Improving the performance of GPU-based genetic programming through exploitation of on-chip memory

Genetic Programming (GP) (Koza, Genetic programming, MIT Press, Cambridge, 1992) is well-known as a computationally intensive technique. Subsequently, faster parallel versions have been implemented that harness the highly parallel hardware provided by graphics cards enabling significant gains in the performance of GP to be achieved. However, extracting the maximum performance from a graphics card for the purposes of GP is difficult. A key reason for this is that in addition to the processor resources, the fast on-chip memory of graphics cards needs to be fully exploited. Techniques will be presented that will improve the performance of a graphics card implementation of tree-based GP by better exploiting this faster memory. It will be demonstrated that both L1 cache and shared memory need to be considered for extracting the maximum performance. Better GP program representation and use of the register file is also explored to further boost performance. Using an NVidia Kepler 670GTX GPU, a maximum performance of 36 billion Genetic Programming Operations per Second is demonstrated.     

On the composition of ISO 25964 hierarchical relations (BTG,BTP, BTI)

Knowledge organization systems (KOS) can use different types of hierarchical relations: broader generic (BTG), broader partitive (BTP), and broader instantial (BTI). The latest ISO standard on thesauri (ISO 25964) has formalized these relations in a corresponding OWL ontology (De Smedt et al., ISO 25964 part 1: thesauri for information retrieval: RDF/OWL vocabulary, extension of SKOS and SKOS-XL. http://purl.org/iso25964/skos-thes, 2013) and expressed them as properties: broaderGeneric, broaderPartitive, and broaderInstantial, respectively. These relations are used in actual thesaurus data. The compositionality of these types of hierarchical relations has not been investigated systematically yet. They all contribute to the general broader (BT) thesaurus relation and its transitive generalization broader transitive defined in the SKOS model for representing KOS. But specialized relationship types cannot be arbitrarily combined to produce new statements that have the same semantic precision, leading to cases where inference of broader transitive relationships may be misleading. We define Extended properties (BTGE, BTPE, BTIE) and analyze which compositions of the original “one-step” properties and the Extended properties are appropriate. This enables providing the new properties with valuable semantics usable, e.g., for fine-grained information retrieval purposes. In addition, we relax some of the constraints assigned to the ISO properties, namely the fact that hierarchical relationships apply to SKOS concepts only. This allows us to apply them to the Getty Art and Architecture Thesaurus (AAT), where they are also used for non-concepts (facets, hierarchy names, guide terms). In this paper, we present extensive examples derived from the recent publication of AAT as linked open data.     

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.