A Novel Framework for Aspect Knowledgebase Generated Automatically from Social Media Using Pattern Rules

One of the factors improving businesses in business intelligence is summarization systems which could generate summaries based on sentiment from social media. However, these systems could not produce automatically, they used annotated datasets. To automatically produce sentiment summaries without using the annotated datasets, we propose a novel framework using pattern rules. The framework has two procedures: 1) pre-processing and 2) aspect knowledgebase generation. The first procedure is to check and correct misspelt words (bigram and unigram) by a proposed method, and tag part-of-speech all words. The second procedure is to automatically generate aspect knowledgebase used to produce sentiment summaries by the sentiment summarization systems. Pattern rules and semantic similarity-based pruning are used to automatically generate aspect knowledgebase from social media. In the experiments, eight domains from benchmark datasets of reviews are used. The performance evaluation of our proposed approach shows the high performance when compared to other approaches.

Efficient Simulations of Large Scale Convective Heat Transfer Problems

We describe an approach for efficient solution of large scale convective heat transfer problems, formulated as coupled unsteady heat conduction and incompressible fluid flow equations. The original problem is discretized in time using classical implicit methods, while stabilized finite elements are used for space discretization. The algorithm employed for the discretization of the fluid flow problem uses Picard's iterations to solve the arising nonlinear equations. Both problems, heat transfer and Navier-Stokes quations, give rise to large sparse systems of linear equations. The systems are solved using iterative GMRES solver with suitable preconditioning. For the incompressible flow equations we employ a special preconditioner based on algebraic multigrid (AMG) technique. The paper presents algorithmic and implementation details of the solution procedure, which is suitably tuned, especially for ill conditioned systems arising from discretizations of incompressible Navier-Stokes equations. We describe parallel implementation of the solver using MPI and elements of PETSC library. The scalability of the solver is favourably compared with other methods such as direct solvers and standard GMRES method with ILU preconditioning.

Optimized jk-nearest neighbor based online signature verification and evaluation of the main parameters

In this paper, we propose an enhanced jk-nearest neighbor (jk-NN) classifier for online signature verification. After studying the algorithm's main parameters, we use four separate databases to present and evaluate each algorithm parameter. The results show that the proposed method can increase the verification accuracy by 0.73-10% compared to a traditional one class k-NN classifier. The algorithm has achieved reasonable accuracy for different databases, a 3.93% error rate when using the SVC2004 database, 2.6% for MCYT-100 database, 1.75% for the SigComp'11 database, and 6% for the SigComp'15 database.The proposed algorithm uses specifically chosen parameters and a procedure to pick the optimal value for K using only the signer's reference signatures, to build a practical verification system for real-life scenarios where only these signatures are available. By applying the proposed algorithm, the average error achieved was 8% for SVC2004, 3.26% for MCYT-100, 13% for SigComp'15, and 2.22% for SigComp'11.

A Meta-Heuristic Approach Based on the Genetic and Greedy Algorithms to Solving Flexible Job-Shop Scheduling Problem

In today’s competitive business world, manufacturers need to accommodate customer demands with appropriate scheduling. This requires efficient manufacturing chain scheduling. One of the most important problems that has always been considered in the manufacturing and job-shop industries is offering various products according to the needs of customers in different periods of time, within the shortest possible time and with rock-bottom cost. Job-Shop Scheduling systems are one of the applications of group technology in industry, the purpose of which is to take advantage of the physical or operational similarities of products in various aspects of construction and design. In addition, these systems are identified as Cellular Manufacturing Systems (CMS). Today, applying CMS and the use of its benefits have been very important as a possible way to increase the speed of the organization’s response to rapid market changes. In this paper, a meta-heuristic method based on combining genetic and greedy algorithms has been used in order to optimize and evaluate the performance criteria of flexible job-shop scheduling problem. In order to improve the efficiency of the genetic algorithm, the initial population is generated in a greedy algorithm and several elitist operators are used to improve the solutions. The greedy algorithm which is used to improve the generation of the initial population prioritizes the cells and the job in each cell, and thus offers quality solutions. The proposed algorithm is tested over P-FJSP dataset and compared with the state-of-the-art techniques of this literature. To evaluate the performance of the diversity, spacing, quality and run-time criteria were used in a multi-objective function. The results of simulation indicate better performance of the proposed method compared to NRGA and NSGA-II methods.

Falcon Optimization Algorithm for Bayesian Networks Structure Learning

In machine-learning, one of the useful scientific models for producing the structure of knowledge is Bayesian network, which can draw probabilistic dependency relationships between variables. The score and search is a method used for learning the structure of a Bayesian network. The authors apply the Falcon Optimization Algorithm (FOA) as a new approach to learning the structure of Bayesian networks. This paper uses the Reversing, Deleting, Moving and Inserting operations to adopt the FOA for approaching the optimal solution of Bayesian network structure. Essentially, the falcon prey search strategy is used in the FOA algorithm. The result of the proposed technique is compared with Pigeon Inspired optimization, Greedy Search, and Simulated Annealing using the BDeu score function. The authors have also examined the performances of the confusion matrix of these techniques utilizing several benchmark data sets. As shown by the evaluations, the proposed method has more reliable performance than the other algorithms including producing better scores and accuracy values.

Overview of Adaptive and Low-Rank Approximation Algorithms for Modeling of The Influence of Electromagnetic Waves Generated by The Cell Phone Antenna on The Human Head

This paper presents an overview of formulations and algorithms dedicated to modeling the influence of electromagnetic waves on the human head. We start from the three-dimensional MRI scan of the human head. We approximate the MRI scan by the continuous approximation span over three-dimensional h adaptive mesh with quadratic polynomials. Next, we introduce time-harmonic Maxwell equations with a 1.8 GHz cell-phone antenna. We solve the problem of the propagation of electromagnetic waves on the human head. We compute the specific absorption rate used as the heat source for the Pennes bioheat equation. Finally, we introduce the Pennes bio-heat equation modeling the heat generated by the electromagnetic waves propagating through the skull, tissue, and air layers in the human head. We discuss the discretization and time-stepping algorithm for the Pennes equation’s solution over the human head. Namely, we focus on the Crank-Nicolson time integration scheme, to solve the bioheat transfer equations. We employ the hp finite elements with hierarchical shape functions and hp adaptive algorithm in three-dimensions. We propose an adaptive algorithm mixed with time-stepping iterations, where we simultaneously adapt the computational mesh, solve the Maxwell and Pennes equations, and we iterative with time steps. We employ the sparse Gaussian elimination algorithm with low-rank compression of the off-diagonal matrix blocks for the factorization of matrices. We conclude with the statement that 15 minutes of talk with a 1.8 GHz antenna of 1 Wat power results in increased brain tissue temperature up to 38.4 Celsius degree.

Sign Detection and Signed Integer Comparison for the 3-Moduli Set {2^n±1,2^(n+k)}

Comparison, division and sign detection are considered complicated operations in residue number system (RNS). A straightforward solution is to convert RNS numbers into binary formats and then perform complicated operations using conventional binary operators. If efficient circuits are provided for comparison, division and sign detection, the application of RNS can be extended to the cases including these operations.For RNS comparison in the 3-moduli set , we have only found one hardware realization. In this paper, an efficient RNS comparator is proposed for the moduli set which employs sign detection method and operates more efficient than its counterparts. The proposed sign detector and comparator utilize dynamic range partitioning (DRP), which has been recently presented for unsigned RNS comparison. Delay and cost of the proposed comparator are lower than the previous works and makes it appropriate for RNS applications with limited delay and cost.

Per-Pixel Extrusion Mapping with Correct Silhouette

Per-pixel extrusion mapping consists of creating a virtual geometry stored in a texture over a polygon model without increasing its density. There are four types of extrusion mapping, namely, basic extrusion, outward extrusion, beveled extrusion, and chamfered extrusion. These different techniques produce satisfactory results in the case of plane surfaces, but when it is about the curved surfaces, the silhouette is not visible at the edges of the extruded forms on the 3D surface geometry because they not take into account the curvature of the 3D meshes. In this paper, we presented an improvement that consists of using a curved ray-tracing to correct the silhouette problem by combining the per-pixel extrusion mapping techniques and the quadratic approximation computed at each vertex of the 3D mesh.

Formal verification of the extension of iStar to support Big data projects

Identifying all the right requirements is indispensable for the success of anysystem. These requirements need to be engineered with precision in the earlyphases. Principally, late corrections costs are estimated to be more than 200times as much as corrections during requirements engineering (RE). EspeciallyBig data area, it becomes more and more crucial due to its importance andcharacteristics. In fact, and after literature analyzing, we note that currentsRE methods do not support the elicitation of Big data projects requirements. Inthis study, we propose the BiStar novel method as extension of iStar to under-take some Big data characteristics such as (volume, variety ...etc). As a firststep, we identify some missing concepts that currents requirements engineeringmethods do not support. Next, BiStar, an extension of iStar is developed totake into account Big data specifics characteristics while dealing with require-ments. In order to ensure the integrity property of BiStar, formal proofs weremade, we perform a bigraph based description on iStar and BiStar. Finally, anapplication is conducted on iStar and BiStar for the same illustrative scenario.The BiStar shows important results to be more suitable for eliciting Big dataprojects requirements.

Population Diversity in Ant-inspired Optimization Algorithms

Finding a balance between exploration and exploitation is very important in the case of metaheuristics optimization, especially in the systems leveraging population of individuals expressing (as in Evolutionary Algorithms, etc.) or constructing (as in Ant Colony Optimization) solutions. Premature convergence is a real problem and finding means of its automatic detection and counteracting are of great importance. Measuring diversity in Evolutionary Algorithms working in real-value search space is often computationally complex, but feasible while measuring diversity in combinatorial domain is practically impossible (cf. Closest String Problem). Nevertheless, we propose several practical and feasible diversity measurement techniques dedicated to Ant Colony Optimization algorithms, leveraging the fact that even though analysis of the search space is at least an NP problem, we can focus on the pheromone table, where the direct outcomes of the search are expressed and can be analyzed. Besides proposing the measurement techniques, we apply them to assess the diversity of several variants of ACO, and closely analyze their features for the classic ACO. The discussion of the results is the first step towards applying the proposed measurement techniques in auto-adaptation of the parameters affecting directly the exploitation and exploration features in ACO in the future.