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Abstract: Recommendation algorithms have been researched extensively to help people deal with abundance of information. In recent years, the incorporation of multiple relevance criteria has attracted increased interest. Such multi-criteria recommendation approaches are researched as a paradigm for building intelligent systems that can be tailored to multiple interest indicators of end-users – such as combinations of implicit and explicit interest indicators in the form of ratings or ratings on multiple relevance dimensions. Nevertheless, evaluation of these recommendation techniques in the context of real-life applications still remains rather limited. Previous studies dealing with the evaluation of recommender systems have outlined that the performance of such algorithms is often dependent on the dataset – and indicate the importance of carrying out careful testing and parameterization. Especially when looking at large scale datasets, it becomes very difficult to deploy evaluation methods that may help in assessing the effect that different system components have to the overall design. In this paper, we study how layered evaluation can be applied for the case of a multi-criteria recommendation service that we plan to deploy for paper recommendation using the Mendeley dataset. The paper introduces layered evaluation and suggests two experiments that may help assess the components of the envisaged system separately. [Copyright &y& Elsevier]

The large deformation of soft rock tunnel is an important problem to be solved urgently with the development of engineering technology. In order to improve the application effect of prestressed anchor cable adaptive active support system, this paper puts forward an optimization method based on adaptive genetic algorithm, reviews the research status of prestressed anchor support in soft rock tunnel, and analyzes and evaluates the existing methods. This paper introduces the construction process of AGA model, establishes the problem description model and genetic optimization algorithm selection model, and constructs the adaptive active support model of prestressed anchor cable. The model adopts an optimization algorithm with a total of 100 iterations. The Simple Genetic Algorithm and Parallel Genetic Algorithm are selected to evaluate the anchoring force, the control effect of surrounding rock displacement, the erosion of supporting structure and the construction time by comparative test. The test results show that AGA has a good effect on the anchoring force, the minimum anchoring force is 457 kN, and the displacement of surrounding rock is controlled within 1.21-1.59mm, which has a good influence on the quality of supporting structure and the construction completion time. Finally, the application of AGA model in parameter optimization of prestressed anchor cable in soft rock tunnel is summarized, and the future research direction is put forward. [ABSTRACT FROM AUTHOR]

With the rise and development of computer networks, network security has become a major issue, and intrusion detection has emerged as a crucial aspect of network security. However, traditional intrusion detection methods suffer from issues like slow detection rates and low accuracy. This necessitates the exploration and development of optimized algorithms for improving intrusion detection efficiency and accuracy. Recently, the multi-objective differential evolution algorithm has shown promising results in the field of intrusion detection. This paper explored the application of the multi-objective differential evolution algorithm in computer network intrusion detection systems. The experimental results on the KDDCUP'99 dataset demonstrated that the algorithm delivers high levels of precision and recall, outperforming other intrusion detection methods. Therefore, combining the multi-objective differential evolution algorithm with traditional intrusion detection methods can lead to significant improvements in intrusion detection efficiency and accuracy. Further optimization of this algorithm can be required to meet complex intrusion detection requirements. The multi-objective differential evolution algorithm presents exciting prospects, which can help enhance the efficiency and accuracy of intrusion detection, and provide strong technical support for ensuring network security and preventing cyber-attacks. [ABSTRACT FROM AUTHOR]

With the rapid development of computer technology. Many scholars pay close attention to computer graphics algorithms, which makes computer graphics widely used in various fields and enters a new revolutionary era. The purpose of this paper is to study the evaluation of computer graphics algorithm demonstration systems. Combining theory with practice, on the basis of careful analysis of the principle of graphics algorithm, a new idea of synchronization of graphics production process and a specific algorithm for realizing graphics are proposed, and most graphics in computer graphics are visualized. It breaks the limitation that only two-dimensional graphic simulation effects can be realized, and increases the interactive user control system. The algorithm complexity is significantly reduced, the graphics creation speed is increased, and the simulation effect is more realistic. After the introduction of this visual teaching demonstration system, these abstract principles can be explained intuitively, which can significantly improve the teaching quality. [ABSTRACT FROM AUTHOR]

Usually a complete computer network system has the following components: source of signals, signal host, cryptographic regime, attack, and interceptor. It is more vulnerable to attacks during the operation of the system, so the security of the system becomes more important. In this paper, by constructing a dual BP neural network combinatorial model and applying it to the computer data communication large integer decomposition encryption process, the constructed model is used to analyze the security against digital certificates, digital signatures, and the algorithms employed in the communication process of connected computer network. [ABSTRACT FROM AUTHOR]

Despite the recent progress of 3D human pose estimation, reconstructing an accurate 3D human posture from a single image without 3D annotation is yet challenging due to the following reasons. First, the reconstructing process is inherently ambiguous, as multiple 3D poses can be projected onto the same 2D pose. Second, camera rotation is difficult to measure precisely without laborious camera calibration. Some approaches resort to traditional computer vision algorithms to address these issues, but they are not differentiable and cannot be optimized through training. In this paper, we propose two geometrically explicit modules to solve the problems without any 3D ground-truth or camera parameters. Relative depth estimation module effectively mitigates depth ambiguity, reducing a number of possible depths for each joint to only two candidates. Differentiable pose alignment module calculates camera rotation via aligning poses from different views. The two modules are geometrically interpretable, reducing the training difficulty and leading to superior performance. Our method achieves state-of-the-art on the standard benchmark datasets among self-supervised methods and even outperforms several fully-supervised approaches that rely on 3D annotations. [ABSTRACT FROM AUTHOR]

Research in the fields of machine learning and intelligent systems addresses essential problem of developing computer algorithms that can deal with huge amounts of data and then utilize this data in an intellectual way to solve a variety of real-world problems. In many applications, to interpret data with a large number of variables in a meaningful way, it is essential to reduce the number of variables and interpret linear combinations of the data. Principal Component Analysis (PCA) is an unsupervised learning technique that uses sophisticated mathematical principles to reduce the dimensionality of large datasets. The goal of this paper is to provide a complete understanding of the sophisticated PCA in the fields of machine learning and data dimensional reduction. It explains its mathematical aspect and describes its relationship with Singular Value Decomposition (SVD) when PCA is calculated using the covariance matrix. In addition, with the use of MATLAB, the paper shows the usefulness of PCA in representing and visualizing Iris dataset using a smaller number of variables. [ABSTRACT FROM AUTHOR]

The aim of this paper was to determine the roughness and Vickers hardness of popular composite materials used for direct tooth fillings. The tests were carried out on the surfaces of samples previously polished with traditional silicone polishing rubbers and discs used in dentistry. The testing methodology and software used for statistical analysis is important to the validity of engineering tests. Measurement systems use sophisticated computer algorithms that are part of the equipment. Analysis of measurement data requires very complex algorithms often based on artificial intelligence. A model and procedure were also prepared to simulate the composite itself under near real-world conditions. [ABSTRACT FROM AUTHOR]

For WSNs deployed in remote tactical edge networks without available infrastructure, using UAVs to collect sensors data can greatly extend sensor networks life. Due to constraints such as energy consumption and communication delay, rapid path planning is necessary for UAVs flight. This paper mainly proposes a new path planning algorithm based on spiral decomposition, which is named as Spiral Path Planning (SPP) algorithm. Besides, the paper improves the existing FPPWR algorithm. Simulation proves SPP is more suitable for a UAV to collect data from sensors nodes evenly distributed in a circle area. Compared with FPPWR, the path SPP plans is shorter, and runtime is less when the number of sensor nodes is small. [ABSTRACT FROM AUTHOR]

In this paper,the traditional Sobel edge detection algorithm for the calculation of the gradient of less template and the multiplication operation is time-consuming, resulting in complex image edge detection is not clear and the slow processing speed,this paper puts forward a method to increase the gradient template and change the way the new multiplication gradient. Sobel edge detection algorithm the new gradient calculation method based on the FPGA technology, parallel processing capability and high reliability, flexibility and reconfigurability of good features using the FPGA chip to complete the design of improved Sobel edge detection algorithm, to solve the traditional Sobel algorithm is not clear enough and algorithm processing speed problem for complex image edge detection. This design uses Verilog HDL language to design and implement each module.The Quartus II 8.0 software is used to simulate the model and download it to the DE2 development board with Altera EP2C35F672C6 as the target chip, and finally achieves the design requirements. [ABSTRACT FROM AUTHOR]

In this paper the algorithm based on SVM (Support Vector Machines) for determining of effectiveness of test drug is proposed. The most time-consuming tasks of medicine include diagnosing and choosing a course of treatment. Traditionally, doctors have solved these problems, relying only on their own intuition and experience. Today in their arsenal are increasingly included methods based on high technology and allowing to process large flows of information. Mathematical description and formulation of the problem are given. The results of experiments based on the algorithm are presented. The main goal of the paper is to create prerequisites for preventive diagnosis of tuberculosis patients. Using the proposed model and monitoring system, specialists can correctly diagnose and develop an optimal treatment course. [ABSTRACT FROM AUTHOR]

This paper presents an approach using Pigeon Inspired Optimization (PIO) for selective harmonic elimination in a cascaded H-bridge (CHB) multilevel inverter fed with unequal dc sources. The aim of this work is to find the optimal combination of switching angles, such that the lower order harmonics are eliminated and the output voltage is constant irrespective of voltage change in the input side. This paper the PIO has been used to find the optimal angles for a 7-level inverter and the method can be scaled to any number of levels. To show the effectiveness of PIO the results have been compared with the results obtained using genetic algorithm (GA). The results show that the PIO is the most accurate and fastest evolutionary algorithm for switching angle optimization for the case of un-equal dc voltages. [ABSTRACT FROM AUTHOR]

This paper proposes exchange market algorithm (EMA) to solve economic emission dispatch and reliability (EEDR) problem in thermal power plants. The aim of EMA, inspired by the method of selling and purchasing of shares by elite stockholders, is to solve the optimization problems. The EMA is a new, robust and efficient algorithm to exploit the global optimum point in optimization problems. Existence of two seeking operators in EMA provides a high ability in exploiting global optimum point. In economic emission dispatch and reliability, it is tried to utilize generating units in a way that we have high reliability in supplying the system load demand as well as the minimum fuel costs. In this paper, the EMA is successfully implemented on two systems with 6 and 26 generating units considering economic and emission dispatch and system reliability. The obtained results are compared with other advanced techniques. The results well demonstrate the practical advantage of the exchange market algorithm over the other approaches. [ABSTRACT FROM AUTHOR]

This paper presents the design of fuzzy control system for an omnidirectional robot. The control structure of omnidirectional robot was described. The kinematics and dynamics of the mobile robot are presented. The design of control system is implemented for position and orientation angle in order to control the linear and angular speed of the omnidirectional robot. The designs of fuzzy controllers are performed and then these are used for the control of the holonomic 4-wheel-driven soccer robots. The designed fuzzy control algorithm has been extensively tested in simulation and provided satisfactory results in run time. The controller presented in this paper provides an optimal solution to minimize the differences between the reference trajectory and the current output. The obtained results demonstrate the effectiveness of proposed algorithms in robot soccer control. [ABSTRACT FROM AUTHOR]

Abstract: With electric power system becoming more and more complex and intelligent these days, its uncertainty also increases. This paper classifies the references of the risk assessment on power system from the aspects of evaluation hierarchy, evaluation category, evaluation model, power system status, time and simulation algorithm. The purpose of this paper is to reveal the research status and compared the advantages and disadvantages of the methods used. And the future development of risk assessment on power system is also prospected. [ABSTRACT FROM AUTHOR]

This paper examines the coarse-grained approach to parallelization of the branch-and-bound (B&B) algorithm in a distributed computing environment. This approach is based on preliminary decomposition of a feasible domain of mixed-integer programming problem into a set of subproblems. The produced subproblems are solved in parallel by a distributed pool of standalone B&B solvers. The incumbent values found by individual solvers are intercepted and propagated to other solvers to speed up the traversal of B&B search tree. Presented implementation of the approach is based on SCIP, a non-commercial MINLP solver, and Everest, a web-based distributed computing platform. The implementation was tested on several mixed-integer programming problems and a noticeable speedup has been achieved. In the paper, results of a number of experiments with the Traveling Salesman Problem are presented. [ABSTRACT FROM AUTHOR]

In this paper, we demonstrate how to extend perfect spatial hashing (PSH) in order to hash multidimensional scientific data. As a use case we employ the problem domain of indexing nodes in a graph that represents Super Carbon Nanotubes (SCNTs). The goal of PSH is to hash multidimensional data without collisions. Since PSH results from the research on computer graphics, its principles and methods have only been tested on 2- and 3-dimensional problems. In our case, we need to hash up to 28 dimensions. In contrast to the original applications of PSH, we do not focus on GPUs as target hardware but on an efficient CPU implementation. Thus, this paper highlights the extensions to the original algorithm to make it suitable for higher dimensions. Comparing the compression and performance results of the new PSH based graphs and a structure-tailored custom data structure in our parallelized SCNT simulation software, we find that PSH in some cases achieves better compression by a factor of 1.7 while only increasing the total runtime by several percent. In particular, after our extension, PSH can also be employed to index sparse multidimensional scientific data from other domains where PSH can avoid additional index-structures like KD- or R-trees. [ABSTRACT FROM AUTHOR]

Creating sessions in scientific conferences consists in grouping papers with common topics taking into account the size restrictions imposed by the conference schedule. Therefore, this problem can be considered as semi-supervised clustering of documents based on their content. This paper aims to propose modifications in traditional clustering algorithms to incorporate size constraints in each cluster. Specifically, two new algorithms are proposed to semi-supervised clustering, based on: binary integer linear programming with cannot-link constraints and a variation of the K-Medoids algorithm, respectively. The applicability of the proposed semi-supervised clustering methods is illustrated by addressing the problem of automatic configuration of conference schedules by clustering articles by similarity. We include experiments, applying the new techniques, over real conferences datasets: ICMLA-2014, AAAI-2013 and AAAI-2014. The results of these experiments show that the new methods are able to solve practical and real problems. [ABSTRACT FROM AUTHOR]

In this paper, a study on development of prediction model based on an intelligent systems is discussed for gas metering system in order to validate the instrument reliability. In providing reliable measurement of gas metering system, an accurate prediction model is required for model validation and parameter estimation. The intelligent prediction system has been developed for gas measurement validation. Then the project focused on the application of particle swarm optimization (PSO) and Genetic Algorithm (GA) in training neural network prediction model in enhancing the performance of Intelligent Prediction System (IPS). In this study, the three experiment has been conducted to improve the accuracy of the neural network prediction model. The comparison of the performance of PSONN and GANN with pure ANN is presented in this paper. The results shows that the proposed PSONN model give promising results in the prediction accuracy of gas measurement. [ABSTRACT FROM AUTHOR]

In this paper, a review of the existing state-of-the-art regarding prediction models in radiotherapy treatments is made. We focus the scope of the paper in data mining techniques used in the context of radiotherapy treatments. There are several data mining algorithms applied to datasets of cancer patients receiving radiotherapy treatments, with very distinct variables and heterogeneous features. The existing literature presents significant advantages in using data mining approaches to predict outcomes in this type of treatments, with an increasing adherence to their use and great potential to explore. Recent published studies are considered, followed by a discussion and some conclusions with the identification of possible future work. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of path following for unmaned cars using only GNSS positionning. Several papers have already addressed the lane keeping, usually restricting the focus to the computation of the optimal steering wheel angle, therefore usually offering a good solution in either straight lines or curves, but rarely both. Moreover the GNSS receiver have a sampling time higher than the required one for the proposed strategies in the literature. In this paper, a control algorithm is proposed for the steering wheel angle, with adaptation of the speed of the car, in order to provide a solution working both for straight lines and curves by using only GNSS. The presented solution has been used in real conditions to realize the demonstration “speed synchronization at intersections using intervehicular communication, x.icars” shown at the Intelligent Transportation System World Congress in Bordeaux, 2015 1 1 https://www.youtube.com/watch?v=KBx6qks8kCI . [ABSTRACT FROM AUTHOR]

We have tendency to exhibit ourselves through the data we share about ourselves including, liking, friendship, follows, disliking, pictures, audio, videos, causes, blogs and sites. Such data about us have already been used by big data companies to create customized ads and marketing tactics. However, while such data being in unstructured and noisy format, utilization and research is at its early stages. In this paper, we elaborate on the idea of understanding individuals through lens of data they produce in context of our main research work for Predicting Educational Relevance For an Efficient Classification of Talent (PERFECT) algorithm engine. We illustrate some of research problems in relevance of such data and identify research problem as ground for this paper. We present sub set of our framework including algorithm and math constructs, for the problem we identify. We conclude that such analytics and cognitive research can help to improve education, healthcare, Job economy, crime control, etc. Thus we coin the phrase “we are what we generate”, with our work in this paper. We suggest future work and opportunities in relevant directions. [ABSTRACT FROM AUTHOR]

Community refers to the group of entities which have similar behavior or characteristic among them. Usually community represents basic functional unit of social network. By understanding the behavior of elements in a community, one can predict the overall feature of large scale social network. Social networks are generally represented in the form of graph structure, where the nodes in it represent the social entities and the edges correspond to the relationships between them. Detecting different communities in large scale network is a challenging task due to huge data size associated with such network. Community detection is one of the emerging research area in social network analysis. In this paper, a spanning tree based algorithm has been proposed for community detection which provides better performance with respect to both time and accuracy. Modularity is the well known metric used to measure the quality of community partition in most of the community detection algorithms. In this paper, an extensive version of modularity has been used for quality assessment. [ABSTRACT FROM AUTHOR]

A novel histogram generation hardware architecture, which can develop histogram for all types of grayscale images of size (256 x 256), is presented in this paper. The histogram produced from the proposed hardware gives exactly the same information as obtained from a histogram plot using simulation tool like Matlab with 100 percent accuracy. Hardware realization of the architecture makes it suitable for generating real-time histogram in medical and military applications which is practically difficult in software based implementations. Experimental results shows that the frequency of operation of the proposed architecture is very high both for Field Programmable Gate Array (FPGA) as well as for Programmable SoC (PSoC) based implementation. To the best of our knowledge, this is the first hardware implementation for grayscale image histogram generator for the selected histogram generation algorithm mentioned in this paper. [ABSTRACT FROM AUTHOR]

Present-day Electric Power Systems are driven under much stressed circumstances when compared to the past and creating a developing need for accuracy, flexibility, and reliability in the areas of Transmission, Distribution and Electric Power Generation. In all stages of power system, voltage stability problems are increasing more and more. So, the only alternate solution for these problems is proper placement and sizing of UPFC. The paper presents the Placement and Tuning of UPFC for a multi-objective function consisting of minimization of transmission losses, load voltage deviation. Here L-index is used to place the UPFC in a specified location i.e., weakest bus, critical line and the weak area of the system. In this paper, a newly developed meta-heuristic algorithm named Krill Herd (KH) is introduced to solve multi-objective problem of optimization. Simulation is carried on IEEE 14-bus system and the results have been compared with the Genetic algorithm with and without UPFC. [ABSTRACT FROM AUTHOR]

Digital Signatures are primarily employed to achieve data integrity, data origin authentication and non-repudiation. In comparison to RSA-based digital signatures, the ECC (Elliptical Curve Cryptography) based Signature schemes like ECDSA (Elliptical Curve Digital Signature Algorithm) are more efficient and suited for a resource-constrained network like WSN. In a WSN, proper entity authentication is to be achieved before the sensor data is exchanged. The use of ECDSA is not suitable for achieving mutual authentication between the entities like the base station, cluster heads and nodes. Based on the study of ECDSA, the paper designs a mutual authentication protocol with the help of a computationally low signature scheme. To the best of our knowledge, this is the first digital signature based scheme which achieves mutual entity authentication. The paper also brings out performance parameters of the developed protocol. [ABSTRACT FROM AUTHOR]

This paper proposes a novel authentication system using hidden biometric trait called finger vein (FV). Proposed finger vein acquisition device captures the superficial vein pattern present under the skin of finger. The vein pattern of all fingers of human being is not same. Each finger of same person has different vein pattern. It is the hidden part which is not seen by normal eye sight hence less possible to forge. The paper highlight the primary set up of NIR based finger vein acquisition device to capture the superficial vein pattern that shows the unique features require for recognition. This Paper explores the bilateral and contrast limited histogram equalization technique to enhance finger vein raw samples captured by proposed finger vein acquisition . The performance is verified using Local Binary Pattern (LBP) algorithm. The recognition accuracy is represented by graph between True Acceptance Rate (TAR) vs. True Rejection Rate (TRR). It is observed that TAR Vs TRR for standard data base is 94. 5% and for own data base is 94.34%. It shows that FV has potential for personal identification. [ABSTRACT FROM AUTHOR]

This paper presents a light-weight online system adaptation technique for multi-core embedded systems running multiple applications. Thus far, online system adaptation techniques are restricted to reconfiguring resource management schemes such as operating frequency scaling or task-to-processor assignment. Additionally, in this paper, we enable to adapt the algorithm at runtime. That is, we selectively apply a suitable algorithm considering the system status when more than one algorithm candidates exist in the target application. We propose a generic and extensible self-adaptive framework with multiple applications in mind by providing generic programming interface that requires minimal changes in the legacy software code. It is shown that the proposed framework adaptively optimizes both resource management and algorithm selection with negligible performance overhead. The effectiveness of the proposed framework is experimentally proven with real-life examples. [ABSTRACT FROM AUTHOR]

In C++, new features and semantics can be added to an existing software package without sweeping code changes by introducing a user-defined type using operator overloading. This approach is used, for example, to add capabilities such as algorithmic differentiation. However, the introduction of operator overloading can cause a multitude of compilation errors. In a previous paper, we identified code constructs that cause a violation of the C++ language standard after a type change, and a tool called OO-Lint based on the Clang compiler that identifies these code constructs with lint-like messages. In this paper, we present an extension of this work that automatically transforms such problematic code constructs in order to make an existing code base compatible with a semantic augmentation through operator overloading. We applied our tool to the CFD software OpenFOAM and detected and transformed 23 instances of problematic code constructs in 160,000 lines of code. A significant amount of these root causes are included up to 425 times in other files causing a tremendous compiler error amplification. In addition, we show the significance of our work with a case study of the evolution of the ice flow modeling software ISSM, comparing a recent version which was manually type changed with a legacy version. The recent version shows no signs of problematic code constructs. In contrast, our tool detected and transformed a remarkable amount of issues in the legacy version that previously had to be manually located and fixed. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive and rigorous study of a novel collision resolution algorithm for wireless sensor networks: 2CS-WSN. It is specifically designed to be used during the contention phase of IEEE 802.15.4. This algorithm has been modelled in terms of discrete time Markov chains (DTMCs) and, using the probabilistic symbolic model checker PRISM, correctness properties and different operation modes of the algorithm have been studied. Moreover, different model abstractions have been used in order to identify any inconsistencies or ambiguities, and to prove interesting properties for non-trivial, practical and relevant scenarios. Finally, since the biggest source of energy waste is the collision, this paper conducts a wide study of energy saving in this algorithm. [ABSTRACT FROM AUTHOR]

The aim of this paper is to further develop the original method proposed by the authors in their previous publications and submitted as a patent to shape frequency response of a vibrating plate according to precisely dened demands. The method is based on modeling the plate together with additional masses and ribs, and applying a sophisticated optimization algorithm, which issues arrangement of the masses and ribs. It has a very high practical potential. It can be used to improve acoustic radiation of the plate for required frequencies or enhance acoustic isolation of noise barriers and device casings. It can be utilized for both passive and active control. For the latter case it allows at the same time to optimally arrange actuators and sensors. In the paper there are presented, compared and discussed simulation results of the method for a plate with dierent boundary conditions: simply supported, fully-clamped and elastically restrained against rotation (corresponding to a mounting in a real device casing). Proposed optimization criteria are followed from practical scenarios, where precise modication of a vibrating plate frequency response is desired. The application of the proposed method for active control is also shown. The important additional outcome of the paper are guidelines on designing device casings in terms of rigidity in order to obtain their required vibration and noise isolation features. [ABSTRACT FROM AUTHOR]

This paper describes our on-going work to accelerate ZENO, a software tool based on Monte Carlo methods (MCMs), used for computing material properties at nanoscale. ZENO employs three main algorithms: (1) Walk on Spheres (WoS), (2) interior sampling, and (3) surface sampling. We have accelerated the first two algorithms. For the sake of brevity, the paper will discuss our work on the first one only as it is the most commonly used and the acceleration techniques were similar in both cases. WoS is a Brownian motion MCM for solving a class of partial differential equations (PDEs). It provides a stochastic solution to a PDE by estimating the probability that a random walk, which started at infinity, will hit the surface of the material under consideration. WoS is highly effective when the problem's geometry is additive, as this greatly reduces the number of walk steps needed to achieve accurate results. The walks start on the surface of an enclosing sphere and can make much larger jumps than in a direct simulation of Brownian motion. Our current implementation represents the molecular structure of nanomaterials as a union of possibly overlapping spheres. The core processing bottleneck in WoS is a Computational Geometry one, as the algorithm repeatedly determines the distance from query point to the material surface in each step of the random walk. In this paper, we present results from benchmarking spatial data structures, including several open-source implementations of k-D trees, for accelerating WoS algorithmically. The paper also presents results from our multicore and cluster parallel implementation to show that it exhibits linear strong scaling with the number of cores and compute nodes; this implementation delivers up to 4 orders of magnitude speedup compared to the original FORTRAN code when run on 8 nodes (each with dual 6-core Intel Xeon CPUs) with 24 threads per node. [ABSTRACT FROM AUTHOR]

This paper intends to achieve high performance in terms of time by implementing various time consuming application on NVIDIA Graphics Processing Unit (GPU) by using parallel programming model NVIDIA Compute Unified Device Architecture (CUDA). NVIDIA CUDA provides platform for developing parallel applications on NVIDIA GPUs. So it gives developers a platform to build high-end parallel processing applications. This paper implements various image processing algorithms on both Central Processing Unit (CPU) and GPU. Implemented point-to-point image processing algorithms are brightening filter, darkening filter, negative filter and RGB to Grayscale filter. Along with various convolution algorithms that consider value of its neighboring pixels are also implemented. Implemented convolution algorithms are sobel filter for edge detection, low pass filter and high pass filter. Performance analysis of the implemented image processing algorithms is done on both CPU and GPU. Analysis is made on images of resolution 3000 X 3000. Color-ed images are used for point-to-point pixel processing algorithms. Grayscale images are used for all convolution algorithms. Performance analysis done for point-to-point processing algorithms by varying number of threads per block. Recursive ray tracing is also implemented on GPU, and found performance gain compare to serial algorithm run on CPU. [ABSTRACT FROM AUTHOR]

Fuzzy clustering is useful clustering technique which partitions the data set in fuzzy partitions and this technique is applicable in many technical applications like crime hot spot detection, tissue differentiation in medical images, software quality prediction etc. In this review paper, we have done a comprehensive study and experimental analysis of the performance of all major fuzzy clustering algorithms named: FCM, PCM, PFCM, FCM-σ, T2FCM, KT2FCM, IFCM, KIFCM, IFCM-σ, KIFCM-σ, NC, CFCM, DOFCM. To better analysis their performance we experimented with standard data points in the presents of noise and outlier. This paper will act as a catalyst in the initial study for all those researchers who directly or indirectly deal with fuzzy clustering in their research work and ease them to pick a specific method as per the suitability to their working environment. [ABSTRACT FROM AUTHOR]

Considering the raw data extraction algorithms designed for different sensor data this paper focuses on the SCATTEROMETER Level-0 data extracted from raw data products. An analysis of extracted signal images is shown. Then a very simple technique but a very important concept is described and implemented on such signal images. Paper shows the approach which can be help full for identification of number of orbits in to the raw data products. The main objective of this approach is to provide the statistics to the application specific user. The approach is based on analyzed behavior of signal images extracted from the raw product. This approach defines a very basic standard to identify number of orbits based on SCATTEROMETER signal images. An implementation algorithm with its time complexity is shown with the corresponding implementation in C language. The proposed approach is for signal images of Data Quality Evaluation Level-0 SCATTEROMETER signal images, but it can also be extended for other signal images. The assumption for the paper is, the scanning geometry of signal image has been established in terms of scans and pixels i.e. header or interpretation format of the signal data. [ABSTRACT FROM AUTHOR]

This paper presents a method for the implementation of facial recognition using filtering techniques that will increase the accuracy of the process as well as distinguish faces more decisively. The process has proved to be invariant to image scale, rotation and illumination. This paper was motivated by the EOH-SIFT approach. The process described in this paper is aimed at improving upon the effectiveness of EOH-SIFT by feeding it a filtered image. After much exhaustive research into various filters, this paper shows that, with two filters which when used in a specific order, significantly boost the potency of the EOH-SIFT approach to identify faces. This approach has given very promising results when tested on the ORL database. Although it is a standard dataset, it does not have much variation as seen in the real world. Hence, our approach has been tested on other datasets and it has produced extremely encouraging results. The recognition of faces proceeds by first obtaining the region of interest and applying the filters on that area followed by the identification of important features in the faces and then matching them using an efficient nearest-neighbor algorithm. This leads to a robust and definitive face recognition system. [ABSTRACT FROM AUTHOR]

Association Rule Mining (ARM) is a popular and well researched method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using different measures of interestingness. Most ARM algorithms focus on a sequential or centralized environment where no external communication is required. Distributed ARM algorithms (DARM), aim to generate rules from different data sets spread over various geographical sites; hence, they require external communications throughout the entire process. DARM algorithm efficiency is highly dependent on data distribution. The Classical algorithms used in DARM are Count Distribution Algorithm (CDA), Fast Distributed Mining (FDM) Algorithm and Optimized Distributed Association Mining (ODAM) Algorithm. This paper presents the implementation details and experimental results of above mentioned algorithms. The paper also highlights the issues of message exchange size in a distributed environment of current DARM algorithms that can affect the communication costs in a distributed environment. [ABSTRACT FROM AUTHOR]

In this paper, we present an efficient reversible logic synthesis algorithm that uses Toffoli and mixed-polarity based Toffoli gate. In this paper, we propose an algorithm to synthesizereversible function in their positive-polarity Reed Muller (PPRM) expansion and usethe Hamming Distance (HD) approach to select suitable transformation path. Once a transformation path is defined, suitable gates for substitution are selected through the gate matching factor and reduction is performed. The algorithm does not generate any extra lines and thus keeping the synthesized function in its simplest form. The algorithm target on efficient way to synthesize three variables based reversible function into a cascade of Toffoli and mixed-polarity based Toffoli gate in term of quantum cost and gate count. Experimental results showthat the proposed algorithm is efficient in terms of the realization of all three variable based reversible functions. [ABSTRACT FROM AUTHOR]

This paper presents about our research in social search. Generally, the research in social search falls into two principal challenges. The first challenge is how to find more relevant answers to the question. The second one is how to increase speed in finding relevant answers. Recently, we had provided two algorithms called Asknext and Question Waves to find more relevant answers compared to the baseline algorithm BFS. But, the search speed of the two proposed algorithms still the subject to improve. In this paper, we introduce the agents’ ability of learning the answers from the interactions with other agents so that they can quickly answer the question of other agents. We model this learning process by implementing the concept of data caching as the short-term memory of each social search agent. The result improvement of the speediness and the reduction of the number of messages used to communicate between agents, after apply agent's short-term memory concept, demonstrates the usefulness of the proposed approach. [ABSTRACT FROM AUTHOR]

The recent buzz around open data highlighted the crucial problem of anonymization in the context of data publishing. Many research efforts were devoted to the definition of techniques performing such an anonymization. However the selection of the most relevant technique and the adequate algorithm is complex. Successful decision depends firstly on the ability of data publishers to understand the anonymization techniques and their associated algorithms. In this paper, we focus on the choice of an algorithm among the different ones implementing one of the anonymization techniques, namely generalization. Through an abstraction process presented in this paper, we provide data publishers with simplified descriptions for the generalization technique and its algorithms. These descriptions facilitate the understanding of the algorithms by data publishers having low programing skills. We present also some other use cases of these abstractions as well as an experimentation conducted to validate them. [ABSTRACT FROM AUTHOR]

The aim of the proposed work presented in this paper is to determine the speech polarity using the knowledge of epochs and the cosine phase information derived from the complex analytic representation of original speech signal. The work presented in this paper is motivated by the observation of variations in the cosine phase of speech around the Hilbert envelope (HE) peaks according to the polarity changes. As the HE peaks represent approximate epochs location, the phase analysis is performed by using algorithms which provide better resolution and accuracy of estimated epochs in the present work. In the present work, accurate epochs locations are initially estimated and significant HE peaks are only selected from the near vicinity of the epochs location for phase analysis. The cosine phase of the speech signal is then computed as the ratio of signal to the HE of speech. The trend in the cosine phase around the selected significant HE peaks are observed to be varying according to the speech polarity. The proposed polarity detection algorithm shows better results as compared with the state of the residual skewness based speech polarity detection (RESKEW) method. Thus, the improvement in the polarity detection rates confirms significant polarity information present in the excitation source characteristics around epochs location in speech. The polarity detection rates are also found to be less affected for different levels of noise addition which indicates the effectiveness of the approach against noises. Also, based on the analysis of mean execution time, the proposed polarity detection algorithm is confirmed to be 10 times faster than the RESKEW algorithm. [ABSTRACT FROM AUTHOR]

The goal of Nearest Neighbour (NN) search is to find the objects in a dataset A that are closest to a query point q. Existing algorithms presume that the dataset is indexed by an R-tree and searching a query point q in a large volume of a dataset, is a tedious task that effects the quality and usefulness of the NNQ processing algorithms which determined by the time as well as space complexity. The simplest solution to the NNS problem is to compute the distance from the query point to every other point in the database. However, due to these complexities issue, the various research techniques have been proposed. It is a technique which has applications in various fields such as pattern recognition, moving object recognition etc. In this paper, a comprehensive analysis on data structures, processing techniques and variety of algorithms in this field is done along with different way to categorize the NNS techniques is presented. This different category such as weighted, additive, reductional, continuous, reverse, principal axis, which are analyzed and compared in this paper. Complexity of different data structures used in different NNS algorithms is also discussed. [ABSTRACT FROM AUTHOR]

Redundancy is incorporated in safety critical systems for dependability and in traditional redundant systems, computing resources are inefficiently utilized under fault free condition. In this paper, an algorithm Resource Reclaimed Scheme (RR scheme) has been developed for scheduling of periodic tasks with critical aperiodic task arrivals in m-redundancy framework with resource augmentation. A novel task allocation and scheduling ensures effective resource utilization and simultaneous fault tolerant capability with graceful degradation. The proposed algorithm guarantees enhanced performance under fault free condition and less than full functionality up to m-1 faults. An earlier work has dealt with a similar dual system and this paper aims at a generalized m-processor scheme and in both cases, a comparison with the traditional redundant scheme has been studied for the significant impact of this novel idea. As a case study, an Avionic Mission System has being considered and simulation and performance evaluation validating the frame work. An effective use of computing resources and improved process speedup which are critical measures for avionics systems, specially the fuel/weight ratio can have wide ramifications and contribute significantly in enhanced performance capability. [ABSTRACT FROM AUTHOR]

Educational Data Mining field concentrate on Prediction more often as compare to generate exact results for future purpose. In order to keep a check on the changes occurring in curriculum patterns, a regular analysis is must of educational databases. This paper focus on identifying the slow learners among students and displaying it by a predictive data mining model using classification based algorithms. Real World data set from a high school is taken and filtration of desired potential variables is done using WEKA an Open Source Tool. The dataset of student academic records is tested and applied on various classification algorithms such as Multilayer Perception, Naïve Bayes, SMO, J48 and REPTree using WEKA an Open source tool. As a result, statistics are generated based on all classification algorithms and comparison of all five classifiers is also done in order to predict the accuracy and to find the best performing classification algorithm among all. In this paper, a knowledge flow model is also shown among all five classifiers. This paper showcases the importance of Prediction and Classification based data mining algorithms in the field of education and also presents some promising future lines. [ABSTRACT FROM AUTHOR]

Community detection is the one of the most relevant and important topics in the field of graph mining, principally for its applications in number of domains such as social or biological networks analysis to transport systems. Different community detection algorithms have been proposed earlier. This paper discusses and proposes a new technique on particular community detection and its retrieval in social community graph. Community detection constitutes a significant role for analysis of a large community graph by enabling and selecting the desired community's graph. It tries to extract a sub-community graph from a very large community graph for analysis. This paper presents both a theoretical and experimental result in this direction with certain example. We have retrieved the sub-community graph from a compressed community graph in the context of graph mining techniques. Observation concludes that the proposed technique is simpler, easier and efficient in terms of complexities. [ABSTRACT FROM AUTHOR]

This paper applies network tomography for load balancing. Load balancing is the allocation of the work of a single application to processors at run-time so that the execution time of the application is minimized. In order to achieve this purpose, load balancing algorithms require various network parameters to make decisions for effectively balancing the load. This paper analyses the scope of the application of network tomography to facilitate load balancing algorithms in estimating network parameters (packet loss and delay) to be used in decision making. This paper also exploits the interdependence of packet loss and delay in the context of load balancing. Evaluation results show that network tomography estimates link delays accurately from the knowledge of path delays and packet loss. This information is instrumental to be used to tune the delay dependence module of various load balancing algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes multi-objective genetic algorithm for the problem of variable selection in multivariate calibration. We consider the problem related to the classification of biodiesel samples to detect adulteration, Linear Discriminant Analysis classifier. The goal of the multi--objective algorithm is to reduce the dimensionality of the original set of variables; thus, the classification model can be less sensitive, providing a better generalization capacity. In particular, in this paper we adopted a version of the Non-dominated Sorting Genetic Algorithm (NSGA-II) and compare it to a mono-objective Genetic Algorithm (GA) in terms of sensitivity in the presence of noise. Results show that the mono-objective selects 20 variables on average and presents an error rate of 14%. One the other hand, the multi-objective selects 7 variables and has an error rate of 11%. Consequently, we show that the multi-objective formulation provides classification models with lower sensitivity to the instrumental noise when compared to the mono-objetive formulation [ABSTRACT FROM AUTHOR]

Human hand detection and segmentation plays an important role in sign language recognition and human machine interaction. In this paper, a novel approach for learning a vision-based hand detection system is introduced. The main contribution of this paper includes robust boosting-based framework for real-time detection of a hand in unconstrained and heterogeneous environments. The proposed system makes use of efficient representative features which allows fast computation while changing the hand appearances and background. Moreover, this proposed strategy efficiently improves the performance while reducing the effort of hand labeling. Experimental results show that the proposed method is practically more flattering as it meets the requirements of real-time performance, accuracy and robustness. This system has been proved to work well with a reasonable amount of training samples and was computationally found to be more effective and efficient. [ABSTRACT FROM AUTHOR]

This paper describes the comparison of the performance of Circular array (CA) and Hexagonal Array (HA) of uniformly excited isotropic antennas which can generate directive beam with minimum relative Side Lobe Level (SLL). The Improved Particle Swarm Optimization (IPSO) method, which represents a novel approach for optimization problems in electromagnetic, is used in the optimization process. Two examples has been presented and solved. In first example, the IPSO is used to determine an optimal set of ‘ON-OFF’ elements in an 12 element thinned array, and in second example, IPSO is used to determine an optimal set of amplitude distributions in an 18 element array that provide a radiation pattern with maximum SLL reduction. This paper is basically concerned with the comparison of the performance of thinned CA and HA in terms of SLL by fixing the other array design variable. Numerical results for synthesizing two different array geometries demonstrated the superiority of Hexagonal Array over the Circular Array for both the examples. [ABSTRACT FROM AUTHOR]

Long Term Evolution is standardized by the 3rd Generation Partnership Project to provide a high data rate up to 100 Mbps and 50 Mbps for downlink and uplink transmission respectively and can operate in different bandwidths ranging from 1.4 MHz up to 20 MHz. The incorporated scheduling mechanisms can significantly contribute to this goal. Scheduling mechanism is the process of allocated the resources (Time and Frequency) to users transmitting different flows in same time. In this paper, a novel scheduling algorithm is presented, modeled and compare to basic scheduler used in downlink LTE transmission, such as Round Robin, Max Rate and Proportional fair schedulers. Simulation results, presented in this paper, show that the newly proposed algorithm improves system capacity as compare with Proportional Fair scheduler with guaranty of 80% fairness between all users. [ABSTRACT FROM AUTHOR]