Reviewerly/exHarmony huggingface dataset:
Academic Performance Predicting Model based on Machine Learning and Keller's Motivation Measure

This article investigates a model for predicting the academic performance of university students using Machine Learning techniques based on the level of motivation achieved with the implementation of the ARCS instructional model and the use of a technological tool called Arduino Science Journal used for learning Topics related to the kinematics of bodies. Analready validated methodology focused on motivation was implemented,which was quantified through the Instructional Material Motivational Survey (IMMS) instrument, which was applied toa group of 36 students of the Kinematics and Dynamics subject from a Civil Industrial Engineering career. Machine learning techniques were used to predict academic performance based on regression algorithms. The results show that Confidence was the IMMS dimension with the best prediction results. At the sametime, the Support Vector Regression algorithm achieves the lowest mean absolute error in the estimated academic performance. This research provides a prediction model of academic performance through emotional variables of the students, showing the potential to act as an early warning system, helping teachers to manage students' academic performance, and allowing students to self assess their performance.
Is refactoring a solution to resolve code smell

In the software industry, systems are often enhanced to meet the requirements of the customer. During the evolution process, various flaws in the structure or design of the code, often known as code smells, are introduced, which might reduce the quality of the software system. Code smells can be removed by refactoring, which is one of the most important approaches for increasing the quality of a piece of software. In this study, twenty projects have been taken from the public repository, Github. Using the Eclipse-based tool Jdeodorant, code smells are recognised, and automated refactoring is applied to eliminate the code smells. An analysis is carried out that refactoring can remove one code smell, but it can also introduce other code.
METHODS OF IMPROVING STAFF MOTIVATION SYSTEM IN EDUCATIONAL INSTITUTIONS

Staff motivation is one of the pressing issues in management of organizations in any field of activity. This also applies to education, since the activities of employees of educational institutions have an impact on the quality of educational services and, subsequently, on the development of the state. Therefore, the administration of educational institutions should pay special attention to staff motivation. The purpose of the article is to identify methods to improve the motivation system and to achieve a balance of internal and external motivation of secondary and higher education staff. This will increase their productivity and provide education system with highly qualified specialists and will also maximize the effectiveness of their theoretical knowledge and professional skills. Using the methods of analysis, synthesis and generalization, the essence of the main concepts of the motivation system has been revealed and the classification of motivational methods has been carried out. Based on the comparative method, an analysis of the level of the average salary in Ukraine’s education system and the number of its employees compared to other types of economic activity has been made. This revealed the crisis and the general financial unattractiveness of work in the education sector of the economy. A survey was conducted among teachers of higher and secondary education institutions of Kharkiv region in order to determine their motivating factors. Problems in meeting economic and socio-psychological needs of both groups of teachers have been identified. The need to feel satisfied with the process and the result of work is significantly reduced due to the low returns of pupils and students. The importance of taking into account the difference in the mentality of generations in understanding their internal motives has been substantiated. The need to optimize bureaucratic processes and technologies used in distance learning has been proven. The use of the obtained results by management of educational institutions will allow reducing the influence of demotivating factors such as unfair pay, lack of free time, stressful working conditions on the overall level of staff motivation and improving the motivation system of secondary and higher education institutionss
A Comparative Analysis of Sentence Embedding Techniques for Document Ranking

Due to the exponential increase in the information on the web, extracting relevant documents for users in a reasonable time becomes a cumbersome task. Also, when user feedback is scarce or unavailable, content-based approaches to extract and rank relevant documents are critical as they suffer from the problem of determining semantic similarity between texts of user queries and documents. Various sentence embedding models exist today that acquire deep semantic representations through training on a large corpus, with the goal of providing transfer learning to a broad range of natural language processing tasks such as document similarity, text summarization, text classification, sentiment analysis, etc. So, in this paper, a comparative analysis of six pre-trained sentence embedding techniques has been done to identify the best model suited for document ranking in IR systems. These are SentenceBERT, Universal Sentence Encoder, InferSent, ELMo, XLNet, and Doc2Vec. Four standard datasets CACM, CISI, ADI, and Medline are used to perform all the experiments. It is found that Universal Sentence Encoder and SentenceBERT outperform other techniques on all four datasets in terms of MAP, recall, F-measure, and NDCG. This comparative analysis offers a synthesis of existing work as a single point of entry for practitioners who seek to use pre-trained sentence embedding models for document ranking and for scholars who wish to undertake work in a similar domain. The work can be expanded in many directions in the future as various researchers can combine these strategies to build a hybrid document ranking system or query reformulation system in IR.
Optimal Trained Bi-Long Short Term Memory for Aspect Based Sentiment Analysis with Weighted Aspect Extraction

Sentiment analysis based on aspects seeks to anticipate the polarities of sentiment in specified targets related to the text data. Several studies have shown a strong interest in using an attention network to represent the target as well as context on generating an efficient representation of features used for tasks while sentiment classification. Still, the attention score computation of the target using an average vector for context is unequal. While the interaction mechanism is simplistic, it needs to be overhauled. Therefore, this paper intends to introduce a novel aspect-based sentiment analysis with three phases: (i) Preprocessing, (ii) Aspect Sentiment Extraction, (iii) Classification. Initially, the input data is given to the preprocessing phase, in which the tokenization, lemmatization, and stop word removal are performed. From the preprocessed data, the weighted implicit and weighted explicit extraction is determined in the Aspect Sentiment Extraction. Moreover, the weighted implicit aspect extraction is done by Stanford Dependency Passer (SDP) method, and the weighted explicit extraction is done through proposed Association Rule Mining (ARM). Subsequently, the extracted features are provided to the classification phase in which the Optimized Bi-LSTM is utilized. For making the classification more accurate and precise, it is planned to tune the weights of Bi-LSTM optimally. For this purpose, an Opposition Learning Cat and Mouse-Based Optimization (OLCMBO) Algorithm will be introduced in this work. In the end, the outcomes of the presented approach are calculated to the extant approaches with respect to different measures like F1-measure, specificity, Negative Predictive Value (NPV), accuracy, False Negative Rate (FNR), sensitivity, precision, False Positive Rate(FPR), and Matthew’s correlation coefficient, respectively.
On the Design of a Modular Wearable Solution for Assistive Technologies

This paper introduces the VibroWear architecture, a solution with a modular design at both hardware and software levels, that integrates an energy-aware engine in order to increase operational autonomy. By providing means of defining policies regarding energy consumption at module or I/O function level, the solution aims at adjusting the operational state of the modules (i.e. full, partial or decreased I/O activity, adjustable sample rates).
Efficient Blind Estimation of Parameters for Multiple Frequency Hopping Signals via Single Channel

Under the condition of single channel interception, blind parameter estimation of multiple frequency hopping (FH) signals intercepted in battlefield can effectively support the tasks of FH networks sorting, jamming and information recovery. It is a hot and difficult issue in the field of communication signal reconnaissance and processing. In this paper, a blind parameter estimation algorithm for multiple FH signals based on the combination of STFT and SPWVD (STFT & SPWVD) is proposed. This algorithm is simple in calculation and adaptable to both synchronous and asynchronous networks. First, the STFT & SPWVD combined time-frequency distribution is used to obtain a clear and robust time-frequency diagram; After that, the frequency, center time and duration of each hop of the FH signals are estimated based on the time-frequency diagram; Then, based on the above information, an efficient blind estimation method is designed for the hop period, frequency set and hop timing parameters of multiple FH signals in different networking modes; Finally, the complete calculation process of the algorithm is summarized, and the effectiveness of the new algorithm is verified by theoretical analysis and Monte Carlo simulation.
A Novel Multimodal Hybrid Classifier Based Cyberbullying Detection for Social Media Platform

Platforms for social networking such as Facebook and Twitter, as well as others, provide a great number of benefits, but they also come with a great number of drawbacks. Cyberbullying is one of the problems that may occur on these social sites. The effect that cyberbullying has on the lives of its victims is incalculable. It is quite subjective, and the approach that each individual would take to this differs greatly. For those who are bullied, the message may seem natural, but for others, it may be intimidating. The ambiguity that may be found in cyberbullying texts makes it very difficult to locate the bullying material. The use of textual postings has been the subject of a significant amount of study, which has been recorded. In this work Extreme Learning Machine (ELM) based cyberbullying detection is proposed. Convolutional Neural Network (CNN) is used to classify emoji posted in the tweets. The text, hashtag, emoji datasets are preprocessed, and features are extracted. ELM classifier is used to detect the cyberbullying. This work achieves accuracy of 99.41, precision of 92.76, recall of 90.17, and f1-score of 91.64.
Comparison of classifiers for use case detection using onboard smartphone sensors

Onboard smartphone sensors provide ample data modalities which can be used to determine the way a phone is being used. However, in order for use case detection systems to be unobtrusive to users, the classification algorithms and the number of sensors should be kept simple and at a minimum. In this paper light, accelerometer and orientation sensor measurements are recorded for 4 different phone use cases and results from 3 different classifiers (K-means, Naive-Bayes, Neural Network) are compared to identify the sensor modality and classification algorithm that provides the highest accuracy for use case detection. The onboard accelerometer is found to be the sensor modality with highest accuracy across all the classifiers, and the neural network is identified as being the best performing classifier. A discussion of the results linking back to theoretical aspects of the classifiers is also given.
A Framework for Recognizing Industrial Actions via Joint Angles

This paper proposes a novel framework for recognizing industrial actions, in the perspective of human-robot collaboration. Given a one second long measure of the human's motion, the framework can determine his/her action. The originality lies in the use of joint angles, instead of Cartesian coordinates. This design choice makes the framework sensor agnostic and invariant to affine transformations and to anthropometric differences. On AnDy dataset, we outperform the state of art classifier. Furthermore, we show that our framework is effective with limited training data, that it is subject independent, and that it is compatible with robotic real-time constraints. In terms of methodology, the framework is an original synergy of two antithetical schools of thought: model-based and data-based algorithms. Indeed, it is the cascade of an inverse kinematics estimator compliant with the International Society of Biomechanics recommendations, followed by a deep learning architecture based on Bidirectional Long Short Term Memory. We believe our work may pave the way to successful and fast action recognition with standard depth cameras, embedded on moving collaborative robots.
Differential confocal measurement of microstructure surface topography based on centering error optimization and wavelet threshold denoising

This paper proposes a precise differential confocal measurement method based on centering error optimization and wavelet threshold denoising (DCM-COWD), which can realize non-contact measurement of microstructure surface topography with high precision and efficiency. Firstly, the influence of pinhole diaphragm size and offset on axial response curve is analyzed through multiple experiments to obtain the optimal pinhole diaphragm diameter and offset of the differential confocal measurement system. Secondly, a probe centering method based on grating rotation successive approximation is designed, in order to reduce the centering error of 3D topography measurement during the spiral scanning process. Thirdly, a translation invariant (TI) wavelet threshold denoising algorithm based on median filtering and whale optimization (WOA) is proposed, which can improve the signal-to-noise ratio (SNR) and reduce the root mean square error (RMSE), and effectively suppress the Pseudo-Gibbs phenomenon of discontinuous points. Finally, the surface topography of a standard sample is measured based on DCM-COWD system. The measurement results show that the center error is within 0.75 μm and the maximum deviation is within 1.45 %. The measurement results show high accordance with the commercial white light interferometer, and the processing speed is improved by 4.17 times. The proposed method has great application potential for the precise and efficient measurement of 3D surface topography.
Introduction

Of all words that have some degree of specialized scientific meaning, scale is one of the most ambiguous and overworked. Webster devotes over half a column to the meaning of the noun, more even than the amount devoted to other similarly overworked terms like space and system. “Scale” is used to refer both to the magnitude of a study (e.g., its geographic extent) and also to the degree of detail (e.g., its level of geographic resolution). Geographic scale is important because it defines the limits to our observations of the Earth. All Earth observation must have a small linear dimension, defined as the limiting spatial resolution, the size of the smallest observable object, the pixel size, the grain of the photographic emulsion, or some similarly defined parameter. Observation must also have a large linear dimension, defining the geographic extent of the study, project, or data collection effort.
Digitizing SU(2) gauge fields and what to look out for when doing so

With the long term perspective of using quantum computers and tensor networks for lattice gauge theory simulations, an efficient method of digitizing gauge group elements is needed.We thus present our results for a handful of discretization approaches for the non-trivial example of SU(2), such as its finite subgroups, as well as different classes of finite subsets.We focus our attention on a freezing transition observed towards weak couplings.A generalized version of the Fibonacci spiral appears to be particularly efficient and close to optimal.
The number of solutions of cubic diagonal equations over finite fields

&lt;abstract&gt;&lt;p&gt;Let $ p $ be a prime, $ k $ be a positive integer, $ q = p^k $, and $ \mathbb{F}_q $ be the finite field with $ q $ elements. Let $ \mathbb{F}_q^* $ be the multiplicative group of $ \mathbb{F}_{q} $, that is $ \mathbb{F}_q^* = \mathbb{F}_{q}\setminus\{0\} $. In this paper, explicit formulae for the numbers of solutions of cubic diagonal equations $ a_1x_1^3+a_2x_2^3 = c $ and $ b_1x_1^3+b_2x_2^3+b_3x_3^3 = c $ over $ \mathbb{F}_q $ are given, with $ a_i, b_j\in\mathbb{F}_q^* $ $ (1\leq i\leq 2, 1\leq j\leq 3) $, $ c\in\mathbb{F}_q $ and $ p\equiv1(\rm{mod} \ 3) $. Furthermore, by using the reduction formula for Jacobi sums, the number of solutions of the cubic diagonal equations $ a_1x_1^3+a_2x_2^3+\cdots+a_sx_s^3 = c $ of $ s\geq 4 $ variables with $ a_i\in\mathbb{F}_q^* $ $ (1\leq i\leq s) $, $ c\in\mathbb{F}_q $ and $ p\equiv1(\rm{mod} \ 3) $, can also be deduced.&lt;/p&gt;&lt;/abstract&gt;
A Domain Specific Parallel Corpus and Enhanced English-Assamese Neural Machine Translation

Machine translation deals with automatic translation from one natural language to another. Neural machine translation is a widely accepted technique of the corpus-based machine translation approach. However, an adequate amount of training data is required, and there is a need for the domain-wise parallel corpus to improve translational performance that shows translational coverages in various domains. In this work, a domain-specific parallel corpus is prepared that includes different domain coverages, namely, Agriculture, Government Office, Judiciary, Social Media, Tourism, COVID-19, Sports, and Literature domains for low-resource English-Assamese pair translation. Moreover, we have tackled data scarcity and word-order divergence problems via data augmentation and prior alignment concept. Also, we have contributed Assamese pre-trained LM, Assamese word-embeddings by utilizing Assamese monolingual data, and a bilingual dictionary-based post-processing step to enhance transformer-based neural machine translation. We have achieved state-of-the-art results for both forward (English-to-Assamese) and backward (Assamese-to-English) directions of translation.
Reduction of uncertainty using adaptive modeling under stochastic criteria of information content

Entropy is the concepts from the science of information must be used in the situation where undefined behaviors of the parameters are unknown. The behavior of the casual parameters representing the processes under investigation is a problem that the essay explores from many angles. The provided uniformity criterion, which was developed utilizing the maximum entropy of the metric, has high efficiency and straightforward implementation in manual computation, computer software and hardware, and a variety of similarity, recognition, and classification indicators. The tools required to automate the decision-making process in real-world applications, such as the automatic classification of acoustic events or the fault-detection via vibroacoustic methods, are provided by statistical decision theory to the noise and vibration engineer. Other statistical analysis issues can also be resolved using the provided uniformity criterion.
The Application of YouTube Media in Learning Speaking: Students’ Perceptions

English has an important role as a global language, especially in the era of globalization. To master English, there are four types of proficiency skills such as listening, speaking, reading, and writing. Speaking is the simplest form of communication in life. However, the teaching techniques in Indonesia used by teachers are generally unattractive and monotonous. Students are only provided with textbooks and conventional materials; therefore, it affects students' opportunities to improve their speaking skills. This study aimed to determine students' perceptions of YouTube as a learning media to improve English-speaking skills. This study used a mixed method which cover quantitative and qualitative designs. Thus, the instruments include the questionnaires distributed via Google Form to 215 of the 11th grade students of State Senior High School No. 58 Jakarta and interview for selected students. The result showed that there were five aspects of student perceptions such as: convenience, motivation, progress, activity, and variation. The largest percentage value was obtained by variation aspect in positive perception of 58.37%. The students' perception of YouTube as a learning media was then positive. In addition, YouTube had a positive impact by proving students’ progress in English-speaking skills.
Figshare: A One-Stop Shop for Research Data Management with Diverse Features and Services

Research data management is recognized as an important concern for institutions. Various academic and research institutions today maintain different institutional repositories operating on different platforms to save and share research data. Researchers require a platform that ensures maximum accessibility, stability and reliability to share research data. Sharing data makes it possible to replicate studies, formulate new research questions, and generate new knowledge. One such platform used for sharing and preserving research data is “Figshare”. The purpose of this paper is to look at the journey of the Figshare platform and investigate the usefulness of this platform for sharing and preserving data. This paper provides an understanding of the platform for research institutions to employ for data management purposes. The research design of this article consists of non-empirical research majorly based on literature review and analysis. The literature analysis focused on the architecture, metadata handling capacities, interoperability, compatibility and services offered to researchers and research institutions by this platform. Focused literature searches were conducted across various electronic databases and this study tried to explore the solution that Figshare offers in supporting research data. Further, the article outlines the benefits of the Figshare repository and shows how this registry helps in the appropriate storage and sharing of research data. This article presents the potential of the Figshare platform that can be applied and used in different research disciplines for preserving and sharing research data.