Reviewerly/exHarmony huggingface dataset:
Knowledge Model Based on Destination Management System Framework For Chinese Gay Tourist in Bangkok by Using Knowledge Engineering

This study is to describe how the knowledge model based on the Destination Management System framework by using Knowledge Engineering can develop the Chinese gay tourist's travelling efficiency when they carry the gay-leisure aspect at the Bangkok. Knowledge engineering methodology provides the process to identify the problem and elicit the knowledge from the experts' experience, research articles, and documents. The knowledge model based on the Destination Management System(DMS) framework matches the needs and requirements of the knowledge worker. The ontology identification and development to organize, distribute and utilize the knowledge were followed by the knowledge worker and expert needs which under the framework of the DMS.
Rate allocation with near-optimal rate-distortion performance for JPEG-LS

Due to performance and low complexity, JPEG-LS become the standard of lossless and near-lossless image compression. However, it can’t accurately control code rate when it is applied in near-lossless compression. This paper is thus devoted to rate control for near-lossless image compression with JPEG-LS. A model of coding bit-rate under a high bit-rate with respect to mean absolute difference (MAD) and coding quantization parameters for prediction coding is first proposed. Then a rate control method for near-lossless compression is designed based on the model for JPEG-LS. In the process of a specific image coding, to control the bit-rate, quantitative parameters are adjusted piecewise based on the model. Experiments show that the proposed method can make final code rate close to a preset rate. It’s different from other methods that quantitative parameter fluctuating within a wide range can be avoided because of the accurate model of bit-rate. As a result, the proposed control method can achieve approximate optimal rate-distortion performance.
FRI Modelling of Fourier Descriptors

Fourier descriptors are used to parametrically represent closed contours. In practice, a finite set of Fourier descriptors can model a large class of smooth contours. In this paper, we propose a method for estimating the Fourier descriptors of a given contour from its partial samples. We take a sampling-theoretic approach to model the x and y coordinate functions of the shape and express them as a sum of weighted complex exponentials, which belong to the class of finite-rate-of-innovation (FRI) signals. The weights represent the Fourier descriptors of the shape. We use the FRI framework to estimate the shape parameters reliably from noisy and partial measurements. We model non-uniformities in sampling using the sampling jitter model and employ a prefiltering process to reduce the effect of measurement noise and jitter. The average sampling interval is estimated by a block annihilating filter, which is then followed by the estimation of Fourier descriptors using least-squares fitting. We demonstrate the robustness of the proposed algorithm to noise and sampling jitter. Monte Carlo performance analysis shows that the variances of the estimators are close to the Cramér-Rao lower bounds. We present results for outlining shapes in synthetic as well as real images.
A remark on quantum cloning

Physical entities are not truly teleported in so-called quantum teleportation, only their states are. Similarly, physical entities are not truly cloned in so-called quantum cloning, only their states are. The celebrated no-cloning theorem tells us that a universal cloning machine, able to perfectly clone arbitrary states, cannot be built. However, this only applies to discernible quantum entities. For the indiscernible ones, which are already perfect clones of one another, the additional cloning of their state is in fact a natural process, which happens in a spontaneous way, as soon as they are allowed to interact.
Postanalysis in Adjustment Computations

In this last chapter of the book, the types of errors I and II are defined for the reader to further explain the DIA of the postadjustment techniques. The chapter presents the blunder detection methods of data snooping, robust estimation, and RANSAC. Solved numerical examples with MATLAB codes are also given.
Risk Assessment Approach to Estimate Security of Cryptographic Keys in Quantum Cryptography

Increased interest in quantum cryptography is observed in recent years. Although this technique is characterized by a very high level of security, there are still challenges of quantum key distribution that should be solved. One of the most important problem remains security mechanisms for the key distillation process which can be effectively controlled by end users. This article presents a new idea for security assessment of cryptographic key based on the risk management in quantum cryptography. This proposal assumes the estimation of risk level using two components: likelihood and impact. The likelihood can be defined by the probability of eavesdropping during the quantum bit estimation. The impact is associated with the effect of key reduction in the all steps of key distillation process. Using this novel approach end users of quantum cryptography will be able to control both efficiency and security level of cryptographic keys.
To Improve the Web Personalization Using the Boosted Random Forest for Web Information Extraction

Background: Web personalization is kind of method that is applied to modify a web site to suit the exact needs of the users, achieving the advantage of data accomplished for the understanding the directional conduct of users concerning inclusion of more materials in the web framework. Methods: In this paper an Finding Large Itemsets produce all blends of things that comprises of a bolstering an incentive over a client illustrated least support. The total number of exchanges which consists of the itemset is nothing but the support for an itemset. For the purpose of ranking the list, the calculated values with the user ranked list are offered to the fuzzy-bat. Results: The result shows that the proposed methodology that perceives the challenge of mining affiliation presides over in an organization of exchanges could be defined as the problem of producing all the affiliation henceforth making a decision which would possess bolster esteem more significant in comparison to a client that exemplified least support as well as certainty esteem more noteworthy than a client characterized least certainty. Conclusion: This paper comprehensively describes the our proposed the preciseness of the system is in contrast to a great extent with the present method for the varied questions that has been provided. The computed esteems with the client positioned rundown are provided to the fluffy bat to rank the rundown. The proposed technique has been compared with the prevailing fuzzy technique with regard to the response time as well as the precision.
A Novel Super-resolution Method Based on Patch Reconstruction with Simk Clustering and Nonlinear Mapping

In this paper, we propose a patch-wise super-resolution (SR) method that combines an external-sample classification tree and a nonlinear-mapping learning stage to simultaneously guarantees reconstruction quality and speed at the stage of patch representation and mapping. We use the low-resolution (LR) to high-resolution (HR) mapping kernel of each patch-pair sample (called SIMK) to complete classification by binary tree branching and provide reasonable training sets for mapping-learning. Then a high accuracy but low cost lightweight network is learned for each tree node to choose the reasonable branch path for the testing LR patches. In the mapping-learning stage, the nonlinear mapping for each class is represented as a full-connected network, which provides satisfying generalization ability for LR patch reconstruction. Comparing with state-of-the-art methods, our approach achieves real-time (>24fps) SR of realistic vision and high quality for different upscaling factors.
Entropy-based Attribute Clustering

Data clustering techniques have been well-studied for several decades. Different from traditional data, attribute clustering focuses on grouping values of attributes or features instead of grouping similar transactions. Similar attributes will be clustered into the same group and different attributes will be clustered into different groups. Two attributes are considered to be similar if they appear in the similar set of transactions. In this paper, we proposed a new improvement to improve the best-known parameter free attribute clustering algorithm. From our intensive experiments on 10 real-world datasets, the results demonstrate that our proposed method not only achieve higher quality clusters but is also faster than the best-known parameter-free attribute clustering method.
A Study on the Vulnerability Assessment for Digital I&amp;C System in Nuclear Power Plant

NPP (Nuclear Power Plant) Operators have approached the problem of cyber security by simply keep up with the never-ending stream of new vulnerability alerts from suppliers and groups like ICS-CERT. Keeping Cyber Security Compliance, NPP Owner must patch vulnerabilities according to their CVSS Score. In fact, NPP Owner often has to deal with hundreds of vulnerabilities, which is not a trivial task to carry out. Unfortunately, the CVSS Score has been shown to be poor indicator for actual exploitation in NPP. This paper analyzes Vulnerability Assessment Methodology about Critical digital asset in NPP. And then give an effective methodology. It approaches the cyber security regulations of NPP from a technical vulnerability point of view, where any given Critical Digital Asset can be assessed for vulnerabilities.
MBT in agile/lightweight processes: a process‐centred review

This study presents a process-driven view on the use of model-based testing (MBT) in agile/lightweight processes. It argues that process-related aspects of MBT and agile processes should be explicitly considered in any practical MBT approach intended for use in agile development (AD). It demonstrates that an effective MBT approach for lightweight processes has to specify how MBT activities are integrated into a development process, how and when MBT artefacts are generated in relation to other development artefacts, and who would carry out MBT activities. Accordingly, a set of integration criteria is introduced for complete incorporation of MBT into agile/lightweight processes. The integration criteria demonstrate the specific characteristics of an appropriate MBT process for AD processes, and help identify the benefits and shortcomings of existing methods on the use of MBT in such processes. Evaluation of existing works based on the proposed integration criteria shows that they have all focused on minimal modelling, and only one method has considered the ‘evolution’ of test models and the ‘reuse’ of test cases, whereas ‘evolution’ and ‘reuse’ are essential characteristics of agile processes, which have to be addressed in any MBT approach intended for such processes.
Visualisasi Data Penjualan dan Produksi PT Nitto Alam Indonesia Periode 2014-2018

PT Nitto Alam Indonesia is a Manufacturing company engaged in screw manufacturing services. The company has a total of 134,252 rows sales and production data, but the data has never been analyzed so that the information is still not fully explored. This research proposes to make a visualization in the form of a dashboard containing sales and production data at PT Nitto Alam Indonesia in 2014 – 2018. It will be shown by using visual data mining (VDM) method with Tableau Software tools. The purpose of this study was to assist PT Nitto Alam Indonesia in analyzing sales and production data to find information that had never been explored before. The results of this study are that the patterns of sales and production can be known for the last 5 years. The sales pattern is included in the type of cycle with the highest sales peak located in the fourth quarter in 2017 at 6.552%. In addition, the performance of sales and production from 2014 to 2018 increased consistently. This research has been validated by applying User Acceptance Tests at PT Nitto Alam Indonesia.
The Future of Snapchat: A Mathematical Model

Saudi Arabia has become one of the leading top five countries based on the number of Snapchat users as of October 2018.In this project, we build a novel mathematical model to explore the future of Snapchat in general and in Saudi Arabia particularly.The model incorporates the trend of "famous Snapchatters" that is highly observed in Saudi Arabia.The model is governed by a system of nonlinear differential equations.We analyze the system qualitatively and numerically.As a result, three equilibrium points are obtained.By considering their stability, we outline different possible scenarios for the future of Snapchat.Moreover, parameter analysis is performed to investigate key parameters in the model.Furthermore, an online survey is conducted to estimate the values for the parameters in the model to explore which scenario is likely to happen in Saudi Arabia.
Educational environment: concept and content

This article deals with the analysis of the educational environment. We considered main definitions of this term, gave its characteristic and its functions were determined.
Multiscale Structure Tensor Total Variation for Image Recovery

This paper proposes multiscale structure-tensor total variation (MSTV) for image recovery. Gradient vectors in local patches usually have similar directions, and thus each local gradient matrix (the set of gradient vectors) tends to be low rank. STV introduces this property by calculating the sum of nuclear norms from all the 10-cal gradient matrices over the input image. By STV regularization, fine textures are recovered efficiently. However, since STV only considers differences of vertically and horizontally adjacent pixels, if neighboring samples are not reliable due to severe degradation, a latent image cannot be recovered efficiently. In this work, we assume that, for any two target pixels in a local patch, two vectors consisting of multiple differences not only between each target and adjacent pixels but also each target and further distant pixels exhibit a similar direction. According to this assumption, our MSTV firstly applies wavelet-based multiscale decomposition to vertical/horizontal gradient vectors and then evaluates the sum of nuclear norms of all the local wavelet coefficients. Experimental results show that the MSTV improves both numerical reconstruction error and subjective visual quality, compared with the conventional STV.
A survey of image corner detection methods

In this paper, we present an overview of various corner detection approaches that have been proposed in the last four decades. Corner detection algorithms can be broadly divided into intensity-based, contour-based and model-based methods. Intensity-based methods are mainly based on measuring local intensity variation of image. Contour-based methods find corners by analyzing the shape of edge contour. Model-based methods extract corners by fitting the local image into a predefined model. Furthermore, the robustness of eight different popular detectors are evaluated using the average repeatability and localization error criterions.
Continuous Cartesian Genetic Programming with Particle Swarm Optimization

Cartesian Genetic Programming (CGP) is a type of Genetic Programming, which uses a sequence of integers to represent an executable graph structure. The most common way of optimizing the CGP is to use a simple evolutionary strategy with mutations, which randomly changes the integer values of integer sequence. We propose an alternative genotype-phenotype mapping procedure for CGP allowing usage of real-valued numbers in genotype. Novel representation allows continuous transition between various functions and inputs of each given node (hence the name, Continuous CGP), which means, that the optimization of CGP individual is transformed from combinatorial optimization problem to continuous optimization problem. This allows leveraging various metaheuristic optimization algorithms. In this paper, we present results obtained by Particle Swarm Optimization algorithm, showing that continuous representation is able to outperform classic CGP in some benchmarks and provides competitive results with one of the best performing symbolic regression systems in literature.
Research on the Tools of Clone Code Refactoring

Clone code is the code fragment that is identical or similar in syntax or semantics, which has great impact on software development and maintenance. According to the large amount of clone code in software and its complex changes, researchers have proposed many methods to eliminate the harmful clone code, in which refactoring is an effective measure. In this paper, concepts of clone code and refactoring are introduced firstly. And then the main methods of current clone code refactoring are compared and analyzed, so the related tools of clone code refactoring can be elaborated and their advantages and disadvantages are summarized. At last, the shortcomings and limitations of the clone code refactoring are discussed.
The Good, the Bad, Algorithmic Noise Tolerance (Ant), the Ugly

Computational units implemented on nanoscale physical substrates are susceptible to errors that can be catastrophic if not mitigated. Statistical error compensation techniques have become prevalent to safeguard computational units against such hardware-failures. Algorithmic Noise Tolerance (ANT) is one such technique that utilizes a low-fidelity replica unit to detect and bypass such failures occurring within the primary (main) computational unit. Connections between ANT and the binary hypothesis testing as well as the information theoretic CEO problem have been explored for sub-exponential error profiles, quadratic and logarithmic distortion functions. However, there exist fundamental performance limits of ANT approach even without such model-dependent restrictions. The purpose of this paper is to explore fidelity-dependent conditions that are universal over the statistical properties of the computational units under which, the overall performance of ANT is arbitrarily close to the fundamental limits.
Infrared target tracking algorithm combining deep features and gradient features

Due to the low contrast between the target and the background of the infrared sequence image, the edge of the image is blurred and the dynamic range of the gray level is small, what features is used to describe the target becomes the key to tracking. Deep features and gradient features are the main features of most current tracking algorithms. However, the target semantic of deep feature extraction pays attention to intra-class classification, ignores intra-class differences, and is easily interfered by similar background (distractor); gradient features as a local area feature, it is not susceptible to background interference, but it cannot adapt to the dramatic deformation of the target. Based on the complementarity of these two features, this paper proposes an infrared target tracking algorithm that combines deep features and gradient features. In this paper, deep features and gradient features are used to represent the semantic and local structure of the target respectively, which enhances the ability to represent arbitrary targets. Next, the tracking model established by different features further improves the robustness of tracking. Finally, this paper establishes a model mutual aid mechanism, and uses the complementarity between the deep feature tracking model and the gradient feature tracking model to accurately target. In the experiment, this paper selects the latest infrared video tracking database (VOT-TIR2016) to verify the effectiveness of the proposed algorithm. The results show that compared with the current mainstream tracking algorithm, the algorithm achieves a 3.8% improvement in accuracy and a 4.3% improvement in success rate, it can effectively handle the effects of similar background and deformation in tracking.
Unshuffling Permutations: Trivial Bijections and Compositions

Given permutations $$\pi $$ , $$\sigma _1$$ and $$\sigma _2$$ , the permutation $$\pi $$ (viewed as a string) is said to be a shuffle of $$\sigma _1$$ and $$\sigma _2$$ , in symbols , if $$\pi $$ can be formed by interleaving the letters of two strings $$p_1$$ and $$p_2$$ that are order-isomorphic to $$\sigma _1$$ and $$\sigma _2$$ , respectively. Given a permutation $$\pi \in S_{2n}$$ and a bijective mapping $$f : S_n \rightarrow S_n$$ , the f-Unshuffle-Permutation problem is to decide whether there exists a permutation $$\sigma \in S_n$$ such that . We consider here this problem for the following bijective mappings: inversion, reverse, complementation, and all their possible compositions. In particular, we present combinatorial results about the permutations accepted by this problem. As main results, we obtain that this problem is $$\mathsf {NP}$$ -complete when f is the reverse, the complementation, or the composition of the reverse with the complementation.
Corpora and lexis

Sciendo provides publishing services and solutions to academic and professional organizations and individual authors. We publish journals, books, conference proceedings and a variety of other publications.
Study of functioning of relay protection system under regular check conditions

An analytical model of the operation of the relay protection of power systems is presented, which takes into account such types of failures as unwanted operation, failure to operate, as well as defects dangerous from the point of view of unwanted operation and failure to operate. Operability checks of relay protection of power systems are conducted with a constant period. The listed events can be divided into two groups: random and regular ones. The presence of random and regular components of events of recovery of relay protection of power systems can be correctly taken into account in the framework of the apparatus of the theory of Markov processes. The model is based on the description of the process of operation of relay protection of power transmission line by a semi-Markov process. The functioning of the system in time is presented in the form of cycles. The cycle of the functioning of the system consists of a subset, where the system is functioning and verifi ed, and a subset, in which it is restored. The model is implemented in a graph with 9 states. Probabilities of events describe the process of changing states on a discrete set of states of relay protection of the power system. The probability of a change of states is the initial characteristic of a semi-Markov process. This model has enabled to obtain the dependence of operation and reliability parameters on the frequency of regular checks. It is established that the frequency of regular checks with the exponential distribution law overstates the value of the unavailability factor, since the time of the onset of a periodic check is greater than the mathematical expectation of a given value of the periodic check under a random distribution law. With a signifi cant time between checks, or in absence of periodic checks, the unavailability factor tends to a value that does not depend on the way of setting the time between periodic checks.