videos. In this paper, we propose an improved video archaeology (I-VA) system by extending our previous work (Shen et al. 2010). The extensions include more comprehensive video manipulation detectors and improved techniques for these detectors. Specially, the detectors are used for two categories of manipulations, i.e., semantic-based manipulations and non-semantic-based manipulations. Moreover, the improved detecting algorithms are more stable. The key of I-VA is the construction of a video migration map, which represents the history of how near-duplicate videos have been manipulated. There are various applications based on the proposed I-VA system, such as better understanding of the meaning and context conveyed by the manipulated videos, improving current video search engines by better presentation based on the migration map, and better indexing scheme based on the annotation propagation. The system is tested on a collection of 12,790 videos and 3,481 duplicates. The experimental results show that I-VA can discover the manipulation relation among the near-duplicate videos effectively.     

A novel two-box search paradigm for query disambiguation

Precision-oriented search results such as those typically returned by the major search engines are vulnerable to issues of polysemy. When the same term refers to different things, the dominant sense is preferred in the rankings of search results. In this paper, we propose a novel two-box technique in the context of Web search that utilizes contextual terms provided by users for query disambiguation, making it possible to prefer other senses without altering the original query. A prototype system, Bobo, has been implemented. In Bobo, contextual terms are used to capture domain knowledge from users, help estimate relevance of search results, and route them towards a user-intended domain. A vast advantage of Bobo is that a wide range of domain knowledge can be effectively utilized, where helpful contextual terms do not even need to co-occur with query terms on any page. We have extensively evaluated the performance of Bobo on benchmark datasets that demonstrates the utility and effectiveness of our approach.     

A survey on enhanced subspace clustering

Subspace clustering finds sets of objects that are homogeneous in subspaces of high-dimensional datasets, and has been successfully applied in many domains. In recent years, a new breed of subspace clustering algorithms, which we denote as enhanced subspace clustering algorithms, have been proposed to (1) handle the increasing abundance and complexity of data and to (2) improve the clustering results. In this survey, we present these enhanced approaches to subspace clustering by discussing the problems they are solving, their cluster definitions and algorithms. Besides enhanced subspace clustering, we also present the basic subspace clustering and the related works in high-dimensional clustering.     

Uncertain term structure model of interest rate

Term structure models describe the evolution of the yield curve through time, without considering the influence of risk, tax, etc. Recently, uncertain processes were initialized and applied to option pricing and currency model. Under the assumption of short interest rate following uncertain processes, this study investigates the term-structure equation. This equation is first derived for valuing zero-coupon bond. Finally, analytic solutions of the uncertain interest rate equation are given when the process of interest rate is assumed to be the uncertain counterparts of the Ho-Lee model and Vasicek model, respectively.     

A model based transformation paradigm for cross-language collaborations

Online collaboration is a big challenge in the field of international product development in a cross-language environment. It serves two purposes: cross-language translation and design requirement clarification. Though many approaches and tools are developed for each of the purposes, not a solution serves both of them well. Especially, the traditional statistical methods for cross-language translation cannot preserve the whole semantic information, which intend to incur misunderstanding and ineffective collaboration. This results in potential problems in clarifying the design requirements. In this paper, we proposed a method to online collaboration, named Cross-Language Transformation based on Recursive Object Model (CLT-ROM). The proposed method consists of two steps. Firstly, a natural language sentence is transformed into a source ROM diagram. Secondly, a corresponding target ROM diagram is generated by a transformation algorithm. The proposed method is a model-based communication tool which facilitates collaborations. Since the ROM has been proven effective in requirements clarification, some examples are given to illustrate that the CLT-ROM has a good capability of semantic preserving in requirement engineering for product development.     

基于改进型MF-DFA的月径流序列多重分形分析

应用改进的多重分形消除趋势波动分析法对长江流域某水文站多年月径流资料进行分析计算,结果显示,月径流序列具有长程相关性和多重分形特征,并采用二项倍增串级模型,对其多重分形谱拟合,表明月径流序列具有较强的多重分形性。通过对其初始序列、重排序列和替代序列进行对比分析,揭示出月径流序列多重分形特征是序列本身的长程相关性和胖尾概率分布共同作用的结果,且胖尾概率分布起主要作用。