Reviewerly/exHarmony huggingface dataset:
A User-friendly Semi-automatic iStar Modeling Approach

iStar modeling is beneficial in the early stage of requirements engineering, helping requirements analysts to analyze requirements and improve the efficiency and quality of the software development procedure. However, it is time-consuming and hard to learn to perform the iStar modeling manually, which can be more practical if the modeling process is automated.To facilitate the distribution of iStar practices, we designed a user-friendly semi-automatic iStar modeling approach to assist users in iStar modeling by extracting model elements from natural language requirement artifacts. Specifically, based on the analysis of the actual modeling process via interviewing, this work proposed an iStar modeling process, and automated three modeling steps: the actor entity extraction, the actor relation extraction, and the intention entity extraction. Then, this work proposes a hybrid method for natural language processing to extract the model elements in requirement sentences to automate the modeling steps. This hybrid method consists of two parts: the deep learning-based method and the logical reasoning method, which utilizes both methods simultaneously, ensuring the high accuracy of the results. Overall, this work proposed a user-friendly semi-automatic approach for aiding the iStar modeling, which proposes an iStar modeling process and automates many steps with hybrid natural language method during the process. We evaluated our proposed approach, and the results show that our proposed approach is efficient and helpful.
RPG-based digital media for mathematics learning post Covid-19 pandemic: Indonesian student's acceptance

Observing the conditions of the current Industrial Revolution 4.0 era requires skills to use digital technology. The use of digital technology, especially smartphones in Indonesia is very high where children and adolescents of school age dominate users, and they use it for games. Children who are still in the development stage to learn have a high interest and prefer to play digital games in RPG. This problem must be solved, considering again in the Covid-19 pandemic era where control learning through direct interaction is limited and given a distance. At the same time, students must be able to learn independently. An innovation effort on this issue is to develop digital educational games to shift high interest in games to be more beneficial to learning activities. To know for sure is necessary to analyze the acceptance of Indonesian students to the media and its aspects with the technology acceptance model (TAM). This is descriptive quantitative research with a survey method. Participants filled out an online questionnaire about media acceptance in mathematics learning, especially during the Covid-19 pandemic, which developed using the TAM. Of the 608 Indonesian students who gave descriptive statistics agree on accepting games for learning during the Covid-19 pandemic.
Manifold learning for dynamic array geometries

Large-scale distributed arrays can obtain high spatial resolution, but they typically rely on a rigid array structure. If we want to form distributed arrays from mobile and wearable devices, our models need to account for motion. The motion of multiple microphones worn by humans can be difficult to track, but through manifold techniques we can learn the movement through its acoustic response. We show that the mapping between the array geometry and its acoustic response is locally linear and can be exploited in a semi-supervised manner for a given acoustic environment. We will also investigate generative modelling of microphone positions based on their acoustic response to both synthetic and recorded data. Prior work has shown a similar locally linear mapping between source locations and their spatial cues, and we will attempt to combine these findings with our own to develop a localization model suitable for dynamic array geometries.
Unsupervised Image Dehazing Using Smooth Approximation of Dark Channel Prior

In this paper we propose a new unsupervised deep learning method for single image dehazing. The method is based on a new loss function that incorporates a smooth approximation of the famous dark channel prior. The method is used to train a neural network and results are compared to state of the art results of supervised neural networks. Evaluation is done by comparing increase in object detection on dehazed images as well as by visual inspection of results of dehazing on natural images.
Exploratory Data Analysis for the Automatic Detection of Question Paraphrasing in Collaborative Environments

Internet searches are a daily occurrence, but we must be aware that more than one person searches the same topic with different words, this is called paraphrasing. Paraphrasing involves syntactic changes and the overlapping of words, linked to the rules of the language in which we work. The identification is a problem of great importance for natural language processing (NLP), especially paraphrasing questions with the same intention. In addition, it has been found that for the study of similarities, some features are not taken into account, which makes the identification yield lower results. In this paper, we address the problem of automatic paraphrase identification in the Quora Question Pair (QQP) dataset, paying special attention to data’s shape through exploratory data analysis (EDA). This is in order to obtain better results in the identification tasks, as well as to compare different classifiers in collaborative environments where resources are limited.
SETFF: A Semantic Enhanced Table Filling Framework for Joint Entity and Relation Extraction

In the study of text understanding and knowledge graph construction, the process of extracting entities and relations from unstructured text is crucial. Lately, joint extraction has achieved more significance in this context. Among them, table filling based method has attracted a lot of research in solving the problem of overlapping relation in complex scenarios. However, most existing table filling works need to deal with many invalid and redundant filling processes. At the same time, some semantic information is not fully considered. For instance, a token should have differentiated semantic representation when decoding triples under different relations. Moreover, the global association information between different relations is not fully utilized. In this paper, we propose a joint extraction framework: SETFF, based on table filling. Firstly, the proposed method filters out the possible relations in sentences through a relation filtering module. Then following the attention mechanism, the pre-trained relation embeddings are used to enhance the differential representation of token semantics under specified relations and obtain the mutual prompting information between different relations. In addition to these, extensive experimental results show that SETFF can effectively deal with the overlapping triples problem and achieve significant performance on two public datasets.
An Analysis on Users’ Emotions Based on Micro-blog in the Context of Prevention and Control of COVID-19 Epidemic

In the era of information explosion, there is information of high value hidden behind a large amount of data.As a platform popular with users, micro-blog contains a large amount of data on users' comments and the analysis of these comments is conducive to grasping the emotions and attitudes of micro-blog users and trends of social public opinions, contributing to the management department to monitor and induce public opinions.The author of this thesis collects data about comments on micro-blog on the subject of COVID-19 Epidemic, divides general data into two periods in time dimension according to the same length of time span, makes emotion analysis on these two data groups by use of the support vector machine, extracts the themed hot words and analyzes reasons for such changes in emotions of micro-blog users.There is a high proportion of negative emotions in both periods, but users' emotions also have corresponding changes in both time periods along with the changes of hot topics.
Chinese Named Entity Recognition Method Based On Multi-head Attention Enhancing Word Information

Chinese named entity recognition (CNER) is one of the important tasks in natural language processing. Unlike the English, Chinese lacks explicit word boundaries. Therefore, many models were designed to address this issue by incorporating word lexicon information into the CNER. However, lots of irrelevant information may be included when matching the entire lexicon for each character. Inspired by the SoftLexicon method, we propose a multi-head attention based model to simplify the introduced lexicon information to generate word-level attention vector. In this method, a word vector matched for each character is first obtained and further weighted by the relevance with the character-level vector to calculate the word-level attention vector. In this way, only the words existing in the sentence are matched, which reduces the scope of word matching. The effectiveness of this method is verified on multiple Chinese datasets.
Analysis of the Paskibraka Acceptance Selection Sensitivity Test Using Topsis and Promethee Methods at the Youth and Sports Office of Bengkulu City

The Youth and Sports Office of Bengkulu City is a selection committee for Paskibraka candidates which is carried out every year. The assessment of post-baraka candidates consists of 8 aspects, namely Parade, PBB, Psychotest, Samapta/Physical, Health and Fitness, Interview, Regional Arts, and General Knowledge (data attached). The results of the assessment will be recapitulated to determine which participants succeeded in passing and which participants failed to pass, so that from this recapitulation it can be announced at the end of the selection activity. The analysis of the sensitivity test for the selection of paskibraka admissions using the TOPSIS method and the Promethee method at the Bengkulu City Youth and Sports Office was carried out by comparing the final results obtained from the two methods based on the assessment data of each paskribraka candidate. The analysis is done by looking at the number of criteria used, the processing time and also the ranking results. In helping the process of comparative analysis of the two methods, an application was built using the Visual Basic .Net programming language. where the application will show the results of the comparison of the TOPSIS and Promethee methods. Based on the tests that have been carried out, the results show that the application functionality can run well and is able to display the results of the assessment on each prospective paskibraka according to the stages of the TOPSIS and Promethee methods, in terms of processing time the TOPSIS method is faster than the Promethee method.
Software Trustworthiness Evaluation Method based on Relationships Between Criteria

Software trustworthiness evaluation (STE) is regarded as a Multi-Criteria Decision Making (MCDM) problem that consists of criteria. However, most of the current STE do not consider the relationships between criteria. This paper presents a software trustworthiness evaluation method based on the relationships between criteria. Firstly, the relationships between criteria is described by cooperative and conflicting degrees between criteria. Then, a measure formula for the substitutivity between criteria is proposed and the cooperative degree between criteria is taken as the approximation of the substitutivity between criteria. With the help of the substitutivity between criteria, the software trustworthiness measurement model obtained by axiomatic approaches is applied to aggregate the degree to which each optional software product meets each objective. Finally, the candidate software products are sorted according to the trustworthiness aggregation results, and the optimal product is obtained from the alternative software products on the basis of the sorting results. The effectiveness of the proposed method is demonstrated through case study.
Productive vocabulary knowledge of pre-service teachers as a predictor of writing performance: basis for English syllabi And instructional modules improvement

Although a growing body of research has indicated that writing performance is determined by the writer’s level of vocabulary knowledge, the relationship between the two still needs to be validated as it has strong pedagogical implication. This study aimed at investigating the productive vocabulary knowledge of pre-service teachers as a predictor of writing performance which serves as basis for English syllabi and instructional modules improvement. Seventy-seven (77) students from different year levels were assessed on their productive vocabulary knowledge using vocabulary profiler along four frequency levels:&nbsp; 1st 1,000 words of high frequency, 2nd 1, 000 words least frequently used, Academic World List (AWL), and Off-list. They were also evaluated on their writing performance in terms of organization, content and mechanics. The study found that the productive vocabulary knowledge of the pre-service teachers was limited to the levels with high frequency words. Findings also indicate that their writing performance (good) was affected by their productive vocabulary test performance. Furthermore, the productive vocabulary knowledge of students was the same regardless of sex, age and year level. Meanwhile, except for sex variations were observed in writing performance along age and year level.&nbsp;&nbsp
Twitter-based Sentiment Analysis of Cryptocurrencies

Digital currency has become increasingly popular in recent years. Cryptocurrencies were the first digital currency to be widely used as an asset by most people. The main difference between cryptocurrencies and traditional assets is that they have some similarities, yet various differences that make it difficult for many people to make a decision on whether they want to dive into the world of cryptocurrencies. This research has been performed by scanning Twint so that it used to determine whether or not the reader wants to enter the cryptocurrency market. Using Twint for scraping, this research has been conducted in order to help people who are interested in cryptocurrencies obtain information about sentiment towards cryptocurrencies. This is accomplished through lexicon opinion that determine the direction and the sentiment of the text. This grouping results tested into LSTM model and obtained 95% accurate.
Specificity of Digital Tools Use in the Formation of Professional Competencies of Teachers – Defectologists

The article discusses digital tools for the formation of professional competencies of teachers-defectologists in the format of an open educational resource that combines the advantages of a training workshop and practical exercises in the field of training. The digital resource contributes to the formation of future teachers-defectologists as subjects of inclusive activities with a readiness for self-education. The aim of the study is to review and analyze the key characteristics of digital student learning tools and the experience of introducing them into the educational process. The authors use the methods of theoretical analysis: the study and analysis of psychological, pedagogical and scientific-methodical literature and Internet resources on the research problem, as well as generalization of the experience of approbation of an open educational resource. The parameters that make it possible to determine the efficiency of the resource use are highlighted: the ability to analyze the anamnestic data of children with disabilities; the ability to diagnose, put a speech therapy conclusion, the amount of rational selection of tools for diagnosis and psychological and pedagogical study of preschoolers with disabilities, taking into account the age and type of impairment; the degree of interaction with other specialists using digital resources, etc. The educational resource LogopedistPro was included in the structure of the educational process with the aim of practice-oriented training and the formation of professional competencies of a teacher-defectologist through a system of training tasks. The results of the level of formation of professional competencies of students are analyzed. As a result, 70% of students improved their level using the open educational resource, activated by workshops. It is concluded that the result of using digital tools is improving the quality of students&amp;apos; knowledge and develop their ability to study independently.
Pandemic Effect on Education System Among University Students

Online exam systems are pieces of software used in online education to evaluate students' performance. However, both the security of the system they respond to and a broad understanding of the risks they may face must be addressed. In this article, we will look into the most recent issue, which is online exams and their security systems. Because most students depended on Google, their friends, and other resources for help, we focused on software-based security and online support for cheating like IP address tracing, block local file accessing, etc. We did this project step by step like collecting data, data pre-processing, data cleaning, training data, and trying to make a prediction of how the student cheated the most. Then we can start our future work as we want to increase the security of online exams. According to the findings of a few study papers we read, individuals that use e-testing do so for all main kinds of assessments, including quizzes, midterms, and final exams. The most common format for these tests is multiple choice, which is certainly the easiest to cheat on, but the faculty admits they aren't routinely employing anti-cheating tactics. So, this research paper will help the new world in the field of online exam systems.
Dual-branch Attention Detection Network for Scene Text Detection

At present, the complexity of the real scene, it has brought many challenges to the scene text detection. there are many problems including the diversity of the layout shape and size of the Chinese line of the natural scene image and the arbitrariness of the direction et al. Using the existing text detector, there may still be a large number of false detections; Therefore, in order to solve the above problems, we propose a dual branch attention detection network for the text detection in natural scenes based on the idea of regional regression, which simplifies the original operation steps and only needs to deal with the data containing threshold differentiation and the non-maximum suppression analysis of predicted geometry; The algorithm proposed in this paper has reached 78.88% F-measure on icdar2015 dataset and 89.02% F-measure on icdar2013 dataset
Research on Two-stage Target Detection Algorithm and its Feature Extraction Network based on Deep Learning

Deep learning two-stage target detection algorithms have been widely used in the field of target detection relying on the advantages of high accuracy. This paper introduces four common two-stage target detection algorithms in detail: R-CNN, Fast R-CNN, Faster R-CNN and Mask R-CNN, and expounds their respective algorithm steps, advantages and disadvantages. At the same time, the feature extraction network that is often used in the current two-stage target detection algorithm is analyzed and summarized from two directions. One direction is to pursue image classification networks with deeper and more complex network structures and higher detection accuracy for feature extraction, such as AlexNet, VGGNet, GoogleNet, ResNet and DenseNet. Another direction is to solve the problem that the network parameters are too large and the detection speed is too slow, a lightweight neural network is proposed, hoping to reduce the parameter calculation amount of the neural network and speed up the detection speed of the network, such as SqueezeNet, MobileNet, ShuffleNet and GhostNet. This paper introduces the network structure of the above nine networks and their respective advantages and disadvantages from the above two directions, and provides a theoretical and method reference for the selection of the feature extraction network of the two-stage target detection algorithm.
Academic problems facing high achieving students at universities

The study aimed to identify the academic problems facing high achieving students in Jordanian universities.The study sample consisted of (353) randomly selected high achieving male and female students from graduate and Bachelor's degrees at Amman Arab University and Balqa Applied University, who obtained a cumulative GPA of 3.7/4 or higher.The researchers followed the descriptive approach, and developed a scale that mainly focused on the following areas (studying problems, emotional problems, problems related to future professional choices, social problems, and family problems).The results indicated that academic problems are the most common problems among high achieving students, which affect them moderately, whereas family problems affect them the least.The results also indicated that there were no statistically significant differences at the level of (α=0.05)due to gender and college variables, and a statistically significant difference at the level of (α=0.05)due to the degree variable in favor of bachelors.Also, differences appeared in emotional problems in favor of females and in studying problems in favor of high achieving students in scientific colleges.
Superiority of Training System of Young Teachers in Chinese Colleges and Universities

The training system of young teachers in Chinese colleges and universities is a distinctive model.It is worth studying its system framework, organisational management, and training content.More importantly, by combining the characteristics of the Chinese traditional education model and the modern education model, the knowledge and teaching ability of young teachers could be shaped and improved.This paper aimed to analyse the advantages of China's young teacher training system from the perspective of the training methods and contents of young teachers.
A Survey on Bayesian Neural Networks

Bayesian Neural Network is the product of combining Bayesian and neural networks, which is one of the most popular neural network branches in deep learning and is widely used in model building in various fields.Bayesian inference is mainly divided into two types: variational inference and MCMC methods.Variational inference uses a predefined simple distribution to approximate the posterior distribution, which is faster and suitable for larger scale data.MCMC approach approaches the posterior distribution by sampling, which is more accurate, but relatively slower.This paper introduces three more representative Bayesian models and briefly introduces the main steps and formulas involved in the models.In addition, a variety of Bayesian neural network models are categorized and organized.Finally, the development of Bayesian is summarized and prospected.
Contactless Detection of Mouse's Licking with a 120 GHz Interferometric Radar

Accurate quantification of behavior has positive in-fluence on the understanding of brain. It is important to monitor movement behaviors for subjects like human and animals. In this paper, a contactless technique for detecting mouse's licking using a custom-designed 120 GHz millimeter-wave radar sensing system is proposed. The experimental scene to test the cognitive judg-ments of mouse is also given. Owing to the miniaturized size, the radar sensor could be placed on the water needle and close to the tongue of mouse without blocking the mouse's sight in a virtual space. The system has been validated and the experimental results show that the movement situations of mouse could be distin-guished precisely based on the proposed technique, such as licking, running on the scroll wheel and touching needle with paws. The detection technique of the behavioral state of mouse lays the groundwork for the study of subsequent association of animal movement and perception.