Reviewerly/exHarmony huggingface dataset:
All passable games are realizable as monotone set coloring games

The class of passable games was recently introduced by Selinger as a class of combinatorial games that are suitable for modelling monotone set coloring games such as Hex. In a monotone set coloring game, the players alternately color the cells of a board with their respective color, and the winner is determined by a monotone function of the final position. It is easy to see that every monotone set coloring game is a passable combinatorial game. Here we prove the converse: every passable game is realizable, up to equivalence, as a monotone set coloring game.
Discrete Representations Strengthen Vision Transformer Robustness

Vision Transformer (ViT) is emerging as the state-of-the-art architecture for image recognition. While recent studies suggest that ViTs are more robust than their convolutional counterparts, our experiments find that ViTs trained on ImageNet are overly reliant on local textures and fail to make adequate use of shape information. ViTs thus have difficulties generalizing to out-of-distribution, real-world data. To address this deficiency, we present a simple and effective architecture modification to ViT's input layer by adding discrete tokens produced by a vector-quantized encoder. Different from the standard continuous pixel tokens, discrete tokens are invariant under small perturbations and contain less information individually, which promote ViTs to learn global information that is invariant. Experimental results demonstrate that adding discrete representation on four architecture variants strengthens ViT robustness by up to 12% across seven ImageNet robustness benchmarks while maintaining the performance on ImageNet.
E-Resource Management and Management Issues and Challenges

E-resources are inevitable, technology has grown and libraries are also adopting the technologies although adopting have many challenges to the library professionals. Whenever something new comes they need to update themselves. A study investigated E-Resources management and management issues of Indian library professional perspectives. For this study, data was collected from various academic institutes/university libraries in India. It includes institutes of national importance, central, state, deemed and private universities. The study finds that the majority of the libraries subscribed to E-journals and E-books, administration related challenges faced by LIS professionals. The t-test results revealed a lack of professional skills is the reason for issues and challenges of Library management.
SLA$^2$P: Self-supervised Anomaly Detection with Adversarial Perturbation

Anomaly detection is a fundamental yet challenging problem in machine learning due to the lack of label information. In this work, we propose a novel and powerful framework, dubbed as SLA$^2$P, for unsupervised anomaly detection. After extracting representative embeddings from raw data, we apply random projections to the features and regard features transformed by different projections as belonging to distinct pseudo classes. We then train a classifier network on these transformed features to perform self-supervised learning. Next we add adversarial perturbation to the transformed features to decrease their softmax scores of the predicted labels and design anomaly scores based on the predictive uncertainties of the classifier on these perturbed features. Our motivation is that because of the relatively small number and the decentralized modes of anomalies, 1) the pseudo label classifier's training concentrates more on learning the semantic information of normal data rather than anomalous data; 2) the transformed features of the normal data are more robust to the perturbations than those of the anomalies. Consequently, the perturbed transformed features of anomalies fail to be classified well and accordingly have lower anomaly scores than those of the normal samples. Extensive experiments on image, text and inherently tabular benchmark datasets back up our findings and indicate that SLA$^2$P achieves state-of-the-art results on unsupervised anomaly detection tasks consistently.
Optimizing Bootstrapping and Evaluating Large FHE Gates in the LWE-Based GSW-FHE

Fully homomorphic encryption (FHE) allows us to perform computations directly over encrypted data and can be widely used in some highly regulated industries. Gentry’s bootstrapping procedure is used to refresh noisy ciphertexts and is the only way to achieve the goal of FHE up to now. In this paper, we optimize the LWE-based GSW-type bootstrapping procedure. Our optimization decreases the lattice approximation factor for the underlying worst-case lattice assumption from \(\tilde{O}(N^{2.5})\) to \(\tilde{O}(N^{2})\), and is time-efficient by a \(O(\lambda )\) factor. Our scheme can also achieve the best factor in prior works on bootstrapping of standard lattice-based FHE by taking a larger lattice dimension, which makes our scheme as secure as the standard lattice-based PKE. Furthermore, in this work we present a technique to perform more operations per bootstrapping in the LWE-based FHE scheme. Although there have been studies to evaluate large FHE gates using schemes over ideal lattices, (i.e. using FHEW or TFHE), we are the first to study how to perform complex functions homomorphically over standard lattices.
Interpretation of Elementary School Mathematics Material in Motion Comic

The era of industrial revolution 4.0 is an inevitability for people living in the 21st century.Many advanced devices are used in interacting in various areas of activity, including education.In mathematics learning can also use sophisticated devices and various platforms or applications according to the development of science and technology.Similarly, students who started their lives in the 21st century, many are familiar with the development of science and technology and use it in daily life.Therefore, it is appropriate if mathematics learning adapts to life in the era of industrial revolution 4.0.to overcome the dislike of mathematics and the difficulty in learning mathematics.Making mathematics motion comic is an effort to attract interest and facilitate learning mathematics.Motion comic is a moving comic, in the form of digital comics that are interesting and light to be read / watched by elementary school-age learners.Packing mathematical material in motion comic is to develop the comic and create the animation.The process of making mathematics motion comic consists of three stages, namely: prep roduction, production and post production.Principles of developing comics, paying attention to ideas or goals, forms, idioms,structures, craft, surfaces.To obtain motion comic continued by making the animation.Mathematical materials in comics are poured in ideas or objectives according to the curriculum.Forms, idioms, and structures are created according to comics and mathematical materials.Craft and surface that polishes math comics into books that are light to read, interesting, and easy for students to learn math.Along with making mathematical comics think about animation based on three main plots of the animation process, namely: designing, executing, analyzing the results of animation.The success of delivering mathematics in motion comic is measured by the suitability of the material with the curriculum and objectives, the delivery of materials in accordance with visual indicators, the presentation of motion comic that is preferred and can understand mathematics.
RESEARCH OF THE LEVEL OF SENIOR GRADE STUDENTS’ SOCIAL AND PSYCHOLOGICAL READYNESS TO ENTER THE INSTITUTIONS OF HIGHER EDUCATION

The article considers the phenomenon of social and psychological readiness of high school students to enter higher education institutions, as it is relevant both for the individual and for society as a whole. Graduates of general secondary education institutions should enter into new social relations, which requires the necessary and sufficient level of knowledge to master the educational and professional program in a higher education institution successfully; ability to adapt to new life circumstances; ability to choose the path of professional development. General secondary education institutions need to pay considerable attention to the professional orientation of students. The components of vocational guidance in schools include the presence of a comprehensive program of vocational guidance; taking into account the needs of each child; availability of qualified career guidance counsellors trained teachers; contact with employers and direct work experience; liaison with educational institutions of the next levels of education; support and understanding of the importance of vocational guidance by school management; access to information on career development and the world of professions. The purpose of the article is to determine the initial level of formation of socio-psychological readiness of high school students to enter higher education institutions. Further steps are planned to form the social and psychological readiness of high school students to enter higher education institutions. Research methods: theoretical - analysis of psychological and pedagogical sources on the researched problems; empirical - Questionnaire to determine the formation of ideas about the world of professions and self-determination of personality, methods of diagnosing personality motivation to succeed T. Ehlers, methods «Motives for choosing a profession» R. V. Ovcharova, method «Readiness of students to choose a profession» V. B Uspensky. The study involved 200 high school students.
The Impact of Online Game Addiction on the Quality of Student Education During the COVID-19 Pandemic

The ongoing Covid-19 pandemic has had a severe impact on the quality of education in Indonesia. This study aims to describe addiction's effect on the quality of student education during the Covid-19 pandemic in Indonesia. This research is a literature study with a qualitative approach. The results show that during the Covid-19 outbreak, game data usage is reported to have increased worldwide, and download volume games have reached highs record in Asia, including Indonesia. This is caused by games used to cope with the psychological stress caused by the outbreak. Traditional games that were once popular have been replaced by new games such as online video games. Stakeholders should be aware of how addiction increases to online games, especially during a pandemic, and can contribute to the risk of disruption to the quality of student learning, especially if the pandemic continues. The government must formulate alternative ways to avoid online game addiction during the Covid-19 pandemic for people, especially teenagers, which can interfere with the quality of their education.
C-STCM: Image Retrieval using Color Based Stretched Text on Co-Occurrence Matrix

In this paper, a novel feature descriptor named stretched texton co-occurrence matrix (STCM) is “proposed for content-based image retrieval (CBIR)”. The STCM represents the shape, color and texture features more efficiently and precisely. The color information is extracted by quantizing each component of HSV color space. The shape features in the form of stretched texton indexes (STI) are computed by quantizing the V component of HSV color space. The earlier texton-based approaches derived only a few textons on a microgrid. The proposed method extracted all probable textons on a 2x2 grid and considered the magnitude relationship among the pixels that are part and not part of texton formation. This aspect is not considered in the earlier methods. The co-occurrence features are obtained by transforming the identified microstructures in the form of STI image into STCM. This paper integrated the color features derived from the histograms of individual HSV color space, with new structural patterns “stretched Texton’s”, texture features in the form of GLCM and this process derived the proposed Color-Stretched Texton Co-occurrence Matrix (C-STCM) .  The experiments are performed on natural databases, i.e., Corel- 1K, Corel 10K, gray and color Broadtaz texture, CMU-PIR, and MIT-VisTex. The results obtained depict the outstanding performance of the proposed C-STCM over the state-of-art methods.
Intelligent Detection Method of English Text in Natural Scenes in Video

With the rapid development of Internet technology, breakthroughs have been made in all branches of computer vision. Especially in image detection and target tracking, deep learning techniques such as convolutional neural networks have achieved excellent results. In order to explore the applicability of machine learning technology in the field of video text recognition and extraction, a YOLOv3 network based on multiscale feature transformation and migration fusion is proposed to improve the accuracy of english text detection in natural scenes in video. Firstly, aiming at the problem of multiscale target detection in video key frames, based on the YOLOv3 network, the scale conversion module of STDN algorithm is used to reduce the low-level feature map, and a backbone network with feature reuse is constructed to extract features. Then, the scale conversion module is used to enlarge the high-level feature map, and a feature pyramid network (FPN) is built to predict the target. Finally, the improved YOLOv3 network is verified to extract key text from images. The experimental results show that the improved YOLOv3 network can effectively improve the false detection and missed detection caused by occlusion and small target, and the accuracy of English text extraction is obviously improved.
Peningkatan Kemampuan Operasi Hitung Siswa Melalui Bimbingan Belajar dan Lomba Cerdas Cermat Di Desa Simandraolo

The COVID-19 outbreak had a major impact on the world of education, so the government decided to conduct online learning as an effort to break the chain of the spread of Covid-19. Online learning involves elements of technology and experiences obstacles when learning, including the network is not smooth, lack of knowledge, to the lack of student participation when learning online. This has an impact on student learning outcomes. Elementary schools in Simandraolo Village conduct online learning, especially mathematics, most students have difficulty in performing arithmetic operations such as addition, subtraction, multiplication and division of integers and fractions. Based on these problems, it is necessary to conduct tutoring activities for students so that students can be helped in overcoming the problems they experience and carry out Smart Contest activities so that students are more motivated in learning. This activity is divided into several stages as follows: preliminary stage, action stage, final stage. Based on the tutoring activities and the quiz competition, it can be concluded that students' difficulties can be overcome by conducting tutoring and quiz competitions. Based on this service activity, it is recommended that students be more active in learning. Doing exercises that are continuous and repeated so that the concepts of integer and fractional operations can be permanently embedded in the mind.
THE „PYGMALION EFFECT“ IN THE CLASSROOM

The article provides a brief literary review of some concepts, which are more characteristic for the psychological sciences, but have adequate application in pedagogical practice. This is the „Pygmalion effect“, which means that expectations of students are high, and this will lead to higher academic results on their part. This concept is related to the so-called „self-fulfilling prophecies“ – the individual develops to the extent that we expect from her/him. Of course, these phenomena are extremely important for the atmosphere in the classroom, because in a sense they determine the teacher’s attitude towards students. An attempt is made to define other terms: „Galatea effect“ and „Golem effect“ as well.
Hierarchical Text Classification As Sub-Hierarchy Sequence Generation

Hierarchical text classification (HTC) is essential for various real applications. However, HTC models are challenging to develop because they often require processing a large volume of documents and labels with hierarchical taxonomy. Recent HTC models based on deep learning have attempted to incorporate hierarchy information into a model structure. Consequently, these models are challenging to implement when the model parameters increase for a large-scale hierarchy because the model structure depends on the hierarchy size. To solve this problem, we formulate HTC as a sub-hierarchy sequence generation to incorporate hierarchy information into a target label sequence instead of the model structure. Subsequently, we propose the Hierarchy DECoder (HiDEC), which decodes a text sequence into a sub-hierarchy sequence using recursive hierarchy decoding, classifying all parents at the same level into children at once. In addition, HiDEC is trained to use hierarchical path information from a root to each leaf in a sub-hierarchy composed of the labels of a target document via an attention mechanism and hierarchy-aware masking. HiDEC achieved state-of-the-art performance with significantly fewer model parameters than existing models on benchmark datasets, such as RCV1-v2, NYT, and EURLEX57K.
SELF-EDUCATION AS A MEANS OF INCREASING DIGITAL SKILLS

Digital skills are an important competence for living and working in the 21st century, which is multiplied mainly by the increase in digitization in everyday and working life. School and education must naturally adapt to the change that is coming with the Industry 4.0, because it will bring about a change in the employment system as we know it today. The need to have at least basic digital skills has been further highlighted by the current pandemic situation. Self-education can ultimately bring us more benefits than we can currently imagine. We will learn to work effectively with the time we have so much right now. The article analyses the possibilities of educating citizens in the context of digitization of the economy and society. Self-education can be a form of raising the level of digital skills of anyone wants to learn and improve their abilities and skills. At present there is no comprehensive system of self-education through which it is possible to acquire specific skills with information and communication technologies. A person who wants to improve their skills on their own will spend a lot of time searching for relevant information. Based on the primary research, design an electronic system enabling citizens to learn in the field of information and communication technologies in the form of detailed instructions and procedures needed to acquire specific skills. In the article we describe the proposed electronic system - a web portal, which will contain a database of instructions and procedures from various areas related to information and communication technologies. It is thanks to such instructions that one should be able to learn specific skills from the comfort of home.
The Cultivation of Self-efficacy in College English Learning

In order to improve the effect of college English learning, it is very necessary to cultivate students' selfefficacy.Self-efficacy theory has extremely high theoretical and practical value for teaching.The mechanism of self-efficacy on English learning is analyzed, and the effective ways to improve English teaching and students' comprehensive application ability are explored in this paper.The key to solving the problem of selfefficacy is that teachers change their teaching concepts and provide students with ways and opportunities to experience success through humanized teaching activities.
AN APP TO CATER FOR EVERY ENGLISH FOR SPECIFIC PURPOSES SKILL

C.N. Albert 1, I. Mudure-Iacob2 1The University of Agricultural Sciences and Veterinary Medicine of Cluj-Napoca (ROMANIA) 2Babes-Bolyai University (ROMANIA)
강수량의 계절 예측을 위한 베이지안 앙상블 MOS방법의 비교연구

본 논물은 기후예측 (climate forecasts)에 있어서 주로 사용되는 통계적 후처리 (statistical post-processing)방법을 검토한다. 특히 베이지안 통계학 (Bayesian statistics)을 이용한 기후예측에서 주로 사용되는 베이지안 선형 회귀모형 (Bayesian linear regression)과 베이지안 모형 평균화 (Bayesian model averaging) 두 가지 앙상블 MOS (ensemble model output statistics)방법을 설명하고 디리크레 과정 사전분포 (Dirichlet process prior)를 이용한 비모수 (nonparametric) 베이지안 접근법을 살펴본다. 세 가지 베이지안 앙상블 방법을 바탕으로 사후분포를 유도하고 마코프 체인 몬테 카를로 (Markov chain Monte Carlo) 방법을 통해 사후추론을 실시한다. 한국 지역의 강수량 자료로부터 leave-one-out 교차타당성 (cross-validation) 방법을 이용하여 모형간의 성능을 비교한다. 모의 실험의 결과 베이지안 통계적 후처리 방법이 일반 순환모형보다 우수한 성능을 보이는 것을 확인하였다.
Some Generalized Multi-sum Chu-Vandermonde Identities

Some generalized multi-sum Chu-Vandermonde identities are presented and proved, generalizing some known multi-sum Chu-Vandermonde identities from literature and adding some quadratic and cubic examples of these identities. Some other closely related identities are provided. The identities are proved with the integral representation method using complex residues, a method also used in some earlier papers.
Constructive Approximation of Continuous Interval-Valued Functions

In this paper we present a Stone-Weierstrass type result in the context of continuous interval-valued functions defined on a compact Hausdorff space. We also provide a Jackson type approximation result involving the modulus of continuity of interval-valued functions based on the gH-difference of intervals. Finally we show how an interval-valued continuous function can be approximated using interval neural networks. All the proofs are constructive.
DIGITAL MEDIA AND EDUCATION: COMMUNICATION BETWEEN FACULTY MEMBERS AND STUDENTS IN A MEXICAN UNIVERSITY DURING THE PANDEMIC

DIGITAL MEDIA AND EDUCATION: COMMUNICATION BETWEEN FACULTY MEMBERS AND STUDENTS IN A MEXICAN UNIVERSITY DURING THE PANDEMIC
THE ALL-DAY EDUCATIONAL SYSTEM AS AN EDUCATIONAL SPACE FOR IMPLEMENTATION OF THE FEUERSTEIN’S INSTRUMENTAL ENRICHMENT AND NYBORG’S CONCEPT TEACHING MODEL

Information about the paper titled "THE ALL-DAY EDUCATIONAL SYSTEM AS AN EDUCATIONAL SPACE FOR IMPLEMENTATION OF THE FEUERSTEIN’S INSTRUMENTAL ENRICHMENT AND NYBORG’S CONCEPT TEACHING MODEL" at IATED Digital Library
Shared-Memory Parallelization of FP-growth with Dynamic Load Estimation and Balancing

Although FP-growth is known as an efficient frequent pattern mining algorithm, it is still a problem how to make it work for large transactional databases. In this paper, we propose a shared-memory, task parallelization method for FPgrowth. In the proposed method, each task receives conditional transactions from the current FP-tree, builds a conditional FP-tree, and generates the next tasks for the successive branches in the search tree. During this process, we dynamically estimate the workloads of such next tasks based on the corresponding conditional FP-trees and balance them among processors in the manner of work-stealing. Furthermore, we found that Borgelt’s FP-tree construction method notably contributes to our shared-memory parallelization. In order to exploit computational resources in a light-weight way, we implement the proposed method with Rust, a compiler language that can handle memory safely without garbage collection. In most of the benchmark datasets we tested, a performance improvement in parallelization was generally observed in comparison with two previous methods.