Reviewerly/exHarmony huggingface dataset:
Investigation of observability property of controlled binary dynamical systems: a logical approach

The property of observability of controlled binary dynamical systems is investigated. A formal definition of the property is given in the language of applied logic of predicates with bounded quantifiers of existence and universality. A Boolean model of the property is built in the form of a quantified Boolean formula accordingly to the Boolean constraints method developed by the authors. This formula satisfies both the logical specification of the property and the equations of the binary system dynamics. Aspects of the proposed approach implementation for the study of the observability property are considered. The technology of checking the feasibility of the property using an applied microservice package is demonstrated in several examples.
A Public Ground-Truth Dataset for Handwritten Circuit Diagram Images

The development of digitization methods for line drawings (especially in the area of electrical engineering) relies on the availability of publicly available training and evaluation data. This paper presents such an image set along with annotations. The dataset consists of 1152 images of 144 circuits by 12 drafters and 48 563 annotations. Each of these images depicts an electrical circuit diagram, taken by consumer grade cameras under varying lighting conditions and perspectives. A variety of different pencil types and surface materials has been used. For each image, all individual electrical components are annotated with bounding boxes and one out of 45 class labels. In order to simplify a graph extraction process, different helper symbols like junction points and crossovers are introduced, while texts are annotated as well. The geometric and taxonomic problems arising from this task as well as the classes themselves and statistics of their appearances are stated. The performance of a standard Faster RCNN on the dataset is provided as an object detection baseline.
METHODS OF TEACHING STEM SPECIALISTS IN HIGHER EDUCATIONAL INSTITUTIONS OF RUSSIA, BELARUS AND HUNGARY: RELEVANCE AND EFFECTIVENESS IN STUDENT ASSESSMENTS

Information about the paper titled "METHODS OF TEACHING STEM SPECIALISTS IN HIGHER EDUCATIONAL INSTITUTIONS OF RUSSIA, BELARUS AND HUNGARY: RELEVANCE AND EFFECTIVENESS IN STUDENT ASSESSMENTS" at IATED Digital Library
DISTANCE LEARNING IN AN ELECTRONIC ENVIRONMENT - AN ADVANTAGE OR CHALLENGE FOR THE MODERN STUDENT

At the beginning of March 2020, when the COVID-19 pandemic escalated, the Minister of Health of the Republic of Bulgaria introduced anti-epidemic measures on the territory of the country in order to reduce the risk of spreading the infection. In accordance with the recommendations for social distance, the visits to public catering establishments, holding mass events, consultations, examinations, the visits of the children in the nurseries and kindergartens were stopped. All classes in educational institutions and organizations, schools and universities were suspended, and if possible it was necessary for the latter to introduce distance learning. University of Ruse ‘Angel Kanchev’ , Bulgaria initiates real-time teaching of learning content for its students, organization and conduct of lesson observation, teaching practice and exam procedures through the BigBlueButton online learning system.The publication reflects the opinion of first-year students, full-time and part-time, in the specialties ‘Preschool and primary school pedagogy’, ‘Social pedagogy’ and ‘Primary school pedagogy with a foreign language’ at the University of Ruse ‘Angel Kanchev’ on the benefits and the shortcomings, difficulties and problems accompanying them during their training in a distance electronic environment during the winter semester of the 2020/2021 academic year.
CATTELL’S SIXTEEN PERSONALITY FACTORS AND ENGLISH AS FOREIGN LANGUAGE PROFICIENCY IN RUSSIAN LINGUISTICS AND NON-LINGUISTICS STUDENTS

CATTELL’S SIXTEEN PERSONALITY FACTORS AND ENGLISH AS FOREIGN LANGUAGE PROFICIENCY IN RUSSIAN LINGUISTICS AND NON-LINGUISTICS STUDENTS
SOLVING A NON-TRIVIAL TASK IN TEACHING SECONDARY SCHOOL MATHEMATICS

Appears in: EDULEARN21 Proceedings Publication year: 2021Pages: 11279-11286ISBN: 978-84-09-31267-2ISSN: 2340-1117doi: 10.21125/edulearn.2021.2352Conference name: 13th International Conference on Education and New Learning TechnologiesDates: 5-6 July, 2021Location: Online Conference
When and Why Do Reasoners Generalize Causal Integration Functions? Causal Invariance as Generalizable Causal Knowledge

Author(s): Bye, Jeffrey K.; Chuang, Pei-Jung; Cheng, Patricia | Abstract: The present paper reports an experiment testing two views of how reasoners learn and generalize potentially complex causal knowledge. Previous work has focused on reasoners’ ability to learn integration functions that best describe how pre-defined candidate causes combine, potentially interactively, to produce an outcome in a domain. This empirical-function learning view predicts that participants would generalize an acquired integration function based on similarity to stimuli they experienced in the domain. An alternative causal-invariance view recognizes that one’s current representation may not yield invariant/useable causal knowledge –– knowledge that holds true when applied to new circumstances. This view incorporates useable causal knowledge as a goal and deviation from causal invariance as a criterion for knowledge revision. It predicts that participants would re-represent causes such that they do not interact with other causes, even when in participants’ experience all (pre-defined) causes in that domain interact. Our results favor the causal-invariance view.
DEVELOPMENT OF READING COMPREHENSION SKILLS IN KINDERGARTEN AND FIRST DEGREE OF PRIMARY SCHOOL

Appears in: EDULEARN21 Proceedings Publication year: 2021Pages: 5701-5704ISBN: 978-84-09-31267-2ISSN: 2340-1117doi: 10.21125/edulearn.2021.1153Conference name: 13th International Conference on Education and New Learning TechnologiesDates: 5-6 July, 2021Location: Online Conference
Pedagogical bases of formation of ecological culture in students of pedagogical higher educational institution

In this article, the formation of the ecological culture of students of higher educational institutions is substantiated as a pedagogical problem, as well as a system and content of the formation of the ecological culture of students is created, a methodology for the formation of ecological culture is proposed and criteria and scientific and methodological recommendations for the formation of the ecological culture of students are formed.
Analisis Bentuk Singkatan dan Akronim Pada Iklan Baris Koran Kompas, Solopos, dan Kaltengpos Serta Implementasinya di SMP Muhammadiyah Pangkalan Bun

The purpose of this study is 1) Describe the abbreviated form of classified advertisements in Kompas, Solopos and Kaltengpos newspapers. 2) Describe the process of forming classified ad abbreviations in Kompas, Solopos and Kaltengpos newspapers. 3) Describe the implementation in learning Indonesian in SMP Muhammadiyah Panggkalan Bun. This research uses the technique of listening and note taking. The technique of listening is to listen to the use of language and forms of abreviation that exist in classified advertisements in newspapers. The note taking technique is used to record the data that contains the abbreviation. The data is classified according to the type of formation process. Analysis of the data in this study uses the Agih Sudaryanto Method. The results of this study are: found forms of abreviation include abbreviations, acronyms, fragments, and letter symbols. as for the process of its formation Abbreviations: a) Consisting of the first letter of each component, b) Consisting of the first two letters, c) Consisting of the first letter of each syllable, d) Consisting of the first letter of the first syllable and the first and last letter of the second syllable of a word, e) Lasting first and last letters of the first syllable and first letter of the second syllable, and f) Irregular binding of letters. The process of forming an acronym: a) Consisting of the first term of each component, b) Consisting of the first two letters of each component. c) Conservation of the first three-letter component and the second two-letter component. The process of forming a fragment; a) The first syllable fragment of a word, and the process of forming the letter symbol: a) The symbol symbol marks the size. The implementation was carried out at the junior high school level in KD 3.4 as teaching material for Indonesian language at the junior high school level.Keywords: Morphological Studies, Forms of Abreviation, Newspaper Ads.
EDUCATIONAL ROLE OF THE CHILDREN'S ILLUSTRATED BOOK THROUGH THE CHILD'S VIEW: RESULTS OF A STUDY

Appears in: EDULEARN21 Proceedings Publication year: 2021Pages: 5329-5333ISBN: 978-84-09-31267-2ISSN: 2340-1117doi: 10.21125/edulearn.2021.1092Conference name: 13th International Conference on Education and New Learning TechnologiesDates: 5-6 July, 2021Location: Online Conference
Forensic Engineering Analysis of a Swimming Pool Electric Shock Injury

This case involves a minor who received an electric shock while swimming in a membership swimming pool. Her family sued the pool association, its president, the electric utility, and others. At some time, tree trimmers had accidentally severed the service drop’s neutral return wire. The electric utility made a temporary splice repair, but did not permanently replace the wire until several years later (after the incident). The forensic engineer (FE) was retained by counsel for the pool and its president to opine on electrical aspects of the plaintiff’s complaints. The FE inspected the pool premises, reviewed documents, and examined the spliced service wires in storage. The FE opined that the pool association and its president were not negligent or careless — and that the electric utility failed its responsibility to maintain the service drop. This report discusses three-phase electric power, current flow, and how a severed neutral can cause a shock.
Meningkatkan Kecerdasan Finansial Anak Usia Dini Melalui Permaianan Monopoli Modifikasi

The purpose of this study is to increase learning activities and improve the financial intelligence of children aged 5-6 years. The research method used is classroom action research with research design according to the Kemmis & Taggart model with a spiral model system with research stages planning, implementing, observing, reflecting. The formulation of the problem in this study is whether the modified monopoly game can increase children's learning activities, and whether the modified monopoly game can improve financial intelligence?. After two cycles of action, the results in the first cycle showed the overall result, namely 50% or with an average score of 2.6 in the Develop as Expected category and in the second cycle, the overall result is 91.7% or with an average score of 3.3 in the Very well developed category. Financial intelligence in the first cycle showed an overall result of 41.7% or with an average score of 2.7 in the Develop as Expected category, and the second cycle, the overall result was 91.7% or with an average of 3.2 in the Very well Developed category. Thus, it was concluded that after two actions were carried out through the application of the monopoly game, the modification of learning activities and the financial intelligence of early childhood could be improved.
어린이집 교사의 이야기를 통해 본 놀이 관찰과 기록의 패러독스

The importance of observing and documenting children’s play is more emphasized in the field of early childhood education and care, before and after the revision of the ｢4th standard childcare curriculum｣, pursuing for a child-and-play-centered. This study aims to explore how childcare teachers conceptualized the purpose of observation and documentation of play, and what difficulties they experienced while observing and documenting play. The data are collected from 189 childcare teachers through distance interviews via e-mail, and the thematic analysis is conducted. The results show that the core purpose of observation and documentation of play is ‘understanding of child development’; and the main difficulty is ‘the realistic limit of childcare environments.’ This study critically discusses the tensions and contradictions in the dominant discourses of ‘development’ and ‘objectivity’ within observation and documentation, and it suggests the expansion of the policy and academic horizons for observation and documentation in which children’s play is alive.
User-Centric Health Data Using Self-sovereign Identities.

This article presents the potential use of the Self-Sovereign Identities (SSI), combining with Distributed Ledger Technologies (DLT), to improve the privacy and control of health data. The paper presents the SSI technology, lists the prominent use cases of decentralized identities in the health area, and discusses an effective blockchain-based architecture. The main contributions of the article are: (i) mapping SSI general and abstract concepts, e.g., issuers and holders, to the health domain concepts, e.g., physicians and patients; (ii) creating a correspondence between the SSI interactions, e.g., issue and verify a credential, and the US standardized set of health use cases; (iii) presenting and instantiating an architecture to deal with the use cases mentioned, effectively organizing the data in a user-centric way, that uses well-known SSI and Blockchain technologies.
Leaf-FM: A Learnable Feature Generation Factorization Machine for Click-Through Rate Prediction

Click-through rate (CTR) prediction plays important role in personalized advertising and recommender systems. Though many models have been proposed such as FM, FFM and DeepFM in recent years, feature engineering is still a very important way to improve the model performance in many applications because using raw features can rarely lead to optimal results. For example, the continuous features are usually transformed to the power forms by adding a new feature to allow it to easily form non-linear functions of the feature. However, this kind of feature engineering heavily relies on peoples experience and it is both time consuming and labor consuming. On the other side, concise CTR model with both fast online serving speed and good model performance is critical for many real life applications. In this paper, we propose LeafFM model based on FM to generate new features from the original feature embedding by learning the transformation functions automatically. We also design three concrete Leaf-FM models according to the different strategies of combing the original and the generated features. Extensive experiments are conducted on three real-world datasets and the results show Leaf-FM model outperforms standard FMs by a large margin. Compared with FFMs, Leaf-FM can achieve significantly better performance with much less parameters. In Avazu and Malware dataset, add version Leaf-FM achieves comparable performance with some deep learning based models such as DNN and AutoInt. As an improved FM model, Leaf-FM has the same computation complexity with FM in online serving phase and it means Leaf-FM is applicable in many industry applications because of its better performance and high computation efficiency.
An Adaptive Approach to Enhance The Quality of Hazy Images

The images in the haze domains have noise and low contrast in dynamic ranges require some analysis. The purpose of this study is to propose an automatic algorithm that will improve the quality of hazy images. In this approach, a sequence of techniques are employed in different steps in order to achieve the quality. The Wiener filter is utilized for image restoration, contrast spreading by RBG color model, YCbCr color model is used for luminance spreading, and the cast removed by white balance. A comprehensive set of experiments are performed on publicly available haze dataset in order to assess the performance of the proposed approach. It is obvious that the proposed method showed significant performance in enhancing hazy images. Moreover, in comparison, the performance of the proposed method is much better against conventional methods such as multi-scale fusion and histogram equalization. Similarly, complexity-wise the proposed algorithm is better than of the existing works, which consequently best choice for real time dehazing.
OPEN SCIENCE IN ALBANIA: FIRST OPEN-SCIENCE POLICY IMPLEMENTED IN ALBANIA

Appears in: EDULEARN21 Proceedings Publication year: 2021Pages: 12267-12271ISBN: 978-84-09-31267-2ISSN: 2340-1117doi: 10.21125/edulearn.2021.2575Conference name: 13th International Conference on Education and New Learning TechnologiesDates: 5-6 July, 2021Location: Online Conference
Analisis Kesulitan Belajar Matematika di Rumah Selama Pandemi Covid-19 pada Siswa Kelas IV Sekolah Dasar

&lt;p&gt;&lt;em&gt;This study aims to describe the purpose of this study is to identify problems in learning mathematics at home during the Covid-19 pandemic that occurred in fourth grade students at SDN Jejeruk in the 2019/2020 school year. The focus of the research in writing this thesis is the method used in this research is descriptive qualitative with a phenomenological approach. The data sources of this study were all fourth grade students, parents, and fourth grade students teachers. Data collection techniques were carried out using questionnaires, interviews, and documentation studies. The validity test of the data used in this research is technical triangulation and source triangulation. Data analysis using Miles and Huberman data analysis model. The results of this study indicate that student learning problems occur because students have difficulty understanding concepts, difficulty solving problems in story problems, and difficulties in numeracy skills. Problem identification is carried out on Fractions material. Difficulty understanding concepts as much as 78%, difficulty in solving story problems as much as 90%, and difficulty in calculating number operations as much as 85%. The problems that occur are caused by several factors. &lt;/em&gt;&lt;/p&gt;
Joint Shapley values: a measure of joint feature importance.

The Shapley value is one of the most widely used model-agnostic measures of feature importance in explainable AI: it has clear axiomatic foundations, is guaranteed to uniquely exist, and has a clear interpretation as a feature's average effect on a model's prediction. We introduce joint Shapley values, which directly extend the Shapley axioms. This preserves the classic Shapley value's intuitions: joint Shapley values measure a set of features' average effect on a model's prediction. We prove the uniqueness of joint Shapley values, for any order of explanation. Results for games show that joint Shapley values present different insights from existing interaction indices, which assess the effect of a feature within a set of features. Deriving joint Shapley values in ML attribution problems thus gives us the first measure of the joint effect of sets of features on model predictions. In a dataset with binary features, we present a presence-adjusted method for calculating global values that retains the efficiency property.
Beth Semantics and Labelled Deduction for Intuitionistic Sentential Calculus with Identity

In this paper we consider the intuitionistic sentential calculus with Suszko’s identity (ISCI). After recalling the basic concepts of the logic and its associated Hilbert proof system, we introduce a new sound and complete class of models for ISCI which can be viewed as algebraic counterparts (and extensions) of sheaf-theoretic topological models of intuitionistic logic. We use this new class of models, called Beth semantics for ISCI, to derive a first labelled sequent calculus and show its adequacy w.r.t. the standard Hilbert axiomatization of ISCI. This labelled proof system, like all other current proof systems for ISCI that we know of, does not enjoy the subformula property, which is problematic for achieving termination. We therefore introduce a second labelled sequent calculus in which the standard rules for identity are replaced with new special rules and show that this second calculus admits cut-elimination. Finally, using a key regularity property of the forcing relation in Beth models, we show that the eigenvariable condition can be dropped, thus leading to the termination and decidability results.
Compound Figure Separation of Biomedical Images with Side Loss

Unsupervised learning algorithms (e.g., self-supervised learning, auto-encoder, contrastive learning) allow deep learning models to learn effective image representations from large-scale unlabeled data. In medical image analysis, even unannotated data can be difficult to obtain for individual labs. Fortunately, national-level efforts have been made to provide efficient access to obtain biomedical image data from previous scientific publications. For instance, NIH has launched the Open-i search engine that provides a large-scale image database with free access. However, the images in scientific publications consist of a considerable amount of compound figures with subplots. To extract and curate individual subplots, many different compound figure separation approaches have been developed, especially with the recent advances in deep learning. However, previous approaches typically required resource extensive bounding box annotation to train detection models. In this paper, we propose a simple compound figure separation (SimCFS) framework that uses weak classification annotations from individual images. Our technical contribution is three-fold: (1) we introduce a new side loss that is designed for compound figure separation; (2) we introduce an intra-class image augmentation method to simulate hard cases; (3) the proposed framework enables an efficient deployment to new classes of images, without requiring resource extensive bounding box annotations. From the results, the SimCFS achieved a new state-of-the-art performance on the ImageCLEF 2016 Compound Figure Separation Database. The source code of SimCFS is made publicly available at https://github.com/hrlblab/ImageSeperation.
A Secure Cloud Based Chatting Application with Hybrid Cryptography

The amount of transmitted data from mobile to cloud computing is becomes larger and larger. Nowadays, Mobile cloud computing is vital field as a result of its availability, usability. Mobile cloud computing is most important role because it connects mobile with cloud computing and cloud data transmitted from mobile to cloud and vice versa, through a network. The purpose of the proposed system is to build an encryption system that can encrypts a data and decrypts the data transmitted from mobile to cloud efficiently. During this system can show system for encrypting mobile to cloud data. In this system, we will help the encryption algorithm. There are multiple hybrid encryption algorithms are used. These algorithms are more secure and end to end will be encrypted.
LibGuides: Library Policies : Computer and Internet Use

Writing library policies and procedures is an important task, but is often at the bottom of a busy library board's to-do list. This guide is designed to provide some sample policies, along with resources to make the process a little faster and easier. Thi
Unsupervised Learning Algorithms in Machine Learning

In this chapter, we will discuss unsupervised learning in general terms. An individual engages in unsupervised learning when they have only access to the input data and do not have access to the corresponding output variables. Unsupervised learning has as its goal the manipulation of the underlying structure and distribution of data to better understand it. Because, unlike supervised learning, which was previously discussed on this thread, there are no correct answers and no instructor present, these are referred to as unsupervised learning. To discover and present the interesting structure within the data, algorithms are left to their own devices. Unsupervised learning problems are further divided into two types: clustering problems and association problems. Clustering problems are the most common type of unsupervised learning challenge.