Reviewerly/exHarmony huggingface dataset:
A New Pedagogical Model for Teaching English for Specific Purposes: Collaborative Educational Project

Raising young professionals is one of the main goals of ESP teachers across the globe, but in today’s rapidly changing world it is complicated by the need to adapt learners to a demanding international working environment that puts heavy pressure on human resources. English communication skills are now sought by employers, so, English teachers worldwide need to help future graduates meet such high expectations. However, the practice has shown that options for improvement within national educational systems are limited. In artificially bilingual classrooms progress requires cooperation and networking of practitioners from different countries. This article presents the results of the first stage of an experimental collaborative educational project, which involved teachers and students from two state universities in Russia and Kazakhstan. It describes the beginning of implementing a relatively new pedagogical model for ESP teaching through designing a virtual crossborder learning environment simulating certain real-life professional communication situations. The aim of the paper is to analyze the initial impact of this model on developing certain soft skills and English communicative competence in the students of non-linguistic majors. The authors used the comparative analysis of data obtained through formal structured questionnaires. The statistical significance of the results was measured by the F-test and the paired Student’s t-test The results suggest that extending ESP learning environment beyond borders helps ESP teachers to shift from purely academic settings to simulating real professional scenarios, thus developing students’ English communicative competence, contributing to their professional socialization, and creating a sufficient level of stress to appeal to their motivation while avoiding discouragement.
Mode Switching Adptive Kalman Filter for low-cost MEMS IMU

In this paper, an Adaptive Extended Kalman Filter algorithm for low-cost MEMS IMU data processing is proposed. Through the innovation calculated in each iteration of Kalman filter, the algorithm judges the motion state of the carrier, and adaptively adjusts the process noise matrix Q and the observation noise matrix R in the Kalman filter. In order to verify the effectiveness of this method in practical application, a serial of real-time experiments are conducted. The experimental results show that the adaptive adjustment of the process noise matrix and the measurement noise matrix can make the filter have better denoising ability when the carrier is nearly stationary, and better tracking ability when the carrier is moving rapidly.
L. S. Vygotsky and His Theory as an Inspiration for Innovations in Education

In recent years, preschool education has become an important topic in the Czech Republic. Since 1990, the system of preschool education has undergone a number of qualitative changes. A modern preschool curriculum has been created and is constantly being reviewed in line with the needs of society and educational policy objectives.One of the problems of the past decade concerns the high number of pupils with postponed compulsory attendance. This has become a challenge for both teachers and researchers. One of the steps that could help was the enactment of compulsory preschool education in 2017. More attention should therefore be given to systematic preparation of children for starting compulsory education on the basis of individualization in education. However, this requires teachers to use high-quality educational diagnostics in order to ensure children’s development with respect to their capabilities, interests and needs. A team of experts at the Faculty of Education at Palacký University is currently developing and validating an online application as an educational diagnostic instrument that should systematically monitor children’s progress as well as highlight potential problems and eliminate them in time. In the development of the instrument, the team builds on the current trends in preschool education, a number of educational theories and the work of renowned authors (Rogers, Piaget, Bandura, etc.) An interesting and inspiring view of the development and education of preschool children is presented by social constructivism and its main proponent Lev Semyonovich Vygotsky, whose ideas and legacy are outlined in the present study.
An Analyze of Fatigue Life Construction of Lifting Poonton for Small Vessel

Developing prototype aluminum pontoon lifting that functions as a floating dock for the repair of small vessels. To ensure the safety aspects of the construction of the Poonton lift, structural analysis is carried out. The purpose of research to determine the strength and fatigue life construction of lifting ponton due to loads. The method used is the finite element method and the palmgren-miner method. This research uses finite element based software. The results showed that the maximum stress value in the construction of the lifting pontoon is (σ max ) = 74,897 MPa which is located in the H-Beam construction that functions as a supporting. The estimated fatigue life of the poonton lifting analyzed during operation is 9.46 years with a total load cycle of 1111680 cycles.
The Intertwining between Corporate Governance and Knowledge Management in the Time of Covid-19 â€“ A Framework

It isnâ€™t â€œLove in the Time of Cholera,â€ but companies will face momentous challenges and trials regarding corporate governance in the time of Coronavirus. Meanwhile, knowledge has been viewed as the most significant asset for organizations in the last decade, this also being reinforced by the knowledge-based view of the company which looks at intangible resources of a company (such as intellectual capital) as the source of high competitive advantage. This paper's objective is to develop an understanding of how responsiveness in an organizational structure may be approached from a combined corporate governance (CG) and knowledge management (KM) view in a context characterized by uncertainty, volatility, precariousness, panic and disorientation. Based on a synthetic review of earlier research and empirical data, this study suggests that managers nowadays have in general a limited understanding of the current developments in the CG â€“ KM interfaces. This knowledge gap has important implications for as regards what should be researched in the area and how, as well as for the usefulness of knowledge, one wants to hold out to. The paper aims to explore how knowledge management governance (KMG) can conduct to positive delivery of KM strategic benefits and the critical issues relating to it. Through the proposed framework, the research argues whether KMG plays a critical role in providing a balance between human resource, processes, and use of technology in KM strategy, KMG being defined as an executive framework which comprises authority, strategy development, organizational culture, risk management, as well as assessment and measurement in regards to KM organization. Therefore, the purpose of this study is to critically evaluate the status of the interdependence relationship between CG and KM in the proposed framework. The paper also explores the CG and KMG perspectives relating to the new Coronavirus socio-economic crisis, identifying CoVid-19 as a systemic risk, providing a CG lens, to focus on Board leadership and effectiveness. The study reveals some recommendations in order for the Boards to optimise the level of appropriateness and adequacy of the governance mechanisms so as to sustain the realization of KM objectives.
A Survey on Human Action Recognition in a Video Sequence

In the recent decade, many new technologies were developed to store and process huge amounts of data. Because of this development, there was a significant increase in video cameras usage and analysis. And also, rapid advancement of machine learning and computer vision led video analysis from recognizing the present state to future state prediction. Such tasks are human action recognition (present state) based on complete actions and human action prediction (future state) based on incomplete actions. These tasks can be used in many areas like surveillance, self driving vehicles, monitoring traffic and entertainment. In last few decades, many robust frame works has been developed for human action recognition and prediction. In this paper, the state-of-art techniques, action datasets, and challenges has been surveyed and presented.
An analysis of the perceptions of the role of social media marketing in shaping the preferences of the electorate: A case study of the 2018 Colombian presidential election

The recent growth in social media platforms and their use amongst individuals have strengthened communication whilst creating new forms of political participation and debate. In recent years, political candidates have begun to integrate social media platforms into their communication strategies whilst also using it as a tool for political marketing to increase their electoral potential and influence in the voting behaviour of the citizens. The overall aim of this study is to explore how digital marketing strategies influence the electorate during political campaigns, by analysing the role of social media marketing in shaping the preference of the electorate in the 2018 Colombian presidential elections. The research was initially developed through a literature review, where the main theories around the topic were discovered, highlighting the role that word of mouth marketing plays in influencing the decisions of the electorate. Interviews were then conducted with political experts and the two directors of the political campaigns. Alongside this a survey was distributed to a sample of the Colombian electorate, to confirm the findings from the literature review and to explore their perspectives towards the case study. As a result the study discovered that the use of social media in political marketing is fundamental as a communication strategy, with word of mouth playing a significantly important role as people tend to believe other members of the public more than the candidates themselves. A further finding is that social media strategies only influence the preferences of swing voters and not electorate as a whole, although amongst the general population it can be used as a tool to reinforce preconceived political positions. Finally, in Colombia, social media still only plays a complementary role to that of traditional marketing methods, as rural areas, which constitute a large proportion of the electorate, have limited Internet access.
Application of Face Data Augmentation Based on Rotate-and-Render-DCGAN in Campus Security

Targeting at solving the problem of low accuracy of existing recognition methods caused by the imbalance of face data sets in the university security system, an unbalanced Data Augmentation method based on Rotate-and-Render-DCGAN is proposed to improve face recognition accuracy campus. Our goal is to correct the angled faces in the data set through Rotate-and-Render and generate artificial images to enrich the image database combined with DCGAN to improve the classifier's performance and the accuracy of face recognition. We evaluate and compare the impact of traditional Data Augmentation and Rotate-and-Render- DCGAN Data Augmentation on face recognition accuracy. The results show that the improvement effect of this method is more significant.
Response to the COVID-19 Outbreak: Virtual Teaching Time

Abstract: The Coronavirus outbreak was a disruptive event at all levels, education among them The Universidad de la Republica responded fast by switching to virtual classes instead of face-to-face ones, when possible The Reading Comprehension in English courses that are given at the Facultad de Odontologia and the Facultad de Humanidades y Ciencias de la Educacion were two of the courses that were offered in the online format Classes were given through videoconference platforms, and different resources were shared through the VLE platform of the university Eighty-six students completed the two courses (forty-four at the Facultad de Odontologia and forty-two at the Facultad de Humanidades y Ciencias de la Educacion) By applying a mixed approach (qualitative and quantitative) questionnaire, students’ considerations regarding the transmission of knowledge through the online course were analyzed Most students stated that the course’s aims could be fulfilled and that they could use the technological tools correctly Some of them mentioned that face-to-face classes are needed to socialize with peers and professors and it is understood that this is an area in need of analysis and improvement It is concluded that the results collected concerning these unexpected teaching experiences show the potential of these courses;more online courses may be offered increasing the amount of diverse options of study for students
Dual Models to Facilitate Learning of Policy Network

Learning to control agents without the prior knowledge of its own kinematic is a challenging problem. Recently neural network architecture models can be utilized for robust nonlinear fitting. However, complex sensor inputs including RGB images and robot joints states bring difficulties to the convergence of the control policy due to large input space and complex network structure. Besides, adding temporal information can bring more perception capabilities to the agent but also increase the number of parameters for policy networks. We present a new method called DualM-Control, which exploits dual models including self model and image model. DualM-Control algorithm can compress high dimensional spatial and temporal sensor inputs into low dimension data, thus making it possible for policy network with a few thousand parameters to evolve with evolution strategy. We test the algorithm on a challenging simulation environment created on gym and the performance exceeds existing approaches.
Implementation of Low Cost ARIA Architecture with Composite Field Optimization and Datapath Modification

Block cipher ARIA is a cryptography consists of similar algorithm structure with AES, which was proposed for obtaining both security and hardware efficiency. In this paper, ARIA architecture implemented with hardware optimization scheme is proposed, in order to meet the increasing demand of implementing cryptographic module with low hardware cost. To optimize the substitution layer, which is the largest component among ARIA architecture, areas of S-boxes are optimized with composite field extension and applying irreducible polynomial coefficients which minimizes hardware cost of S-boxes. Datapath through the substitution layer is modified from 128bit to 32bit to minimize the number of S-boxes which constitute the substitution layer. With these optimization schemes, proposed architecture is implemented and synthesized using 65nm CMOS process, obtained 9942 GE of hardware area which is 78.43% less than previous ARIA implementation work.
Kalman Filtering Versus Voting: Which Strategy is Best for Multi-Sensor Localization?

The Kalman filter has a long history in multi-sensor localization systems. Its assumption of normal distribution has however encountered difficulties in handling multipath effects for GNSS-based localization. In the meantime, the voter also has the possibility to be developed as a data fusion method, with the goal to maximize safety or availability of the system. In this paper, the Kalman filter and the voter are compared to discover their characteristics in multi-sensor localization systems. The simulation-based comparison specifically researches the improvement brought to the dependability of the system by the Kalman filter and the voter. The results show that both strategies can enhance certain aspects of dependability.
The Data-Driven Deep Learning to Localization Product in Convenience Store

Abstract The penetration of Industry 4.0 in the convenience store is increasingly visible. The combination of Artificial Intelligence (AI), the Internet of Things (IoT), and robotic were always under evaluation, including applied AI in the whole system that is demanded robust when integrated with online shop. Meanwhile, the problem of speed detection and precision is still a challenge in AI. In this study data-driven deep learning was developed to improve the speed and maximize localization of consumer products. After the selected data-driven received from the online shop platform then triggers deep learning and activates a modified YOLOv2 in parallel. Thus, each detector per product is needed. Our system performance is proven by experiments to meet expectations in evaluating speed, precision, and performance in recognition and localization.
Random Maclaurin Feature-based Fuzzy Clustering

Traditional kernel-based fuzzy clustering helps to discover the complex structures hidden in data, but it suffers from high computational complexity. This paper introduces random Maclaurin feature (RMF) into fuzzy clustering, and proposes a random Maclaurin feature-based fuzzy clustering algorithm (RMFCM). In this method, the RMF is used to approximate polynomial kernel, and the fuzzy clustering with linear computational complexity is conducted in generated feature space. The experiments carried out on four synthetic datasets and four UCI real-world datasets prove the effectiveness and efficiency of the proposed RMFCM method. The influence of the dimension of the RMF is also studied in the experiments.
A high‐dimensional classification rule using sample covariance matrix equipped with adjusted estimated eigenvalues

High‐dimensional classification has challenges mainly due to the singularity issue of the sample covariance matrix. In this work, we propose a different approach to get a more reliable sample covariance matrix by adjusting the estimated eigenvalues. This procedure also brings us a nonsingular matrix as a by‐product. We improve the optimization procedure to obtain a linear classifier by incorporating the adjusted sample covariance matrix and a shrinkage mean vector into the original optimization problem. We have shown that our proposed binary classification rule is better than some other rules in terms of the misclassification rate through most of various synthetic data and real data sets.
Hyperion : A Simulation of High Places in the Form of Virtual Reality for Acrophobia Sufferers

Acrophobia is the generally high common phobia. This phobia can make activity and the person's lifestyle limited because the person is avoiding high places. Acrophobia can be overcome by the centrifugal therapy method, which is the person who had this phobia is involved in the situation that triggers this phobia step by step. In virtual reality format and the sound effect, VR can simulate like in the high places in virtual. Scientists used virtual reality technology to make an application that can visualize high places' conditions through option mode. The purpose of making this application is to reduce Acrophobia by using virtual reality technology. The simulation of this research is served by showing a virtual environment of high places by using an accelerometer and gyroscope on a smartphone to interact with the social environment. The method used in this research is the prototyping model-Adobe photoshop to make application interface and game engine unity as an authoring tool to build the application. As a result of this research, the user can train by itself or with a therapist using the smartphone android and VR headset application.
Virtual to Real Unsupervised Domain Adaptation for Image-Based Localization in Cultural Sites

The ability to localize the visitors of a cultural site from egocentric images can allow applications to understand where people go and what they pay attention to in the site. Current pipelines to tackle the problem require the collection and labeling of large amounts of images, which is challenging, especially in large-scale indoor environments. On the contrary, virtual images of a cultural site can be generated and automatically labeled using dedicated tools with minimum effort. In this paper, we investigate whether unsupervised domain adaptation techniques can be used to train localization models on labeled virtual data and unlabeled real data, and deploy them to work with real images. To perform this study, we propose a new dataset of both real and virtual images acquired in a cultural site which are labeled for room-based localization as well as for 3 DOF camera pose estimation. We hence compare two approaches to unsupervised domain adaptation: mid-level representations and image-to-image translation. Our analysis shows that both approaches can be used to reduce the domain gap arising from the different data sources and that the proposed dataset is a challenging benchmark for unsupervised domain adaptation for image-based localization.
Countermeasures and System Design of Campus Network Information Security Defense Based on Data Encryption Technology

With the surge of educational informationization, campus network, as an important infrastructure of school informationization, takes on the tasks of school teaching, scientific research, management and foreign exchange. Due to many reasons, such as the uneven ability and level of equipment, users and managers, various security problems emerge one after another, and the information security of campus network directly affects all the work of the school. Computer system paralysis, campus network paralysis, state secrets, school secrets leakage, personal files damaged, changed, leaked and other issues make network security an important work in current network construction and network management, and it is also a problem that every individual who uses the network and computer should consider and pay attention to. Using data encryption technology to solve security risks has become a development upsurge of computer industry. This paper explores the current situation of campus network information security, and puts forward relevant suggestions according to the current specific situation, hoping to improve the security and reliability of campus network information.
Toward a Contextual-Conceptual Web of Wisdom (WOW) Model

The semantic web has been proposed as a web of data that facilitates machine (program) reasoning of semantic concepts. The original idea of the semantic web has largely remained unfulfilled, although many standards and semantic web-enabling tools have been developed and formalized. Ontology is a standardized of knowledge representation for the semantic web. In developing domain ontologies, both contextual and conceptual information is useful and necessary. In addition, when actionable knowledge is distilled longitudinally and across multiple user groups, the resultant can become long-term wisdom, which can be used to guide future decision making. This paper investigates the use of an emerging web of wisdom (WOW) for a question-and-answer tasks and demonstrates the efficacy of this approach.