Reviewerly/exHarmony huggingface dataset:
Mind Maps Aid EFL Students’ Vocabulary Building

Mind mapping is a way to facilitate access to knowledge and retrieval by visual evidence such as pictures, arrows and colors. Young students need one place to register mind maps and the possibility of retrieving their memory. Through this study, the researcher found that the elementary student assistant on the creation of personal, bright and interesting using mind maps can alleviate the difficulties of recall and memorization of vocabulary for a longer period of time. On the other hand, teaching and learning through the mapping of the mind is a fun and entertaining way as it helps to motivate students to study new things and seek to learn and encourage learners to use new vocabulary habitually and increase the desire of students to know more. In addition, drawing skills is one of the easiest ways to manage, handle and develop vocabulary retention.
A Novel Approach to Classify Video Scenes via Mining Weighted Association Rules of Actions in Temporal Sequence

Scene classification is a basic issue in computer vision. In contrast to the feature-based method, we propose a novel approach based on the association rules of actions. In this paper, it is used to mine the association relationship of the basic concepts in corresponding complex video scenes or events. Meanwhile, its association rules are taken as a kind of detection classification criteria of complex video scenes or events for complex video scenes classification. Given the fact that the traditional mining algorithm cannot take the relationship among the semantic concepts of video action with time sequence into consideration, which will affect the results of detailed semantic concept association analysis. Thus, the weighted association rule of video action semantic concepts mining algorithm based on time sequence is proposed. First, the temporal window is introduced to mine the association rules in each window. Furthermore, the weights of different windows are calculated to update the support and confidence of each rule. Then, the association rules mining is carried out on a dataset to work out the optimal association rule set. Finally, the classification of a given video can be obtained by the score on each class of the rule set. Experiments show that our method can classify the video scene effectively. Furthermore, it can also provide high level semantic information other than the traditional approaches.
MOTIVATION OF EDUCATIONAL ACTIVITY OF THE FIRST-YEAR STUDENTS IN THE PROCESS OF THEIR ADAPTATION AT THE CLASSICAL UNIVERSITY

The article deals with the study of academic motivation of first-year students at the classical university. The following methods of research were used: theoretical − in order to understand the essence and peculiarities of educational motivation, formulation of the conclusions of the conducted research, etc. (analysis and synthesis, comparison, generalization, etc.); empirical − to reveal the real state of academic motivation of freshmen students in the conditions of the classical university (electronic and paper questionnaires, pedagogical observation); mathematical − for processing the received results of the questionnaire (method of registration, ranking, descriptive statistics). Student motivation can be considered as a complex system of factors and motives that are constantly changing and mutually influence each other, encourage to acquire knowledge actively, develop and form abilities and skills, personal qualities, a positive attitude to the future profession and the world around. The peculiarity of training motivation for first-year students lies in the fact that it is not only polymotive, but also one of the important results in the process of adaptation and at the same time one of the mechanisms/factors of adaptation of young people to the educational process at university. Thus, the activity of students during their educational adaptation, on the one hand, determines the motivation for learning, and on the other hand it is a consequence of it. The authors conducted a questionnaire for students of three academic years and calculated the indicators of internal, external positive and external negative motivation. The majority of students (over 70%) have optimal motivational complexes during all academic years, which contributes to their successful adaptation in the educational process at university; internal and external positive motivations either prevail or their values are the same, but they are bigger than negative external motivation. We can also notice a certain increase in the proportion of students with the optimal motivational complexes for the specified period. At the same time, we can see a gradual decrease in the percentage of students with the indicated motivational complexes for the period under study. To compare indicators of academic motivation of first-year students in different academic years, the c2 criterion was used.
WHEN CORPORATE CULTURE CAN BE A PROBLEM: SYMPTOMS AND CAUSES OF «TOXICITY»

The issues of a «toxic» corporate culture, having a negative impact on interpersonal relations, the working atmosphere in a team, the involvement and loyalty of personnel, have been considered. On the basis of theoretical and empirical studies of foreign and Russian scientists, the main manifestations of a «toxic» culture have been summarized. The behavioral attributes and main behavioral patterns of managers, determining the formation of a «toxic» culture, have been highlighted. The methods, aimed at changing both the working environment as a whole, and interaction with «toxic» managers have been proposed.
The Mediating Effect of Customer Knowledge Sharing on Organizational Performance

Customer knowledge is a vital external factor which can affect organizational performance and survival. Customer knowledge consists of knowledge for customers (KfC), knowledge about customers (KaC) and knowledge from customers (KfrC). Many researchers regard customer knowledge as a strategic resource for companies to improve innovation, to facilitate the detection of new market opportunities, and to support long-term relationships with customers. However, there is still a lack of understanding of the role of knowledge sharing in improving organizational performance. This study aimed to investigate the impact of customer knowledge, and innovative customer knowledge sharing, on organizational performance. A second aim was to study the mediating effect of innovative customer knowledge sharing. The findings showed that customer knowledge positively influences customer knowledge sharing (CKS) except regarding the factor of knowledge about customers (KAC). Organizational performance (OP) was positively impacted by customer knowledge sharing (CKS). The findings also showed that customer knowledge sharing (CKS) has a mediating effect between customer knowledge (CK) and organizational performance (OP).
BrainEE at SemEval-2019 Task 3: Ensembling Linear Classifiers for Emotion Prediction

We present a homogeneous ensemble of linear perceptrons trained for emotion classification as part of the SemEval-2019 sharedtask 3. The model uses a matrix of probabilities to weight the activations of the baseclassifiers and makes a final prediction using the sum rule.The base-classifiers are multi-class perceptrons utilizing character and word n-grams, part-of-speech tags and sentiment polarity scores.The results of our experiments indicate that the ensemble outperforms the base-classifiers, but only marginally.In the best scenario our model attains an F-Micro score 1 of 0.672, whereas the base-classifiers attained scores ranging from 0.636 to 0.666.
A three-phase mapreduce-based algorithm for searching biomedical document databases

Retrieving information from large document databases is in the focus of scientific research in recent years. In this paper, a parallel algorithm for searching biomedical documents based on the MapReduce technique is presented. The algorithm consists of three phases: preprocessing phase, document representation phase, and searching phase. In the first phase, lemmatization and elimination of stop words are performed. In the second phase, each of the documents is represented as a list of pairs (word, tf-idf index of the word). The third phase represents the main searching procedure. It uses a specially designed ranking criterion, which is based on a combination of the term frequency - inverse document frequency (tf-idf) index and the indicator function for each query word. Four different versions of ranking criteria are proposed and analyzed. The algorithm performances are tested on different subsets of the large and well-known PubMed biomedical document database. The results obtained by the experiments indicate that the proposed parallel algorithm succeeds in finding high-quality results in a reasonable time. Comparing to the sequential variant of the algorithm, the experiments show that the parallel algorithm is more efficient since it finds high-quality solutions in significantly less time.
THE ANALYSIS OF SPEECHNOTE APPLICATION FOR MEASURING THE ACCURACY OF STUDENTS’ PRONOUNCIATION

Technology, education, and internet have been one of education’s part in this era. Learning independently is one of the choice for many students, not only effective to get understand but also time effectiveness are some of the reasons. All the thing that we learnt in the school is how to improve speaking, listening, writing, and reading skill. That’s why pronunciation become hidden subject which students don’t have to learn it. The problem in our education is English still foreign language for us, so it would be difficult to learn pronunciation autonomuosly or independently. Speechnote is one of the application which was created by WellSource for dictating spoken language to written language with high accuracy. The application helped students to learn pronunciation independently without being watched by the expert. The used method is descriptive-kualitatife and data collective which are recording and filling out questionnaire by 36 participants of students X.IS-1 SMA N 15 Semarang. The result of this research are the error of aplication, the accuracy of recording,recognizing misspronunciation, and the connection of increasing pronunciation by using speechnote.
POLYGLISM OF COMMUNICATION IN SOCIAL NETWORKS: FORMS AND GENRES

The article is devoted to the study of the features of verbal communication in social networks. Internet communication research is one of the topical issues of modern journalism, which is due to the processes of globalization, virtualization and openness of the world. In the Internet space, speakers actively express their own views on acute issues, exchange information, enter into discussions, congratulate each other on holidays, and more. Active virtual communication takes place first of all in social networks, which is a public space for information exchange.The purpose of the article is to determine the main genres and forms of communication in social networks, which led to the solution of the following tasks: to characterize the essence of communication in social networks, to identify its features, to use the main genres and forms of communicative interaction and to find out their specifics. Subject of research − genre types of representations of communicative interaction in social networks, object of research − Internet communication, presented in the Ukrainian segment of the social network Facebook. The features of communicative interaction in social networks as a new form of virtual communication are determined. The genres of the post and commentary as the show of communicative interaction of users of social networks are considered, and representative representations of the reports of the users are determined. The analysis of multilevel comments and branching of messages, which determine the polylogical nature of network communication. The main forms of representation of information in profiles of social network users are highlighted. The research is based on the Ukrainian-language segment of the Facebook network.As a result of the research on the polylogical nature of the communicative interaction of users of social networks, we have identified the genres and forms of communication. Among the genres, we distinguish a comment and a comment, and within the scope of the reports such genre varieties as ritual, emotional, informational, argumentative and search. Among the forms of communication are distinguished verbal, iconic and creolosed with the provision of the advantages of the latter. The perspectives of studying communication in social networks include developing a typology of genre types of posts and comments, as well as analysis of strategies and tactics of network communication.
La Competencia Digital ante contextos de exclusión: un estudio de caso en Educación Primaria

The results of an investigation that focus on the study of Digital Competence from the analysis of a case study are presented in this article. The unfavorable context of a 12-year-old child (economic, social and cultural), with a family situation with high levels of destructuring, is described to analyze the influence of the different agents involved in his education, in depth in the Formal spaces, in the school environment, as informal at the family level and the potential group of equals. Large gaps are identified and emerge by contrasting the developed skills of the case with the research that analyses the proposed dimensions for Digital Competence. The results uncover that the development of them is scarce and focus mainly on processes related to communication and collaboration and related to the search for information. The research also highlights the difficulties that emerge from the school institution in order to work on this type of learning and act as a true compensatory agent. We mainly identify two limiting areas when it comes to working with digital skills: the oversizing of the curriculum of content in subjects of Primary Education and the search for low levels of conflict with families by conditioning the processes of educational innovation.
Human Face Recognition Using Combination of ZCA Feature Extraction method and Deep Neural Network

Nowadays, there are deep learning-based face recognition systems with reliable accuracy rates. But these systems need high computational powers because they should do feature extraction. This research presents the consequences of recognition rates on the AT&T face database, using the hybrid methods of ZCA feature extraction and ANN in order to reduce computational power usage.
Challenges in Time-Stamp Aware Anomaly Detection in Traffic Videos

Time-stamp aware anomaly detection in traffic videos is an essential task for the advancement of the intelligent transportation system. Anomaly detection in videos is a challenging problem due to sparse occurrence of anomalous events, inconsistent behavior of a different type of anomalies and imbalanced available data for normal and abnormal scenarios. In this paper, we present a three-stage pipeline to learn the motion patterns in videos to detect a visual anomaly. First, the background is estimated from recent history frames to identify the motionless objects. This background image is used to localize the normal/abnormal behavior within the frame. Further, we detect an object of interest in the estimated background and categorize it into anomaly based on a time-stamp aware anomaly detection algorithm. We also discuss the challenges faced in improving performance over the unseen test data for traffic anomaly detection. Experiments are conducted over Track 3 of NVIDIA AI city challenge 2019. The results show the effectiveness of the proposed method in detecting time-stamp aware anomalies in traffic/road videos.
A comparative analysis of mammography breast imaging reporting and data system score and fine needle aspiration cytology in the evaluation of palpable breast lump

Background: Breast lump is one of the commonest complaints with which patients present in breast clinics. As in most cases breast lump ultimately leads to a diagnosis of breast cancer, it is important for women with a breast lump to receive appropriate evaluation. So initial diagnostic approach such as utilization of rapid, inexpensive, presumptive and safe method is utmost important as such methods not only would benefit both patients and clinicians by prompting proper preoperative diagnosis and management but also further limits unnecessary testing and procedures. Aim and objectives of this study was to analyse the diagnostic accuracy of mammogram according to Breast Imaging Reporting and Data System (BI-RADS) score and fine-needle aspiration cytology (FNAC) in patients presented with benign and malignant breast lumps in authors centre.Methods: During this retrospective study from January 2018 to December 2018, the subjects concerned included all the female patients, who were referred to the department of pathology for FNAC of their breast mass and had radiological reports (mammography) with BI-RADS score according to the latest guidelines. The sensitivity, specificity, accuracy, positive and negative predictive values of BI-RADS scores and FNAC in diagnosis of breast diseases was done on the basis of final diagnosis.Results: In this study, the sensitivity, specificity, positive predictive value, negative predictive value and accuracy was 88.57%, 82.46%,75.61%,92.16% and 84.78%, respectively for Mammography (BIRADS) score and 100% for all the parameters on FNAC.Conclusions: According to authors above study findings concluded that FNAC could be considered an ideal initial diagnostic modality in all breast lumps recognized by means of imaging techniques. It is not only considered as safe, cost effective, rapid and presumptive diagnostic method but also further limits unnecessary testing and procedures.
Graph convolutional networks for exploring authorship hypotheses

This work considers a task from traditional literary criticism: annotating a structured, composite document with information about its sources.We take the Documentary Hypothesis, a prominent theory regarding the composition of the first five books of the Hebrew bible, extract stylistic features designed to avoid bias or overfitting, and train several classification models.Our main result is that the recently-introduced graph convolutional network architecture outperforms structurallyuninformed models.We also find that including information about the granularity of text spans is a crucial ingredient when employing hidden layers, in contrast to simple logistic regression.We perform error analysis at several levels, noting how some characteristic limitations of the models and simple features lead to misclassifications, and conclude with an overview of future work.
Using Latency Metrics in NoSQL Database Performance Benchmarking

This paper presents an experimental study evaluating the performance of NoSQL database management systems. The study compares&nbsp;two NoSQL database management systems (Cassandra and MongoDB)&nbsp;and considers the following parameters/factors: workload and degree of&nbsp;parallelism. Two different workloads (update heavy and mostly read) were&nbsp;used, and different numbers of threads. The measured results are related&nbsp;to average latency: update latency and read latency. Our study shows&nbsp;that with the only exception of 1000 operations, both latency indicators&nbsp;have a quasi-parabolic behavior, where the minimum (i.e. the best performance) depends mainly on the number of threads and slightly varies with&nbsp;the increase in the number of operations. In the case of 1000 operations,&nbsp;there is also a maximum point (i.e. worst performance) case, after which&nbsp;the latency decreases.
Self labeling online sequential extreme learning machine and it’s application

In the field of big data machine learning, the data volume is large, but the labeled data is few. Due to this, it may lead to that the distribution of labeled data (source domain) is not similar to that of unlabeled data (target domain). In traditional machine learning field, this problem is a kind of transfer learning problems. To address this problem, a self labeling online sequential extreme learning machine is presented, which is called SLOSELM. Firstly, an ELM classifier is trained on the labeled training dataset of the source domain. Secondly, the unlabelled dataset of the target domain is classified by the ELM classifier. In the third step, the high confident samples are selected and the OSELM is employed to update the original ELM classifier. Tested on the real-world image dataset and the daily activity dataset, the results show that our algorithm performs well.
Grammar Learning with Communicative Approach for Foreign Students in BIPA Education and Training

Various opinions also took place when looking at the diverse Indonesian language learning needs of BIPA students. Not only technical language learning in the classroom, but also activities in the implementation of the program to achieve the learning needs of language and culture of BIPA students. Departing from the various problems that exist, the discussion this time will only discuss grammar that can be approved using communicative.Keywords: BIPA Learning, Grammar, Communicative Approach, Learning and Education
Research on Cold-Start Problem in User Based Collaborative Filtering Algorithm

In order to solve the cold-start problem existing in traditional user based collaborative filtering algorithm, we propose a novel user clustering based algorithm, which firstly prefills user-item rating matrix, and then considers user characteristics as well as ratings when computing user similarities, and applies optimized k-means algorithm to cluster users. MovieLens is used as the test dataset. It is proved that the algorithm proposed in this paper can solve the cold-start problem and improve the accuracy of recommendation to some extent.
RANCANG BANGUN APLIKASI SISTEM PENDUKUNG KEPUTUSAN SELEKSI PEMILIHAN DESTINASI PARIWISATA SUMATERA BARAT MENGGUNAKAN METODE ELECTRE

Tourism is one of the activities carried out by humans that has a purpose as entertainment. Human who travel are referred to as tourist. West Sumatra Province is one of the regions with the best tourism is sea tourism and land tourism. Marine tourism consists of beach tourism, island tourism and lake tourism, while mainland tourism consists of tourist panorama, mountain tours, hill tours and others. However, only a portion of West Sumatra's tourist attractions are known to tourists, because of this information is obtained so that there are still many tourist attractions in West Sumatra unknown to tourists from outside the West Sumatra region. To overcome this problem, a system is needed that can be used to support the electoral decision-making process in West Sumatra. This system is built on the Web, using the PHP programming language and Laravel Framework and MySQL as a Database. The method used in this system is the Elimination of Et Choix Traduisant la Realite (ELECTRE). The use of Elimination Et Choix Traduisant la REalite (ELECTRE) can help provide information in West Sumatra, which is in accordance with the wishes of tourists according to the specified criteria. With the help of this system, the information obtained will make it easier for tourists to find tourist attractions.