Reviewerly/exHarmony huggingface dataset:
A Comparative Study And Analysis Of Lattice Boltzmann Method And Exemplar Method For Still Color Image Inpainting Technique

Inpainting techniques are used for image restoration and object removal. In which restoration is a complicated and important area of research. Restoration of damaged regions of an image is done with the help of image inpainting techniques, so that the image looks natural, complete and beautiful for human eyes. Lattice Boltzmann Method and Exemplar methods are implemented here in color images which must be inpainted and a comparative analysis of the two methods on its variables are done. Exemplar based approaches perform well for two-dimensional textures. Image restoration is done using Lattice Boltzmann Method and Exemplar method in color image. The LBM scheme can also be considered as a special discretized form of the continuous Boltzmann equation. After implementation of inpainting techniques in the image their signal to noise ratio, image quality and time taken for execution is calculated and compared. From the result we have arrived a conclusion that Lattice Boltzmann Method gives a better physical appearance when compared to Exemplar method in image inpainting of color still images.
Architecture-Centric Bottleneck Analysis for Deep Neural Network Applications

The ever-growing complexity and popularity of machine learning and deep learning applications have motivated an urgent need of effective and efficient support for these applications on contemporary computing systems. In this paper, we thoroughly analyze the various DNN algorithms on three widely used architectures (CPU, GPU, and Xeon Phi). The DNN algorithms we choose for evaluation include i) Unet - for biomedical image segmentation, based on Convolutional Neural Network (CNN), ii) NMT - for neural machine translation based on Recurrent Neural Network (RNN), iii) ResNet-50, and iv) DenseNet - both for image processing based on CNNs. The ultimate goal of this paper is to answer four fundamental questions: i) whether the different DNN networks exhibit similar behavior on a given execution platform? ii) whether, across different platforms, a given DNN network exhibits different behaviors? iii) for the same execution platform and the same DNN network, whether different execution phases have different behaviors? and iv) are the current major general-purpose platforms tuned sufficiently well for different DNN algorithms? Motivated by these questions, we conduct an in-depth investigation of running DNN applications on modern systems. Specifically, we first identify the most time-consuming functions (hotspot functions) across different networks and platforms. Next, we characterize performance bottlenecks and discuss them in detail. Finally, we port selected hotspot functions to a cycle-accurate simulator, and use the results to direct architectural optimizations to better support DNN applications.
Edge Detection in Range Images Using a Modified Canny Filter

Image segmentation is a crucial step in any image analysis process. It consists in preparing the image to make it more usable by an automatic process, such interpreting and understanding its content. In this paper, we introduce a novel method for range image segmentation. The proposed method proceeds by adapting the principle of the Canny filter, commonly utilized for greyscale and colour images, to be applied for range images, where depths in these images should be differently handled. Instead of using raw image data, a new image of angles between normal vectors according some given directions is computed and then used to compute a new gradient image. Canny steps are then applied on the latter image, producing precise and well located edges. Experimentation on real images from the ABW database shows that edges in range images are correctly detected.
Using Knowledge Discovery Techniques to Support Tutoring in an Open World Intelligent Game-Based Learning Environment

This study covers the attempt to incorporate a proposed knowledge discovery framework in an open world Intelligent Game-Based Learning Environment (IGBLE) to further improve the use's experience. Four phases were done in order to come up with the framework: (1) sequential pattern mining using PrefixSpan of user's action log, (2) association rule mining of the generated patterns, (3) extracting generic rules using Informative Generic Base (IGB) algorithm and (4) integrating it to the IGBLE. The framework produced a learned Procedural Task Knowledge (PTK) that served as a mechanism to support tutoring for the selected game. This implies that the new IGBLE incorporated with the framework provided significantly better guidance to the players through recommending good and easy-to-follow sequences of actions.
Application of Discrete Whale Optimization Hybrid Algorithm in Multiple Travelling Salesmen Problem

For the standard whale optimization algorithm cannot directly solve the multiple travelling salesmen problem(MTSP), this paper proposes a discrete whale optimization hybrid algorithm (DWOHA) . A small amount of optimization of ant colony optimization is used to provide some elite individuals for the initial population to reduce the number of global searches. Propose a principal and subordinate chromosome coding method with smaller solution space to reduce the space complexity of the algorithm and improve the efficiency of optimization. Redefine the operation rules to apply the characteristics of MTSP discrete solution space based on the whale's unique location update method. Construct a triangular neighborhood structure to increase the local mining efficiency of the algorithm and improve the convergence accuracy. Through simulation experiments on several test sets, the results are compared with other algorithms which proves that DWOHA has an excellent performance in solving MTSP.
Performance Comparison of Genetic Algorithm and Particle Swarm Optimization in Solving Product Storage Optimization

Product storage provides considerable influence in obtaining profit for traders in selling products. However, the existing product storage is inefficient because products with high selling prices are stored in large quantities even though this does not necessarily give a high profit because it can also provide high losses. Traders must be able to determine the number of products stored at high selling prices and smaller losses. We propose Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) as an optimization method. We use GA with real code representation, one cut point crossover, insertion mutation and elitism selection. We also use PSO to solve the same problem. Both GA and PSO have been proved that can solve optimization problem. We compare which performance of them is better based on profit gained, fitness value and computational time. The experiment result shows that with the same problem and data set, PSO is better in gaining profit than GA but it needs longer computational time than GA.
A Bi-Level Approach for Hyper-Parameter Tuning of an Evolutionary Extreme Learning Machine

One of the critical challenges in the implementation of machine learning algorithm is hyperparameter optimization as performance of any machine learning model is sensitive to the setting of their hyperparametersr. Evolutionary Algorithms (EA) is widely used for hyperparameter optimization due to its efficient intellectual tuning strategies. The time complexity is appreciably changed with respect to the size of dataset used for training. On the other hand, large dataset is required for pursuing the better prediction. In this paper, we have proposed a methodology namely Bi-Level Evolutionary Extreme Learning Machine(bL-EELM) based on bi-level programming approach for tuning hyperparameter of an Evolutionary Extreme Learning Machine(EELM). we divided our problem into two levels. We consider an E-ELM module as a lower level optimization problem. In our upper level we placed a evolutionary module whose task is to create a population of hyperparameters and feed to lower Level as an input of EELM. We have chosen ten benchmark classification problems for the experiment and analysis of our proposed approach. Experimental results proofs that our proposed approach has better prediction accuracy as well as generalization performances compare to Extreme learning machine(ELM) and EELM.
Integration and Evaluation of Quantum Accelerators for Data-Driven User Functions

Quantum computers hold great promise for accelerating computationally challenging algorithms on noisy intermediate-scale quantum (NISQ) devices in the upcoming years. Much attention of the current research is directed towards algorithmic research on artificial data that is disconnected from live systems, such as optimization of systems or training of learning algorithms. In this paper we investigate the integration of quantum systems into industry-grade system architectures. In this work we propose a system architecture for the integration of quantum accelerators. In order to evaluate our proposed system architecture we investigated various data-driven functions for various accelerators, including a classical system, a gate-based quantum accelerator and a quantum annealer. The data-driven function predict user preference and is trained on real-world data. This work also includes an evaluation of the quantum enhanced kernel, that previously was only evaluated on artificial data. In our evaluation, we showed that the quantum-enhanced kernel performs at least equally well to a classical state-of-the-art kernel when simulated. We also showed a low reduction in accuracy and latency numbers within acceptable bounds when running on the gate-based IBM quantum accelerator. We therefore conclude it is feasible to integrate NISQ-era devices in industry-grade system architectures in preparation for future advancements in quantum hardware.
Routing Optimization for High Speed Photon State-Channel Architecture

Compared with mainstream payment systems such as Visa, the biggest obstacles to blockchain based technologies such as Bitcoin and Ethereum becoming mainstream means of payment in human daily life lie in their low transaction rate and slow response time. A potentially promising solution is the state channel architecture. State channels are more general than payment channels, which provide off-chain transaction settlement without much need for expensive on-chain operations. We investigate the routing optimization problem for Photon, a state channel network for Spectrum, which is similar to Raiden being a 2nd layer state channel network on top of Ethereum. Yet Photon possesses interesting characteristics that Raiden lacks. Extensive simulations show our proposed algorithm can effectively achieve high success rate and throughput with low deposit lockup.
Semantic Traffic Light Understanding for Visually Impaired Pedestrian

A visually impaired pedestrian normally requires information of walking/stop signs with time to decide whether it is available to cross a road on crosswalk. Since traffic lights are installed in verity formats such as, man figure, color and numerical characters, it is complicate to simply understand the meaning. This paper proposes a method of semantic traffic light understand for a visually impaired pedestrian. In the method, a picture of traffic light is initially assumed to extract and type of traffic light are first classified into man figure, color and numerical characters by some classification tools such as signature. In man figure case, color and man gesture are classified and both information are decided base on Fuzzy logic. In the case of numerical characters, they are first enhanced and then classified by a classifier. The experiments have been performed with 200 samples of pedestrian traffic lights and results reveal acceptable accuracy.
Feature Selection for Anomaly Detection in Call Center Data

In this study, we present the process of designing machine learning models for the detection of call center agent malpractices. Based on the features extracted from audio recordings of a given telephone conversation, appropriate one-class support vector machine, isolation forest, and multivariate Gaussian models are trained, evaluated and compared in order to determine the best use case. The labeled data used in the experiments was obtained from a real call center and the results obtained indicate that the system is usable in a real-world scenario. The accuracy of used machine learning models are validated by using the F1 score as a metric.
Breast Microcalcification Visualization Using Pseudo-Color Image Processing

In this study, we aimed to find an effective method for improving visibility of microcalcifications on breast mammograms by using pseudo-color image processing techniques. We had considered twelve pseudo-coloring algorithms or color scales including autumn, bone, cool, copper, hot, rainbow, pink, spring, summer, winter, springtime-1, and springtime-2 color scales. Experiments were performed on 20 mammograms including 10 benign images, and 10 malignant images. The resultant images of using pseudo-color image processing with the twelve color scales were evaluated by radiologists. They determined that at what degree the pseudo-color images are helpful for improving visibility of micro-calcium spots on the mammograms by using 5-level score (5=very high, 4=high, 3=neutral, 2=low, 1=very low). The results using Ian McDowell's rating scale showed that the performances of bone and autumn color scales are very good. The performances of copper, winter, summer, springtime-1, pink, and hot color scales are good, and the performances of springtime-2, spring, cool, and rainbow color scales are fair. The bone color scale, which varied from black, through gray-blue, to white, outperformed other color scales, followed by autumn color scale which the important benefit is that the micro-calcium spots were illustrated shinier and clearer.
Explaining Machine Learning

Multi-layered Perceptrons, also widely known as Artificial Neural Networks, are one of the most used subsets of Machine Learning systems. They consist of a series of layered and interconnected Perceptrons, also referred to as Artificial Neurons, where each layer can perform different types of transformations on data. Because Multi-layered Perceptrons can consist of 10 to 30 layers, these transformations quickly become extremely complex and incomprehensible and these systems therefore become black boxes. This, coupled with the increasing presence of these systems in our lives, has led us to focus on the necessity of their human-understandability and transparency. As Information Visualization has proved to be one of the most effective ways of explaining datasets, our hypothesis is that it can be equally useful for understanding the processes of decision-making in Machine Learning systems. We approached this by developing an algorithmic data-driven visualisation of the most elementary processing element in these systems: the Perceptron. Our latest iteration, which received a user interface, has shown us to allow debugging, generating human expectation of the Perceptron's decision-making. This interdisciplinary and practice-based research focused on investigating the inner workings of a Perceptron and was developed through iterative cycles of research, design and implementation.
A Novel Ontology-based Approach for Describing Cloud Services

The development of Cloud computing led to an increase in the number of services ready for use and therefore the number of consumers' requests. Cloud providers advertise the description of their offered services in different formats on the Internet. This makes the discovery and selection of services more difficult for users. Thus, a uniform description of Cloud services would be needed. The aim of this study is to provide a new ontology for uniform description of Cloud services coming from heterogeneous sources. The proposed model takes into account the different relationships and dependencies between services across Cloud's layers. These dependencies are represented by the different constraints of composition and deployment. To validate our proposal, qualitative and quantitative evaluations are conducted. The qualitative evaluation is carried out by making comparisons with both state-of-the-art works and the Bitnami platform according to some criteria. For the quantitative evaluation, comparisons are performed with the output results specified by users. Globally, the results are promising and encouraging.
Partial migration technique for GPGPU tasks to Prevent GPU Memory Starvation in RPC‐based GPU Virtualization

Summary Graphics processing unit (GPU) virtualization technology enables a single GPU to be shared among multiple virtual machines (VMs), thereby allowing multiple VMs to perform GPU operations simultaneously with a single GPU. Because GPUs exhibit lower resource scalability than central processing units (CPUs), memory, and storage, many VMs encounter resource shortages while running GPU operations concurrently, implying that the VM performing the GPU operation must wait to use the GPU. In this paper, we propose a partial migration technique for general‐purpose graphics processing unit (GPGPU) tasks to prevent the GPU resource shortage in a remote procedure call‐based GPU virtualization environment. The proposed method allows a GPGPU task to be migrated to another physical server's GPU based on the available resources of the target's GPU device, thereby reducing the wait time of the VM to use the GPU. With this approach, we prevent resource shortages and minimize performance degradation for GPGPU operations running on multiple VMs. Our proposed method can prevent GPU memory shortage, improve GPGPU task performance by up to 14%, and improve GPU computational performance by up to 82%. In addition, experiments show that the migration of GPGPU tasks minimizes the impact on other VMs.
A multi-dimensional view of collocations in academic writing

This chapter discusses the identification of the major sets of interrelated collocations in academic writing across different disciplines, or dimensions of collocation. A corpus of textbooks and research articles from Social, Behavioral and Economic Sciences, containing 10.6 million words across 230 texts was analyzed using a collocational multi-dimensional analysis framework (Berber Sardinha 2017). Results show four dimensions of collocation for Social, Behavioral and Economic Sciences, namely: (1) Human Nature, Culture and Research Methods vs. Economics; (2) Human Evolution and Society; (3) Business and Finance; and (4) Statistics. The chapter highlights a methodology for identifying collocational patterns through multi-dimensional analysis, and contributes to a better understanding of academic writing in Social, Behavioral and Economic Sciences.
Intelligent Image Retrieval Using Texture and Color Features

This study proposes an effective method for content-based texture image retrieval. We focus on the analysis of texture and color features in the Lab color space to achieve effective image retrieval. Five texture features are computed in the “L” component, i.e. coarseness, contrast, busyness, complexity and strength. In addition, two color features are extracted from the “ab” components. Experiment results show that the proposed method based on texture and color features is capable of achieving more effective image retrieval.
Cloud Data Monitoring Management and Visual Application System Based on Spring Boot

The system has developed cloud-based data monitoring management and visualization applications based on Spring Boot. The springboot micro-service framework make system configuration and monitoring easier. In order to adapt to various page layouts, the front-end page used HTML5, CSS, JavaScript, bootstrap and other technologies. The system realized multiple functions of data management, data downloading and Baidu map visualization. The use of Baidu maps has enabled the visualization of more intuitive and distinctive air quality data. The program has been deployed in the Ali Cloud, which facilitates users to remotely access web projects. The combination of Spring Boot framework and AJAX has achieved complete separation of front and back ends, which makes the system have good stability, real-time and high efficiency.
On Polyhedral Estimates of Reachable Sets of Discrete-Time Systems with Uncertain Matrices and Integral Bounds on Additive Terms

We consider discrete-time systems of bilinear type for the case when interval bounds on the coefficients of the system are imposed, additive input terms are restricted by integral non-quadratic constraints, and initial states belong to given sets, which are assumed to be parallelepipeds. An approach for estimating the reachable sets is presented. It is based on considering reachable sets in the “extended” space and constructing external and internal estimates of them in the form of polytopes of some special shape. The specific cross-sections of these polytopes provide the parallelepiped-valued or parallelotope-valued estimates of the reachable sets in the “initial” space. Evolution of the estimates in the “extended” space is determined by recurrence relations. All the estimates can be calculated by explicit formulas. The main attention is paid to internal estimates. Illustrative examples are presented.
Order statistics and random matrix theory of multicarrier continuous-variable quantum key distribution

Summary In a multicarrier continuous‐variable quantum key distribution (CVQKD) protocol, the information is granulated into Gaussian subcarrier CVs and the physical Gaussian link is divided into Gaussian sub‐channels. Here, we propose a combined mathematical framework of order statistics and random matrix theory for multicarrier continuous‐variable quantum key distribution. The analysis covers the study of the distribution of the sub‐channel transmittance coefficients in the presence of a Gaussian noise and the utilization of the moment generation function (MGF) in the error analysis. We reveal the mathematical formalism of sub‐channel selection and formulation of the transmittance coefficients and show a reduced complexity progressive sub‐channel scanning method. We define a framework to evaluate the statistical properties of the information flowing processes in multicarrier CVQKD protocols. Using random matrix theory, we express the achievable secret key rates and study the efficiency of the adaptive multicarrier quadrature division‐multiuser quadrature allocation (AMQD‐MQA) multiple‐access multicarrier CVQKD. The proposed combined framework is particularly convenient for the characterization of the physical processes of experimental multicarrier CVQKD.
Improved GAC Model-based Pulmonary Artery Segmentation of CTPA Image Sequence

Aiming at the problem that the traditional geodesic active contour (GAC) model is prone to produce boundary leakage and cannot segment adaptively, this paper proposes an improved GAC model and realizes the automatic segmentation of pulmonary artery in computed tomographic pulmonary angiography (CTPA) image sequence. Firstly, the variable velocity C(I) is used to replace the constant velocity c of the traditional GAC model, and base on the improved GAC model with C(I) to segment the first frame image of the CTPA sequence to obtain a convergent pulmonary artery contour. Secondly, the grayscale information of the target area is used to improve C(I) to V(I), which makes its direction variable. Finally, based on the improved GAC model with V(I), automatic segmentation of pulmonary artery in subsequent images is realized. Among them, the initial contour of each subsequent image is the final contour of the previous image. These two improvement strategies can solve the problem that the model is easy to be over-segmented and drive the curve evolve adaptively inward or outward to the target contour. Experimental results show that the proposed algorithm can realize automatic segmentation of pulmonary artery, and has a high coincidence rate with the results of physician segmentation.