Earticle

Teaching ability of aerobics is crucial to the development of aerobics athletes. It is also an effective way to evaluate the qualification of an aerobics coach. Aerobics teaching has many limitations. Current evaluation model of teaching ability of aerobics is far from complete. Thus, this paper proposes an optimized evaluation method and model of teaching ability of aerobics based on multiple intelligence theory. It constructs an evaluation index system of teaching ability of aerobics, taking into account aerobics teaching design, teaching implementation and teaching effective. Indicators are assigned weight. By analyzing the grey correlation degree between different indicators of teaching ability of aerobics and the optimal indicator, we can get the evaluation result for teaching ability of aerobics. An empirical study proves that the evaluation method has efficacy.

A robust beamforming with quadratic constraints, formulated as a semidefinite programming (SDP) problem, is proposed in this paper. With this formulation, the constraints on magnitude response can be easily imposed on the adaptive beamformer. And the non-convex quadratic constraints can be transformed into linear constraints. Therefore, the proposed method can be robust against the steering direction error. In practice, there are many array imperfections except steering direction error. In order to resist all kinds of array imperfections, the adaptive beamformer based on worst-case optimization technique is proposed by minimizing the array output power with respect to the worst-case array imperfections. Simulation results demonstrate that the proposed method is effective and can achieve a better performance.

A human chromosome is a DNA molecule with approximately 108 base pairs. The techniques developed to date for sequencing are restricted to pieces of DNA with up to tens of thousands of base pairs. This means that when a piece is sequenced, only an extremely small part of a chromosome can be seen. Molecular biologists use special techniques to deal with DNA molecules comparable in size to a chromosome. These techniques enable them to create maps of an entire chromosome or of significant fractions of chromosomes. Computational techniques were studied that could potentially aid biologists in the map-generation process. An algorithm that solves the consecutive 1s problem was studied. Such a problem is a good model of hybridization mapping when there are no errors and when probes are unique. If errors are present, another approach is needed, and the approximation algorithm is a prospective problem solver for hybridization physical mapping of DNA with errors.

Chip-multiprocessors have played a significant role in real parallel computer architecture design. For integrating tens of cores into a chip, designs tend towards with physically distributed last level caches. This naturally results in a Non-Uniform Cache Access design, where on-chip access latencies depend on the physical distances between requesting cores and home cores where the data is cached. Therefore, data movement and management impact access latency and power consumption. Remote misses limit the performance of multi-threaded applications, so using data locality is fundamental importance in Chip-multiprocessors. In this work we observed that a shared data writing behavior dramatically wastes precious on-chip cache resource and seriously affects the whole system performance. Therefore, we emphasis on improving the performance of applications that exhibit high data sharing, and propose a new prediction mechanism to predict accurately the impact of shared data and a scalable, efficient hybrid shared-aware cache coherence transition strategy which collaborate with directory-based MESI cache coherence protocol. In order to evaluate our proposal transition strategy, we experiment with the NAS Parallel Benchmarks and a modern Intel Harpertown multi-core machine. Results show the whole performance gains of up to 21% opposed to the traditional write-invalidate cache coherence transition strategy.

The widespread availability and extensive use of Internet across the world has caught attention of the criminals and digital crimes are occurring at an epidemic scale nowadays. The field of digital forensics is constantly evolving by employing new tools and technique to counter novel approaches employed by the criminals as well as to investigate the nature of the criminal activity and bring the culprits to justice. Traditionally, the static analysis was used to investigate the digital incidents. But due to advancement in technology and the fact that hackers are developing malware that do not leave footprint on the hard disk, the need for performing live digital forensic analysis in addition to the static analysis has become imperative. Live forensic analysis techniques have evolved during the last decade to analyses the memory content to get a better picture of the running application programmers, processes and active binaries. In this study, we look into different techniques of live analysis and critically review them by identifying their benefits and limitations. The key areas focused in this study pertain to virtualization, pagefile extraction and identifying the encryption keys.

Breast imaging case not only image low level features but also has image semantic features. In order to implement multimode retrieval of breast imaging, using feature selection algorithm based on association rules to select features, digging out the associated relationship between image low level features and image semantic features, and then taking advantage of association classification algorithm to get image visual semantic features, which reduced the semantic gap between image low level features and visual semantic features, at last, making similarity measure combined with low level features, to make multimode retrieval come true. As the results show, this method improve the performance of breast imaging case retrieval and provide more meaningful decision support for doctors.

In drawing area, aiming at the problems of drawing breadth and angle cannot be adjusted individually and not convenient to carry, a kind of multi-functional folding drawing board is designed by us. The appearance of the multi-functional folding drawing board is kind of drawing board, some light materials like basswood and rigid plastics are used in main structure of the drawing board to make it light and convenient to carry. The main structure can be folded and it is suitable for A1 and A2 drawings. In the designing procedure, dip angle adjusted mechanism by worm wheel and gear, magnetic pull-in mechanism, leading rail, slide-way, drawer, tool library, paper bag and horn locks are applied based on modularity designing theory. Finite element analysis of key components of drawing board is analyzed.

Identifying influential spreaders is an important issue in understanding the dynamics of information diffusion in social networks. It is to find a small subset of nodes, which can spread the information or influence to the largest number of nodes. The conventional approaches consider information diffusion through the network in a coarse-grained manner, without taking into account the topical features of information content and users. However, for messages with different topics, the target influential spreaders may vary largely. In this paper, we propose to harness historical propagation data to learn the information diffusion probabilities on topic-level, based on which we use a greedy algorithm to iteratively select a set of influential nodes for a given topic. Specially, we design a three-stage algorithm named TopicRank to mine the most influential spreaders with respect to a specific topic. Given observed propagation data, we first use Latent Dirichlet Allocation (LDA) model to learn a topic mixture for each propagation message. Then, the topic-level diffusion probability of an edge is computed by exploiting the propagation actions occurred to it and the topic distribution of these propagation messages. Last, based on the learned topic-level diffusion probabilities, we apply optimized greedy algorithm CLEF to identify influential nodes with respect to a specific topic. Experimental results show that our method significantly outperforms state-of-the-art methods when used for topic-sensitive information spread maximization.