Reviewerly/exHarmony huggingface dataset:
A Study on Critical Factors Affecting English Language Anxiety in Selected Schools

This paper discussed a study proposed to analyse the critical factors affecting English language anxiety among upper secondary students in selected schools in Malaysia.In countries where English is taught formally as second language, learners faced different levels of anxiety and by individually they had developed specific types of anxiety in learning the English Language.Also, where English was taught as formal school subject, school climate was considered as one of the variables that affects English Language anxiety.Another factor with regards to rapid globalization and modernization was teacher's competency in utilizing technological tools which was also a concern that affects English Language Anxiety.The objective of the research was to study the relationship between school climate and Technology, Pedagogy, and Content Knowledge (TPACK) in affecting English Language Anxiety.An amount of 315 student participants will answer an adapted questionnaire known as the English Language Classroom Anxiety Scale (ELCAS) and a self-developed questionnaire known as School Climate English Language Anxiety (SCELA).Meanwhile, the English Language teachers involved in the study will answer an adopted questionnaire Technology Pedagogical Content Knowledge (TPACK).The data obtained from all three questionnaires will be presented in the form of descriptive statistics and will be analysed using the Statistical Package for Social Science (SPSS) software version 22.
Detecting Abnormal Behaviors in Smart Home

Recent popularity of smart home devices, due to the widespread use of Internet of Things (IoT) technology, has enabled infinite possibilities to provide people with more convenient, safe and healthy life experiences. Among various ideas and applications in smart home environments, the problem of abnormal behaviors detection arises people's attentions and concerns. In our work, we propose to achieve anomaly detection as a one-class classification problem. In particular, we use two machine learning models, which are Autoencoder and one-class Support Vector Machines, to learn the normal behavior patterns and then detect any behavior that deviates from the learned normality. We further use Samsung SmartThings, an open platform for smart homes and the consumer IoT, to build a real-time detection and notification mechanism, and evaluate effectiveness of our model in both offline and online detection scenarios.
Addressing the Polysemy Problem in Language Modeling with Attentional Multi-Sense Embeddings

Neural network language models have gained considerable popularity due to their promising performance. Distributed word embeddings are utilized to represent semantic information. However, each word is associated with a single vector in the embedding layer, disabling the model from capturing the meanings of polysemous words. In this work, we address this problem by assigning multiple fine-grained sense embeddings to each word in the embedding layers. The proposed model discriminates among different senses of a word with attention mechanism in an unsupervised manner. Experiments demonstrate the benefits of our approach in language modeling and ASR rescoring. Investigations are also made on standard word similarity tasks. The results indicate that our proposed method is efficient in modeling polysemy and therefore obtains better word representations.
Seed-Guided Deep Document Clustering

Different users may be interested in different clustering views underlying a given collection (e.g., topic and writing style in documents). Enabling them to provide constraints reflecting their needs can then help obtain tailored clustering results. For document clustering, constraints can be provided in the form of seed words, each cluster being characterized by a small set of words. This seed-guided constrained document clustering problem was recently addressed through topic modeling approaches. In this paper, we jointly learn deep representations and bias the clustering results through the seed words, leading to a Seed-guided Deep Document Clustering approach. Its effectiveness is demonstrated on five public datasets.
An Analysis of Mathematics Contents and Teaching and Learning Methods of International Baccalaureate: Focusing on Primary and Middle School Level

International Baccalaureate(IB) that has been well known as international curriculum has been used by many countries and schools all around the world. The schools usually use IB with their own national curriculum. This study analyzed primary and middle school mathematics contents and teaching and learning methods of IB. As a result, mathematics contents of PYP are similar with Korean primary mathematics curriculum. However, in middle school level, some of mathematics contents of MYP are included in elective subjects in Korean high school. It indicates that the level of mathematics contents of MYP is higher than those of Korean middle school curriculum. In addition, PYP takes a transdisciplinary teaching approach and MYP takes an interdisciplinary teaching approach, so they teach multi subjects with one main theme. They also take learner-centered inquiry oriented teaching method. On the basis of the results, we discussed some implications for Korean mathematics curriculum and textbooks in regard to interdisciplinary integration and the form of curriculum document of IB. We also suggested the necessity of in-depth case study on IB certified schools.
Improved GSO Algorithms and Their Applications in Multi-Target Detection and Tracking Field

Fast detection and high-precision tracking of multiple UAVs are the keys to achieving efficient low-altitude defense.However, as there are many hyper-parameters which need to be optimized in target detection network, commonly used methods such as random search method are computationally intensive and cannot quickly obtain multiple optimal hyper-parameters combinations.In addition, angular random walk due to low-frequency noise of speed sensor in servo loop can cause target tracking accuracy to decrease.Fortunately, those two problems can be regarded as a single-mode function optimization problem and a multi-mode function optimization problem, respectively.In the paper, in order to overcome the aforementioned problems, GSOM (glowworm swarm optimization mutation) algorithm and GSOMLDW (glowworm swarm optimization mutation linearly decreasing weight) algorithm are firstly proposed.Furthermore, the global convergence of GSOMLDW has been proven in the paper, which has not been analyzed in currently available literature.Then, experimental results on four multi-modal benchmark functions have strongly illustrated that the novel GSOM algorithm can enhance glowworms' memory ability and improve peak detection rate effectively.When it is used for optimizing hyper-parameters of multi-target detection network, it can be expected to obtain much more hyper-parameter combination selections.Meanwhile, experimental results on ten uni-modal benchmark functions have obviously demonstrated that GSOMLDW algorithm can balance glowworms' exploration and exploitation abilities powerfully and obtain superior global solution accuracy at last.When the GSOMLDW algorithm is used for servo system identification and drift error model identification, the final position error fluctuation after compensation is almost zero while it reaches 1500 urad before compensation.Consequently, the proposed method can effectively improve target tracking precision.
Risk Analysis of Wild Fire on Electric Grid Transmission Lines Considering Cascading Failure

Wild fire is one of the most serious natural disasters threatening the electric grid. This article analyzes the risk of wild fire on electric grid transmission lines considering cascading failure. In order to meet the timeliness requirement of on-line analysis of power grid under wild fire disaster, an improved sampling method considering the sequence of fire tripping is proposed. Thus, the risk ranking of transmission lines considering cascading failures of wild fire can be obtained, according to that, the optimal emergency strategy can be formulated for the electric grid. The effectiveness and validity of the proposed framework and method are demonstrated by application examples.
Mutual Graph Embedding in LBSN: Capturing Multi-Factor Influence for Point-of-Interest Prediction

With the emerging of various location-based social networks (LBSNs), point-of-interest (POI) prediction becomes a hot research topic which helps to provide personalized location recommendation for users and help them discover new places and activities. Many factors affect user behaviors, such as geographical effect, temporal effect, and social effect. However, most of the previous work mainly focused on incorporating more factors, and lack consideration on combining multi-factor influence with an integrated model. Through investigating the specific graph structure in LBSNs, we propose the Mutual Graph Embedding in LBSNs (MGEL) model which innovatively captures the co-effect of multiple influential factors by incorporating them into POI and user attribute networks. Collaborative prediction is realized through embedding these factors into a shared low-dimensional semantic space. Under this scheme, MGEL is flexible to take new factors into consideration without changing the model structure. We conduct extensive experiments on large real-world datasets. Result shows that when considering the same factors, our method outperforms the state-of-the-art POI prediction models.
Writing Poster at Higher Education: Victor Schwab Theory Analysis

The management changing is started by an election of a new chairman. Supporting academic of the students, English lecturer assigns a project of poster creation of election using English. this research is to find what extent the poster is delivered to its audience. This study is qualitative research with an analytical descriptive as the method. The data are 5 English posters of Students’ election candidates. All of the posters are analyzed based on 5 criteria of Schwab’s good writing poster theory. Content analysis is used to validate the data findings. The study reveals all the 5 posters don’t meet the criteria. However, all of them gives similarity in showing advantage criterion. Next research should assess into a single poster to give a brief explanation of making a good poster.
KEEFEKTIFAN COMPUTATIONAL THINGKING (CT) DAN PROBLEM BASED LEARNING (PBL) DALAM MENINGKATKAN KREATIVITAS SISWA TERHADAP PENYELESAIAN SOAL-SOAL CERITA MATERI PERBANDINGAN (SKALA PADA PETA) DI SEKOLAH DASAR

This study aims to determine whether there is an influence of creativity on student learning outcomes in learning Computational Thingking (CT) and learning with Problem Based Learning (PBL) on solving comparative story material questions (scale on maps) in elementary schools, and to find out whether there is a difference between the acquisition of learning outcomes using Computational Thingking (CT) learning and learning with Problem Based Learning (PBL). This research is a true experimental study using Control group pre-test post test design. The population is Sambiroto 02 Public Elementary School students as many as 75 students in the 2018/2019 school year. The results of the study, namely the average learning outcomes in group E1 (learning with Computational Thinking) 80, with the lowest score of 78 and the highest value of 100. The average learning outcomes in group E2 (learning with Problem Based Learning) 87, with the lowest value of 82 and the highest value of 100 Average learning outcomes in the Control group (Expository learning) 66, with the lowest value 62 and the highest value 76. The effect of creativity on student learning outcomes in the E1 group was 79.95%. The effect of creativity on student learning outcomes in group E2 was 88.12%.
A Detection Method of Abnormal Event in Crowds Based on Image Entropy

The occurrence of group abnormal events will pose a harm to social public safety. In order to improve the detection efficiency of abnormal events in dense populations. This paper proposes an algorithm for crowd abnormal event detection based on image entropy. The algorithm firstly extracts the amplitude of the optical flow of each frame in the video by Farneback optical flow method, and then constructs the image representation of the amplitude of the optical flow. The difference of the amplitude of the optical flow of two consecutive frames will provide us with the characteristic map; A population anomaly event is identified by comparing the entropy difference between the feature maps to a particular threshold. The experimental results show that the algorithm has high detection efficiency and good real-time performance.
Training ASR Models By Generation of Contextual Information

Supervised ASR models have reached unprecedented levels of accuracy, thanks in part to ever-increasing amounts of labelled training data. However, in many applications and locales, only moderate amounts of data are available, which has led to a surge in semi- and weakly-supervised learning research. In this paper, we conduct a large-scale study evaluating the effectiveness of weakly-supervised learning for speech recognition by using loosely related contextual information as a surrogate for ground-truth labels. For weakly supervised training, we use 50k hours of public English social media videos along with their respective titles and post text to train an encoder-decoder transformer model. Our best encoder-decoder models achieve an average of 20.8% WER reduction over a 1000 hours supervised baseline, and an average of 13.4% WER reduction when using only the weakly supervised encoder for CTC fine-tuning. Our results show that our setup for weak supervision improved both the encoder acoustic representations as well as the decoder language generation abilities.
A Novel Relationship Extraction Scheme Based on Negative Feedback Attention

Entity relationship extraction is an important task in natural language processing (NLP). Extracting sentences expressing entity relationship from large knowledge bases (KBs) by distance supervision will cause excessive noise. The existing attention model cannot filter such noise since it is difficult to express well when turning sentences from text such as novels and fictions. We propose a novel entity relationship extraction scheme, in which we add a new negative feedback mechanism to the attention model. The involved negative feedback mechanism can improve the accuracy by considering the contextual information of the corpus to highlight the misleading effect of the turn in sentences. Moreover, in order to retain the semantic information in sentences and optimize the effect of sentence embedding, a variety of features, such as words, parts of speech, distance between non-entity words and entities, are adopted to encode sentences. Besides, the prevalent Bidirectional Long Short-Term Memory (BILSTM) is used as the sentence encoder in the proposed scheme. Experimental results show that our proposed scheme outperforms the traditional entity relationship extraction methods, the traditional attention model and the single characteristic encoder using convolutional neural network (CNN).
TEACHERS, PROFESSIONAL GUIDING PRINCIPLES IN PEDAGOGICAL UNIVERSITY

Statement of the problem. The article deals with professional guiding principles among teachers in a pedagogical university, which are an element of the quality of a staff. The quality problem of the staff of teachers in a pedagogical university is relevant, and the real professional guidelines of teachers that they implement while training future teachers are poorly studied. The purpose of the article is to identify and describe professional guiding principles among teachers in a pedagogical university based on empirical analysis. The methodology of the study is an empirical study based on the conceptual conclusions of a sociological, socio-psychological and pedagogical nature, described in classical works (E. Durkheim, P. Sorokin, L.S. Vygotsky, A.N. Leontiev, V.V. Davydov). The article is based on the materials of a sociological survey conducted by the center for comprehensive sociological research of the KSPU named after V.P. Astaf,iev among teachers of the pedagogical university. Research results. Theoretical analysis and empirical research have shown that teachers,&nbsp;professional guiding principles can be represented by a meaningful relationship between guidelines in their teaching activities and guidelines that are significant in evaluating students. The factor analysis made it possible to identify three significant factors of professional guiding principles among teachers of the pedagogical university: formal – academic; professionalism – adaptation; pedagogical-social. The results obtained showed that the professional guidelines of university teachers are highly dependent and related to their work experience at the university. Conclusion. The research approach to the study of professional guidelines among the teaching staff of a pedagogical university is tested in this study and in this article. The majority (82 %) of the teaching staff of the pedagogical university are teachers with long experience. The very structure of professional guidelines is related and highly differentiated by the length of service of teachers at the university. The effective research continuation could be further development of elements and content of the quality of the teaching staff to train teachers of the future in modern conditions.
Analisa Metode VIKOR pada Rekomendasi Alat Musik Keyboard Electone Terbaik

The purpose of this study is to recommend the best electone keyboard. The method used in this research is Visekriterijumsko Kompromisno Rangiranje (VIKOR) Algorithm where the data source used is obtained from the results of questionnaires and interviews. Decision Support System is an alternative to making appropriate and efficient decisions in the process of selecting electune keyboard instruments. The VIKOR algorithm is used to find the best alternative. The alternatives used are Yamaha PSR S970 (A1), Korg PA 50 (A2), Casio MZX (A3), Technis KN 2000 (A4). The results of the study using VIKOR are in the form of ranking, where the first ranking is Yamaha PSR S97 (A1) and the second is Technis KN 2000 (A4).
Testing the Validity and Reliability of the “Learn, Pick, Flip, Check, Reward” (LPFCR) Card Game in Homophone Comprehension

The "Learn, Pick, Flip, Check, Reward" (LPFCR) Card Game was developed to enhance Primary 4 Pupils' comprehension of homophones.This paper reports the pilot test of the instruments used in the implementation of the game.It aims to examine the validity and reliability of the instruments.Four data collection instruments which were the pre and post tests, observation checklist and questionnaire were analysed statistically and thematically through the method of data analysis.20 Primary 4 pupils of similar characteristics with the actual samples were involved in this pilot study.Findings of the pilot tests revealed that both pre and post tests achieved a high internal consistency and reliability of over, α<0.8.Meanwhile, the observation checklist was analysed thematically and was found to be valid and reliable as similarity in the given themes of observations were obtained.The questionnaire was analysed through SPSS Version 25 and a reliability coefficient of over, α<0.8 was obtained, which also indicated high internal consistency and reliability.These results proved that the instruments are valid and reliable to be used in evaluating the LPFCR card game.
Clinico-pathological study 200 cases of Breast lesions in a tertiary care centre of Rohtas, Bihar, India

Background: Breast cancer is the second most common cancer in Indian women. The incidence of Breast cancer in India is increasing slowly and has bad prognosis if detected in late stages. But, its incidence can be decreased if its detection is made at earliest stages, i.e. in the preinvasive stage. This present study was undertaken to assess clinico-pathological aspect of breast lesions in a tertiary care centre of Rohtas, Bihar. Material and Methods: A retrospective study was conducted in the department of Pathology, NMCH, Jamuhar, Sasaram. The data was collected form medical records department. The study period was of two years from January, 2017 to December, 2018 after approval from institutional ethics committee. Results: A total of 200 cases of breast lesions were analyzed. The ratio of benign to malignant lesions was 3.9:1. The overall mean age of patients with breast lesion was 34.5 years. Among 20.5% malignant cases, the age group affected most was 41-50 years. Only 6% of breast lesions were of inflammatory nature. This result is less as compared to past studies and all the lesions histologically were of chronic non-specific mastitis. The most common type of malignancy observed was invasive carcinoma of no special type (NST). Conclusion: The pattern of breast lesions provides valuable information regarding clinic-pathological profile of patients. The clinical diagnosis of breast lesions should be correlated with histopathological features for correct and adequate management of the case.
Online Identity Theft on Consumer Purchase Intention: A Mediating Role of Online Security and Privacy Concern

This study measures the influence of fear of financial loss (FOFL), fear of reputational damage (FORD), with the mediating effect of online security and privacy concern (OSPCON) towards online purchase intentions in an emerging economy's context. Data was conveniently collected from University students of four of the public higher institutions in Ghana. Out of the 201 questionnaires distributed, 179 were eligible for analysis. A Quantitative methodological approach was adopted which relied on the Partial Least Square approach to Structural Equation Modelling (PLS-SEM) for the statistical analysis. Seemingly, FOFL and FORD constructs were not seen to be a significant direct predictor of online purchase intention. However, the mediating effect of OSPCON for both FOFL and FORD towards online purchase intention in the Ghanaian context was found to be significant, hence the mediated-hypotheses were supported. Nonetheless, we have highlighted the need for additional and further research taking a cue from the study's limitations. The study contributes to our knowledge of how online identity theft practices lead to the unwillingness of online customers to embark on online transactions in an emerging economy, given the rampant outburst of online transactions in the developed world. The originality of this study is in the fact that it focuses on an emerging economy, which is under-researched.
Design and Performance Modeling of A YARN-based GPU Resource Scheduling System

The training of Deep Learning (DL) model can be accelerated significantly with Graphics Processing Unit (GPU). Accessing a server with GPUs attached and training model directly on it is the most common way of using GPUs, but this way may cause unbalanced load and low GPU utilization in multiple-server-multiple-application situation. This paper gives a mathematical definition of this problem and presents a YARN-based solution. Key of the solution is to build a resource scheduling platform with a customized Application Master (AM) optimized for DL. Performance model of the platform is also presented in this paper as well as the proof of higher GPU utilization. The benchmark of training ResNet on ImageNet shows that there is little performance degradation on platform.