Reviewerly/exHarmony huggingface dataset:
Differential Evolution with Cluster-Based External Archive and Local Search for Multimodal Optimization

One of the main objectives in multimodal optimization is to find multiple optima solutions in a search space. Hence, population-based metaheuristics are suitable for this class of problems but their loss of diversity while converging may become a problem when tracking multiple optima. In this paper, we propose the use of a cluster-based external archive maintenance strategy along with the jDE algorithm, namely NCjDE-HJ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ar</sub> . The DBSCAN algorithm is employed to group candidate solutions in an external archive representing multiple peaks that will feed the Hooke-Jeeves local search algorithm. Also, the Michalewicz mutation strategy is applied to refine the solutions found by the jDE algorithm. The proposed approach is compared with five state-of-the-art algorithms in terms of peak ratio and the results obtained show that the proposed modifications favor the finding more multimodal peaks.
Image Regularization with Total Variation and Optimized Morphological Gradient Priors

For image restoration, an image prior that is obtained from the morphological gradient has been proposed. In the field of mathematical morphology, the optimization of the structuring element (SE) used for this morphological gradient using a genetic algorithm (GA) has also been proposed. In this paper, we introduce a new image prior that is the sum of the morphological gradients and total variation for an image restoration problem to improve the restoration accuracy. The proposed image prior makes it possible to almost match the fitness to a quantitative evaluation such as the mean square error. It also solves the problem of the artifact due to the unsuitability of the SE for the image. An experiment shows the effectiveness of the proposed image restoration method.
Alpha-DBL: A Reasonable High Secure Double-Block-Length Hash Function

We propose a new double-block-length compression function which is called Alpha-DBL. This scheme using two parallel secure single block length schemes based on a block cipher with 2n-bits key and n-bits block size to compress a 3n-bits string to a 2n-bits one. We show that Alpha-DBL scheme attains nearly optimal collision security and preimage security bounds (up to 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> and 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2n</sup> queries for finding a collision and a preimage, respectively). More precisely, for n = 128 no adversary making less than 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n-1.27</sup> = 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">126.73</sup> queries can find a collision with probability greater than 1/2. With our knowledge, this collision security bound is better than other of such a compression function. In addition, we give a preimage security analysis of Alpha-DBL that show security bound of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2n-5</sup> = 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">251</sup> queries for n = 128. Using this scheme in the iterated hash function construction can preserve the collision resistance security and the preimage resistance security.
Fast Serial Iterative Decoding Algorithm for Zigzag Decodable Fountain Codes by Efficient Scheduling

Fountain codes are erasure correcting codes realizing reliable communication systems for the multicast on the Internet. The zigzag decodable fountain (ZDF) codes are one of generalization of the Raptor codes, i.e., applying shift operation to generate the output packets. The ZDF codes are decoded by a two-stage iterative decoding algorithm, which combines the packet-wise peeling algorithm and the bit-wise peeling algorithm. By the bit-wise peeling algorithm and shift operation, ZDF codes outperform Raptor codes under iterative decoding in terms of decoding erasure rates and overheads. However, the bit-wise peeling algorithm spends long decoding time. This paper proposes fast bit-wise decoding algorithms for the ZDF codes. Simulation results show that the proposed algorithm drastically reduces the decoding time compared with the previous algorithm.
INTERCULTURAL COMMUNICATIONS AS A SCIENTIFIC AND PROFESSIONAL PHENOMENON

In the course of a theoretical analysis, the concepts of “communication” and “intercultural communication” are clarified, the relevance of intercultural professional and scientific - pedagogical communications in an information society, expanding the scale of intercultural interaction of teachers, is revealed. The necessity of the development of intercultural communicative competence of the teacher as a result of the development of intercultural communications in professional - pedagogical activity is substantiated.
Visualisation of a physical model of interacting of hard objects in a computer game

Abstract The problem of this research consists of relevant need to develop a physical model of interaction of hard objects, and herewith physical modelling of interaction of hard objects is often used in computer games. The purpose of this research is visualization of a physical model of interaction of hard objects in a computer game. Methodology of this research consists of analysing of domestic and foreign authors’ scientific researches: method of system-information analyse, method of mathematical and informational modelling: method of computer test. As a result of research the existing methods realizing collisions of objects in computer games are analyzed; the model of a game engine is developed. The algorithms in this model were visualised and tested. In conclusion, there are inferences, which confirm that dynamics of an absolute hard and deformabale agents, modelling of gas and liquid are basic part of any physical process.
Management Model: Employee Database model for Spatio-Temporal Relationship

ST data is identified with a significant number of the issues for example, satellite pictures, climate maps, transportation system, etc. Besides, this data is normally not fixed and can modify over the time. Along these lines the idea of this sort of information are large, examining information is a perplexing errand Having a powerful structure respects to quick information changing is one of the most significant requests in STdata. ST data have exceptional connections concerning spatial and temporal qualities. The two kinds of information are perplexing as far as their various properties and the progressions displayed after some time. The idea of this information model is motivated from the idea of hair which has explicit properties and its development over the time. So as to have better looking and quality, the information is expected to keep up over the time, for example, brushing, cutting, shading, covering, cleaning and so on. The data model is created dependent on the current relational and OO data models. An data model that can expand the exhibition of data storage and request reactions from a STframework is requested. The structure of the connections between STdata copies the natural structure of the hair, which has a 'Root' that are spatial qualities and a 'Pole'  that are temporal qualities and experiences development. The information structure and activities are actualized by SQL articulations that are identified with the ideas of ORDBMS. The outcomes demonstrate that HODM has a lower storage size and a quicker query response time for every examined kind of spatio-transient queries.
On multiple blocking sets and blocking semiovals in finite non-Desarguesian planes

In this paper we first provide an infinite family of minimal (q−1)-fold blocking sets of size q3 in every affine translation plane of order q2. Next, we focus on the regular Hughes plane H(q2) of order q2. It is well known that π=PG(2,q) is embedded as a Baer subplane in H(q2). Let ℓ be a line of H(q2) having q+1 points in common with π and let us denote by H(q2)ℓ the affine plane obtained from H(q2) by deleting ℓ and all the points incident with ℓ. We exhibit a family of minimal (q−1)-fold blocking sets of size q3 in H(q2)ℓ. Furthermore, we consider the polarity of H(q2) which has q3−q2+q2+1 absolute points and we show that its absolute points form a blocking semioval. Finally, we describe a class of Baer subplanes of H(q2) distinct from π and admitting an automorphism group of order q3(q−1).
Hardware-Aware Sum-Product Decoding in the Decision Domain

We propose hardware-aware sum-product (SP) decoding for low-density parity-check codes. To simplify an implementation using a fixed-point number representation, we transform SP decoding in the logarithm domain to that in the decision domain. A polynomial approximation is proposed to implement an update rule of the proposed SP decoding efficiently. Numerical simulations show that the approximate SP decoding achieves almost the same performance as the exact SP decoding when an appropriate degree in the polynomial approximation is used, that it improves the convergence properties of SP and normalized min-sum decoding in the high signal-to-noise ratio regime, and that it is robust against quantization errors.
Development of Listening Comprehension Model by Using You tube for English Students

Abstract Learning a foreign language by using social media is getting important because it has great potential to support student-centered learning, as it is flexible, interactive, and content–rich in nature. This research was aimed to develop an Englishlistening comprehension model by using Social Media You tube. A Research and Development methodology ADDIE was used: analyzing students’ and teachers’ needs, designing a new learning materials, Developing material, Implementing it in a group, and evaluating the existing learning materials, validating the learning materials by experts, revising learning materials, trying out the learning materials, and revising learning materials. It was found that the existing learning materials were not appropriate for the students’ characteristics and were not organized in a systematic way. Students and lecturers indicated that they appreciated enjoyable English language learning materials such as songs, stories and games using You Tube. Based on these findings a new model for developing materials was developed for English Listening Comprehension Program at the Islamic University of Riau.
Requirements Analysis for Tangible User Interfaces for Collaborative Use

On a requirements analysis process, software designers identify and collect information about users needs, requirements of the system and its user interface. In order to design user interfaces for collaborative use it is important to know what are functional requirements, individual and social users needs. This paper presents a process of requirements analysis for Tangible User Interfaces design, specifically for software that are used by a group of people working to achieve a common goal. The process followed to obtain requirements was based on Computer Support Cooperative Work perspective. As a case of study we considered the use of tangible medias in collaborative web design.
Simple ConvNet Based on Bag of MLP-Based Local Descriptors

Deep convolutional neural network (ConvNet) is applied to versatile image recognition tasks with great success, though demanding high computation cost. Toward efficient computation, we propose a simple ConvNet architecture based on local descriptors in the bag-of-features framework. The local descriptors are formulated in a simple form of MLP and thus are efficiently computed on various ROI in a flexible manner. The proposed method is effectively trained in an end-to-end manner by reformulating the MLP descriptor into the form of deep ConvNet stacking convolution layers linearly. Through projection-based visual word encoding, the local descriptors are aggregated and fed into a classifier for image recognition tasks, which enables us to compute the network forwarding pass by matrix-vector multiplication. In the experiments on image classification, the proposed method is analyzed thoroughly, exhibiting favorable generalization performance on various tasks.
Strategies for Construction of Majors in Universities with Different Characteristic Based on Social Needs

Abstract We used the basic principles of game theory to investigate development strategies for standards of professional competence in universities in the same geographic area, under conditions of non-uniform distribution of social demand preference. The results revealed that, under various conditions in our model, different approaches for differential strategies were selected by university U 2 compared with university U 1 . Specifically, in the competition between U 1 and U 2 , there is a differential strategy compression point of U 2 to U 1 ; When U 2 universities choose the biggest differentiation strategy, university U 1 also have differentiated strategy control points. Arbitrary decision-making of university U 1 in relation to university U 2 was based on the requirements of professional competence standards, according to the change of the social demand for professional competency standard, universities U 2 will adopt the strategy of “no difference”, “maximum differentiation” and “comprehensive intermediate strategy”, respectively. The current results have theoretical implications for the selection of professional development and professional competence development strategies in asymmetric universities operating in the same geographic area.
Incremental learning-based visual tracking with weighted discriminative dictionaries

Existing sparse representation-based visual tracking methods detect the target positions by minimizing the reconstruction error. However, due to complex background, illumination change, and occlusion problems, these methods are difficult to locate the target properly. In this article, we propose a novel visual tracking method based on weighted discriminative dictionaries and a pyramidal feature selection strategy. First, we utilize color features and texture features of the training samples to obtain multiple discriminative dictionaries. Then, we use the position information of those samples to assign weights to the base vectors in dictionaries. For robust visual tracking, we propose a pyramidal sparse feature selection strategy where the weights of base vectors and reconstruction errors in different feature are integrated together to get the best target regions. At the same time, we measure feature reliability to dynamically adjust the weights of different features. In addition, we introduce a scenario-aware mechanism and an incremental dictionary update method based on noise energy analysis. Comparison experiments show that the proposed algorithm outperforms several state-of-the-art methods, and useful quantitative and qualitative analyses are also carried out.
Novel Segmentation Metrics for Use in Augmented Reality Advertisement Integration

A major application area for Augmented Reality (AR) is advertisement. To achieve easy advertisement integration and management for stakeholders, a quantitative semantic understanding of the world, that adheres to human perception is necessary. Current deep learning-based segmentation algorithms such as Mask R-CNN provide a mask of the real world (e.g. building facades) in a greedy manner, which, although quantitatively accurate, does not adhere to the needs of the AR advertisers for appropriate ad placement, such as ad size, mask continuity, etc. In this paper, we propose three intuitive metrics for evaluating building facade segmentation specifically for advertisement integration, namely, average discontinuity, normalized continuous area, and resolution ratio. Each of these metrics is inspired from the way advertisers may want to view segmentation results within an AR world editor, or the way users may want to experience AR advertisements. Experimental results on a test segmentation shows the importance of such intuitive metrics, where we show how an accurate placement area for a sample 2D advertisement can be easily isolated from a segmented facade using our metrics.
Decision Making of Flue Gas Desulphurization Technologies Based on Cloud Model and Grey Relation Projection Method

Evaluation information quantification and gray correlation relationship of evaluating indexes. Cloud model was used to represent the natural language evaluation information and transform qualitative concepts to quantitative expression. Based on the grey system theory and the vector projection principles, the priority membership of evaluation objects was calculated for the optimization of flue gas desulphurization technologies.
Analysis of Korean Classes and Directions for Improvement-Focusing on the Schools in Daegu

This articles analyzed in-depth interview data on Korean language class management for 10 class teachers in 10 schools of Daegu Metropolitan Office of Education in 2019.As a result of analyzing in-depth interview data, the status of Korean class operation was largely divided into structural aspects, organizational support climate, and cultural aspects. Examining the structural aspects, there were mentoring for university students, bilingual education, linking with relevant institutions, and counseling for multicultural students in the Korean language class. The shortage was causing difficulties in operation. In addition, the process-centered evaluation of Korean language students was not well conducted, and many new teachers were operating Korean language classes, so they were inexperienced in their duties and complained about difficulties in teaching Korean language (KSL). According to the organizational support climate, there are 15 Korean language classes, making it difficult to proceed with individualized lessons, resulting in more class hours per week than Korean class teachers. In addition, many Korean language (KSL) textbooks were so difficult that many schools used worksheets produced by the school or Korean language textbooks on the market. It was appealing difficulties. Lastly, from the cultural point of view, the class teacher recognized that the Korean class teacher was easy because the work and class hours were small. In addition, the teachers had a false sense of Korean language class. Based on these operating conditions, the improvement direction should be presented. First, the college student mentoring system should satisfy the various needs of mentees through mentee-centered matching, not mentor-centered matching. Second, in order to reinforce cooperation among local agencies, the KSI needs to provide data to schools by synthesizing various programs conducted by local agencies. Third, it is necessary to provide specific guidelines and manuals on the curriculum-based evaluation and grading of Korean language students. Fourth, there is a need to mix the Korean language class selection method with the competition system and the designation system in the competition system. Fifth, when the teacher in charge of the Korean language class transfers, it is necessary to improve the personnel system that can be first transmitted to the school with the Korean language class. You will have to provide. Sixth, the number of Korean class (15 students) needs to be adjusted according to the reality, and if it is difficult to adjust the size, it is necessary to consider introducing a two-teacher system. Seventh, Korean language (KSL) textbooks are so difficult to play a role as a diagnostic assessment and learning tool. Therefore, Korean language (KSL) textbooks should be developed and distributed according to the students
Research on visual analysis technology and application of urban road data

Abstract Many cities use the current wealth of multi-source data to create intelligent transportation systems, which visually process road data so that traffic consumers and traffic managers can better understand urban traffic conditions. Due to the heterogeneity, complexity and mass of these data, it is not easy to conduct effective analysis on them, and it is often necessary to integrate human perception in the analysis process, so as to cause extensive application of visualization. In this paper, we first briefly introduce the pre-processing of urban trajectory data, then systematically analyze the visualization of two data types of road traffic flow and traffic events, and finally briefly summarize the research trend of traffic visualization in recent years and put forward corresponding challenges. The results show that the early research focused on the visualization of road flow, including the main arrow diagram, Mosaic diagram, track wall, etc. With the deepening of visual analysis, the analysis of traffic flow increases to the analysis of traffic events. In recent years, there has been a new trend of mining and utilizing the attributes or semantics of traffic trajectories or traffic events. The research on traffic data visualization has been greatly expanded in breadth and depth.
Assessment of the Crypto Market Efficiency: Empirical Evidence from Unit Root Tests with Different Approximations

The purpose of this study is to examine whether the weak form of the efficient market hypothesis (EMH) is valid for the Bitcoin market. To that end, we consider the recent developments in unit root analysis utilizing daily data from February 2, 2012 to November 23, 2018. More specifically, we employ unit root tests with and without sharp breaks and also a unit root test with gradual breaks in order to obtain efficient and unbiased output. Our findings show that the EMH appears to be valid for the Bitcoin market. We discuss theoretical and practical implications of these findings.
Movie Recommendation System Using Social Network Analysis and k-Nearest Neighbor

Many types of research have been conducted in recommendation system to develop approaches to solve the challenges for collaborative filtering problem such as cold start problem; in this paper, we proposed the approach to solving the problem of collaborative by using social network analysis and k-nearest neighbor (k-NN). We used the centrality of social network to detect the community or cluster group to the user, and then apply the k-NN method to find a group for new users with similar personal information such as age, gender, and occupation after that recommendation system will recommend items that users in the group were previously interested for the new.
A Multi-Center PSO Algorithm with Memory Ability and Its Application to the Online Modelling of an Underwater Vehicle Thruster

To improve the performance of Particle swarm optimization (PSO) in online optimization problems, a multi-center PSO algorithm with memory ability was proposed. Main strategies of the proposed algorithm include the initial population optimization based on historical optimal solution and improved chaos mapping and the multi-center collaborative search. To verify online optimization performance, the proposed algorithm is applied to the online modelling process of an underwater vehicle thruster to optimize the modeling parameters. Result proves the superiority of the proposed algorithm in online optimization problem.
Analytic Hierarchy Process Based Cloud Service Assessment Model Using Service Measure Index

Cloud service have strength that can increase the satisfaction of customers’ various requirements. In that environment, customers should be able to choose the service that best suits their requirements through quality verification. Though service providers use SLAs for quality standards, it is difficult for customers to compare multiple services themselves. Therefore, in this paper, we propose a cloud ranking decision model. For our proposed model, we choose service assessment indicators from SMI model by CSMIC and apply AHP method to measure service scores. And we also show process through a case study how our proposed model assesses cloud services.