Reviewerly/exHarmony huggingface dataset:
MODEL FOR APPLICATION OF STEM APPROACH IN CONDITIONS OF DISTANCE LEARNING – STEM HOME LABORATORY

The sudden shift to distance learning caused by the COVID-19 virus emergency has posed a serious challenge to schools. This article presents a successful model for applying the STEM approach in science education at the Alexander Georgiev-Kodzhakafaliyata Primary School in Burgas. In the conditions of synchronous distance learning in MS Teams environment, students conduct experiments with handy tools and materials at home, during online classes or as homework assignments, then describe the experiments in presentations and share them with their classmates during the project week ( April holidays and at the end of the year). The application of STEM home laboratory combines knowledge from different disciplines, diversifies and facilitates learning, enables learning by doing things, which most stimulates the curiosity of students. Thus, they casually acquire skills for planning and conducting a scientific experiment, generating hypotheses and reasoning, measuring results and formulating conclusions. At the same time, soft skills for teamwork, creative and critical thinking, presentation skills are formed and upgraded.
Sustainability Shout Out: HolyGrail 2.0 - Intellegent Sorting Enabling the Ciruclar Economy - Viewpoints on Innovation

HolyGrail 2.0 is a pilot project facilitated by AIM, the European Brands Association, in collaboration with 85 companies and organizations. They are piloting digital watermarking technologies for intelligent sorting which will lead to higher-quality recy
COVID- 19: Impacto na Doença Oncológica

The COVID-19, a disease associated with the SARS-CoV-2 virus (Severe Respiratory Acute Syndrome - Coronavirus - 2), is currently recognized as a pandemic according to the World Health Organization statement in March of 2020. Most patients with COVID-19 have mild symptoms that disapper spontaneously. However, a minority evolves into more serious situations and, in some cases, can be deathly. Situations with a higher risk of serious illness are recognized in patients with active malignancy, or immunosuppression status.The few data limits the knowledge about the relationship between COVID-19 and cancer. In this sense, efforts are being made to reduce the risk of infection in these patients associated with hospital routines and to reduce the impact of this situation on the normal performance of the planned treatments.This brief review aims to focus on the impact of COVID-19 on cancer patients and to discuss paradigm changes and new treatment approaches and recommendations.
BME-TUW at SR’20: Lexical grammar induction for surface realization

We present a system for mapping Universal Dependency structures to raw text which learns to restore word order by training an Interpreted Regular Tree Grammar (IRTG) that establishes a mapping between string and graph operations. The reinflection step is handled by a standard sequence-to-sequence architecture with a biLSTM encoder and an LSTM decoder with attention. We modify our 2019 system (Kovacs et al., 2019) with a new grammar induction mechanism that allows IRTG rules to operate on lemmata in addition to part-of-speech tags and ensures that each word and its dependents are reordered using the most specific set of learned patterns. We also introduce a hierarchical approach to word order restoration that independently determines the word order of each clause in a sentence before arranging them with respect to the main clause, thereby improving overall readability and also making the IRTG parsing task tractable. We participated in the 2020 Surface Realization Shared task, subtrack T1a (shallow, closed). Human evaluation shows we achieve significant improvements on two of the three out-of-domain datasets compared to the 2019 system we modified. Both components of our system are available on GitHub under an MIT license.
PALMYRA 2.0: A Configurable Multilingual Platform Independent Tool for Morphology and Syntax Annotation

We present PALMYRA 2.0, a graphical dependency-tree visualization and editing software. PALMYRA 2.0 is designed to be highly configurable to any dependency parsing representation, and to enable the annotation of a multitude of linguistic features. It uses an intuitive interface that relies on drag-and-drop utilities as well as pop-up menus and keyboard shortcuts that can be easily specified.
Learning a Latent Simplex in Input Sparsity Time

We consider the problem of learning a latent k-vertex simplex K∈Rd, given A∈Rd×n, which can be viewed as n data points that are formed by randomly perturbing some latent points in K, possibly beyond K. A large class of latent variable models, such as adversarial clustering, mixed membership stochastic block models, and topic models can be cast in this view of learning a latent simplex. Bhattacharyya and Kannan (SODA 2020) give an algorithm for learning such a k-vertex latent simplex in time roughly O(k⋅nnz(A)), where nnz(A) is the number of non-zeros in A. We show that the dependence on k in the running time is unnecessary given a natural assumption about the mass of the top k singular values of A, which holds in many of these applications. Further, we show this assumption is necessary, as otherwise an algorithm for learning a latent simplex would imply a better low rank approximation algorithm than what is known. We obtain a spectral low-rank approximation to A in input-sparsity time and show that the column space thus obtained has small sin⁡Θ (angular) distance to the right top-k singular space of A. Our algorithm then selects k points in the low-rank subspace with the largest inner product (in absolute value) with k carefully chosen random vectors. By working in the low-rank subspace, we avoid reading the entire matrix in each iteration and thus circumvent the Θ(k⋅nnz(A)) running time.
Relational Learning with Variational Bayes

In psychology, relational learning refers to the ability to recognize and respond to relationship among objects irrespective of the nature of those objects. Relational learning has long been recognized as a hallmark of human cognition and a key question in artificial intelligence research. In this work, we propose an unsupervised learning method for addressing the relational learning problem where we learn the underlying relationship between a pair of data irrespective of the nature of those data. The central idea of the proposed method is to encapsulate the relational learning problem with a probabilistic graphical model in which we perform inference to learn about data relationships and other relational processing tasks.
Data explosion, disclosure and stepping stones

Disclosure laws are a key facet of corporate regulation. The advent of increased technology is having a significant effect on compliance with, and monitoring of, disclosure, misleading and deceptive conduct and data security laws. Particular factors are the explosion of new corporate communication channels and the vastly increased amount of information available to, and about, companies. When a company breaches such laws a question arises as to whether the directors or officers of the company might be personally liable. This question has traditionally involved discussion of accessory liability (in addition to contravention of specific statutory provisions). However, a mode of liability known as ‘stepping stones’ is frequently raised in Australian courts. Stepping stones cases highlight the potential for directors in common law jurisdictions to be liable for breach of the duty of care where the company contravenes laws relating to disclosure, misleading and deceptive conduct and data security.
Reversible Image Processing using ‘Magical Triangle’

The original images were converted in to a reversible matrix values by discovery, the values of the pixels using payload technique.The payload is to calculate the pixels values on reversible data hiding, we converting the image pixels into a value.Generating the matrix form is converting an image using matrix algorithm.Data Embedding is used to converting the values in to a matrix pixels, if we give an input value then it form the matrix by using that input value.Thus, Combination of two results the reversible matrix form.
Mobile Learning Development in Security Intelligence Technical Functions Courses for Police Academy Cadets

This research is based on the fact that most Akpol Cadets have difficulty in understanding the materials at the subject of Security Intelligence Technical Functions. This is because of the many foreign terms in intelligence that are quite difficult to understand. In addition, it is also caused by the limitations of teaching materials that exist today in loading visual images or illustrations that can facilitate Akpol Cadet in understanding the material. This research aims to produce mobile learning in the Security Intelligence Technical Functions as a source of learning that is of high innovation value so that cadets can learn the subject matter of Security Intelligence Technical Functions more easily and gain more real experience of technical learning materials, and the learning process can become more effective because Akpol Cadets can re-review the learning materials outside the learning process. This research will apply Research and Development (R & D) method using Dick and Carey research and development model, while for product development model to be done using Hannafin and Peck model at the learning material development stage.
Linear discriminant analysis via the Growth Curve model and restrictions on the mean space

A linear classification function is applied when the means follow a Growth Curve model with restriction on the mean space. If the underlying assumption is that different groups in the experimental ...
IMPLEMENTASI PEMBELAJARAN DIGITAL PADA MODUL PEMBELAJARAN SEKOLAH MENGGUNAKAN AUGMENTED REALITY (AR)

Abstract: In this era, students are not just only want to study with just read a book. But, they want study, and see the the object  directly. With the development current technology, Augmented Reality brings a new education system with using camera to support the education system. Nowadays, the programming applications has many variation, from android studio aplication to a game development aplication, like  Unity. Unity 3D  support to make 3D animation model. In this application,  will using vuforia SDK to make Augmented Reality.
Catching the Long Tail in Deep Neural Networks

Learning dynamics in deep neural networks are still a subject of debate. In particular, the identification of eventual differences regarding how deep models learn from frequent versus rare training examples is still an area of active research. In this work, we focus on studying the dynamics of memorization in deep neural networks, where we understand memorization as the process of learning from rare or unusual training examples that are part of the long-tail of a dataset. As a working hypothesis, we speculate that during learning some weights focus on mining patterns from frequent examples while others are in charge of memorizing rare long-tail samples. Using this idea, we develop a method for uncovering which weights focus on mining frequent patterns and which ones focus on memorization. Following previous studies, we empirically verify that deep neural networks learn frequent patterns first and then focus on memorizing long-tail examples. Furthermore, our results show that during training a small proportion of the total weights present an early convergence to model frequent patterns, while the vast majority of the weights present a slow convergence to model long-tail examples. We also find that memorization happens mostly at the first layers of a network and not at the level of classification. Finally, by analyzing performance differences for models trained with varying levels of long-tail samples, we find that a larger number of long-tail samples has a negative impact on learning frequent patterns, by a process we conjecture to force the model to learn frequent patterns as memorization.
▵Breakpad: Diversified Binary Crash Reporting

This paper introduces {\Delta}Breakpad. It extends the Breakpad crash reporting system to handle software diversity effectively and efficiently by replicating and patching the debug information of diversified software versions. Simple adaptations to existing open source compiler tools are presented that on the one hand introduce significant amounts of diversification in the code and stack layout of ARMv7 binaries to mitigate the widespread deployment of code injection and code reuse attacks, while on the other hand still supporting accurate crash reporting. An evaluation on SPEC2006 benchmarks demonstrates that the corresponding computational, storage, and communication overheads are small.
Paradigm Shift in the Law and Policy of Data Protection in Intelligent Information Society

Currently, South Korea s data protection law can be characterized as a rigid protection system focused on criminal punishment based on prior consent of data subject. It is known as the world s most powerful and strict regulatory legislation. Compared to the European Union s General Data Protection Regulation(GDPR), Korea s data protection law is considered one of the strictest privacy regulations in the world. Many say that the current protection model in Korea has lost its balance of protection and utilization. It requires prior consent of the data subject in order to collect, use, and provide personal data on the premise that personal data must be protected like a private secret. And it imposes excessive administrative fines and criminal penalties for violations. This article was written with the aim of providing clues as to how Korea s data protection law should find a reasonable balance between protection and use in the future. Fortunately, the Personal Data Protection Act, which was revised in February 2020, can be positively evaluated as a legislative attempt to find a reasonable balance between protection and utilization. Similar to the European Union s GDPR, the revised law permits the change of purpose to the extent reasonably related to the purpose of collection. And it introduced the concept of pseudonymized data to increase the possibility of using pseudonymized data, and integrated the enforcement system into the Personal Data Protection Committee to secure the expertise and independence of the data protection agency. This can be assessed as a step closer to global standards. Nevertheless, Korea s data protection law still has a rigid protection model from the perspective of global standards. This article presents three urgent legislative tasks to be improved. First, as in the European Union s GDPR, the six grounds of lawfulness for collection, use, and provision of personal data must be acknowledged, and in principle, the same grounds must be applied in the field of information and communications networks. Second, the requirements for the exercise of the right to the protection of personal data, reasons for defense, and exceptions must be prepared in a reasonable manner. Third, excessive criminal punishment should be reorganized. Like Germany or the UK, criminal sanctions should be imposed only for serious violations of the law, or, as in Japan, criminal sanctions should be imposed for violations of corrective orders of the Personal Data Protection Committee.
Information Design in Blockchain: A Role of Trusted Intermediaries

This study clarifies that blockchain cannot replace the strategic value of trusted intermediaries, despite sufficient technological advancement for its implementation. Given the progress expected in the future, this study assumes that blockchain can implement various commitment devices for communication explored in the information design literature, without disclosing their details to anonymous record keepers. By considering revelation incentives explicitly, we show that substituting the verification task of playersâ€™ pre-owned private signals with a trusted intermediary can reduce transaction costs in liability, which cannot be achieved non-judicially by blockchain. Hence, trusted intermediaries play a significant role in executing information design through blockchain.
Modelling and Optimization of Peroxide Pulp Bleaching Process

The wood pulp industry has been around for a long time, but new higher quality pulp require more advanced solutions to old processes. One of these processes is the peroxide (PO) bleaching process, which is the last of a whole chain of bleaching processes at the Morrum pulp processing plant. The aim of this thesis was to develop and study a model for the peroxide pulp bleaching process, and thereafter optimize the process with the model. The PO-stage is a multivariable, non-linear process with a variable retention time of a few hours. The models tested was a kinetic reaction model, a Gaussian process regression (GPR) model and hybrid models of the two. Before the models could be tested a retention time estimation model was made to compensate for the variable retention time. The bleaching simulations showed the kinetic model could not accurately model the brightness output. The kinetic lacked variability in the maximum brightness parameter C¥. However, the combination of estimating C¥ for the kinetic model with an GPR model proved to be a good performing model. Prediction on slow brightness changes was accurate, but fast changes was harder and large error could occur. The optimization of the hybrid model showed that chemical dosages could be lowered while achieving a smoother and more precise brightness. Further studies on robustness of the brightness model and the optimization model are needed before implementation on the real process can be done. (Less)
Select good regions for deblurring based on convolutional neural networks

The goal of blind image deblurring is to recover sharp image from one input blurred image with an unknown blur kernel. Most of image deblurring approaches focus on developing image priors, however, there is not enough attention to the influence of image details and structures on the blur kernel estimation. What is the useful image structure and how to choose a good deblurring region? In this work, we propose a deep neural network model method for selecting good regions to estimate blur kernel. First we construct image patches with labels and train a deep neural networks, then the learned model is applied to determine which region of the image is most suitable to deblur. Experimental results illustrate that the proposed approach is effective, and could be able to select good regions for image deblurring.
Reader Comment Digest Through Latent-Stock Market

Examine the monetary information for causing forecasts in securities exchanges by utilizing enormous information to examine and prescribe the client interest in different classification financial exchanges dependent on web media. At the point when a noteworthy occasion happens, numerous news stories from various newsagents frequently report it. Additionally, these newsagents likewise give stages to their perusers to compose remarks communicating their perspectives or comprehension. Through processing these peruser remarks, we can pick up bits of knowledge into the responses, proposals, individual encounters, or popular sentiments regarding the developing occasion. Be that as it may, these peruser remarks from various sources are frequently quickly gathered bringing about a huge volume. It gets hard to physically break down these remarks. Right now, propose a system that can process per user remarks consequently through inert occasion features and news particularity. An occasion feature alludes to the part of the occasion worried by numerous perusers. In particular, a portion of the peruser remarks, in spite of originating from various sources, talk about a specific aspect of the occasion. Such features give a compelling way to sort out news remarks in a worldwide way. Then again, a few remarks talk about the particular subject of the relating news story. These particular themes show the particular focal point of perusers on the bit of news locally. Such peruser remarks digest in various granularities encourages more profound comprehension of these colossal remarks. To accomplish the above alluring objective of processing peruser remarks, we propose an unaided model called EFNS which is equipped for catching the multifaceted fine-grained relationship among occasions, news, and remarks. We additionally build up a multiplicative-update strategy to derive the parameters and demonstrate the intermingling of our calculation. Our system can likewise picture peruser remarks as indicated by the relationship with idle occasion aspects and the level of news particularity. Test results show that our proposed EFNS model can give a successful method to process news peruser remarks and beat the best in class strategy.
Журнал для преподавателей информатики «Педагогика информатики»

The article analyzes the content of the electronic scientific and methodological journal “Pedagogy of Informatics”, dedicated to the specifics of computer training. The main topics of the journal are: global e-learning, artificial intelligence, robotics, media and information literacy, neuroeducation, creative and heuristic technologies in computer learning, issues of professional training and retraining of teachers. In particular, modern approaches to teaching computer science for 5–11 grades are considered, forms of their additional education in combination with olympiads, intellectual contests, tournaments, seminars, summer schools are described, and the results of Belarusian participants in international competitions in informatics are presented
Penerapan Model Pembelajaran Kooperatif Tipe Numbered Heads Together (NHT) Untuk Meningkatkan Aktivitas dan Hasil Belajar Siswa PKn di SD

This study aims to improve student activity and learning outcomes by applying the Numbered Heads Together (NHT) cooperative learning model. This type of research is classroom action research. Observations in this classroom action research are carried out in 2 cycles. Each cycle goes through the stages of planning, implementing, observing and reflecting. To collect data in this study, the techniques used are guided by the qualitative and quantitative research paradigms. The data in this study were analyzed using descriptive analysis. The research subjects were fourth grade students of SDN 105316 Beranti which consisted of 17 male students and 13 female students, so that the total number of students was 30 students. From the results obtained, it can be concluded that there is an increase in student learning activities by 34%, namely the percentage of students who are actively learning in cycle II (87%) is greater than in cycle I (53%), and there is an increase in student learning completeness by 36.6 %, namely the percentage of classical student learning completeness in cycle II (90%) is greater than that in cycle I (53.4%).
How to play Flash games after Adobe discontinues Adobe Flash Player

﻿Introduction On the 31st of December Adobe will be finally pulling the plug and formally making Adobe Flash Player End Of Life (EOL). Going into ...