Reviewerly/exHarmony huggingface dataset:
Foreign Language Anxiety and English Language Achievement in Higher Education: The Mediating Role of Student Engagement.

The study aims to examine the mediating role of engagement in the relationship between foreign language anxiety and English language achievement. The research design of the study is prediction research design. The participants consisted of 605 English preparatory class students at Foreign Languages Department at university level. Path analysis was used to analyse the data. The results show that student engagement mediated the relationship between foreign language anxiety and English language achievement. It was concluded that when engagement predicted English language achievement, the effect of foreign language anxiety on English language achievement partially disappeared. It means that the effect of foreign language anxiety on the English language achievement is not able to predict solely by engagement in EFL classroom. Therefore, other mediator variables can be investigated in the relationship between foreign language anxiety and English language achievement. Article visualizations:
An Analysis of Students’ Speaking Ability on Specific Purpose of Learning

ABSTRACTThe objectives of this research were to find out the students’ speaking English ability on specific purpose of learning in academic year 2018/2019. This research focus on the specific material in learning at the second semester students’ of Planology Study Program in academic year 2018/2019This research used a descriptive quantitative research. The total number of population of this research was 50 students. The researcher took the sample by using a purposive sampling. Thus, the total number of sample was 20 students. In data collection, this research used oral monologue test. The researcher used descriptive statistics to analyzed the data that indicated general tendencies in the data (range, mean, and median). Based on the data found that the students got minimum score was 45, maximum score was 82, range score was 37 and mean score 63.75. In this case, the students’ speaking English ability on specific purpose of learning were on the good level. It can be concluded that the students were able to speak English on specific purpose of learning
Comparative Analysis of XML Schema Languages for Improved Entropy Metric

The eXtensible Markup Language (XML) is a data set to represent data in a format that is both human readable and machine readable. For XML documents to provide understanding about data exchange between applications, XML schema documents should be validated against the schema language. Most existing schema metrics were implemented differently in Document Type Definition (DTD), XML Schema Definition (XSD) and Regular Language for Next Generation (RNG) but never compare XML schema languages on any metric. Hence this paper compared three different schema languages on Improved Entropy Metric (IEM) using the Number of Attributes (NA), Number of Equivalence Class (NEC), Frequency Occurrence of Class (FOCi) and Number of Elements (NE). The proposed metric was applied on real schemas documents data are acquired from Web Service Description Language (WSDL) and implemented in DTD, XSD and RNG. The result showed that RNG reduce complexity of class elements, reflect strong support for class elements to appear in any order which showed more reusability and flexibility traits and overall understanding of the schema documents becomes much easier because RNG can be algorithmically converted and partner with other schema languages therefore this reduces maintenance effort. Keywords— XML Schema Language, Schema Documents, Schema Metrics
PROFESSIONAL AND PERSONAL QUALITIES OF THE FUTURE TEACHERS OF VOCATIONAL EDUCATION

In this article revealed the professional and personal qualities of the future teacher of vocational training. For quality that characterizes professional pedagogical orientation are also teaching duty andresponsibility. Equipped with the essence of professional and educational activities, orientation of the personality and professional qualities of the future teacher.
The impact of the Story Dogs reading program on student wellbeing: Report for Story Dogs

This is a report prepared for Story Dogs. It was an evaluation of the Story Dog reading program that examined the impact of Story Dogs on student wellbeing. The evaluation employed a mixed methods exploratory design. Findings suggest the program is having a positive impact on student wellbeing.
A Case Study of Cluster-based and Histogram-based Multivariate Anomaly Detection Approach in General Ledgers

The rapid development of financial markets results in data variability and unpredictability. Anomaly detection in financial data is a very important issue. Finding anomalies can result in error reduction and corrections in due time. The main aim of this research was to find anomalies in general ledgers of a real company in Bosnia and Herzegovina. Anomalies are defined as input errors of accountants. Main concepts of anomaly detection are defined, a summary of the current progress is given, and challenges of future work are presented. Cluster-based and histogram-based anomaly detections were performed on a real-life dataset of a microcredit organization. Results of algorithms were presented, as well as results achieved using synthetic data.
The Research on Face Recognition and Segmentation Based on Intelligent Background

Affected by factors such as attitude, light, expression, etc., it is impossible to accurately identify the identity in a wireless visual sensor network in an uncontrollable environment.In traditional visual identity recognition, it is necessary to convert uncontrollable factors into controllable and stable feature factors for identity recognition in a relatively uncontrollable environment where the node distribution is relatively complicated.The conversion process leads to long recognition time and low efficiency.An adaptive recognition method for identity features in wireless visual sensing networks based on LBP face recognition is proposed.A strong classifier is obtained for cascading, and the underlying features are extracted.The final Harr face cascade classifier is applied to the face Check it out.The PCA dimensionality reduction processing of the facial area feature vector is performed to obtain the low-dimensional feature vector, the dimensionality reduction coefficient, and the average face of the person.For the face image in the wireless local area, its LBP operation is given.Perform histogram statistics on face feature information, obtain face LBP histograms, and perform feature matching on the face feature database to complete recognition.The improved algorithm has improved the cumulative matching score of traditional algorithms by 17.8%; the accuracy rate has improved It is 32.7%, and the recognition time is shortened by 3.9s.Simulation results show that the proposed algorithm has high accuracy and recognition efficiency.
LSTM Network and OCR Performance for Classification of Decimal Dewey Classification Code

Background of the study: Giving book code by a librarian in accordance with the Decimal Dewey Classification system aims to facilitate the search for books on the shelf precisely and quickly. Purpose: The first step in giving code to determine the class of books is the principal division which has 10 classes.Method: This study proposed Optical Character Recognition to read the title text on the book cover, preprocessing the text, and classifying it by Long Short-Term Memory Neural Network. Findings: In general, a librarian labeled a book by reading the book title on the book cover and doing book class matching with the book guide of DDC. Automatically, the task requires time increasingly. We tried to classify the text without OCR and utilize OCR which functions to convert the text in images into text that is editable. BY the experimental result, the level of classification accuracy without utilizing OCR is higher than using OCR. Conclusion: The magnitude of the accuracy is 88.57% and 74.28% respectively. However, the participation of OCR in this classification is quite efficient enough to assist a beginner librarian to overcome this problem because the accuracy difference is less than 15%.
The Analysis of Sales by Using Apriori and FP-Growth at PT. Panca Putra Solusindo

Panca Putra Solusindo is a company which sells Electronic equipment. It has a lot of transactions. Among these data, the company has not been able to provide such platform that can be used as information for management to find out which products are most preferred by consumers. Data mining is present to provide information patterns to companies in answering these needs and helping in marketing to be more effective. In this research, the function of the Apriori algorithm association is used to find out the minimum support and minimum confidence and FP-Growth in knowing the frequent itemset and both of them can find out the best rule that occurs in the transaction. These two algorithms are tested by using Weka application version 3.8.
Scheduling of Task in Cloud Environment Using Optimization Algorithms: Survey

Cloud computing, with its huge potential has started evolving as a new age technology. The varied applications of cloud that includes QoS, Charging based on usage, virtualization, offering self-services that are in demand, elasticity etc. make it a viable option for businesses in today’s IT industry. Allocating the optimal task over the available set of resources is termed to be task scheduling. The task scheduling must be done effectively for the sake of maximizing the usage of cloud computing. For this purpose various task scheduling strategies have been adopted. In this paper, a systematic survey on various task scheduling approaches in cloud computing has been carried out and the same has been presented herewith.
AI for reading screening mammograms: the need for circumspection

• The studies on AI reading of screening mammograms have methodological limitations that undermine the conclusion that AI could do better than radiologists. • These studies do not informon numbers of extra breast cancers found by AI that could represent overdiagnosis. • The ability of AI to detect breast cancers is overestimated because there are no result on biopsy procedures that should be performed when mammograms are positive at AI reading but not at radiology reading.
Investigating the Challenges and Methodologies of Sentiment Analysis

Humans act as a social animal and drive on intuitions. These intuitions are the result of sentiments which one perceived through the life experiences. Applying analytics on Opinion Mining can help governments, businesses and educational institutions to define policy that best suits the consumer base. This study aims to review researches on sentiment analysis where techniques vary from early attempts to newly developed. The researches implementing supervised and unsupervised techniques which would identify the hidden sentiments from the texts have been categorized and discussed. Challenges and methodologies are the key points for selection of literatures. Tabular comparisons of the past researches have been provided. This paper is designed to provide a precise study for Sentiment Analysis.
An Incremental Learning Framework for Skeletal-based Hand Gesture Recognition with Leap Motion

Hand gesture recognition has become the focus of researchers lately because of its manifold applications in various fields. Leap Motion (LM) is a device to obtain useful and accurate information of the hand action, which is suitable for collecting the three-dimensional (3D) human hand gesture. In this paper, a novel framework which consists of an incremental learning (IL) algorithm without deep structure is proposed and applied to hand gestures classification that explicitly aimed to the LM data. The same datasets are used to train the proposed framework and the conventional Long Short Term Memory Recurrent Neural Network (LSTM-RNN). Due to the structural advantage of the proposed model, the recognition performance is improved distinctly in robustness and training time than the LSTM network. Moreover, convincing experiment results are given to illustrate that the solution is more efficient in static gesture classification.
Enriching the Transformer with Linguistic and Semantic Factors for Low-Resource Machine Translation.

Introducing factors, that is to say, word features such as linguistic information referring to the source tokens, is known to improve the results of neural machine translation systems in certain settings, typically in recurrent architectures. This study proposes enhancing the current state-of-the-art neural machine translation architecture, the Transformer, so that it allows to introduce external knowledge. In particular, our proposed modification, the Factored Transformer, uses factors, either linguistic or semantic, that insert additional knowledge into the machine translation system. Apart from using different kinds of features, we study the effect of different architectural configurations. Specifically, we analyze the performance of combining words and features at the embedding level or at the encoder level, and we experiment with two different combination strategies. With the best-found configuration, we show improvements of 0.8 BLEU over the baseline Transformer in the IWSLT German-to-English task. Moreover, we experiment with the more challenging FLoRes English-to-Nepali benchmark, which includes both extremely low-resourced and very distant languages, and obtain an improvement of 1.2 BLEU. These improvements are achieved with linguistic and not with semantic information.
Use of two inpainting Techniques to Restore Partially detected Cartographic Features

The continuous use of methodologies to extract cartographic features of digital images have been of great importance in the area of cartography. Many techniques can be used by features extraction processes, however, the results obtained by these techniques usually have partially detected features, culminating in loss of quality of the extraction process. To keep searching for better results, it is possible to use techniques based on inpainting, that has as its main purpose image restoration and removal of occlusions. Therefore, the main objective of this article is to show a methodology of reconstruction of partially detected features using two inpainting techniques proposed by [1] and [2], aiming to improve the quality of results in the process of extraction of cartographic features and digital images. Observing the final analysis of the results obtained with the techniques in three entry images, the technique of [1] showed an improvement of 0.61% compared to the extracted feature.While the technique of [2], an improvement of 6.82%.The good results obtained regarding the improvement of the quality of the process of extraction of partially detected cartographic features will be of great use in the area of cartography.
Classification of potential blood donors using machine learning algorithms approach

Blood donation is the process of taking blood from someone used for blood transfusions. Blood type, sex, age, blood pressure, and hemoglobin are blood donor criteria that must be met and processed manually to classify blood donor eligibility. The manual process resulted in an irregular blood supply because blood donor candidates did not meet the criteria. This study implements machine learning algorithms includes kNN, naïve Bayes, and neural network methods to determine the eligibility of blood donors. This study used 600 training data divided into two classes, namely potential and non-potential donors. The test results show that the accuracy of the neural network is 84.3 %, higher than kNN and naïve Bayes, respectively of 75 % and 84.17 %. It indicates that the neural network method outperforms comparing with kNN and naïve Bayes.
Specific Emitter Identification Over Fading Channels

In this paper, specific emitter identification(SEI) problem has been investigated over fading channel. Different emitters are distinguished by the non-linear nature of the power amplifiers used by the emitters. This non-linearity is the fingerprint for the specific emitters. Based on this non-linearity SEI problem is solved. In this paper, we have considered several features like energy entropy, first and second order moment and correlation coefficient, which are calculated from the intrinsic mode functions(IMFs) obtained by using Variational mode decomposition(VMD). The SEI performance using EMD-EM2 method is compared with VMD-EM2 over fading channel, where entropy and first, second order moments are calculated from the Hilbert spectrum. The performance in terms of probability of correct identification for the second method is better than the first method for both single-hop and relaying case. Also the performance of VMD-CB is superior to VMD-EM2.
NAIVE BAYES ALGORITHM IMPLEMENTATION TO DETECT HUMAN PERSONALITY DISORDERS

We live in a society that still sees problems regarding one's soul and personality as taboo, even though mental health is as important as physical health. A personality disorder itself is a disorder that can be seen from behavior, mindset, and attitude, which brings difficulties to life. Based on this problem, this study applies the method of Naive Bayes classifier as early detection of human personality disorders. Using a data set of 130 correspondences from the AMIKOM university scope with the age limit of 18-25 years and identified personality disorders is a borderline type disorder. The data obtained was 94 with undiagnosed classes and 36 with undiagnosed classes, with the research variables in the form of questionnaire questions as many as 13 questions. The testing process is done with 10 fold and 5 fold cross-validation, and confusion matrix with the results in the form of accurate 10 folds superior with a value of 88.8% compared to 5 folds that is 88.2%, for precision 10 folds superior with 88.7%, but for 5 fold recall superior with 88.3%, while the final results of these two performances in F1-Score, produce the same value, which is 86.1%.
GraphWGAN: Graph Representation Learning with Wasserstein Generative Adversarial Networks

Graph representation learning aims to represent vertices as low-dimensional and real-valued vectors to facilitate subsequent downstream tasks, i.e., node classification, link predictions. Recently, some novel graph representation learning frameworks, which try to approximate the underlying true connectivity distribution of the vertices, show their superiority. These methods characterize the distance between the true connectivity distribution and generated connectivity distribution by Kullback-Leibler or Jensen-Shannon divergence. However, since these divergences are not continuous with respect to the generator's parameters, such methods easily lead to unstable training and poor convergence. In contrast, Wasserstein distance is continuous and differentiable almost everywhere, which means it can produce more reliable gradient, allowing the training more stable and more convergent. In this paper, we utilize Wasserstein distance to characterize the distance between the underlying true connectivity distribution and generated distribution in graph representation learning. Experimental results show that the accuracy of our method exceeds existing baselines in tasks of both node classification and link prediction.
An Exploration of Recurrent Units for Automatic Speech Recognition with RNN based Acoustic Model

Recurrent neural network (RNN) has become a popular technology for automatic speech recognition (ASR). However, the vanilla RNN is difficult to train due to the problem of vanishing gradient and thus has poor performance. Some units with gate mechanism have been proposed to solve the problem, such as gated recurrent unit (GRU), long short-term memory (LSTM), projected LSTM (LSTMP), projected GRU (PGRU) and output-gated PGRU (OPGRU). In this work, we aim to evaluate the performance of above RNN units for acoustic modeling in a Mandarin ASR task. We evaluate three conditions, including unidirectional RNN, bidirectional RNN (BRNN) and time delay neural network (TDNN) – RNN. The experiments were done on Aishell-1 corpus by using Kaldi toolkit. The results show that PGRU gets the best performance on all three conditions and its model size is also smaller than that of LSTM and LSTMP.
Live project: understanding the design process from the project brief to post occupancy evaluation

The live project was delivered to second year students in the BA (Hons) Architecture program at Birmingham School of Architecture and Design at Birmingham City University in the academic year 2018/19. The students were asked to design an external structure for an outdoor area in a Montessori Kindergarten. They had the option of either designing a tree house, a new play house, a shelter for the forest school or an amphitheatre. All projects were to be located in the extended garden of the nursery on adjacent sites. After the initial design stage ended in February 2019 with a presentation of all projects to the client (the nursery managers), two selected projects were then built in June 2019, during the school’s events week with a group of students. A few weeks after the hand-over of the completed projects, some of the students as well as the tutors went back to the nursery to conduct a Post Occupancy Evaluation (POE) of the projects. In order to experience the design and construction phases, this live project was composed in eight stages, directly related to the RIBA Plan of Work, which “recognises the stages that a building project goes through and promotes the importance of recording and disseminating information about completed projects.”vi The project therefore required the students to integrate structural and technological solutions into their design proposals
Deep Neural Network Comparison for Spanish Tweets Polarity Classification

Two deep neural network models were compared in the task of polarity classification in Spanish text retrieved from social networks. For each model accuracy, precision, recall and F1 was calculated over a particular corpus. Also, the effect of adding gaussian noise on the inputs over the classifier results was evaluated.
Eaglesong: an ARX hash with fast diffusion

We propose a hash function based on three design principles: the sponge construction, ARX operations, and the wide trail strategy. While the sponge construction applies generically to any sufficiently strong permutation, the wide trail strategy and the ARX operations are naturally somewhat incompatible. We show that while the ARX operations provide only very weakly nonlinear S-boxes, it is possible to build very strong linear diffusion layers with them. As a result, the wide trail argument, which bounds the attacker’s success probability in terms of the minimum number of active S-boxes across two rounds, survives. The proposed hash function is one of a very select group of ARX ciphers featuring rigorous bounds against differential and linear cryptanalysis.
Improvement and Application of Apriori Algorithm Based on Equalization

In the era of information explosion, data collection and analysis have become more and more important, and association rule mining has become an important research area of the data mining field. As a classic association rule algorithm, Apriori often falls into the problem of frequently scanning the database and generating a large number of candidate sets. Aiming at these problems, an improved Apriori algorithm MD_Apriori based on the idea of equalization is proposed, which adopts the vertical data structure to mine frequent items and then the proposed approach is applied to mining frequent items of massive alarm information. Experimental results show that the proposed algorithm is significantly superior to the current association rule mining algorithms.