Vol. 38 · No. 3

Compute Cloud comprises a distributed set of High-Performance Computing (HPC) machines to stipulate on-demand computing services to remote users over the internet. Clouds are capable enough to provide an optimal solution to address the ever-increasing computation and storage demands of large scientific HPC applications. To attain good computing performances, mapping of Cloud jobs to the compute resources is a very crucial process. Currently we can say that several efficient Cloud scheduling heuristics are available, however, selecting an appropriate scheduler for the given environment (i.e., jobs and machines heterogeneity) and scheduling objectives (such as minimized makespan, higher throughput, increased resource utilization, load balanced mapping, etc.) is still a difficult task. In this paper, we consider ten important scheduling heuristics (i.e., opportunistic load balancing algorithm, proactive simulation-based scheduling and load balancing, proactive simulation-based scheduling and enhanced load balancing, minimum completion time, Min-Min, load balance improved Min-Min, Max-Min, resource-aware scheduling algorithm, task-aware scheduling algorithm, and Sufferage) to perform an extensive empirical study to insight the scheduling mechanisms and the attainment of the major scheduling objectives. This study assumes that the Cloud job pool consists of a collection of independent and compute-intensive tasks that are statically scheduled to minimize the total execution time of a workload. The experiments are performed using two synthetic and one benchmark GoCJ workloads on a renowned Cloud simulator CloudSim. This empirical study presents a detailed analysis and insights into the circumstances requiring a load balanced scheduling mechanism to improve overall execution performance in terms of makespan, throughput, and resource utilization. The outcomes have revealed that the Sufferage and task-aware scheduling algorithm produce minimum makespan for the Cloud jobs. However, these two scheduling heuristics are not efficient enough to exploit the full computing capabilities of Cloud virtual machines.

The suffix array is a classic full-text index, combining effectiveness with simplicity. We discuss three approaches aiming to improve its efficiency even more: changes to the navigation, data layout and adding extra data. In short, we show that i) the way how we search for the right interval boundary impacts significantly the overall search speed, ii) a B-tree data layout easily wins over the standard one, iii) the well-known idea of a lookup table for the prefixes of the suffixes can be refined with using compression, iv) caching prefixes of the suffixes in a helper array can pose another practical space-time tradeoff.

Argumentation plays an important role in reasoning and allows the justification of opinions, especially when applied to collaborative decision making. Reaching consensus is not a trivial task where arguments exchanged in a dialogue and common knowledge are important for consensus. This paper presents a model of argumentative dialogue to support the formation of common knowledge in a group of agents that communicate by sending arguments, and proposes a semantics for consensus decision making. The output of the model is a weighted argumentation graph in which semantics is used to decide the preference of the group.

Collaborative filtering (CF) is the most successful and widely implemented algorithm in the area of recommender systems (RSs). It generates recommendations using a set of user-product ratings by matching similarity between the profiles of different users. Computing similarity among user profiles efficiently in case of sparse data is the most crucial component of the CF technique. Data sparsity and accuracy are the two major issues associated with the classical CF approach. In this paper, we try to solve these issues using a novel approach based on the side information (user-product background content) and the Mahalanobis distance measure. The side information has been incorporated into RSs to further improve their performance, especially in the case of data sparsity. However, incorporation of side information into traditional two-dimensional recommender systems would increase the dimensionality and complexity of the system. Therefore, to alleviate the problem of dimensionality, we cluster users based on their side information using k-means clustering algorithm and each user's similarity is computed using the Mahalanobis distance method. Additionally, we use fuzzy sets to represent the side information more efficiently. Results of the experimentation with two benchmark datasets show that our framework improves the recommendations quality and predictive accuracy of both traditional and clustering-based collaborative recommendations.

Foreign-key relationship is one of the most important constraints between two tables. Previous works focused on detecting inclusion dependencies (INDs) or foreign keys in relational database. To discover foreign-key relationship is obviously helpful for analyzing and integrating data in web tables. However, because of poor quality of web tables, it is difficult to discover foreign keys by existing techniques based on checking basic integrity constraints. In this paper, we propose a hybrid human-machine framework to detect foreign keys on web tables. After discovering candidates and evaluating their confidence of being true foreign keys by machine algorithm, we verify those candidates leveraging the power of the crowd. To reduce the monetary cost, a dynamical task selection technique based on conflict detection and inclusion dependency is proposed, which could eliminate redundant tasks and assign the most valuable tasks to workers. Additionally, to make workers complete tasks more effectively and efficiently, sampling strategy is applied to minimize the number of tuples posed to the crowd. We conducted extensive experiments on real-world datasets and results show that our framework can obviously improve foreign key detection accuracy on web tables with lower monetary cost and time cost.

This paper describes a plaintext related image encryption algorithm that utilizes the Mojette transform for computation of bins that are subsequently combined with pixels of the processed image. While the bins are computed solely from pixel intensities of a plain image and also the combination depends only on intensities of plain image pixels, the parameters of bins are rearranged according to used key. This design results in a great sensitivity of the proposed image encryption algorithm to both plain images and keys, which is verified by a set of experiments. The paper also tests the resistance of the proposal against statistical and differential attacks by means of commonly used measures as correlation coefficients, entropy, NPCR and UACI. Furthermore, the paper analyses computation speed reached by the proposed solution. Computed values of all parameters are discussed and then compared with results obtained by some recent plaintext related image encryption algorithms.

We present LSQ, an algorithm learning SPARQL queries from a subset of expected results. The algorithm leverages grouping, aggregates and inline values of SPARQL 1.1 in order to move most of the complex computations to a SPARQL endpoint. It operates by building and testing hypotheses expressed as SPARQL queries and uses active learning to collect a small number of learning examples from the user. We provide an open-source implementation and an on-line interface to test the algorithm. In the experimental evaluation, we use real queries posed in the past to the official DBpedia SPARQL endpoint, and we show that the algorithm is able to learn them, 82

In the Privilege-based Visual Secret Sharing Model (PVSSM), each share has a unique privilege and a higher-privilege share contributes with more privilege to reveal the secret image. However, in PVSSM, when several shares with the higher priority are stacked, the secret image can be visibly displayed. This security problem is solved by applying a two-dimensional Logistic-Adjusted Sine Map (2D-LASM) to each share. This method is called Chaotic Encryption-based PVSSM. In this paper, we aim to present how Chaotic Encryption-based PVSSM is applied to color images. In order to assess the efficiency of this method, histogram analysis, data loss attack, salt-pepper noise attack, differential attack, chi-square analysis and correlation analysis tests were applied. The performance of this method has been evaluated according to NCPR, UACI, PSNR, SSIM and CQM. The proposed method achieved a good test values and showed better results compared to similar studies in literature.

Discrete-Event Systems (DES) are discrete in nature. Petri Nets (PN) are one of the most widespread tools for DES modelling, analyzing and control. Different kinds of PN can be used for such purposes. Some of them were described in 5, being the first part of this paper. Here, the applicability of Labelled PN (LbPN) and Interpreted PN (IPN) for modelling and control of nondeterministic DES, especially with uncontrollable and/or unobservable transitions in the models, will be pointed out. Moreover, another kinds of nondeterminism in DES (errors, failures) will be modelled, and the possibilities of the error recovery of failed system will be presented.