Reviewerly/exHarmony huggingface dataset:
Using Warshall to Solve the Density-linked Density Clustering Algorithm

Clustering algorithm has a wide range of applications in data mining, pattern recognition and machine learning.It is an important part of data mining technology.The emergence of massive data makes the application of data mining technology endless.Cluster analysis is the basic operation of big data processing.The clustering algorithm is to divide similar elements into one class, and to divide elements with large differences into different classes.Aiming at the computational complexity of the density clustering algorithm, this paper proposes an improved algorithm W-DBSCAN which uses Warshall algorithm to reduce its complexity.In the density clustering algorithm, the data with high similarity are densely connected.In this paper, aiming at the complexity of the density clustering algorithm, an improved algorithm W-DBSCAN using the Warshall algorithm to mitigate its complexity is proposed.In the density clustering algorithm, the data with high similarity is density-connected.This paper constructs a matrix n×n where the element (x, y) is marked as 1 means that the data x and data y are directly reachable, and then the reachability matrix of the matrix is calculated using the Warshall algorithm.The solution density connection problem is transformed into the solution reachability matrix problem, thus reducing the complexity of the algorithm.
The Evolution of Researchers’ Bibliography: From Systematic Organization to Citation

Purpose/Thesis: This paper analyses the publications of two authors from different countries through a number of citations identified by Google Scholar. Jean Meyriat and Edson Nery da Fonseca are outstanding researchers in France and Brazil, respectively, both contributing to the formation and advancement of information science in their countries. The “traditional” bibliography and the development trajectory of their scientific achievements were considered in this analysis.Approach/Methods: The analysis of the researchers’ publications identified by Google Scholar based on their names helps to understand the role that search engines play in the evaluation of science and its effect on information seeking and in the evaluation of the scientific production. The qualitative research is based on a bibliological analyses focusing on the way in which the written production of the selected authors is reported and highlighted. The carried out study is exploratory in nature, so it proposes to raise questions and to emit hypotheses that could suggest directions for further research.Results and conclusions: The uniformity of controlled metadata used in traditional bibliographies make it possible to follow the progress of an author’s thought. The same is true of other activities he has performed in and out of the field and, it is also true for what has been produced about this author. Searching for information in Google Scholar can show a researcher’s production and publi­cations about her/him in the order of citations but not in the order of the progress of the researcher’s scientific achievements or development of research focused on these achievements. Placed in the context of the analysis of the scientific field as it was defined by Bourdieu (1976), the observation of the visibility of the production Jean Meyriat and Edson Nery da Fonseca, two major actors in information science in France and Brazil , by Google Scholar, shows a tendency to underemphasize the role they played.Practical implications: The functioning of the scientific field has specific effects on the production of research. The search engines such as Google Scholar, made the research output more accessible on Internet. However, the criteria they prioritize, i.e., the number of citations, ignores important aspects of a scientific career. Qualitative analysis that considers the networks of sociability and the development trajectory of researchers’ scientific achievements may reveal relevant contributions to their scientific field.
MULTIPLE KERNEL FUZZY CLUSTERING FOR UNCERTAIN DATA CLASSIFICATION

Traditional1call tree classifiers work1with information whose values1area unit celebrated and precise.We1have a tendency to extend1such classifiers to handle information1with unsure info.Worth uncertainty1arises in several applications throughoutthe1informationassortmentmethod.Example1source of uncertainty embrace1measurement/quantization errors, information staleness, and multiple recurrent measurements.With1uncertainty, the worth of a knowledge item1is commonly depicted not by one1single worth, however by1multiple values forming a probability distribution.Instead of1abstracting unsure information by applied1math derivatives (such as1mean and median), we have a1tendency to discover that1the accuracy of a call1tree classifier will be a lot1of improved if the "complete1information" of a knowledge item is utilized.Since1process pdf's is computationall1 a lot of pricey than1process single values (e.g., averages), call tree1construction on unsure information1is more electronic equipment1demanding than that sure information.To1tackle this problem, we have a tendency1to propose a series of pruning1techniques which will greatly improve1construction potency.
The Malta case: distributed ledger technologies (dlts) and Islamic commercial law in the European union. A European controversy.

The Shariʿa Law has a comprehensive vision of all human activities, including commerce. The peculiarities of the commercial legal system that derives from the legal principles of Shariʿa emanates from the concepts of forbidden or Haram and permissible or Halal. These principles are applied today to breakthrough commercial developments such as the Blockchain/Digital Ledger Technologies. On the other hand, there is a growing debate about the possibility of the application of Shariʿa Law in the Member States of the European Union, either for social reasons or for commercial reasons. The controversy and opportunities created in the smallest State of the Union, Malta, serves as a sample.
A New Seismic Horizon Picking Method Based on the Edge Detection and the Kalman Filter

Seismic horizon picking plays an important role in the seismic exploration. Most of the present picking methods can only recognize horizons as some discrete disordered points, not a connected line, and work not well under the strong noise. A new horizon picking method based on the edge detection and the Kalman filter is proposed. Firstly the Canny edge detection is applied in the seismic profile to detect the points of horizons, which can include false points and noise. Then the points of horizons, as the measurement data, are input into the Kalman filter to track and recognize the seismic horizons trace by trace. Finally the multiple horizons can be recognized automatically and simultaneously. The proposed method can intelligently link the relevant points into a trajectory, and get a series of time and space data of every horizon. The simulation on the synthetic record and the practical picking on the real profile show that the proposed method can improve the continuity of horizons, and work well under the background of strong noise.
Not quite what’s on paper? Comparison between theoretical and actual information-sharing networks in the Ugandan rural water service sector

Under Uganda’s decentralised system, rural water service delivery remains to some extent problematic. Several studies attribute the possible causes of deficiencies in the water sector to governance issues. This article applies social network analysis to map upward and downward water-related information flows between the actors of local government from village to district level. Comparing the actual information-sharing network with what’s on paper reveals a less reciprocal and more centralised network than that theoretically envisaged. Some actors, such as the district water officer, are more central than expected in terms of sending and receiving information, while others seem to underperform. Our findings show, however, that it is not the political–administrative information exchange which is the biggest obstacle, but rather information flows between higher (district and sub-county) and lower (parish and village) levels of the local governance structure. Adding water users to the analysis reveals the village chairperson as the most crucial broker of information upward to duty bearers at district level. The limited role of water user committees also becomes apparent. The authors conclude that information communication technology holds potential to overcome some of the bottlenecks (eg distance) hindering the flow of water-related information between actors at different levels.
Motion Recognition and Students’ Achievement

Human motion has multifarious meanings that can be recognized using a facial detection machine. This article aims to explore body motion recognition to explain the relationship between students’ motions and their achievement, as well as teachers’ responses to students’ motions, and especially to negative ones. Students’ motions can be identified according to three categories; facial expression, hand gestures, and body position and movement. Facial expression covers four categories, namely, contempt, fear, happiness, and sadness. Contempt is used to express conflicted feelings, fear to express unpleasantness, happiness to express satisfaction, and sadness to express that the environment is uncomfortable. Hand gestures can likewise be grouped into four categories: conversational gestures, controlling gestures, manipulative gestures, and communicative gestures. Conversational gestures refer to communicative gestures. Controlling gestures refer to vision-based interface communications, like the ones popular in current technology. Manipulative gestures refer to ones used in human interaction with virtual objects. Communicative gestures relate to human interaction, and therefore involve the field of psychology. Body position and movement also can be classified into four categories, namely: leaning forward, leaning backward, correct posture, and physical relocation. Leaning forward happens when a user is working with a high level of concentration. Leaning backward occurs when a user has been highly concentrated on work for several hours, and needs a break or change. Correct posture is the sign of an enjoyable working position which involves sitting in a free and relaxed manner. Movement refers to a change to the student’s sitting location, reflecting some inadequacy of the learning environment. Teachers can anticipate changes of students’ emotions by good learning design, teaching metacognitive skills, self-regulated performance, exploratory talks, mastery approach/avoidance, using hybrid learning environments, and controlling space within classrooms. Teachers’ responses to students’ motions will be explored in this article
SCHONA: A Scholar Persona System Based on Academic Social Network

Recently, social network including academic social network has developed rapidly. It is a challenge to utilize massive academic data including academic social network data and academic achievement data to analyze and mine scholars' important information such as behavioral characteristics and research interests. In this paper, we present a scholar persona system SCHONA which is composed of two parts, data collection and scholar labels generation. It collects three types of data first and finally generates the labels which can accurately represent scholars by extensively using big data analysis methods such as Word2Vec, K-means and TextRank.
Classification of Voice Content in the Context of Public Radio Broadcasting

With the rapid development of mass media technology, content classification of radio broadcasting has emerged as a major research area facilitating the automation of radio broadcasting monitoring process. This research focuses on the voice dominant content classification of radio broadcasting by employing a multi-class Support Vector Machine (SVM) in order to automate monitoring of radio broadcasting in Sri Lanka. This study investigates the performance of “One Vs. One” and “One Vs. All” methods known to be two conventional ways to build a multi-class SVM. These two multi-class SVM models are trained to classify five voice dominant classes as news, conversations, and advertisements without jingles, radio drama and religious programs. One of the substantial measures in creating such a classification is selection of the optimal feature sets. For that purpose, time domain features, frequency domain features, cepstral features, and chroma features are manually analyzed for each binary SVM classifier independently. Two multi-class SVM models are trained based on the selected features and the “One Vs. One” model was able to better classify the recordings with an 85% accuracy compared to 83% accuracy achieved by “One Vs. All” model. Further, the results revealed the importance of careful feature selection in order to achieve higher classification accuracies.
AN IMPLEMENTATION OF SOFTWARE EFFORT DURATION AND COST ESTIMATION WITH STATISTICAL AND MACHINE LEARNING APPROACHES

In software industry estimation of effort, cost (EDC) and duration is a troublesome procedure.The exertion itself is in charge of experiencing trouble in evaluating EDC.In any software estimation process, the preeminent advance is to characterize and comprehends the framework to be assessed.Software cost estimation algorithmic methods are estimating by analogy, expert judgment method, price to win method, topdown method, bottom-up method are developed by researchers in the field of software engineering.Any methods are not superior to the other methods.In fact, the qualities and shortcomings of those strategies are differentiating to one another.Presently there are two types of software EDC models to estimate the software those are statistical approaches and machine learning approaches.Many of the software cost estimation methods follows statistical approaches, which are not having the capability to present causes and strong or accurate results.Machine learning techniques are suitable in software engineering because it produces accurate results, which estimates by training rules and iterations.Machine learning techniques resolve the challenges like developing programs in a computer and to give the effective outputs by using the experience
A research on the establishment of automated driving scenario database using natural language processing method

Scenario definition for Automated Driving Function Validation is the state-of-the-art research area for autonomous driving, unification of the scenario description methods and language is the crucial step for the scenario definition. Based on the simulation scenario database established by China Automotive Technology and Research Center (CATARC) using natural language processing methods, to connect the human readable scenario definition with programmable scenario coding, provide the bridge between scenario definition with simulation testing, for better unify between scenario definition and virtual scenario building. With the participation of many enterprises, the corresponding test of automatic driving has become an access condition for the implementation of the corresponding technologies of automatic driving. Enterprises and institutions all over the world are trying to establish a unified scenario database to achieve the wide sharing and unification.
Evaluation of Proportional Odds and Continuation Ratio Models for Smoker in Indonesia

The polytomous model is a model used for more than two categorical response data.Some models that can use for ordinal scale responses are the Proportional Odds Model, Continuation Model, Partial Proportional Odds Model, and Adjacent Model.The Proportional Odds model has the assumption of "proportionality" or parallelity to the cumulative logit.If the parallel logits assumption not fulfilled, the alternative models that can use are Adjacent Model and Continuation-Ratio.The purpose of this study is to evaluate the proportional Odds (PO) model and Continuation-Ratio (CR) for smokers in Indonesia.The data used was taken from 2017 Indonesian Demographic and Health Survey (IDHS) by classifying smokers in ordinal categories (mild, moderate, and severe).The results show there was a violation of the assumptions in the PO Model so that the CR Model was an alternative to use.Gender is a factor that has a significant influence on all response categories.Based on the value of Goodness of fit, deviance, Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Mac Fadden R2 indicate that the CR Model is better to use than the Model PO.
Vega: A Computer Vision Processing Enhancement Framework with Graph-based Acceleration

The popularity of Computer Vision (CV) algorithms has been on the rise given their growing dependence on machine learning and deep neural networks.The resulting improvement in inference accuracy has revolutionized a number of fields.However, given that CV algorithms consist of many different stages, each having different computing characteristics, their execution is frequently irregular and inefficient, unable to leverage the full potential of the computing platform.Presently, supporting real-time video processing for high resolution images on edge systems involves a significant amount of programming effort and performance tuning.To overcome this challenge, we present Vega, a parallel graph-based framework that enables better utilization of multi-core edge computing platforms.Vega provides a highly flexible and user-friendly interface to execute a range of CV algorithms efficiently, leveraging multiple external libraries for performance.First, Vega maps independent stages of a CV algorithm to nodes in a pipeline graph.Next, it dynamically schedules nodes on a multi-core CPU using multi-threading.From our experimental results, our framework improves performance of all selected algorithms by at least 1.75x and up to 4.82x on the same platform.We analyze the impact of using our framework in terms of hardware utilization, frame processing latency and throughput.
Total Focusing Algorithm of Double Layer Media Based on Iterative Process

In order to ensure the position of the refraction point of the double-layer medium in the full-focus imaging algorithm when in immersion test, this paper carries out the time of flight trajectory of the research, put forward by the method of extreme points to look for refraction and put forward the ray intersection method as a Newton iterative method of the initial test point. The theory of this method is deduced to satisfy Snell's law of refraction and Fermat principle. A 64 array full matrix acquisition and full focus imaging algorithm is used to verify the computational efficiency and imaging quality of the extreme iteration method (EIM). The results show that the imaging speed of the EIM is about 50 times faster than that of the analytical solution model.
Zero-Knowledge User Authentication: An Old Idea Whose Time Has Come

User authentication can rely on various factors (e.g., a password, a cryptographic key, and/or biometric data) but should not reveal any secret information held by the user. This seemingly paradoxical feat can be achieved through zero-knowledge proofs. Unfortunately, naive password-based approaches still prevail on the web. Multi-factor authentication schemes address some of the weaknesses of the traditional login process, but generally have deployability issues or degrade usability even further as they assume users do not possess adequate hardware. This assumption no longer holds: smartphones with biometric sensors, cameras, short-range communication capabilities, and unlimited data plans have become ubiquitous. In this paper, we show that, assuming the user has such a device, both security and usability can be drastically improved using an augmented password-authenticated key agreement (PAKE) protocol and message authentication codes.
NEW PRIORITIES OF BOOK CULTURE OF THE INFORMATION SOCIETY: SUPPORTING BOOKS, READING AND CHILDHOOD (RESULTS OF THE III REGIONAL BOOK FORUM, SEPTEMBER 12–13, 2019)

The results of the scientific and practical arrangement with international participation – the book forum are presented, brief excerpts from the reports on the plenary session are given, topics of sections, speakers, and the range of issues addressed are named.
A Note on Ciuciura’s mbC1

This note offers a non-deterministic semantics for mbC1, introduced by Janusz Ciuciura, and establishes soundness and (strong) completeness results with respect to the Hilbert-style proof system. Moreover, based on the new semantics, we briefly discuss an unexplored variant of mbC1 which has a contra-classical flavor.
Odd Harmonious Labeling on Edge Amalgamation from Double Quadrilateral Graphs

A graph that has odd harmonious labeling properties is called an odd harmonious graph. The purpose of this research is to obtain a new class graphs construction which is a family of odd harmonious graphs. The research method used consisted of several stages, namely research preparation, research investigation and verifivation of research results. The results of this study, we will give a line amalgamation construction of n double quadrilateral graphs DQ, denoted by *DQ(n) with n&gt;= 1 and graph obtained by connecting between two graphs *DQ(n) with line graph L2, denoted by *(DQ(n),L2,DQ(n)). It has further been proven that *DQ(n) and *(DQ(n),L2,DQ(n)) have odd harmonious labeling properties, such that all of them are odd harmonious graphs.
Color Image Compression with Transform Domain Down-Sampling and Deep Convolutional Reconstruction

In this paper, we build up a new block-based color image compression scheme based on our proposed transform domain down-sampling method and deep convolutional reconstruction algorithm. Specifically, our proposed down-sampling scheme aims to down-sample each N × N transform block into the N/2 × N/2 block for the saving of bit-cost. On the other hand, the proposed deep convolutional reconstruction algorithm is employed to reconstruct the down-sampled block for a full- resolution reconstruction. We apply our proposed methods to both the chrominance components to compress color images. Experimental results show that our proposed method achieves excellent results when used in practice.
Utilizing a Recorded-Video to Assess Speaking and Presentation Skills of Pre-Service Teachers

Teachers have employed in-class oral presentations to practice the pre-service teachers’ speaking skills as they are prepared to become future English teachers who are fluent in communication using English in a globalization era, However, effectively assessing student presentations is challenging, particularly in terms of practical administration of assessment. Therefore, a video recording as an alternative assessment tool was utilized to provide opportunities for learners and their peers to evaluate their speaking and presentation skills. They will need to combine video-camera on their cell phones or tablets and the use e-google form to give immediate feedback for their improvement. The study used a qualitative descriptive method. The data were gathered from the students’ recording videos, reflective journal, feedback from e-google form. The results showed that students got more enthusiastic in presenting their ideas using the video-camera. Furthermore, presentation skills such as the use of eye contact, body posture, and voice were found as other factors that supported the English speaking skills of the pre-service teachers.