Reviewerly/exHarmony huggingface dataset:
Design and development of portable smart traffic signaling system with cloud-artificial intelligence enablement

With increasing traffic, &lt;span lang="EN-US"&gt;apart from the major traffic junctions, there are few smaller junctions which witness heavy traffic only during a certain period of the day. For such cases, deploying of conventional traffic lights are not a viable option. A cost-effective internet of things (IoT) enabled portable smart traffic signaling system is designed using ESP32 dual core microcontroller, to assist traffic personnel working at small traffic junctions. It uses a foldable mechanical structure which can be carried easily. The system is designed to work with and without internet connectivity depending on its functionality and place of deployment. The system can be pre-programmed with default time value to work without human intervention. Using an android application, the user can manually control the traffic signal by analysing the traffic density. System gathers the traffic density information based on the operations performed by the traffic personnel and stores it in the cloud. In Smart mode, system computes the mean value and also runs K-means clustering algorithm on the dataset to generate optimized time values. Comparison of the data generated using manual and automatic modes infer the credibility of the system in generating optimized time values and reducing human effort.&lt;/span&gt;
Recognition of Toxicity of Reviews in Online Discussions

The paper solves some problems belonging to the field of recognition of asocial behaviour in online space.Nowadays, it seems to be an important issue when we must question our way of dealing with the pandemic crisis.Social network users must deal with such unhealthy phenomena in online space as toxic comments and toxic troll authors that prevent constructive communication and knowledge sharing through the web space.We have proposed a new multimodal approach to social network analysis, which combines two methods, the first one to recognize toxic posts using machine learning and the second one to identify toxic authors in online space using sentiment analysis.The recurrent neural network was trained with different numbers of neurons in the hidden layers using three different types of hidden layers and optimizers along with various learning rates.Finally, the paper provides detailed results of extended experiments with deep learning models for recognition of toxic reviews, where a model generated by a combined deep learning architecture achieved accuracy over 0.9, and results of our novel approach to the detection of toxic troll reviewers achieving accuracy of 0.95.Our approach to troll recognition is based on a comparison of the sentiment related to the authors' posts to sentiment related to all comments of an online discussion.
GRADUATE STUDENTS’ MOTIVATION REGULATION STRATEGIES IN FACING ACADEMIC WRITING AMID COVID-19 PANDEMIC

Abstract To be more proficient and self-regulated, L2 academic writers, the deployment of motivation regulation strategies should be incorporated at the commencement of academic writing learning enterprises, particularly during this pandemic situation. According to previous studies, the establishment of robust motivation regulation strategies will potentially help L2 academic writers to better manage the ongoing writing learning dynamics and cope with varied upcoming challenges. Specifically, this study was conducted qualitatively by lending support from qualitative content analysis in order to offer more overarching findings derived from actual events experienced by the participants. To comply with this objectivity, two research instruments were also utilized in this study namely the Likert-Scale questionnaire and open-ended interviews. Further, the Likert-Scale questionnaire was administered via Google Forms to be filled out by 36 English education master students from batch 2018 and 2019, and the open-ended interview inquiries were sent through WhatsApp application in order to obtain the responses from the three random selected interviewees. Results from this study revealed that the majority of English education master students had successfully become more competent and resilient academic writers through five major streams of motivation regulation strategies namely interest enhancement, performance self-talk, mastery self-talk, self-consequating, and environmental structuring. Keywords: Motivation regulation strategies, academic writing, graduate students, Covid-19 pandemic
Quantum nonequilibrium dynamics from Knizhnik-Zamolodchikov equations

We consider a set of non-stationary quantum models. We show that their dynamics can be studied using links to Knizhnik-Zamolodchikov (KZ) equations for correlation functions in conformal field theories. We specifically consider the boundary Wess-Zumino-Novikov-Witten model, where equations for correlators of primary fields are defined by an extension of KZ equations and explore the links to dynamical systems. As an example of the workability of the proposed method, we provide an exact solution to a dynamical system that is a specific multi-level generalization of the two-level Landau-Zenner system known in the literature as the Demkov-Osherov model. The method can be used to study the nonequilibrium dynamics in various multi-level systems from the solution of the corresponding KZ equations.
The Linguistic and Situational Features of WhatsApp Messages Among High School and University Canadian Students

WhatsApp messages can be such a rich source for creative and spontaneous language geared toward more individual expression. WhatsApping provides us with a unique view into language and is an interesting prototype for thinking about language use, the various functions of this variety and how it is used to render different kinds of meanings. This study aims to explore the linguistic features of text messaging’s communicative intent, content and context. Selected samples of messages were drawn from a high school student population in Canada who provided a corpus of 100 different texts already sent and/or received for personal, educational and professional purposes. The collected data were analyzed using Biber and Conrad’s qualitative approach to register, genre, and style analysis. The result is that people use clipped sentences in a free flow of casual speech and slang. While certain abbreviations have come into such common use, to the point of becoming standard, a wide array of individualistic variance in terms of style and language usage has emerged. It is concluded that avid texters, while appearing to greatly deviate from more traditional, standard written English, are a rich source for studying creative and spontaneous language adaptation of register, genre and text according to context and text users.
Knowledge Enhancement and Understanding of Diversity

In many societies, diversity has been neglected. The importance of diversity in the society for knowledge generation has become increasingly apparent in recent years. In order to achieve diversity, we need to take into account people from various backgrounds and backgrounds, and to understand that every individual is unique. Differentiating people can be based on race or ethnicity, nationality, age, gender, sexual orientation, economic background, and disabilities. We must recognize that each of these differences results in different experiences for individuals, and that all of these experiences can be synthesized to generate knowledge that is both more inclusive and more comprehensive. Diverse initiatives can be used to promote diversity in educational institutions, such as affirmative action and diversity programs. As diversity enhances receptivity to learning from others' experiences, it is essential for creativity and innovation. Furthermore, diversity promotes productivity and performance as a team can draw on the experience of each individual to find solutions to problems. Individuals must be aware of different opportunities and possibilities in order to grow as individuals, and diversity allows this to happen. As diversity prepares individuals for a diverse world, it is also important to students' learning. Ultimately, diversity contributes significantly to the creation of new knowledge due to the fact that it helps people draw on their own experiences and the experiences of others in order to discover new approaches to problems.
Difference-in-relation: Diffracting human-robot encounters

This article adopts Donna Haraway’s (1992) and Karen Barad’s (2007) lenses of reflection and diffraction to probe into human-robot relationships in-the-making. Dominant practices of human-robot interaction aspire to an optics of reflection based on the belief that the differences inherent to machines need masking or assimilating. I propose that diffracting human-robot encounters requires becoming-with and co-worlding with artefacts and their asymmetries. Entering the robot lab to witness my collaborative Machine Movement Lab project and its diffractive strategies in-the-making, as well as the material-bodily knowledges they enact, offers situated insights into how they make tangible difference patterns and relational ontologies at work in our more-than-human encounters.
Improving Action Quality Assessment Using Weighted Aggregation

Action quality assessment (AQA) aims at automatically judging human action based on a video of the said action and assigning a performance score to it. The majority of works in the existing literature on AQA divide RGB videos into short clips, transform these clips to higher-level representations using Convolutional 3D (C3D) networks, and aggregate them through averaging. These higher-level representations are used to perform AQA. We find that the current clip level feature aggregation technique of averaging is insufficient to capture the relative importance of clip level features. In this work, we propose a learning-based weighted-averaging technique. Using this technique, better performance can be obtained without sacrificing too much computational resources. We call this technique Weight-Decider(WD). We also experiment with ResNets for learning better representations for action quality assessment. We assess the effects of the depth and input clip size of the convolutional neural network on the quality of action score predictions. We achieve a new state-of-the-art Spearman's rank correlation of 0.9315 (an increase of 0.45%) on the MTL-AQA dataset using a 34 layer (2+1)D ResNet with the capability of processing 32 frame clips, with WD aggregation.
An Interpretation of Long Short-Term Memory Recurrent Neural Network for Approximating Roots of Polynomials

This paper aims to present a flexible method for interpreting the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) for the relational structure between the roots and the coefficients of a polynomial. A database is first developed for randomly selected inputs based on the degrees of the univariate polynomial which is then used to approximate the polynomial roots through the proposed LSTM-RNN model. Furthermore, an adaptive learning optimization algorithm is used specifically to update the network weights iteratively based on training deep neural networks data. Thus, the method can exploit the ability to find the individual learning rates for each variable through adaptive learning rate strategies to effectively prevent the weights from fluctuating in a wide spectrum. Finally, several experimental results are performed which shows that the proposed LSTM-RNN model can be used as an alternative approach to compute an approximation of each root for a given polynomial. Furthermore, the results are compared with the conventional feedforward neural network based artificial neural network model. The results clearly demonstrate the superiority of the proposed LSTM-RNN model for roots approximation in terms of accuracy, mean square error and faster convergence.
Deep Learning with Word Embedding Improves Kazakh Named-Entity Recognition

Named-entity recognition (NER) is a preliminary step for several text extraction tasks. In this work, we try to recognize Kazakh named entities by introducing a hybrid neural network model that leverages word semantics with multidimensional features and attention mechanisms. There are two major challenges: First, Kazakh is an agglutinative and morphologically rich language that presents a challenge for NER due to data sparsity. The other is that Kazakh named entities have unclear boundaries, polysemy, and nesting. A common strategy to handle data sparsity is to apply subword segmentation. Thus, we combined the semantics of words and stems by stemming from the Kazakh morphological analysis system. Additionally, we constructed a graph structure of entities, with words, entities, and entity categories as nodes and inclusion relations as edges, and updated nodes using a gated graph neural network (GGNN) with an attention mechanism. Finally, through the conditional random field (CRF), we extracted the final results. Experimental results show that our method consistently outperforms all previous methods by 88.04% in terms of F1 scores.
Analysis on the Innovative Approaches of College English Translation Teaching Based on New Media Technology

Information technology is developing rapidly in China. New media technologies of different types and adapting to different environments continue to appear, and produce good application effects in various fields, so as to provide a solid backing for the development and innovation of China’s education industry. Specifically, English translation teaching and related activities in Colleges and universities, We can also see the role of new media technology in promoting innovation. When using new media technology, we should pay attention to its improvement on the depth and breadth of students’ thinking, and carry out thinking from the perspectives of application innovation, basic innovation, combined innovation and practical innovation, so as to finally provide support for the improvement of English translation teaching quality.
HEMClust: An Improved Fraud Detection Model for Health Insurance using Heterogeneous Ensemble and K-prototype Clustering

Health insurance plays an integral part of society's economic well-being; the existence of fraud creates innumerable challenges in providing affordable health care support for the people.In order to reduce the losses incurred due to fraud, there is a need for a powerful model to predict fraud on the data accurately.The purpose of the paper is to implement a more sophisticated technique for fraud detection using machine learning: HEMClust (Heterogeneous Ensemble Model with Clustering).The first phase of the model aims in improving the quality of claims data by providing effective preprocessing.The second stage addresses the overlapping instances in provider specialties by grouping them using k-prototype clustering.The final stage includes building the model using a heterogeneous stacking ensemble that performs classification on multiple levels, with four base learners in level 0 and a meta learner in level 1.The results were assessed using evaluation metrics and statistical tests such as Friedman and Nememyi to compare the performance of base classifiers against the proposed HEMClust.The empirical results show that the HEMClust produced 94% and 96% overall precision-recall rates on the dataset, which was an increase of 45% to 50% in the fraud detection rate for each class in the data.
Boarding House Search Information System Database Design

Technological advancements are causing humans to produce several inventions and developments to accomplish anything. Which one utilizes internet technology to obtain information as quickly and simply as possible? For example, if someone moves to a new area for work or school, they will need boarding houses. However, this information is difficult to obtain because it is traditionally passed down from person to person, and the boarding house owner finds it difficult to promote his boarding house. As a result, individuals require a computerized system to resolve this issue. This study aims to create a database design for a boarding home information system. This study employs the DBLC (Database Life Cycle) technique, which illustrates the life cycle of a database that may be developed at any time. Database design is separated into three parts: conceptual, logical, and physical database design.
Network Education Resource Information Sharing System Based on Data Mining

To enter a new stage of development of education informatization, network education has become a global trend of education training, the development of lifelong education, and resource sharing platform for higher vocational colleges to some extent, with network technology and multimedia technology as the core of the rapid development of modern information technology. The sharing of network education resource sharing system and the application in the whole advancement provide the conditions, it promotes the construction and development of higher vocational education resource system. This study examines the problems that exist in the construction of higher vocational college education resources, such as unreasonable management of education resources, unclear goals of higher vocational colleges, and unclear school-enterprise cooperation, based on the research status and achievements of higher vocational college education resources at home and abroad. On the basis of combing and analyzing the related concepts, theoretical basis, and construction principles of higher vocational education resources, the paper designs the network sharing system of higher vocational education resources in detail from its design objectives, design ideas, user needs, overall framework system, hierarchical structure, and functional modules. The core part is the realization of system function modules, including the resource management function module, system management function module, and user management function module. At present, domestic scholars have carried out a lot of research on the construction of basic education network resources. Although the research focuses on resource allocation, coconstruction and sharing mechanisms, and the current state of building and development methods, it also serves as a guide for the development of basic education network resources. However, most of these research results analyze problems from the qualitative perspective, and most of the research results are speculative research at the macro level, lacking practicality. With the rapid development of network education, the construction of network resource libraries is developing in the direction of intelligence and individuation to meet the different learning needs of learners. Based on the analysis of the problems encountered by learners in using the resource library for learning, this paper puts forward the application of data mining technology (DM) to guide the construction of the resource library and discusses how to improve the organizational structure of the resource library, improve the utilization rate of the resource library, and adapt to personalized learning. At present, the system is still in the system testing and experience stage, through the continuous testing of relevant functional modules, further improves the function of the sharing system, and constantly increases the scalability of the system function, in order to establish a perfect, comprehensive, and nationwide higher vocational education resource network sharing system.
Multi-objective optimization design of steel structure building energy consumption simulation based on genetic algorithm

Abstract In order to solve the problems of data acquisition, quantitative analysis and model solving in the field of construction schedule optimization, a construction schedule optimization system based on genetic algorithm was constructed. On this basis, the construction schedule two-stage multi-objective optimization models of “duration-cost” and “fixed duration-resource equilibrium” are established, which aim at the lowest cost and resource equilibrium. Through the investigation and analysis of the project contract documents, the energy consumption and cost of the normal construction and emergency construction state of the contract plan of the basic project part (from the beginning of precipitation activities to the end of +0) are obtained. This section was optimized for the analysis. The genetic algorithm is used to solve the model, and the optimal duration of each process and the optimal start time of non-critical process are determined. The feasibility and effectiveness of the system and model are verified by practical application in the actual project, which provides support for determining the construction schedule scientifically and reasonably and helps to improve the construction schedule technical application effect and construction schedule management level.
Ransomware Incident Preparations With Ethical Considerations and Command System Framework Proposal

Concerns with cyber-attacks in the form of ransomware are on the mind of many executives and leadership staff in all industries. Inaction is not an option, and approaching the topic with real, honest, and hard discussions will be valuable ahead of such a possible devastating experience. This research note aims to bring thoughtfulness to the topics of ethics in the role of cybersecurity when dealing with ransomware events. Additionally, a proposed set of non-technical recovery preparation tasks are outlined to help organizations bring about cohesiveness and planning for dealing with the real potential of a ransomware event. Constraints from many factors come into focus during preparations for ransomware, and a method to categorize them is detailed. Finally, the use of Incident Command Systems is well known and documented in emergency management, and a proposed model for integrating this process for ransomware episodes is sketched.
Design rules application in manufacturing industries: a state of the art survey and proposal of a context-aware approach

In manufacturing industries, the design of a product needs to comply with many design rules. These rules are essentials as they help industrial designers to create high quality designs in an efficient way. [Problem] However, the management of an ever-increasing number of design rules becomes a real problem, especially for novice designers. Even if there exists some knowledge engineering tools for managing design rules, their capabilities are still limited and many companies continue to store their design rules in unstructured documents. Nowadays, the application of design rules remains a difficult task that needs a circular validation process between many experts in a manufacturing company. [Proposition] In this paper, we will analyze the main existing approaches for the application of design rules and we will demonstrate the need of a new approach to improve the current state-of-the-art practices. To minimize rule application impact on the design process, we propose to develop a Context-Aware Design Assistant that will recommend design rules on the fly while using computer-aided design software. Our design assistant relies on the modelling of the design rules and the design context in a single knowledge graph that can fuel a recommendation engine. [Future Work] In future work, we will describe the technical structure of the Context-Aware Design Assistant and develop it. The potential outcome of this research are: a better workflow integration of design rules application, a proactive verification of design solutions, a continuous learning of design rules and the detection and automation of design routines.