Reviewerly/exHarmony huggingface dataset:
Research on Innovation and Entrepreneurship Education Reform in Vocational Colleges

At in 2014, Premier Li put forward the call of "mass entrepreneurship and innovation" for the first time, and wrote the term "mass entrepreneurship and innovation" into the 2015 government work report as never before.Since that time, the new situation of "everyone innovation" and "mass innovation" has become popular throughout the country.As the backbone of high-quality skilled personnel training, vocational colleges should correctly understand the employment needs of society and the country, gradually create an innovation and entrepreneurship education environment, and better cultivate and educate students, promote students to become excellent skilled talents gradually.In this context, vocational colleges across the country have carried out innovation and entrepreneurship education, aiming to improve students' innovation ability and learning initiative and enthusiasm on the basis of mastering certain professional skills.In view of the poor effect of innovation and entrepreneurship education reform in China's vocational colleges at the present stage, this paper expounds the necessity of improving the innovation and entrepreneurship ability of vocational college students in the "double innovation" environment, analyzes the current situation of innovation and entrepreneurship education, and puts forward some ways to improve the innovation and entrepreneurship education of vocational college students combined with the teaching characteristics of higher vocational education, In order to improve the practical ability of innovation and Entrepreneurship of higher vocational students.
Improving NER in Social Media via Entity Type-Compatible Unknown Word Substitution

Named entity recognition (NER) is a fundamental task for information extraction (IE), and current state-of-the-art methods try to address this issue and achieve high performance on clean text (e.g., newswire genres). However, most of these algorithms do not generalize well when they transit to the noisy domain such as social media. To alleviate the noisy expression in social media data, we present a novel word substitution strategy based on constructing an entity type-compatible (ETC) semantic space. We substitute unknown words with the ETC words found by deep metric learning (DML) and nearest neighbor (NN) search. Comprehensive experiments show that the proposed framework achieves state-of-the-art performance on the W-NUT2017 dataset and the novel strategy brings good generality to multiple NER tools and previous works.
Practical Learning Application Program to Enhance Online Course Using Oculus Quest Virtual Reality

Virtual Reality (VR) is a technology that since the beginning of the 19th has started to be introduced and developed which creates the illusion of being a user who is present in a nonphysical place at the same time and place. Through this technology the user can communicate with the 3 Dimensional Environment which is disturbed by the computer as if the user is safe in the real world. The aim is to help simulate an object that is difficult to present so it can be more practical. Today it has developed various VR headsets from various companies, one of which is Oculus. Its use is also implemented for various fields, the application in the field of learning is still minimal. As the development of learning systems that lead to online methods, there are complexities of participants who are learning need practicum learning. Ask for the help of a virtual reality application program to help support practicum activities on the online learning system using Unity 3d software and Oculus Quest. This research contributes to the development of practical simulations on online learning systems.
Evaluation of cybersickness in virtual reality in driving simulator

Virtual reality (VR) devices are becoming a more popular and widespread tool for learning, gaming and entertainment purposes. One of familiar problems of emerging in VR is side effect known as cybersickness, which can be a nuisance for consumers of VR content. This occurrence can be explained as visual and vestibular conflict. The problem with cybersickness lies within the fact that the body is stationary, but eyes perceive motion in virtual reality, also known as vection. Cybersickness symptoms that often occur include blurred vision, headache, vertigo, upset stomach and other. Aim of this research is to observe changes in cybersickness symptoms in two tested conditions (2D display and VR). In this paper, subjective and objective metric of evaluation regarding cybersickness in VR driving simulation are used. Subjective metric is survey and objective metric is electroencephalogram (EEG). Results of the survey indicate which symptoms of cybersickness are more pronounced during driving in virtual environment compared with classic 2D screen experience. Statistically significant difference was found for 6 variables, which include vertigo, blurred vision and headache. Objective metric showed that highest average beta wave was in VR setting, as well as beta/alpha ratio, which is associated with stress and excitement.
Shape retrieval using angle-wise contour variance

Abstract In this study, we propose a geometric feature set for 2D shape retrieval. Conventional Hough feature gives the edge locations along with angle and creates Hough table if there are multiple intersections at borders. In this paper, a statistical way to represent the relation of repeating contours at each angle around the shape centroid is presented. The main contribution of this paper is to use the standard deviation of repeating contours. We calculate the angle between the shape centroid and each point on the contour. For each integer angle value, three features were extracted: the number of contour repetitions, the average distance of the points at that angle to the centroid, and the standard deviation of the points at the same angle. Thus, a 2D image was represented by a constant sized matrix, regardless of its size. In the case of similarity between two images, instead of merging features within a single expression, the algorithm picked the feature with the highest similarity rate for that comparison. We tested the proposed method on MPEG-7, Kimia99, ETH-80 datasets for a benchmark with the state-of-the-art. It outperformed most of the recent methods in terms of retrieval rate.
The Estimation Algorithm of Behind-the-Meter Solar PV Capacities Considering Lighting Load

In the era of energy transitions, solar photovoltaic(PV) resources and wind generators are rapidly increasing. Most small-scale solar PV generators in South Korea are located behind-the-meter(BTM). It is difficult for system operators to monitor the amount of PV generations in real time. Therefore, the amount of BTM solar PV generations have caused increasing the uncertainty of load forecasting. In order to improve uncertainty of load forecasting, this paper proposes an algorithm for BTM solar PV capacity estimation considering lighting load. The capacities of BTM solar PV resources are estimated using reconstituted load based temperature filtering process and lighting load estimation. The proposed algorithm was stable for performance on BTM solar PV capacity estimation and improved the accuracy of load forecasting throughout case studies.
Designing and Analyzing Experiments

This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real-life questions; to carry out data analysis; and to visualize and interpret results to support better decisions in business, economics, and public policy. Data wrangling and exploration, regression analysis, machine learning, and causal analysis are comprehensively covered, as well as when, why, and how the methods work, and how they relate to each other. As the most effective way to communicate data analysis, running case studies play a central role in this textbook. Each case starts with an industry-relevant question and answers it by using real-world data and applying the tools and methods covered in the textbook. Learning is then consolidated by 360 practice questions and 120 data exercises. Extensive online resources, including raw and cleaned data and codes for all analysis in Stata, R, and Python, can be found at www.gabors-data-analysis.com.
Student’s Perception of TED TALK Video to Improve Listening Skill

This present study aimed at describing the students’ perception towards their listening skill and the media that used by the researcher is using TED TALK Videos. The study applied qualitative study with descriptive approach and the instrument using a questionnaire. A total of 10 questions were distributed to the participants. The participants who already filled the questionnaire are 30 participants. The result from 30 respondents’ through questionnaire indicates the students give a positive perception toward Ted Talk videos to improve their listening skill based on the overall data presented in the previous parts, it could be seen that the listening skills material which was equipped with TED Talk videos as a media to improve listening skill got a positive perception and great appreciation.&#x0D;
MPIA: Multiple Preferences with Item Attributes for Graph Convolutional Collaborative Filtering

Personalized recommender systems are playing an increasingly critical role in a variety of online applications. In recent years, advancements in graph-structured deep neural networks have attracted considerable interest and achieved state-of-the-art performance on recommender system benchmarks. However, existing graph-based recommendation methods generally characterize each user with just one representation vector, which is insufficient to convey diverse preferences of users. To address this issue, in this paper, we approach the learning of user representations from a different perspective, by modeling users based on multiple representation embeddings. We propose a Multiple Preferences with Item Attributes for Graph Convolutional Collaborative Filtering (MPIA) framework built upon the message-aggregation concept of graph neural networks, which can generate preference-specific user representations to better model the diverse preferences of users. By taking advantage of graph representation learning techniques, MPIA learns preference-specific embeddings for users and attribute-specific embeddings for items. Moreover, we utilize shared embeddings for user and item representations to obtain the commonalities in multiple networks. Specifically, we construct a user-item bipartite graph for each preference, and enrich the representation of each node with the topological structure and features of its neighbors. We also design a preference-attribute fusion method to acquire more accurate item retrievals for every aspect of interest. Extensive experiments conducted on three real-world datasets demonstrate the effectiveness of the proposed MPIA framework.
Interactive Computational Medical Device Deployments within Virtual Reality

Abstract 3D modeling of anatomical features and medical devices are being used at increasing rates within the medical field. These 3D models allow for a wide variety of uses, such as educational or for medical device optimization. Next steps in such utilization are to perform computational and simulated device deployments within unique human anatomies. Such computational deployments have and will yield new perspectives and understandings relative to how given devices fit within the varied anatomies of varied patient populations. While these simulated device deployments offer many benefits, they are often time consuming to both develop and perform. Here, we present new functionalities to perform computational cardiac device deployments within virtual reality (VR). This functionality offers increased control of where the device is to be deployed, reducing the times required to perform computational device deployments in unique anatomical models.
Block chain technology and its impact on education sector

In the current COVID situation education industry has been faced with disruption, digitisation and educational transformation. Block Chain technology (BCT) can change the education domain drastically and can enhance its working. In addition to higher efficiency, the key reasons for using BCT include improving the education system, improving learner experience, improving analytics, launching innovative services and enhancing stakeholder experience. However, organisations struggle with the implementation of BCT initiatives. There is a lack of credible literature written on the impact of BCT in education. This study aims to identify the variables that drive educational transformation using BCT and to study the impact of BCT in the education industry, as confirmed by research studies conducted by European Universities (Grech et al., 2017). The results of the study show that strategy, learner needs, technology, regulation, business processes, skills and competencies affect the BCT implementation in the education domain (Ali Alammary et al., 2019).
English Learning Achievement of Multilingual Learners Through Digital Literacy Practices

Developing and implementing digital literacy practices for multilingual learners can gradually enhance their achievement of learning English.They have different first language backgrounds by developing and implementing digital literacy practices in multilingual learners, progressively, but they are literate digitally.They can improve their learning of English achievement.This article aims to describe how multilingual learners develop and implement digital literacy practices to improve their English learning achievement.The researcher applied observation sheets and field notes to find out the data.The subjects were English learners who have learned English in Surabaya in 2019.The research results showed that developing and implementing digital literacy practices in multilingual learners could improve learning English achievement.Three contributing factors to the advantages of digital technology were: comfortable access to learning resources, various learning resources, and updated learning resources.Therefore, English language learning and teaching are more exciting and challenging.Thus, digital literacy practices in multilingual learners play a crucial role in improving learning English in Indonesia.
A Study of Computational Trust Models in Cloud Security

The interest in cloud computing and its techniques are gaining exponentially in IT industries because of its cost-effective architecture and services. However, these flexible services of cloud bring many security and privacy challenges due to loss of control over the data. This paper focuses on an analysis of various computational trust models in cloud security environment. The computational trust models that are used to build secure cloud architectures are not available in a blended fashion to overcome security and privacy challenges. The paper aims to contribute to the literature review to assist researchers who are striving to contribute in this area. The main objective of this review is to identify and analyse the recently published research topics related to trust models and trust mechanisms for cloud with regard to research activity and proposed approaches. The future work is to design a trust mechanism for cloud security models to achieve the higher level of security.
Social Manipulation and Disinformation in the Philippines

This chapter examines how, under President Rodrigo Duterte’s leadership, the Philippines government has implemented a unique method of digital repression to advance its political objectives. It focuses on four issues. First, it discusses what set of political factors and governance trends enabled an illiberal populist like Duterte to get elected in the first place. Second, it describes key drivers of digital repression in the Philippines—focusing on social manipulation and disinformation strategies deployed by Duterte’s administration. Third, it explains how digital repression works in the Philippines, outlining the mix of strategies Duterte employs to advance his political objectives. Fourth, it investigates the extent to which outside actors—such as Facebook or the Chinese government—are responsible for enabling the spread of digital repression in the country.
FORECASTING COSTS OF CYBER ATTACKS USING ESTIMATION THE GLOBAL COST OF CYBER RISK CALCULATOR V 1.2

Purpose – due to the constant increase of cyber-attacks not only the measures of identifying and controlling cyber risks are created, but also the methods of estimating possible cyber-attacks financial costs should be developed to increase business preparedness. The purpose of this research is to forecast potential costs of cyber-attacks in Baltic countries. Research methodology – to achieve the aim of the article and prepare a prognosis of possible cyber-attacks costs the Estimation the Global Costs of Cyber Risk Calculator V 1.2 tool was used. Findings – estimated costs of cyber-attacks in Lithuania, Latvia and Estonia are highest in the public business and ser-vices sector and also in the defense sector. According to conducted calculations the costs of cyber-attacks in Lithuania will reach 1% of GDP of Lithuania by 2026. Research limitations – in this research the costs of cyber-attacks are estimated regarding industries of business but not excluding specific cyber threats. Therefore, for the future research possibilities could be the analyses of specific cyber risks and their impact to various business sectors. Practical implications – the results of the research may be useful in practical approach for preparing the risk manage-ment tools, evaluating possible damage and effect of cyber-attacks to business, also increasing preparedness level and business resilience. Originality/Value – this estimation model has been not used to evaluate and discuss cyber-risks costs in Lithuania among previous researches, therefore the topic and conducted results are original and significantly relevant for further analyses of cyber security issues in Lithuania.
Pengembangan Keahlian Penggunaan Microsoft Excel bagi Instruktur Pelatihan TIK Nasional bagi Disabilitas

Bakti Kominfo has tasks in the ICT section, namely building IT network infrastructure in disadvantaged areas. In addition, BAKTI Kominfo also has a role in developing human resources who have expertise in the IT field and keep up with the current developments, namely the Digital world. Therefore, BAKTI Kominfo conducts training for ICT teachers to provide insight as a provision to guide students with disabilities to take part in National ICT Training and Competitions. One of the training materials is Excel. In this activity in collaboration with ISB Atma Luhur to train ICT teachers with Microsoft Excel material. This training lasts for 3 consecutive days using Zoom Meeting. In the conditions of the Covid-19 Pandemic, this did not discourage us from always innovating in making and holding training courses to develop skills from ICT teacher teacher resources to assist students with disabilities. The goal is to capture the hidden competences and talents of students with disabilities. With this training activity, the ability of ICT teachers to use Microsoft Excel 2016. And from the training participants suggested to always innovate in making training training similar to other materials and to always be accompanied during training and even after training.
System Error Estimate using Combination of Classification and Optimization Technique

The representation of software must produce flawless significances without any inadequacies.Software imperfection evaluations scheme determines defective mechanisms in software.The eventual creation would have minor or negligible shortcomings to harvest great eminence software.Software quality metrics are a division of software metrics that spotlight the quality aspects of the product.The software flaw prediction system helps in the early discovery of flaws and contributes to talented removal and producing a quality software system through numerous metrics.The aim of the paper was to show how static model of data mining is used to extract defects and the PSO algorithm.Another aim of the research was to develop an optimized software flaw prophecy system on data mining techniques namely Association Rule mining, Decision Tree, Naive Bayes and Classification integrated with Particle Swarm Optimization technique.The proposed software flaw prediction system is deliberated through Data Mining techniques with Particle Swarm Optimization algorithm has been verified and compared the results.This proposed system is very useful to identify the relationships between the quality metrics and the potential defective modules.The optimized data mining systems have pragmatic perfect prediction of these defective modules.In the future, optimized data mining systems can be improved by the use of different platforms and particularly by improving data mining using PSO algorithms.It is necessary to develop algorithms that can identify faults in advance, which will minimize costs and promote the quality of developed software systems.Future optimized data mining systems will improve the relationship between quality metrics and the potential defective modules, which will lead to improved performances, productivity and lower operation costs.
An Improved Approach to the Construction of Chinese Medical Knowledge Graph Based on CTD-BLSTM Model

In the process of constructing the knowledge graph, entity recognition and relationship extraction are not only the most fundamental but also the most important tasks, and the effect of their model directly affects the final result of the graph. To establish a more refined knowledge graph, this paper proposes a model of extending Bi-LSTM structural units with Double-word vector and combining Semi-supervised Co-training method. The improved model is used in Chinese named entity recognition and entity-relationship extraction in the Chinese medical field, named Co-Training Double Word embedding conditioned BLSTM (CTD-BLSTM). Experiments show that the CTD-BLSTM model obtains higher accuracy and recall rate than BLSTM in the Chinese medical named entity recognition and entity-relationship extraction. It performs better recognition and adaptability to support the construction of the knowledge graph.
Implementation of Data Based Record Security using The Luc Method

Cryptography is defined as the study of how to hide messages so that they cannot be read by those who are not blessed. Security in cryptography has become a very important aspect of an information system. Security problems often get less attention from designers and processors. Cryptography applications can be used to secure data. Therefore, database record users need help to meet the security needs of data stored in a Database Record or database. Records are the contents or data of managed tables that are interrelated and various data are stored in the table and cannot be manipulated. One of the safest methods for the above purposes is to use the Luc safety technique. Luc is a public cryptographic algorithm that uses a set of prime numbers to generate public and private keys. In this research, we will try to implement a mathematical science called cryptography. With the existence of cryptography which includes the encryption and decryption process, messages, data, and information can be coded so that unauthorized people cannot read the information and return it to its original state, other than people who do not know the key to decrypt it.