We present an approach under the framework of abstract interpretation to analyze list-manipulating programs by combining shape and numerical abstractions. The analysis automatically divides a list into non-overlapping...
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ISBN:
(纸本)9781450316569
We present an approach under the framework of abstract interpretation to analyze list-manipulating programs by combining shape and numerical abstractions. The analysis automatically divides a list into non-overlapping list segments according to the reachability property of pointer variables to list nodes. The list nodes in each segment are abstracted by a bit-vector wherein each bit corresponds to a pointer variable and indicates whether the nodes can be reached by that pointer variable. Moreover, for each bit-vector, we introduce an auxiliary integer variable, namely a counter variable, to record the number of nodes in the segment abstracted by that bit-vector. On this basis, we leverage the power of numerical abstractions to discover numerical relations among counter variables, so as to infer relational length properties among list segments. Our approach stands out in its ability to find intricate properties that involve both shape and numerical information, which are important for checking program properties such as memory safety and termination. A prototype is implemented and preliminary experimental results are encouraging. Copyright 2013 ACM.
The Internetware" paradigm is fundamentally changing the traditional way of software development. More and more software projects are developed, maintained and shared on the Internet. However, a large quantity of...
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ISBN:
(纸本)9781450323697
The Internetware" paradigm is fundamentally changing the traditional way of software development. More and more software projects are developed, maintained and shared on the Internet. However, a large quantity of heterogeneous software resources have not been organized in a reasonable and efficient way. Software feature is an ideal material to characterize software resources. The effectiveness of feature- related tasks will be greatly improved, if a multi-grained feature repository is available. In this paper, we propose a novel approach for organizing, analyzing and recommend- ing software features. Firstly, we construct a Hierarchical rEpository of Software feAture (HESA). Then, we mine the hidden affnities among the features and recommend relevant and high-quality features to stakeholders based on HESA. Finally, we conduct a user study to evaluate our approach quantitatively. The results show that HESA can organize software features in a more reasonable way compared to the traditional and the state-of-the-art approaches. The result of feature recommendation is effective and interesting. Categories and Subject Descriptors D.2.9 [Software Engineering]: Mining Software Reposi- tory;H.3.3 [Information Storage and retrieval]: Fea- ture Model, Clustering, Query formulation General Terms Algorithms, Human Factors.
Nowadays, the demand for software resources on different granularity is becoming prominent in software engineering field. However, a large quantity of heterogeneous software resources have not been organized in a reas...
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Wireless sensor networks (WSN) is a key technology extensively applied in many fields, such as transportation, health-care and environment monitoring. Despite rapid development, the exponentially increasing data emana...
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To reduce the time required to complete the regeneration process of erasure codes, we propose a Tree-structured parallel Regeneration (TPR) scheme for multiple data losses in distributed storage systems. Under the sch...
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To reduce the time required to complete the regeneration process of erasure codes, we propose a Tree-structured parallel Regeneration (TPR) scheme for multiple data losses in distributed storage systems. Under the scheme, two algorithms are proposed for the construction of multiple regeneration trees, namely the edge-disjoint algorithm and edge-sharing algorithm. The edge-disjoint algorithm constructs multiple independent trees, and is simple and appropriate for environments where newcomers and their providers are distributed over a large area and have few intersections. The edge-sharing algorithm constructs multiple trees that compete to utilize the bandwidth, and make a better utilization of the bandwidth, although it needs to measure the available band-width and deal with the bandwidth changes; it is therefore difficult to implement in practical systems. The parallel regeneration for multiple data losses of TPR primarily includes two optimizations: firstly, transferring the data through the bandwidth optimized-paths in a pipe-line manner; secondly, executing data regeneration over multiple trees in parallel. To evaluate the proposal, we implement an event-based simulator and make a detailed comparison with some popular regeneration methods. The quantitative comparison results show that the use of TPR employing either the edge-disjoint algorithm or edge-sharing algorithm reduces the regeneration time significantly.
Probabilistic Neural Networks (PNN) represent knowledge in the form of simple well understood Bayesian models, which are most suitable for data mining applications that also need confidence levels. However if the numb...
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ISBN:
(纸本)9781450318518
Probabilistic Neural Networks (PNN) represent knowledge in the form of simple well understood Bayesian models, which are most suitable for data mining applications that also need confidence levels. However if the number N of pattern neurons grows large, the evaluation of a single unknown sample has still O(N) complexity, which is not scale well. parallelism can efficiently work towards speeding up the large neural networks. Whereas the PNN run time can be reduced by parallelism, its computational cost can be decreased by keeping only the globally most important pattern neurons. While such parsimonious global models have been studied for a long time, there is another way that is related to local learning algorithms. Maintain all N pattern neurons but to classify any unknown x use only the local ones, the k nearest neighbor to x neurons. This local learning PNN is investigated here. By using confidence ratio outputs we optimize the number of k nearest neighbor neurons for the best PNN performance. Combined with the parallel approach using p processors the method can reduce the cost of classifying an unknown x from O(N) to O(k/p). Copyright 2013 ACM.
The resource allocation problem in data centre networks refers to a map of the workloads provided by the cloud users/tenants to the Substrate Network(SN)which are provided by the cloud *** studies consider the dynamic...
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The resource allocation problem in data centre networks refers to a map of the workloads provided by the cloud users/tenants to the Substrate Network(SN)which are provided by the cloud *** studies consider the dynamic arrival and departure of the workloads,while the dynamics of the substrate are *** this paper,we first propose the resource allocation with the dynamic SN,and denote it as ***,we propose an efficient mapping algorithm for *** performance of the proposed algorithm is evaluated by performing simulation *** results show that the proposed algorithm can effectively solve the GraphMap-DS.
An improved algorithm is proposed for the reconstruction of singular connectivity from the available pairwise connections during preprocessing phase. To evaluate the performance of the algorithm, an in-house computati...
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An improved algorithm is proposed for the reconstruction of singular connectivity from the available pairwise connections during preprocessing phase. To evaluate the performance of the algorithm, an in-house computational fluid dynamics (CFD) code is used in which high-order finite-difference method for spatial discretization running on the Tianhe-1A supercomputer is employed. Test cases with a varied amount of mesh points are chosen, and the test results indicate that the improved singular connection reconstruction algorithm can achieve a speedup of 2000× at least when compared with the naive search method adopted in the former version of our code. Moreover, the parallel efficiency can benefit from the strategy of local communication based on the algorithm.
Maximum common sub-graph isomorphism (MCS) is a famous NP-hard problem in graph processing. The problem has found application in many areas where the similarity of graphs is important, for example in scene matching, v...
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Maximum common sub-graph isomorphism (MCS) is a famous NP-hard problem in graph processing. The problem has found application in many areas where the similarity of graphs is important, for example in scene matching, video indexing, chemical similarity and shape analysis. In this paper, a novel algorithm Qwalk is proposed for approximate MCS, utilizing the discrete-time quantum walk. Based on the new observation that isomorphic neighborhood group matches can be detected quickly and conveniently by the destructive interference of a quantum walk, the new algorithm locates an approximate solution via merging neighborhood groups. Experiments show that Qwalk has better accuracy, universality and robustness compared with the state-of-the-art approximate MCS methods. Meanwhile, Qwalk is a general algorithm to solve the MCS problem approximately while having modest time complexity.
Hierarchical text classification is an important task in many real-world applications. To build an accurate hierarchical classification system with many categories, usually a very large number of documents must be lab...
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