Reviewerly/exHarmony huggingface dataset:
Building a reverse dictionary with specific application to the COVID-19 pandemic

A Reverse Dictionary maps a natural language description to corresponding semantically appropriate words. It is of assistance, particularly to the language producers, in finding the correct word for a concept in mind while writing/speaking. As the COVID-19 pandemic intensely impacted almost all the functionalities across the globe, texts on this subject appear in a significant amount in various forms, including news updates, awareness and safety articles, notices and circulars, research articles, social media posts, etc. A Reverse Dictionary on this subject is a requisite in view of the following reasons, hence addressed. Firstly, the varied text forms involve a diverse range of language producers ranging from professional doctors to the general mass. Secondly, the COVID-19 pandemic’s glossary is more specific than the general English language, hence unfamiliar to the language producers. We have carried out an implementation based on the Wordster Reverse Dictionary architecture, owing to its outperformance of the commercial Onelook Reverse Dictionary benchmark. We report an accuracy of 0.49 based on top-3 system responses. To address the limitations of the current implementation, we bring into consideration Zadeh’s paradigm of the Computational Theory of Perceptions. Notably, the compilation of the COVID-19 glossary as a part of this study is another contribution in view that it is of assistance to the concerned readers.
Anticausativization in Latin and Early Italo-Romance: The Semantics of Predicates and the Syntax of Voice

This article discusses the distribution of anticausativization strategies in Latin and one early Italian vernacular, Old Florentine, in light of the current debate on the role played by the event structure of verbs in determining argument realization. It is shown that the root (including also templatic entailments) and event structure template aspects of verb meaning play an important role in shaping the syntax of the anticausative alternation in Latin and Old Florentive, interacting with changes in the encoding of voice and argument structure in the transition from Latin to Romance.
Using Adjective Monsters Kit to Improve the Pupils’ Mastery of Comparative and Superlative Adjectives

The teaching of grammar has always been a disputable topic among the ESL teachers, since the teaching approach is frequently dull and mundane.In response to this issue, 21st century teachers are encouraged to engage their pupils in a fun and interesting manner throughout the teaching and learning process.In light of the progressive adoption of digital technologies, more innovative tools are created in today's education field.Throughout years of teaching experiences, the researchers discovered that the majority of their Year 4 pupils were having problems mastering comparative and superlative adjectives.On that account, an innovation, namely Adjective Monsters Kit was created to help solve the said learning problem.It consists of three significant items, namely Adjective Monsters Family, Adjective Monsters Game and Adjective Monsters Worksheet.Accordingly, this paper aimed to investigate the use of Adjective Monsters Kit in improving the pupils' mastery of comparative and superlative adjectives.The participants of this research comprised a total of 30 Year 4 pupils from a primary school in Terengganu, Malaysia.Data were collected using the pupils' pre-test and post-test results as well as survey questionnaire.They were then analysed and discussed quantitatively in descriptive analysis.The findings revealed that Adjective Monsters Kit has improved the pupils' understanding of comparative and superlative adjectives.Results of the study showed that the pupils were more motivated to learn and participate in a fun learning environment.Future research is suggested to modify the learning kit for other relevant grammar items.
VISUAL ODOMETRY OF A MOBILE PALETTE ROBOT USING GROUND PLANE IMAGE FROM A FISHEYE CAMERA

Abstract. In this paper, we present a method of mobile robot’s visual odometry using the visual feature tracking in the ground plane image generated from the fisheye image. In order to extract the feature information on the ground, we use a fisheye camera that has a larger FOV than a general pinhole camera, so that we can capture more information on the ground plane. However, due to the large distortion, it is difficult to extract the visual features in the fisheye image. The distortion can be eliminated, but various problems arise, such as a decrease in the resolution of an image or losing the wide angle of the fisheye camera. We propose the EUCM-Cubemap projection model to convert the fisheye image into the cubemap image without losing the FOV of the fisheye image. And we create the Ground Plane Image, a virtual image that vertically looks at the ground from a cube map image. So, the ground plane image is generated so that it is captured from a virtual camera perpendicular to the ground. In the ground plane image, the motion vector obtained by feature tracking between previous and current frames is proportional to the actual robot’s motion in the 2D ground plane. Thus, if we know the actual scale of the motion vector, we can estimate the mobile robot’s velocity and steering angle on the virtual wheel generated by the ground plane image. The scale of the vector can be estimated using the position and focal length of the camera. Using these parameters, we estimate the mobile robot’s pose by applying the bicycle kinematic model. Experimental results show that the proposed method can replace other conventional odometry methods for mobile robots. And, in the future, it is expected to be used in a variety of fields such as visual-based control or path planning.
Machine Reading Comprehension Using Multi-Passage BERT with Dice Loss on Thai Corpus

Nowadays there is an advancement in the field of machine reading comprehension task (MRC) due to the invention of large scale pre-trained language models, such as BERT. However, the performance is still limited when the context is long and contains many passages. BERT can only embed a part of the whole passage equal to the input size; thus, sliding windows must be used which leads to discontinued information when the passage is long. In this paper, we aim to propose a BERT-based MRC framework tailored for a long passage context in the Thai corpus. Our framework employs the multi-passage BERT along with self-adjusting dice loss, which can help the model focuses more on the answer region of the context passage. We also show that there is an improvement in the performance when an auxiliary task is used. The experiment was conducted on the Thai Question Answering (QA) dataset used in Thailand National Software Competition. The results show that our method improves the model’s performance over a traditional BERT framework.
Analisis UI/UX Untuk Perancangan Website Apotek dengan Metode Human Centered Design dan System Usability Scale

Website based of technology information system widely using by organizations and companies. One of it’s application are inventory on Apotek rakyat anda. Apotek rakyat anda are pharmacy shop who sells medicines. Following the technology growth on the use of database electronic system digital based knowing as application or information system. This system has been made for the user based on user interface and user experience. The used of user interface for fulfing user needs and user experience for success parameter on the system that has been build. Methods for designing are Human-Centered Design (HCD) approach, for knowing system utility are System Usability Scale (SUS) method. Hoping this designing information system will help on inventory on Apotek Rakyat Anda.
Green HRM and Organizational Sustainability: The Mediating Effect of Employees’ Attitudes during COVID-19 in the Jordanian Commercial Banking Sector

This article tested a structural model that investigates the relationship between Green Human Resource Management (GHRM) and Organizational Sustainability (OS), Green HRM and employee&amp;rsquo;s attitude during COVID-19 (CO), and CO and OS, further, the focus is on mediating role of CO in the link of GHRM and OS. Using a random sampling method, a survey was conducted on Jordanian commercial banks. In this study, the sample included 245 staff members working in Jordanian Commercial Banking. Analysis of the data involved the use of Structural Equation Modelling (SEM) by using SPSS AMOS tool. The results pointed out that GHRM was positively related to OS, GHRM exerted a significant positive effect on CO, and CO had a significant positive effect on OS. In regard to the CO role mediation on GHRM and OS relationship has equally been evaluated. Based on the theoretical and practical relevance, they entail highlighting the likelihood for future research direction building an evidence-based practice is the area of focus in the evidence-based aspects focused in this study.
Self-Determination Of Students With Different Levels Of Self-Realization

The need to study the role, place and significance of self-determination of university students during their self-realization explains the relevance of the given paper.During adolescence, at the time of professional, personal, and social self-determination and the beginning of an individual's self-realization, the capability to make an independent choice appears to be a necessary personality quality.The ability to selfdetermination allows an individual to make decisions, choose the sphere of self-realization and realize his potential according to his interests and desires, complying with external conditions and requirements.The object of the study is the self-determination of personality.The subject of the study is the manifestation of self-determination among university students with different levels of self-realization.As a result of an empirical study involving 280 university students, the authors determined factor structures of selfdetermination in subjects with harmonious, adaptive, inert, and irrational levels of self-realization.The parameters of self-determination that ensure the success of self-realization are presented as follows (in order of importance): 1) life awareness; 2) a person's confidence in the availability of choice (autonomy) and a sense of conformity of life to desires (self-expression); 3) the presence of a subjective model of the desired result (goal setting), the possibility and ability to evaluate the achieved result (evaluation criteria); 4) self-management skills.
A Combined Sensing System for Intrusion Detection Using Anti-Jamming Random Code Signals

In order to prevent illegal intrusion, theft, and destruction, important places require stable and reliable human intrusion detection technology to maintain security. In this paper, a combined sensing system using anti-jamming random code signals is proposed and demonstrated experimentally to detect the human intruder in the protected area. This sensing system combines the leaky coaxial cable (LCX) sensor and the single-transmitter-double-receivers (STDR) radar sensor. They transmit the orthogonal physical random code signals generated by Boolean chaos as the detection signals. The LCX sensor realizes the early intrusion alarm at the protected area boundary by comparing the correlation traces before and after intrusion. Meanwhile, the STDR radar sensor is used to track the intruder’s moving path inside the protected area by correlation ranging and ellipse positioning, as well as recognizing intruder’s activities by time-frequency analysis, feature extraction, and support vector machine. The experimental results demonstrate that this combined sensing system not only realizes the early alarm and path tracking for the intruder with the 13 cm positioning accuracy, but also recognizes the intruder’s eight activities including squatting, picking up, jumping, waving, walking forward, running forward, walking backward, and running backward with 98.75% average accuracy. Benefiting from the natural randomness and auto-correlation of random code signal, the proposed sensing system is also proved to have a large anti-jamming tolerance of 27.6 dB, which can be used in the complex electromagnetic environment.
Economic Dynamics As A System

This article touches upon the issue of the development of an optimal model of economic dynamics.The economic processes associated with the modern economy are becoming more and more complicated, and their dynamism calls for an everyday search for solutions to fundamental issues of economic disciplines.One of these deep-seated problems is the problem of economic dynamics.Studies of the processes of economic dynamics are important because the cyclical rhythm, the pulsation of the economic and social sphere are very indicative in the economic sphere, also there is a constant change in the cycles of economic prosperity, stagnation, and decline as well as wave-like dynamics of generalizing indicators.Many researchers do not pay due attention to this problem, although we have the right to believe that a competent choice of this model can have a key impact on the formation of the trajectory of economic development.This question has to be faced every time there is a practical need to improve the efficiency of an existing or developing economic model.To do this, it is necessary to analyse the most famous of the existing models of economic dynamics and substantiate the processes that have a direct impact on the simulation of the economic course from a scientific point of view.
Eight Factors Bringing about Students’ Speaking Disfluency in Indonesia

English, among thousands of languages, is the most widespread and spoken by people around the world. Students in Indonesia, in particular, oftentimes measure their English mastery by evaluating to what extent their speaking fluency. It, thus, becomes challenging since they are not merely called for producing words but are required to apply the appropriate way of delivering the messages as well. With that in mind, this article aimed at figuring out the common challenges, which in turn lead to students’ speaking disfluency. Moreover, to unearth the answer to the objectives, this study applied qualitative library research which primarily focused on analyzing the secondary data such as online books, and articles as the sources. Finally, the challenges found encountered by the students to get fluency were affected by eight factors such as; Vocabulary, Grammar, Anxiety, Lack of Motivation, Learning Environment, Teachers’ Teaching Strategies, Self-Confidence, and Age. Besides, this article also proposed alternative solutions for both teachers and students in overcoming the issues. In a nutshell, the researcher hopes that this paper would benefit the upcoming researchers to be cited as an additional reference in English studies, and for teachers to be a guide to promote students’ speaking fluency.
Hierarchical Representation of Complex Intervention Sequences for Automated Subgroup Analysis in Critical Care Settings

Our understanding of the impact of interventions in critical care is limited by the lack of techniques that represent and analyze complex intervention spaces applied across heterogeneous patient populations. Existing work has mainly focused on selecting a few interventions and representing them as binary variables, resulting in oversimplification of intervention representation. The goal of this study is to find effective representations of sequential interventions to support intervention effect analysis. To this end, we have developed Hi-RISE (Hierarchical Representation of Intervention Sequences), an approach that transforms and clusters sequential interventions into a latent space, with the resulting clusters used for heterogeneous treatment effect analysis. We apply this approach to the MIMIC III dataset and identified intervention clusters and corresponding subpopulations with peculiar odds of 28-day mortality. Our approach may lead to a better understanding of the subgroup-level effects of sequential interventions and improve targeted intervention planning in critical care settings.
ML Enabled WhatsApp Chatbot using IBM Watson

A chatbot mimics human interaction through voice or text interactions.Chatbots are used by businesses to interact with consumers in addition to the traditional client care channels like via phones, mails or social media.The most basic type of chatbot system handles jobs by interpreting user input and then searching its directory for content related to particular words or phrases.In a nutshell, it works as a keyword-based content retrieval system.Watson Studio provides a collaborative environment and resources to work on data in order to solve business challenges.Users may select tools for analyzing and displaying their data, cleaning and shaping it and using it for constructing and training machine learning models.Watson Assistant is a business-oriented AI chatbot.This enterprise AI technology can be used to create conversational AI applications.Since, loan application process can be extremely cumbersome, so we have developed a chatbot using IBM Watson that could help in easing this process.The chatbot has been integrated with WhatsApp using Twilio.By answering a series of standard questions related one's application, one can see the risk percentage involved in taking the loan.This has been enabled using Machine Learning models.
Classification of Toxic Plants on Leaf Patterns Using Gray Level Co-Occurrence Matrix (GLCM) with Neural Network Method

Poisonous plants are plants that must be avoided and not consumed by humans, because the presence of poisonous plants is also often found in the surrounding environment without realizing it. Because of the lack of knowledge to classify poisonous plant species, it will be more difficult to find out. With the help of a computer system, it will be easier to identify the types of poisonous plants. There are 3 types of poisonous plants that will be used in this study, namely cassava, jatropha, and amethyst. There are also 3 types of non-toxic plants with almost the same morphology as a comparison, namely cassava, figs, and eggplant. In this study, researchers tried to classify poisonous plant species using leaf pattern features that would be extracted using shape features and Gray Level Co-occurrence Matrix (GLCM). The value taken from the shape feature is the values ​​of area, width, diameter, perimeter, slender, and round. While the value of contrast, entropy, correlation, energy, and homogeneity for Gray Level Co-occurrence Matrix (GLCM) attributes. To classify data using Neural Network with RapidMiner application. From this study, it is known that from 300 total datasets used, the highest accuracy is 96.13% using the Neural Network method. With an AUC value of 0.986 and is included in the very good category.
Strategies to Improve Oral Communication Ability through Early Children's Show and Tell Method

Oral communication has an important aspect in early childhood. There are various activities that can be carried out in developing oral communication for early childhood, but sometimes children feel bored and bored in class because teachers teach only focused on magazines which causes the development of oral communication has not developed. The purpose of this study was to develop children's oral communication through the show and tell method at Karana Jaya Kindergarten, Lampung. This research is a descriptive research with a qualitative approach. The research subjects were 19 children aged 5-6 years. Data collection techniques using observation, interviews and documentation. The results showed that in addition to the activities contained in the magazine to develop children's creativity, This show and tell activity is one of the right activities to do in developing children's oral communication. The conclusion of this research is to develop and improve oral communication skills through the show and tell method.&#x0D;
Blockchain-Based Neural Network Model for Agricultural Product Cold Chain Coordination

This paper adopts a blockchain fusion neural network algorithm to conduct an in-depth study on the model of agricultural cold chain coordination. We aim to enable HDFS to meet the demand of storing many small files of various types generated by various stages of agricultural cold chain coordination and then propose an improved balanced merging and index caching strategy based on file types and size grouping. The main three modules are the file preprocessing module, file balanced merging module, and index caching module. The experimental results show that this method can significantly improve the overall performance of HDFS when storing and reading large amounts of small files. Simulation experiments using the UCI test dataset show that the improved spectral clustering algorithm not only reduces the error rate but also significantly reduces the time spent on the clustering process, demonstrating the effectiveness and feasibility of the improved spectral clustering algorithm. The improved spectral clustering algorithm of this paper is used to cluster and analyze nearly one thousand cold chain coordination-related data, and the optimal city is successfully selected as the construction point of a large cold storage transit station. This study can effectively improve the efficiency of cold chain coordination resources and their time utilization and maximize the profit creation for cold chain coordination enterprises, selecting data features for prediction, experimenting with different models and parameters to optimize accuracy, and embedding the resulting learning system for prediction and further operations. The two models of coordination market demand forecasting models and methods are analyzed separately. Finally, after analyzing the prediction results of the two different prediction methods, it is found that it conforms to the actual situation of coordination development in Jiangxi Province. It shows that the coordination market prediction model established in this paper is meaningful and the prediction analysis made has some practical value.
Online learning during COVID-19 pandemic as perceived by the students of Graphic engineering and design

In this work we wanted to determine how students of Graphic engineering and design perceived online learning a year after the transition from traditional to online classes. The goal was to better understand problems, challenges, and good sides of online learning in this study field, and deter-mine factors that impede knowledge transfer. The students' responses were collected via an anonymous online survey, which consisted of a series of questions that covered: conditions in which online classes are attended, communication channels, problems and how they are dealt with, under-standing and motivation. The biggest challenges for our students were to sustain their attention and concentration during classes despite the distractors in their environments, and to keep their motivation despite the lack of direct interaction, social isolation, and too much computer time. Their technological and digital literacy were of great use in adapting to the online platforms and applications, as well as in solving technical problems that prevented them from following the classes. Even though online courses allow students to be more flexible and potentially combine work and study, most of our students do not think that their attendance and engagement in the studies increased in the last period. It is also undisputed that our students were dealing with a lot of stress and anxiety and that their emotional state highly influenced their perception of online learning. Hence, we firmly believe that apart from making online courses more engaging and interactive, it is of utmost importance to find a proper way to motivate and psychologically support students in the online learning environment, especially in times that require a high degree of resilience.