Toward a classification of discourse patterns in asynchronous online discussions

The goal of this study was to develop a classification for a range of discourse patterns that occur in text-based asynchronous discussion forums, and that can aid in the distinction of three modes of discourse: knowledge sharing, knowledge construction, and knowledge building. The dataset was taken from Knowledge Forum® databases in the Knowledge Building Teacher Network in Hong Kong, and included three discussion views created for different classes: Grade 5 Science, Grade 10 Visual Arts, and Grade 10 Liberal Studies. We used a combination of qualitative coding and narrative analysis as well as teachers’ understanding of online discourse to analyze student discussions. Nine discourse patterns were identified. These patterns revealed a variety of ways in which students go about their collaborative interactions online and demonstrated how and why students succeed or fail in sustaining collaborative interactions. This study extended the three modes of online discourse and developed different discourse patterns, which are efforts to provide instructional guidance. The implications of supporting productive discourse and the enactment of CSCL innovations in classrooms are discussed.     

Hybrid immune algorithm based on greedy algorithm and delete-cross operator for solving TSP

This paper first introduces the fundamental principles of immune algorithm (IA), greedy algorithm (GA) and delete-cross operator (DO). Based on these basic algorithms, a hybrid immune algorithm (HIA) is constructed to solve the traveling salesman problem (TSP). HIA employs GA to initialize the routes of TSP and utilizes DO to delete routes of crossover. With dynamic mutation operator (DMO) adopted to improve searching precision, this proposed algorithm can increase the likelihood of global optimum after the hybridization. Experimental results demonstrate that the HIA algorithm is able to yield a better solution than that of other algorithms, which also takes less computation time.     

Band selection for hyperspectral images using probabilistic memetic algorithm

Band selection plays an important role in identifying the most useful and valuable information contained in the hyperspectral images for further data analysis such as classification, clustering, etc. Memetic algorithm (MA), among other metaheuristic search methods, has been shown to achieve competitive performances in solving the NP-hard band selection problem. In this paper, we propose a formal probabilistic memetic algorithm for band selection, which is able to adaptively control the degree of global exploration against local exploitation as the search progresses. To verify the effectiveness of the proposed probabilistic mechanism, empirical studies conducted on five well-known hyperspectral images against two recently proposed state-of-the-art MAs for band selection are presented.     

Image inpainting algorithm based on TV model and evolutionary algorithm

With the development of modern image processing techniques, the numbers of images increase at a high speed in network. As a new form of visual communication, image is widely used in network transmission. However, the image information would be lost after transmission. In view of this, we are motivated to restore the image to make it complete in an effective and efficient way in order to save the network bandwidth. At present, there are two main methods for digital image restoration, texture-based method and non-textured-based method. In the texture-based method, Criminisi algorithm is a widely used algorithm. However, the inaccurate completion order and the inefficiency in searching matching patches are two main limitations of Criminisi algorithm. To overcome these shortcomings, in this paper, an exemplar image completion based on evolutionary algorithm is proposed. In the non-textured-based method, total variation method is a typical algorithm. An improved total variation algorithm is proposed in this paper. In the improved algorithm, the diffusion coefficients are defined according to the distance and direction between the damaged pixel and its neighborhood pixel. Experimental results show that the proposed algorithms have better general performance in image completion. And these two new algorithms could improve the experience of network surfing and reduce the network communication cost.     

Scientific workflows for process mining: building blocks,scenarios, and implementation

Over the past decade process mining has emerged as a new analytical discipline able to answer a variety of questions based on event data. Event logs have a very particular structure; events have timestamps, refer to activities and resources, and need to be correlated to form process instances. Process mining results tend to be very different from classical data mining results, e.g., process discovery may yield end-to-end process models capturing different perspectives rather than decision trees or frequent patterns. A process-mining tool like ProM provides hundreds of different process mining techniques ranging from discovery and conformance checking to filtering and prediction. Typically, a combination of techniques is needed and, for every step, there are different techniques that may be very sensitive to parameter settings. Moreover, event logs may be huge and may need to be decomposed and distributed for analysis. These aspects make it very cumbersome to analyze event logs manually. Process mining should be repeatable and automated. Therefore, we propose a framework to support the analysis of process mining workflows. Existing scientific workflow systems and data mining tools are not tailored towards process mining and the artifacts used for analysis (process models and event logs). This paper structures the basic building blocks needed for process mining and describes various analysis scenarios. Based on these requirements we implemented RapidProM, a tool supporting scientific workflows for process mining. Examples illustrating the different scenarios are provided to show the feasibility of the approach.     

Textual image compression at low bit rates based on region-of-interest coding

In this paper, we deal with those applications of textual image compression where high compression ratio and maintaining or improving the visual quality and readability of the compressed images are of main concern. In textual images, most of the information exists in the edge regions; therefore, the compression problem can be studied in the framework of region-of-interest (ROI) coding. In this paper, the Set Partitioning in Hierarchical Trees (SPIHT) coder is used in the framework of ROI coding along with some image enhancement techniques in order to remove the leakage effect which occurs in the wavelet-based low-bit-rate compression. We evaluated the compression performance of the proposed method with respect to some qualitative and quantitative measures. The qualitative measures include the averaged mean opinion scores (MOS) curve along with demonstrating some outputs in different conditions. The quantitative measures include two proposed modified PSNR measures and the conventional one. Comparing the results of the proposed method with those of three conventional approaches, DjVu, JPEG2000, and SPIHT coding, showed that the proposed compression method considerably outperformed the others especially from the qualitative aspect. The proposed method improved the MOS by 20 and 30 %, in average, for high- and low-contrast textual images, respectively. In terms of the modified and conventional PSNR measures, the proposed method outperformed DjVu and JPEG2000 up to 0.4 dB for high-contrast textual images at low bit rates. In addition, compressing the high contrast images using the proposed ROI technique, compared to without using this technique, improved the average textual PSNR measure up to 0.5 dB, at low bit rates.     

Numerical simulation of contaminant transport in groundwater using software tools of $$r^3t$$

When a realistic modelling of radioactive contaminant transport in flowing groundwater is required, very large systems of coupled partial and ordinary differential equations can arise that have to be solved numerically. For that purpose, the software package \(r^3t\) is developed in which several advanced numerical methods are implemented to solve such models efficiently and accurately. Using software tools of \(r^3t\) one can treat successfully nontrivial mathematical problems like advection-dominated system with different retardation of transport for each component and with nonlinear Freundlich sorption and/or precipitation. Additionally, long time simulations on complex 3D geological domains using unstructured grids can be realized. In this paper we introduce and summarize the most important and novel features of numerical simulation for radioactive contaminant transport in porous media when using \(r^3t\).