Reviewerly/exHarmony huggingface dataset:
Analysis of Media Editorial Discourse Strategy on 'K-Quarantine' -Focused on the Contrast of Different Discourse Formation

이른바 ‘K-방역’이라는 이름으로 지칭되어 온 한국 정부의 방역지침은 개인과 의료, 경제, 교육 등 사회 모든 영역에 구속력을 지니며 주체 및 영역에 따라 가치와 이익이 상충될 수 있다는 점에서 이데올로기적이다. 따라서 ‘K-방역’ 담론은 방역 지침 수립과 집행의 주체가 정부라는 점에서 정치 담론과 혼합되어 나타나기도 하며 이는 진보와 보수, 또는 여야 진영에 따라 다른 양상을 보인다. 이와 같은 현상에 주목하여, 본 연구에서는 ‘K-방역’에 대한 상반된 담론이 담화적, 언어적으로 어떻게 형성되는지 분석하였다. 이를 위해 담론 형성에 핵심적인 역할을 하는 미디어 담론을 살펴보되, 특히 한국의 신문들 가운데 보수와 진보라는 이념적 정향을 가장 잘 보여 주는 것으로 평가되고 있는 ≪경향신문≫, ≪동아일보≫, ≪조선일보≫, ≪한겨레≫ 등 네 신문 사설 텍스트를 비교 및 대조 분석하는 데 주된 관심을 두었다. 연구 결과, ‘K-방역’ 담론은 특정한 언어 사용과 담화 전략을 통해 언론사의 정파성을 드러내며, 시간이 지남에 따라 공공 보건 영역의 담론에서 정치 영역의 담론으로 성격이 변화함을 실증적으로 확인하였다. 이를 통해 정파성의 이데올로기는 궁극적으로 자기 성찰과 비판을 배제하며 코로나-19 팬데믹과 같은 문제 해결에 방해가 될 수 있음을 밝혔다.The Korean government s quarantine guidelines, which have been referred to as so-called K-Quarantine , are ideological in that they are binding on individuals, in all areas of society, such as medical care, economy, and education, and may conflict with values and interests depending on the subject and area. Therefore, the discourse of K-Quarantine sometimes appears mixed with political discourse in that the government is the subject of establishment and enforcement of quarantine guidelines, and it shows different aspects depending on the progressives, conservatives, or the opposition camps. Focusing on such a phenomenon, in this study, we analyzed how contradictory discourses on ＇K-quarantine＇are formed discourse and linguistically. For this reason, let s take a look at media editorials, which play a central role in the formation of editorials. The main focus was on the comparison and contrast analysis of the editorial texts of the four newspapers such as ＇Dong-a Ilbo＇, ＇Chosun Ilbo＇, and ＇The Hankyoreh＇. As a result of the analysis, it is empirically confirmed that the partisanship of the media is revealed through the use of language and discourse strategies in the discourse of ‘K-Quarantine , and this discourse changes over time from the discourse in the public health field to the discourse in the political field. Through this, it was revealed that the ideology of partisanship ultimately excludes self-reflection and criticism, and can be an obstacle to solving problems such as the COVID-19 pandemic.
Graph Neural Networks for the Global Economy with Microsoft DeepGraph

Graph Neural Networks (GNNs) are AI models that learn embeddings for the nodes in a graph and use the embeddings to perform prediction tasks. In this talk, we present how we developed GNNs for the LinkedIn economic graph. LinkedIn economic graph is a digital representation of the global economy with 1B nodes and 200B edges, consisting of social graphs about members' connections, activity graphs between members and other economic entities, and knowledge graphs about members', companies', job postings' attributes. By applying GNN to this graph, we can utilize the full potential of the economic graph in many search and recommendation products across LinkedIn.
Research on infrared image sub-pixel super-resolution reconstruction algorithm based on deep learning

Infrared imaging system compared with visible light imaging system, infrared imaging system can operate in all-weather conditions with high anti-interference ability and the ability to penetrate smoke and haze. The current infrared technology still has the disadvantage of low signal-to-noise ratio and lack high frequency detail information. The most direct way to improve the infrared imaging system is to improve the hardware design by increasing the size of the photoreceptor and the size of the image source, but the manufacturing process is complicated, the cost is high, and the improvement effect is limited. With the development of the field of computer vision, deep learning has become the most effective solution for super-resolution reconstruction[6]. Therefore, in this paper, a Video frame Infrared image super-resolution reconstruction method IVSR (Infrared Video super-resolution) is designed based on the deep learning method and the dual-path operation mode combining the single-frame and multi-frame super-resolution reconstruction algorithms. The innovation of IVSR net lies in that after the optical flow module extracts the inter-frame information of each frame and the current frame, the sub-pixel convolution layer of the multi-frame image fusion module can effectively utilize the sub-pixel information. IVSR can be regarded as two reconstruction processes, the first stage is the output of high-resolution information from the optical flow motion estimation module to the projection module, and the second stage integrates the high-resolution information of each projection module for fusion reconstruction. This method can effectively improve the visual effect of IVSR reconstruction, effectively solve the problem of poor reconstruction quality due to the lack of high frequency detail information. Compared with traditional reconstruction algorithms and other typical deep learning algorithms, reconstructed images of IVSR are more exquisite, with more prominent high-frequency details, no image distortion and significant advantages in objective evaluation indicators.
GRAPH LITERACY OF A VOCATIONAL EDUCATION TEACHER

The concepts of “literacy”, “graphical information”, “computer graphics”, “graph literacy”, “pedagogical literacy” are considered. Graph literacy is considered in the context of vocational education teacher training. The role of computer graphics in graphic and pedagogical activities is described. The article describes the trends in the use of information and communication and computer technologies both in the process of learning graphic disciplines and in the pedagogical process as a whole. The definition of graph literacy of a vocational education teacher is given, which is described as a complex multicomponent personal education, characterized by the presence of knowledge, skills and abilities to create, transform and perform mental operation with visual images, figures and blueprints, the ability to transmit visual information accurately and quickly with the use of graphic aids, including digital ones, in teaching activities.
Microsoft Azure

Over the past couple of years, cloud computing has grown to gigantic proportions, and it is not hard to see why. Companies can potentially save tons of money by not buying and maintaining their own server hardware; instead, they can opt for a pay-per-use model that can scale from one server to hundreds and back to one in mere minutes. Cloud providers like Microsoft make it very easy to get your software up and running on a cloud service and at the same time give you power tools to fully configure a cloud-based network that can even be an extension to your on-premise network.
Korišćenje elektronske knjige u vreme zdravstvene krize od strane studenata učiteljskih i pedagoških fakulteta Zapadnog Balkana

The closure of educational and cultural institutions during the COVID-19 pandemic has led to changes in the traditional teaching model and the transition to online teaching. Since access to textbooks and professional literature was significantly difficult in completely new conditions, the resources of information and communication technologies were included in the teaching process, among which the e-book took an important place. The authors examine the experiences of students of teacher training faculties and faculties of education (N = 394) in relation to (a) the use of e-book for study purposes; (b) the impact of the COVID-19 pandemic on the use of e-book for study purposes and (c) the factors that initiated the use of e-book. Research results show that two thirds of the respond- ents use this book format. According to the experience of more than a third of students, the current health crisis has contributed to the use of e-books to a greater extent than before, while a quarter of the respondents were motivated to start using it. The use of e-books was initiated mostly under the influence of professors and by independent internet search. Future class teachers and preschool teachers from the Western Balkans are open to the use of modern technologies, and the current pandemic has intensified the use of e-books for the purpose of their education, which has contributed to overcoming the dif- ficulties caused by the health crisis, as well as to gaining competence in efficient work with future generations of readers of various forms of text.
2. Unveiling the blockchain potential for the future of manufacturing: a multipurpose blockchain network

In the past decades, new technologies that relate to the Internet have been able to reshape the economy completely. Nowadays, blockchain technology is recognized as one of the new technologies with potential for completely transforming sectors such as banking. Nevertheless, the philosophy and basic principles in which the technology is developed may be applicable in other sectors such as manufacturing. Despite the technology is still immature, the technology can offer solutions to improving management and traceability in supply chains, fostering collaborative and open innovation, enhancing the use of certification, optimizing human resources management, developing improved R&D funding strategies and creating trust by means of online accounting. In this chapter, a multipurpose blockchain network is proposed to be used by all manufacturing actors involved in activities such as the aforementioned.
Generalized Net Model of Biometric Multifactor Authentication System

The aim of the current research work is to investigate biometric multifactor system. The basics of the theories of multifactor systems and biometric systems are presented in the paper. Generalized net model of biometric multifactor authentication system is constructed. The presented model use hand (palm) geometry, palm vein and cardiac signal biometrics. The biometrics and factors are acquired in the same time. The biometric identification methods are applied. The features are compared to the stored information for individual. If it is no information for the user in the database the process of assignment have to be started. In the end rank level fusion is applied and the process of decision making is made.
Proposed two variable selection methods for big data: simulation and application to air quality data in Italy

In this era of big data, considerable amounts of information data are produced daily with the rapid development of technology. In various fields, such as engineering, computer science, and finance, several statistical and machine learning methods are used to uncover useful information and patterns behind these enormous datasets. Neural networks (NN) and random forests (RF) are the common model selection (variable selection) methods in machine learning. The least absolute shrinkage and selection operator (LASSO) and principal component analysis are the statistical methods. In this study, we propose two methods: a combination of NN and LASSO and a combination of NN and RF. We use Monte Carlo simulation and a real data application (air quality data in Italy) to investigate the performance of the classical methods (ordinary least square and feed-forward NN) and two proposed methods by the goodness of fit criteria. The results showed that the proposed methods perform better than the classical methods.
Mining Frequent Patterns on Knowledge Graphs

The adoption of knowledge graphs is growing across various domains. And their construction, when automated, can result in verbose sparse graphs. In this talk, we discuss how to mine frequent patterns and subgraphs in a calculation knowledge graph in order to factor out verbose representations and allow for each of maintenance, reduction of storage, and an increase in runtime speed.
Estimation of steady-state quantities of an HMM with some rarely generated emissions

Abstract We propose to apply the importance sampling and the antithetic variates statistical techniques to estimate steady-state quantities of an Hidden Markov chain (HMM) of which certain emissions are rarely generated. Compared to standard Monte Carlo simulation, the use of these techniques, allow a significant reduction in simulation time. Numerical Monte Carlo examples are studied to show the usefulness and efficiency of the proposed approach.
Travelex knocked offline by malware

Online currency exchange service Travelex has been knocked offline by a ransomware attack, most likely using the Sodinokibi (aka REvil) malware. There are reports that the attackers are demanding a $6m ransom for the restoration of the company's systems and data.
Prototypical Convolutional Neural Network for a Phrase-Based Explanation of Sentiment Classification

The attention mechanisms are often used to support an interpretation of neural network based classification of texts by highlighting words to which the network paid attention while making a prediction. Following recent studies, the attention technique does not always provide a faithful explanation of the model. Thus, in this paper we study another idea of prototype-based neural networks. Although for texts they obtain promising results, they may provide explanations in the form of comparisons of whole (potentially long) documents or also run into problems with providing reliable explanations. To overcome it, in this work a new prototype-based convolutional neural architecture for text classification is introduced, which provides predictions’ explanations in the form of similarities to phrases from the training set. The experimental evaluation demonstrates that the proposed network obtains similar classification performance to the black-box convolutional networks while providing faithful explanations. Moreover, it is shown that a new method for dynamic tuning of the number of prototypes introduced in this paper offers performance gains against static tuning.
Breast Cancer Risk Analysis using Machine Learning

When cells in and around breast are affected and damaged due to cancer, we call it Breast Cancer. It is commonlyfound among women and a very few men. Breast Cancer poses major health hazard and best way to handle itis to identify the symptoms as early as possible. Recently, Machine Learning techniques have been aggressivelyused by many researchers for different types of analysis and predictions in medical domain. In literature, BreastCancer classification and prediction through Machine Learning techniques, based on Breast Cancer WisconsinData Set, has come into picture many times. Typical attributes like radius, perimeter, tumour size (often fetchthrough X-Rays) apart from others, provides comprehensive inputs for the prediction process, often at a laterstage. However, till date, work on evaluation of the Risk of Breast Cancer is quite limited. We have worked onthe problem of risk prediction of breast cancer on the basis of self-assessed parameters, to find if the patient islikely to get the disease, at a very early stage. Risk evaluation / prediction tries to identify if a person is at riskof getting infected with disease. Risk analysis can not only save money but also enable the patient to undertakecourse correction in terms of food intake and medication, before it is too late. In this paper we present our analysisbased on well-defined parameters, discuss our results and then compare those results with one other similar work.
Balanced Scheduling Method for Big Data of Network Traffic Based on Set-pair Analysis Strategy

Abstract The continuous surge in the total amount of big data of network traffic leads to excessive data transmission load on the network channel. To effectively alleviate this problem, a scheduling method for regulating big data load is designed and proposed. The link discovery is applied to perform set-pair analysis of network topology awareness; the cloud computing algorithm is used to perform calculations for data transmission; based on the measurement results, traffic routing is controlled and measured to achieve link analysis and reorganization, as well as completing balanced scheduling of traffic big data, which proves that the method can effectively alleviate the big data scheduling load problem and has an important role for research in network optimization fields.
Synchronized Conceptual Representations in Unsupervised Generative Learning

Representations are essential in learning of natural and artificial systems due to their ability to identify characteristic patterns in the sensory inputs. In this work we examined latent representations of a dataset of images of basic geometric shapes as a basis for sharing semantic information about observations between neural network models of unsupervised generative learning. Individual models trained in an unsupervised process with minimization of generative error were exposed to a process of synchronization of symbolic tokens associated with characteristic regions in the latent representations identified with two different strategies. It was demonstrated that conceptual representations with good decoupling of characteristic patterns can be produced with generative models of limited complexity; and that a simple process of conceptual synchronization can enable effective sharing of information between individuals in the collective via associating common symbols with latent regions associated with characteristic patterns in the sensory observations. The results demonstrate the potential of conceptual latent representations that emerge in unsupervised generative learning as a natural platform for abstract concept learning and communication.
Long-term correlation filter tracking algorithm based on adaptive feature fusion

In the complex tracking environment, most of existing correlation filter-based tracking algorithms are often unable to track the target stably for a long time. To solve this challenging problem, in this paper, we propose a long-term correlation filter tracking algorithm based on adaptive feature fusion. Firstly, we normalize the response peaks of different features to dynamically assign feature weights for the purpose of combining HOG features and color histogram features adaptively. Secondly, a detection filter is learned to detect the tracking results, and we judge the confidence of detection results by the detection response peak and the normalized value of average peak correlation energy. If the target is in a low confidence state, the re-detection module is employed to relocate the target to achieve long-term tracking. Our experimental results on the OTB-2013 and OTB-2015 benchmark datasets demonstrate that the proposed method performs favorably against some state-of-the-art methods in terms of accuracy and robustness. Furthermore, the proposed method satisfies the accuracy and real-time requirements of long-term tracking in complex environments.
Introduction of PMI-SO Integrated with Predictive and Lexicon Based Features to Detect Cyberbullying in Bangla Text Using Machine Learning

The increasing use of social media is causing a huge escalation in cyberbullying. Cyberbullying causes significant emotional and psychological distress. Previous research has shown good accuracy in detecting cyberbullying from textual data. In this research, we introduced PMI-SO to develop a feature-based model which detects cyberbullying in Bangla text with remarkable accuracy. The developed model utilizes PMI-SO as a new input feature along with other predictive and lexicon based features to detect cyberbullying using Machine Learning classifiers. We created two datasets in order to conduct this research.There were 5000 Bangla texts in the Social Media Dataset and 10277 Bangla texts in the PMI Dataset. We used the PMI dataset to generate PMI-SO for the Social Media Dataset. The Social Media Dataset was used to perform classification. Performance analysis revealed that XGBoost classifier was able to classify texts with an accuracy score of 93%. The lowest accuracy was 85% and it was obtained by SVM. We also developed a web page, which takes a Bangla text, its likes and reply count as input and predicts cyberbullying in that text.
Parts of Speech Tagging: A Setswana Relative

Abstract Setswana qualificatives consists of multiple words. This makes part of speech tagging a challenging task especially for the relative part of speech. The Setswana relative has a wide variety of structure in terms of number of words, form, tense, and negation. A few studies have looked at part of speech tagging for Setswana complex parts of speech including the relative. However, these studies did not explore in detail all the different forms of a relative. In this study, we investigate the different forms of a Setswana relative and convert them into a general pattern. The relative patterns are stored in a trie data structure which is used to detect relative’s parts of speech in a given Setswana sentence. Tests show that most of the relative forms are consistent giving a performance rate of 78% for the test data. The direct relative structure gives a higher performance rate as its structure is simpler and less ambiguous compared to the indirect relative structure.
Rule-Based Extraction of Tuple-Based Service Demand from Natural Language-Based Software Requirement for Automated Service Composition

In recent years, automated service composition (ASC) has become popular and prevalent in software engineering and services computing. Nevertheless, although there are many existing research works on ASC, a number of problems and issues in this research area remain to be addressed. Based on our observations, one of the major problems and insufficiencies of current ASC approaches is that many of them accept and consider only tuple-based service requirements, whereas it is unrealistic to assume that all users of an ASC approach can express their service demands in this manner. To most service and software stakeholders, the most natural and best way to describe their requirements is to express them in natural language. Due to this difference in formations, most existing ASC approaches are useless or difficult to use for ordinary users. In this paper, we attempt to bridge this gap between the conventional descriptions of user requirements and the acceptable inputs of current ASC approaches by proposing and implementing a new approach. This approach uses natural language processing (NLP) technologies to obtain the required information from natural language-based requirement descriptions to generate tuple-based service demands for ASC approaches. Based on the part-of-speech and dependency information parsed and provided by an adopted NLP processor, we define a set of rules and preprocessing methods to extract the intended service requirement elements. Finally, with a real-world software description dataset, the developed system is fully tested and demonstrates accurate extraction performance.
A Corpus of Biology Analogy Questions as a Challenge for Explainable AI

Analogy is a powerful tool for student learning. A commonly used analogy question template is: A is to B as C is to what? Here, A, B, and C are concepts in the domain of interest. An example instance of such a question is: Phospholipid is to lipid as margarine is to what? We give a description of a large corpus of questions in this form, and their answers and explanations that are generated from a curated biology knowledge base (KB). The KB helps achieve high accuracy and detailed explanations on such questions. The investment required in a curated KB is difficult to understand and justify in relation to methods based on word embeddings that have been used for answering such questions by automatically processing the text. To better characterize the difference between the two approaches, we report preliminary results on answering the questions in this biology analogy corpus using contextual word representations and deep learning architectures based on GloVe, ELMo, Seq2Seq, and Seq2Vec models. As none of the current approaches to word embeddings produce any explanation for an answer, our work provides a useful baseline as well as a new challenge for the future development of explainable AI systems.