Computation Of Similarity Measures Research Articles

A Topological Similarity Measure Between Multi-Resolution Reeb Spaces.

Searching similarity between a pair of shapes or data is an important problem in data analysis and visualization. The problem of computing similarity measures using scalar topology has been studied extensively and proven useful in the shape and data matching. Even though multi-field or multivariate (consists of multiple scalar fields) topology reveals richer topological features, research on building tools for computing similarity measures using multi-field topology is still in its infancy. In the current article, we propose a novel similarity measure between two piecewise-linear multi-fields based on their multi-resolution Reeb spaces - a newly developed data-structure that captures the topology of a multi-field. Overall, our method consists of two steps: (i) building a multi-resolution Reeb space corresponding to each of the multi-fields and (ii) proposing a similarity measure between two multi-resolution Reeb spaces by computing a list of topologically consistent matching pairs (of nodes) and the similarity between them. We demonstrate the effectiveness of the proposed similarity measure in detecting topological features from real time-varying multi-field data in two application domains - one from computational physics and one from computational chemistry.

Reviewer Assignment Using Weighted Matching and Hungarian Algorithm

After closing submission system of the conference, the organizing committees’ duties are huge and among these tasks are assigning at least three reviewers (at least) for each paper. This task involves matching a set of papers against reviewers in order to assign most suitable group of reviewers to specific paper to be reviewed. In this paper, a proposed weighted similarity measure is computed between each pair of (reviewer, author) work. This can be done by keeping a sample of published papers for each reviewer in order to extract the field of expertise of the reviewer and measure the relevance with the author's paper. The proposed weighted similarity measure is based on dividing each paper of authors and reviewers into five sections (i.e. Title, Abstract, Keywords, References, and the rest text in the paper) to compute the matching degree; each paper part is based on its importance thus TF-IDF and cosine similarity techniques are adopted to calculate the degree of correspondence between each pair of coincidence parts. Hereafter, Hungarian optimization algorithm is employed to assign each paper to the most relevance reviewers using computed similarity measure. The experimental results on NTICT 2018 dataset showed that the proposed method achieved its goal.

Improved quality of service-based cloud service ranking and recommendation model

One of the ongoing technologies which are used by large number of companies and users is cloud computing environment. This computing technology has proved that it provides certainly a different level of efficiency, security, privacy, flexibility and availability to its users. Cloud computing delivers on demand services to the users by using various service-based models. All these models work on utility-based computing such that users pay for their used services. Along with the various advantages of the cloud computing environment, it has its own limitations and problems such as efficient resource identification or discovery, security, task scheduling, compliance and sustainability. Among these resource identification and scheduling plays an important role because users always submits their jobs and expects responses in least possible time. Research is happening all around the world to optimize the response time, make span so as to reduce the burden on the cloud resources. In this paper, QoS based service ranking model is proposed for cloud computing environment to find the essential top ranked services. Proposed model is implemented in two phases. In the first phase, similarity computation between the users and their services is considered. In the second phase, computing the missing values based on the computed similarity measures is calculated. The efficiency of the proposed ranking is measured and the average precision correlation of the proposed ranking measure is showing better results than the existing measures.

Semantic web service composition using semantic similarity measures and formal concept analysis

One of the main assets of the Service Oriented Architecture (SOA) is composition, which consists in developing higher-level services by re-using well-known functionality provided by other services in a low-cost and rapid development process. In this paper, we present IDECSE a new integrated approach for composite services engineering. By considering semantic Web services, IDECSE addresses the challenge of fully automating the classification, discovery and composition while reducing development time and cost. The classification and the discovery processes rely on adequate semantic similarity measures. Both semantic and syntactic descriptions are integrated through specific techniques for computing similarity measures between services. Formal Concept Analysis (FCA) is used then to classify Web services into concept lattices in order to facilitate relevant services identification. A graph based semantic Web service composition process was proposed within the IDECSE framework. Using semantic similarities in grouping classes of services and in composing services shows a significant improvement compared to other approaches.

Learning contextual superpixel similarity for consistent image segmentation

This paper addresses the problem of image segmentation by iterative region aggregations starting from an initial superpixel decomposition. Classical approaches for this task compute superpixel similarity using distance measures between superpixel descriptor vectors. This usually poses the well-known problem of the semantic gap and fails to properly aggregate visually non-homogeneous superpixels that belong to the same high-level object. This work proposes to use random forests to learn the merging probability between adjacent superpixels in order to overcome the aforementioned issues. Compared to existing works, this approach learns the fusion rules without explicit similarity measure computation. We also introduce a new superpixel context descriptor to strengthen the learned characteristics towards better similarity prediction. Image segmentation is then achieved by iteratively merging the most similar superpixel pairs selected using a similarity weighting objective function. Experimental results of our approach on four datasets including DAVIS 2017 and ISIC 2018 show its potential compared to state-of-the-art approaches.

Low-Complexity Scalable Architectures for Parallel Computation of Similarity Measures

Processor array architectures have been employed, as an accelerator, to compute similarity distance found in a variety of data mining algorithms. However, most of the proposed architectures in the existing literature are designed in an ad hoc manner without taking into consideration the size and dimensionality of the datasets. Furthermore, data dependencies have not been analyzed, and often, only one design choice is considered for the scheduling and mapping of computational tasks. In this work, we present a systematic methodology to design scalable and area-efficient linear (1-D) processor arrays for the computation of similarity distance matrices. Six possible design options are obtained and analyzed in terms of area and time complexities. The obtained architectures provide us with the flexibility to choose the one that meets hardware constraints for a specific problem size. Comparisons with the previously reported architectures demonstrate that one of the proposed architectures achieves less area and area-delay product besides its scalability to high-dimensional data.

Simulation-assisted machine learning.

MotivationIn a predictive modeling setting, if sufficient details of the system behavior are known, one can build and use a simulation for making predictions. When sufficient system details are not known, one typically turns to machine learning, which builds a black-box model of the system using a large dataset of input sample features and outputs. We consider a setting which is between these two extremes: some details of the system mechanics are known but not enough for creating simulations that can be used to make high quality predictions. In this context we propose using approximate simulations to build a kernel for use in kernelized machine learning methods, such as support vector machines. The results of multiple simulations (under various uncertainty scenarios) are used to compute similarity measures between every pair of samples: sample pairs are given a high similarity score if they behave similarly under a wide range of simulation parameters. These similarity values, rather than the original high dimensional feature data, are used to build the kernel.ResultsWe demonstrate and explore the simulation-based kernel (SimKern) concept using four synthetic complex systems—three biologically inspired models and one network flow optimization model. We show that, when the number of training samples is small compared to the number of features, the SimKern approach dominates over no-prior-knowledge methods. This approach should be applicable in all disciplines where predictive models are sought and informative yet approximate simulations are available.Availability and implementationThe Python SimKern software, the demonstration models (in MATLAB, R), and the datasets are available at https://github.com/davidcraft/SimKern.Supplementary information Supplementary data are available at Bioinformatics online.

Algorithms to compute the Burrows-Wheeler Similarity Distribution

The Burrows-Wheeler transform (BWT) is a well studied text transformation widely used in data compression and text indexing. The BWT of two strings can also provide similarity measures between them, based on the observation that the more their symbols are intermixed in the transformation, the more the strings are similar. In this article we present two new algorithms to compute similarity measures based on the BWT for string collections. In particular, we present practical and theoretical improvements to the computation of the Burrows-Wheeler Similarity Distribution for all pairs of strings in a collection. Our algorithms take advantage of the BWT computed for the concatenation of all strings, and use compressed data structures that allow reducing the running time with a small memory footprint, as shown by a set of experiments with real and artificial datasets.

A personalised recommendation algorithm based on probabilistic neural networks

Collaborative filtering is widely used in recommendation system. Our work is motivated by the observation that users caught in their attention relationship network, and their opinions about items will be directly or indirectly affected by others through such a network. Based on behaviours of users with similar interest, the technique focuses on the use of their opinions to recommend items. Therefore, the quality of similarity measure between users or items has a great impact on the accuracy of recommendation. This paper proposes a new recommendation algorithm with graph-based model. The similarity between two users (or two items) is computed from the connections on graph with nodes of users and items. The computed similarity measure is based on probabilistic neural networks to generate predictions. The model is evaluated on a recommendation task which suggests that which videos users should watch based on what they watched in the past. Our experimental results on the YouKu and Epinions datasets demonstrate the effectiveness of the presented approach in comparison with both collaborative filtering with traditional similarity measures and simplex graph-based methods and further improve user satisfaction, our approach can better improve the overall recommendation performance in precision, recall and coverage.

A personalised recommendation algorithm based on probabilistic neural networks

Collaborative filtering is widely used in recommendation system. Our work is motivated by the observation that users caught in their attention relationship network, and their opinions about items will be directly or indirectly affected by others through such a network. Based on behaviours of users with similar interest, the technique focuses on the use of their opinions to recommend items. Therefore, the quality of similarity measure between users or items has a great impact on the accuracy of recommendation. This paper proposes a new recommendation algorithm with graph-based model. The similarity between two users (or two items) is computed from the connections on graph with nodes of users and items. The computed similarity measure is based on probabilistic neural networks to generate predictions. The model is evaluated on a recommendation task which suggests that which videos users should watch based on what they watched in the past. Our experimental results on the YouKu and Epinions datasets demonstrate the effectiveness of the presented approach in comparison with both collaborative filtering with traditional similarity measures and simplex graph-based methods and further improve user satisfaction, our approach can better improve the overall recommendation performance in precision, recall and coverage.

A semantic matching engine for web service composition

One of the main assets of service-orientation is composition, which consists of developing higher-level services by re-using well-known functionality provided by other services in a low-cost and rapid development process. However, considerable differences on structural, semantic and technical levels along with the growing number of available web services makes their discovery a significant challenging task. Therefore, services compatibility is an essential pre-requisite to service composition. Measuring the similarity of services is an important and valuable task to get useful information about their compatibility. Similarity measure can be considered as an optimisation step before composing services since it enables to reduce the search time by functionally classifying similar services. This paper presents a practical approach to measure the similarity of web services. Both semantic and syntactic descriptions are integrated through specific techniques for computing similarity measures between services. Formal concept analysis (FCA) is then used to classify web services into concept lattices, and therefore generate a hierarchy of classes of similar web services. Service clustering is used to narrow down the search space and to enable rapid semantic matching of a service request against a large size pool of services. Following this step, a composition engine takes as inputs the set of similar services and the specification of the required service, and generates the candidate composition plans that realise the goal. To determine the composition plan, the composition is reduced to a planning problem.

A semantic matching engine for web service composition

One of the main assets of service-orientation is composition, which consists of developing higher-level services by re-using well-known functionality provided by other services in a low-cost and rapid development process. However, considerable differences on structural, semantic and technical levels along with the growing number of available web services makes their discovery a significant challenging task. Therefore, services compatibility is an essential pre-requisite to service composition. Measuring the similarity of services is an important and valuable task to get useful information about their compatibility. Similarity measure can be considered as an optimisation step before composing services since it enables to reduce the search time by functionally classifying similar services. This paper presents a practical approach to measure the similarity of web services. Both semantic and syntactic descriptions are integrated through specific techniques for computing similarity measures between services. Formal concept analysis (FCA) is then used to classify web services into concept lattices, and therefore generate a hierarchy of classes of similar web services. Service clustering is used to narrow down the search space and to enable rapid semantic matching of a service request against a large size pool of services. Following this step, a composition engine takes as inputs the set of similar services and the specification of the required service, and generates the candidate composition plans that realise the goal. To determine the composition plan, the composition is reduced to a planning problem.

Encrypted Image Retrieval System based on Features Analysis

Abstract – Content-based search provides an important tool for users to consume the ever-growing digital media repositories. However, since communication between digital products takes place in a public network, the necessity of security for digital images becomes vital. Hence, the design of secure content-based image retrieval system is becoming an increasingly demanding task as never before. This paper, presents a mechanism that addresses the secure CBIR as a novel improvement and application for the image retrieval. The proposed system consists of six phases briefly described as follows: first, feature extraction phase, which produces the low-level quantitative description of the image (color and texture) that allows the computation of similarity measures, the definition of the ordering of the images, and the indexing of the search processes. Second, indexing for search process phase, hash table and bloom filter were employed for classification. Third, feature encryption phase, where content protection is performed using a method developed by us (including Chaotic Logistic Map). Fourth, image encryption phase, as security mechanism for CBIR, we combine two research fields in computer science, CBIR and image cryptography, which grow up to meet the trends of security and speed in current computer sciences, chaos and stream cipher systems were applied as an image encryption system. Fifth, the retrieval phase, which provides a subset of images answering the query based on the similarity between images computed over the feature vector extracted from each image. Finally, Relevance feedback phase, a technique that attempts to capture the user’s needs through iterative feedback. Although the system proved its efficiency in search performance (with 88% of average precision), security strength, and computational complexity, it does not mean the optimal system is designed, since some weakness points still can be found that are suggested to be improved as a future work.

Approach for moving object detection using visible spectrum and thermal infrared imaging

Moving object detection is one of the most promising research areas, which is required in different applications, such as video monitoring and surveillance systems, human activity recognition systems, vehicle counting, and anomaly detection. Various methods for object detection using single sensor and a few using multimodal techniques have been reported in the literature. However, such systems fail to handle adverse or challenging atmospheric conditions such as illumination variations, scale and appearance change of objects or targets, occlusions, and camouflaged conditions. We have presented an approach for the detection of moving objects using structural similarity metric (SSIM) and Gaussian mixture model (GMM). SSIM is used to compute similarity between reference mean background frame and foreground frame of visible spectrum (VIS) and thermal infrared (IR) independently. The computation of similarity measure is performed in an image spatial domain. The threshold results of SSIM are fused together using different pixel-level fusion methods such logical “OR,” discrete wavelet transform, and principal components analysis. Temporal analysis is performed to eliminate noise and false positives (unwanted background regions) using GMM on fused results. We have compared the results with recent methods for different complex scenarios and found out that approximately F -measure increases up to 80%. Hence, the proposed method proves to be a robust moving object detection technique in multimodality domain.

MERPSYS: An environment for simulation of parallel application execution on large scale HPC systems

In this paper we present a new environment called MERPSYS that allows simulation of parallel application execution time on cluster-based systems. The environment offers a modeling application using the Java language extended with methods representing message passing type communication routines. It also offers a graphical interface for building a system model that incorporates various hardware components such as CPUs, GPUs, interconnects and easily allows various formulas to model execution and communication times of particular blocks of code. A simulator engine within the MERPSYS environment simulates execution of the application that consists of processes with various codes, to which distinct labels are assigned. The simulator runs one Java thread per label and scales computations and communication times adequately. This approach allows fast coarse-grained simulation of large applications on large-scale systems. We have performed tests and verification of results from the simulator for three real parallel applications implemented with C/MPI and run on real HPC clusters: a master-slave code computing similarity measures of points in a multidimensional space, a geometric single program multiple data parallel application with heat distribution and a divide-and-conquer application performing merge sort. In all cases the simulator gave results very similar to the real ones on configurations tested up to 1000 processes. Furthermore, it allowed us to make predictions of execution times on configurations beyond the hardware resources available to us.

Construction cost prediction on the basis of multiple parameters using case-based reasoning method

Nowadays, case-based reasoning (CBR) can be viewed as an effective and efficient method for cost prediction for building construction. Even if users have not very much experienced all the cases of knowledge base or database, this method makes it possible to retrieve the information of similar or exact cases, which are known to them, for new experiences. However, there are challenges related to the rapid retrieval process from the database library. One of the most important retrieval processes is the computation of similarity measures. With these issues, this research work attempts to build CBR in terms of similarity measuring method based on the concept of Euclidean distance and Chebyshev distance. The novelty of this paper is enhancing the accuracy of cost prediction as well as checking the effectiveness of the CBR model and both are relevant to academia and industry. In order to obtain the result, I have used indigenous tool.

Benchmarking Performance of a Hybrid Intel Xeon/Xeon Phi System for Parallel Computation of Similarity Measures Between Large Vectors

The paper deals with parallelization of computing similarity measures between large vectors. Such computations are important components within many applications and consequently are of high importance. Rather than focusing on optimization of the algorithm itself, assuming specific measures, the paper assumes a general scheme for finding similarity measures for all pairs of vectors and investigates optimizations for scalability in a hybrid Intel Xeon/Xeon Phi system. Hybrid systems including multicore CPUs and many-core compute devices such as Intel Xeon Phi allow parallelization of such computations using vectorization but require proper load balancing and optimization techniques. The proposed implementation uses C/OpenMP with the offload mode to Xeon Phi cards. Several results are presented: execution times for various partitioning parameters such as batch sizes of vectors being compared, impact of dynamic adjustment of batch size, overlapping computations and communication. Execution times for comparison of all pairs of vectors are presented as well as those for which similarity measures account for a predefined threshold. The latter makes load balancing more difficult and is used as a benchmark for the proposed optimizations. Results are presented for the native mode on an Intel Xeon Phi, CPU only and the CPU + offload mode for a hybrid system with 2 Intel Xeons with 20 physical cores and 40 logical processors and 2 Intel Xeon Phis with a total of 120 physical cores and 480 logical processors.

Lọc cộng tác với độ đo tương tự dựa trên đồ thị

Collaborative filtering is a technique widely used in recommender systems. Based on the behaviors of users with similar taste, the technique can predict and recommend products the current user is likely interested in, thus alleviates the information overload problem for Internet users. The most popular collaborative filtering approach is based on the similarity between users, or between products. The quality of similarity measure, therefore, has a large impact on the recommendation accuracy. In this paper, we propose a new similarity measure based on graph models. The similarity between two users (or symmetrically, two products) is computed from connections on a graph with vertices beeing users and products. The computed similarity measure is then used with k – nearest neighbor algorithm to generate predictions. Empirical results on real movie datasets show that the proposed method significantly outperforms both collaborative filtering with traditional similarity measures and pure graph-based collaborative filtering.

Improved Brain Extraction Tool using Marker-Controlled Watershed Segmentation

MRI head scan images contains some non-brain tissues and it is necessary to remove these nonbrain tissues for further processing. The Brain Extraction Tool (BET) is one of the most popular brain segmentation technique which removes the non-brain tissues in MRI head scan volumes. It is a simple and robust brain extraction method. It is a boundary based method and uses deformable surface to detect brain boundaries. Due to its over smoothing capabilities it always under-segment the brain images by including more non-brain tissues in its final segmentation. The aim of this proposed method is to overcome the problems encountered with the existing BET method by using the marker-controlled watershed segmentation. The proposed improved brain extraction tool (IMBET) was tested with 20 volumes of brain images and its performance was compared with the popular existing methods BET, BSE, WAT, HWA and GCUT. The computed similarity measures Dice (D) and Jaccard (J) show that the proposed method outperform the BET and produced better brain extraction result than the existing methods.

The semantic similarity ensemble

Computational measures of semantic similarity between geographic terms provide valuable support across geographic information retrieval, data mining, and information integration. To date, a wide variety of approaches to geo-semantic similarity have been devised. A judgment of similarity is not intrinsically right or wrong, but obtains a certain degree of cognitive plausibility, depending on how closely it mimics human behavior. Thus selecting the most appropriate measure for a specific task is a significant challenge. To address this issue, we make an analogy between computational similarity measures and soliciting domain expert opinions, which incorporate a subjective set of beliefs, perceptions, hypotheses, and epistemic biases. Following this analogy, we define the semantic similarity ensemble (SSE) as a composition of different similarity measures, acting as a panel of experts having to reach a decision on the semantic similarity of a set of geographic terms. The approach is evaluated in comparison to human judgments, and results indicate that an SSE performs better than the average of its parts. Although the best member tends to outperform the ensemble, all ensembles outperform the average performance of each ensemble's member. Hence, in contexts where the best measure is unknown, the ensemble provides a more cognitively plausible approach.