Reviewerly/exHarmony huggingface dataset:
Enhancing Embedding-Based Chinese Word Similarity Evaluation with Concepts and Synonyms Knowledge

Word similarity (WS) is a fundamental and critical task in natural language processing. Existing approaches to WS are mainly to calculate the similarity or relatedness of word pairs based on word embedding obtained by massive and high-quality corpus. However, it may suffer from poor performance for insuf- ficient corpus in some specific fields, and cannot capture rich semantic and sentimental information. To address these above problems, we propose an enhancing embedding-based word similarity evaluation with character-word concepts and synonyms knowledge, namely EWS-CS model, which can provide extra semantic information to enhance word similarity evaluation. The core of our approach contains knowledge encoder and word encoder. In knowledge encoder, we incorporate the semantic knowledge extracted from knowledge resources, including character-word concepts, synonyms and sentiment lexicons, to obtain knowledge representation. Word encoder is to learn enhancing embedding-based word representation from pre-trained model and knowledge representation based on similarity task. Finally, compared with baseline models, the experiments on four similarity evaluation datasets validate the effectiveness of our EWS-CS model in WS task.
Trajectory Tracking Control of UR5 Robot Manipulator Using Fuzzy Gain Scheduling Terminal Sliding Mode Controller

In dealing with the trajectory tracking control of robotic manipulator many scholars have had to work in a sliding mode controller.However, SMC has two major limitations one is chattering the other is asymptotical convergence.The chattering problem occurs in traditional SMC due to the switching function of the discontinuous controller and the constant gain parameter of K. Though in this study a Fuzzy Gain Scheduling Terminal Sliding Mode (FGSTSM) controller with a hyperbolic tangent function instead of signum function, is proposed and applied for tracking control of the UR5 robot manipulator.Moreover, the mathematical model of the UR5 robot manipulator using the Newton-Euler algorithm was developed on Maple software of version Maple 18.2.Hence the trajectory tracking control of the UR5 robot manipulator simulation was conducted on MATLAB/SIMULINK of version Matlab R2016a.Then for simulation purposes, a curve (arc) was taken as the desired trajectory for the robot manipulator to track and finally the result is compared with the conventional SMC controller.
English to Hindi Multi Modal Image Caption Translation

When it comes to describe image by words, the possibilities are numerous.Generating caption automatically from image is a well researched area.The concept is now advanced to utilise multiple models for translating captions in another language.One of the major hurdle in machine translation is word sense disambiguation.Visual cues from image can be helpful in disambiguate source words.System are being developed to generate image descriptions in a target language from an input image and one or more descriptions in source language.This paper describes the multi modal Neural Machine Translation systems for translating image captions from English to Hindi.Various multi-modal architectures were explored using local visual feature, global visual features, attention mechanisms, and pre-trained embedding.The features obtained form various models are integrated in various ways.We also tried re-ranking method.The systems are evaluated on BLEU score, RIBES score and AM/FM score.Re-ranking method proves to be best over all our other methods.
Compression Priors Assisted Convolutional Neural Network for Fractional Interpolation

Fractional interpolation has been extensively utilized in a series of video coding standards to generate fractional precision prediction to remove the temporal redundancy in consecutive frames. In the traditional interpolation filter based methods, the fractional samples are interpolated through a linear combination of the neighboring integer samples. This method is simple yet unable to accurately characterize the nonstationary video signals. Recently, convolutional neural network has been utilized in the fractional interpolation and shows superior performance compared with the traditional methods. However, only the reconstruction of the reference frame is used as the infer information source. All the other information contained in the bitstream or generated during the encoding/decoding procedure denoted as the compression prior is not utilized at all. In this paper, we give the first trial to involve some compression priors into the CNN to improve the performance of a in-loop coding tool. Specifically, we propose a Compression Priors assisted Convolutional Neural Network (CPCNN) to further improve the fractional interpolation efficiency. In addition to the reconstructed component, we additionally utilize two other compression priors - the corresponding residual component and col-located high quality component to boost the performance. Specifically, the residual component that indicates the prediction efficiency and contains effective texture information is utilized as a complementary input to the reconstructed one. While the col-located component provides more useful high quality information to help the reconstruction get rid of the quality fluctuation. Furthermore, a special network structure is designed to learn powerful representations of these triple input components. Comprehensive experiments have been conducted to demonstrate the effectiveness of our proposed CPCNN. The experimental results show that compared to HEVC, our proposed CPCNN achieves on average of 5.3%, 2.8% and 1.9% BD-Rate savings under LDP, LDB and RA configurations, respectively.
Wisatawan Flashpacker: Sebuah Pendekatan Epistemologis

This paper examines flashpacker as an area of research; which discusses the new phenomenon of the flashpacker originating it’s term from “backpacker”. In this review, a deeper analysis of the evolution and development is presented, charting the growth of the literature, focusing both chronology and epistemology. A framework for understanding and creating knowledge about flashpacker is presented, forming the basis which signposts established research themes and concepts and outlines some future directions for research. In addition, this article focuses on constraining and propelling forces, ontological advances, contributions from key journals, emerging themes and issues. The paper suggests that flashpacker has numerous foundational origins that have complicated its growth as a viable and valued concept. This paper also proposes a number of research activities in understanding the flashpacker.&#x0D; Keywords: flashpacker, epistemology, framework, creating knowledge, research activities
More Low Differential Uniformity Permutations over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mrow><mml:msup><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mi>k</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:msub></mml:math> with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mi>k</mml:mi></mml:math> Odd

Permutations with low differential uniformity, high algebraic degree, and high nonlinearity over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mrow><mml:msup><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mi>k</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:msub></mml:math> can be used as the substitution boxes for many block ciphers. In this paper, several classes of low differential uniformity permutations are constructed based on the method of choosing two permutations over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mrow><mml:msup><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mi>k</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:msub></mml:math> to get the desired permutations. The resulted low differential uniformity permutations have high algebraic degrees and nonlinearities simultaneously, which provide more choices for the substitution boxes. Moreover, some numerical examples are provided to show the efficacy of the theoretical results.
The Energy Sector and the Internet of Things – Sustainable Consumption and Enhanced Security through Industrial Revolution 4.0

With integration between machine automation and data being the hallmark of industrial revolution 4.0, the resilience of energy infrastructure in modern economy has taken a new significance. The study aims at discussing the potential and examining the impacts of the Internet of Things (IoT), which are smart devices with embedded sensors and connectivity, enabling data exchange to the energy sector. This technology contributes towards enhancing industry’s sustainable practices through the industrial internet of things. With data from the “edge of the grid,” these sensors assist in efficient energy consumption, providing constant monitoring for the regulatory authority, particularly on pollutant emissions. IoT technology may complement the national electric smart-grid, enhancing its reliability by feeding these raw data into machine learning neural network for the optimal operation. All these technologies shall complement one another, as Malaysia transform from a net energy exporter into an energy importer. Practicing efficient energy consumption can reduce this external dependency, and enhance national energy security. This paper derives statistical data sourced from the Energy Commission and technical data from publications of other scholars. On smaller scale, IoT implementation in manufacturing plants may resulted in 15% operating cost reduction. The benefits on national level implementation however remains unknown.
Salient Object Detection Based on Weighted Hypergraph and Random Walk

Recently, salient object detection based on the graph model has attracted extensive research interest in computer vision because the graph model can represent the relationship between two regions better. However, it is difficult to capture the high-level relationship between multiple regions. In this algorithm, the input image is segmented into superpixels first. Then, a weighted hypergraph model is established using fuzzy C-means clustering algorithm and a new weighting strategy. Finally, the random walk algorithm is used to sort all superpixels on the weighted hypergraph model to obtain the salient object. The experimental results on three benchmark datasets demonstrate that the proposed method performs better than some other state-of-the-art methods.
ADFAC: Automatic detection of facial articulatory features

Using computer-vision and image processing techniques, we aim to identify specific visual cues as induced by facial movements made during monosyllabic speech production. The method is named ADFAC: Automatic Detection of Facial Articulatory Cues. Four facial points of interest were detected automatically to represent head, eyebrow and lip movements: nose tip (proxy for head movement), medial point of left eyebrow, and midpoints of the upper and lower lips. The detected points were then automatically tracked in the subsequent video frames. Critical features such as the distance, velocity, and acceleration describing local facial movements with respect to the resting face of each speaker were extracted from the positional profiles of each tracked point. In this work, a variant of random forest is proposed to determine which facial features are significant in classifying speech sound categories. The method takes in both video and audio as input and extracts features from any video with a plain or simple background. The method is implemented in MATLAB and scripts are made available on GitHub for easy access.•Using innovative computer-vision and image processing techniques to automatically detect and track keypoints on the face during speech production in videos, thus allowing more natural articulation than previous sensor-based approaches.•Measuring multi-dimensional and dynamic facial movements by extracting time-related, distance-related and kinematics-related features in speech production.•Adopting the novel random forest classification approach to determine and rank the significance of facial features toward accurate speech sound categorization.
THE RELATIONSHIP BETWEEN TEACHER-STUDENT RELATIONSHIP, SELF-CONFIDENCE, AND ACADEMIC ACHIEVEMENT IN THE CHINESE CONTEXT

This study examined the relationship between teacher-student relationship, self-confidence in learning, and academic achievement of high school students in China.In this study, 14,021 11th graders in China took part in the National Standardized Tests in three subject areas: Chinese language, mathematics, and English language.Teacher-student relationship and self-confidence in learning were measured with the questionnaires adapted from PISA2012, the psychometric properties of which have been examined in large-scale assessments.Results showed that teacher-student relationship in each subject area, self-confidence in learning, and their corresponding academic achievement were all statistically significantly related to each other.Structural equation models were used to verify the mediating effect of self-confidence in learning between teacher-student relationship and students' academic achievement in each subject area.Results indicated that self-confidence in learning mediated the significant associations between teacher-student relationship and students' academic achievement.This mediating effect was the largest in the subject area of Chinese language, which was 8.1% greater than that in the subject area of English language, and 3.9% greater than that in the subject area of mathematics.This study suggests that good teacher-student relationship is conducive to enhancing students' self-confidence in learning, which had positive relationships to academic achievement.
Literasi Membaca Puisi Guru SD

The purpose of this study is to describe the implementation of poetry reading literacy in elementary schools. The approach used is qualitative and quantitative. The subjects of this study were 22 Silaing Bawah Public Elementary School teachers and 22 Silaing Bawah Public Elementary School teachers. Based on the ability to read poetry obtained the average value of the results of the pre-test reading poetry evaluation of the teachers of SD Negeri 07 Silaing Bawah and SD Negeri 08 Silaing Bawah is 68.33 and is in a pretty good qualification. Whereas the average results of the reading poetry reading the evaluation of the teachers of SD Negeri 07 Silaing Bawah and SD Negeri 08 Silaing Bawah after the training was 78.58 and were in good qualifications. Thus it can be seen that the implementation of poetry reading literacy can improve the ability to read the poetry of the teachers of SD Negeri 07 Silaing Bawah and SD Negeri 08 Silaing Bawah.
THE METRICS FOR TRACKING ALGORITHMS EVALUATION

The work is devoted to a review of existing metrics for assessing the quality of the task of tracking objects on video with various algorithms. When evaluating tracking algorithms for their subsequent com-parison, it is not enough to use one metric, and algorithms should be evaluated using a set of different independent estimates. To this end, a study was conducted of existing metrics for evaluating algorithms, the results of which are given in the article. The review involves many different approaches to evaluating algorithms. For example, approaches based on the assessment of the definition of the center of the track-ing object, which are one of the first and still popular metrics for evaluating tracking algorithms. The main disadvantages of such approaches include the difficulty of determining the true center of the object, as well as the interpretation of estimates for various sizes of the object. To eliminate these shortcomings, anew metric is introduced in the article: an unbiased (window) error in determining the center of an object, which takes into account the constant component of the error in determining the center. Other approach-es include metrics based on the analysis of the intersection over union. Also, the article considers ap-proaches based on the analysis of tracking failures, which take into account the tracking length and fail-ure rate. A new method is proposed for evaluating algorithms in case of loss of visual contact with an tracking object, taking into account the number of frames in which visual contact with the object was lost. During the study, approaches to evaluating algorithms for simultaneous tracking of several objects were considered. Integral metrics were proposed whose task is to obtain a comprehensive assessment of the tracking algorithm. For the formation of a comprehensive assessment, it is desirable to use various un-correlated metrics. Complex estimates provide the ability to compare algorithms with each other. As a comprehensive assessment, the article proposes the use of a metric combining the accuracy and robust-ness of the algorithm. As a rule, the intersection over union is used as the accuracy metric, however, for problems where the accuracy of tracking the center of the object is fundamental, the authors propose using an unbiased error in determining the center as the accuracy metric.
Correction to: Reoptimization Time Analysis of Evolutionary Algorithms on Linear Functions Under Dynamic Uniform Constraints

In the article Reoptimization Time Analysis of Evolutionary Algorithms on Linear Functions Under Dynamic Uniform Constraints, we claimed a worst-case runtime of and for the Multi-Objective Evolutionary Algorithm and the Multi-Objective Genetic Algorithm, respectively, on linear profit functions under dynamic uniform constraint, where denotes the difference between the original constraint bound B and the new one. The technique used to prove these results contained an error. We correct this mistake and show a weaker bound of for both algorithms instead.
The Development of Kato Nan Ampek Picture Storybook Models Through Literacy of Minangkabau Culture to Develop the Character During Early Childhood

This study aims to develop a model of picture books through literacy ampek kato nan Minangkabau culture to develop character early childhood.This story book development through literacy Minangkabau culture to develop the character of children aged 5-6 years.This type of research is the research and development (Research and Development), with model Brog and Gall.The result of the development of kato nan ampek story book contains local cultural values Minangkabau subjects related charge based on the rules of manners/ethics in communication which is known as Kato nan ampek.Judging from the results of this study by the validation test was obtained on average 92.96%.77.79% teachers' questionnaire responses, the value of characters that appear in 90% of children.Based on the results of the research it can be concluded that the development of models of kato nan ampek storybook characters to develop early childhood is very valid
Analisis Pertambahan Pasien COVID-19 di Indonesia Menggunakan Metode Rantai Markov

COVID-19 is a new disease that is affecting almost all of the world. Until now there has not been a single drug (vaccine) that can be used to cure it. Many attempts were made to prevent the spread of this disease but COVID-19 patients are increasing every day, although at the same time some are recovering. This study will calculate the probability of additional patients occurring over a long period of time, referred as a steady state state condition, using the Markov chain method. Nine states have been formed to represent the daily increase ranges of COVID-19 patients number. The calculation results show that the possibility of additional patient number between 1 to 91, 92 to 182, 182 to 272, 273 to 363, 364 to 454, 455 to 545, 546 to 636, 637 to 727, or greater than 728 people a day are 0.21197, 0.05644, 0.08408, 0.16337, 0.13999, 0.14512, 0.07189, 0.07695, and 0.05014, respectively.
The Criminal Law Regulation of the Behavior of Fabricating and Deliberately Disseminating False Information of Epidemic

As of January 2020, the outbreak of the COVID-19 epidemic began that affected China and the whole world.Contrary to the efforts of people from all walks of life to contain the epidemic, illegal activities, even crimes, that interfere with the prevention and containment of the virus frequently occur, among which, making and maliciously spreading misinformation related to the epidemic is very common, which action, obstructs the efforts of virus prevention and containment as well as results in a substantial harm to the social stability and public order.The state authorities concerned, encountered by such challenge, have made efforts to crack down upon this kind of act.However, as in criminalization of such acts, it remains disputed in theory and difficult in identification in practice, determination of such a crime of making and maliciously spreading epidemic-related misinformation remains unsatisfactory in effect.This thesis conducts an analysis and puts forward proposals on the questions that arise in the criminalization of such act by the Criminal Law, which may be broken into three parts: firstly, it analyzes the justifiableness of criminalization of making and maliciously spreading epidemic-related misinformation from the perspective of jurisprudence; secondly, it offers after analysis and comparison principles that should be followed in the criminalization of such act; and thirdly, it reviews the issues of categorization, legal basis thereof and crime determination and exculpations under Article 291.1.1,Article 291.1.2and Article 293 of the Criminal Law, and offers proposals on criminal law application.
Just Fun and Games? Utilitarian and Hedonic Chatbot Perceptions and Their Role for Continuance Intentions

Conversational agents (CAs) offer huge potential for service companies by creating social closeness and enabling fast and scalable communication with customers. However, investigation of utilitarian and especially hedonic value as driving motivations for using CAs is still nascent. We found social presence to be an important predictor for hedonic and utilitarian value and subsequent continuance intention. Moreover, we reveal customers’ continuance intention is determined primarily by hedonic value when expecting a CA, whereas focus shifts to utilitarian values if customers expect a human employee. With our results, CA services can be better tailored to customer needs and company service goals.
Improved Model for Facial Expression Classification for Fear and Sadness Using Local Binary Pattern Histogram

In this study, a Local Binary Pattern Histogram model was proposed for Facial expression classification for fear and sadness. There have been a number of supervised machine models developed and used for facial recognition in past researches. The classifier requires human effort to perform feature extraction which has led to unknown changes in the expression of human face and incomplete feature extraction and low accuracy. This study proposed a model for improving the accuracies for fear and sadness and to extract features to distinguish between fear and sadness. Images of different people of varying ages were extracted from two datasets got from Japanese female facial expression (jaffe) dataset and Cohn cade got from Kaggle. In other to achieve an incremental development, classification was done using Linear Support Vector Machine (LSVM) and Random Forest Classifier (RFC). The accuracy rates for the LSVM models, LSVM1 and LSVM2 were 88% and 87% respectively while the RFC models, RFC1 and RFC2, were 81% and 82% respectively.