Reviewerly/exHarmony huggingface dataset:
Reverse Derivative Ascent: A Categorical Approach to Learning Boolean Circuits.

We introduce Reverse Derivative Ascent: a categorical analogue of gradient based methods for machine learning. Our algorithm is defined at the level of so-called reverse differential categories. It can be used to learn the parameters of models which are expressed as morphisms of such categories. Our motivating example is boolean circuits: we show how our algorithm can be applied to such circuits by using the theory of reverse differential categories. Note our methodology allows us to learn the parameters of boolean circuits directly, in contrast to existing binarised neural network approaches. Moreover, we demonstrate its empirical value by giving experimental results on benchmark machine learning datasets.
On the question of collective liability in innovation and creativity networks deploying blockchains–<i>A discussion concerning joint liability for the unauthorised application of proprietary information</i>

As legal and business interests continue to arise concerning the deployment of blockchains to different use cases, concerns have equally followed as to how legal liability would attach to participants within blockchain networks. This paper aims to contribute to the discussion concerning the attribution of joint legal liability to nodes within blockchain-based networks. It argues that there should be no broad-brush legal rule governing the issue and that it is the modus operandi of each network that should be determinative of the applicable standard of legal liability in every situation. This paper adopts the use case of innovation and creativity networks to advance the argument that individual liability, as a standard, may in some cases be superior to and more socially beneficial than the use of joint legal liability.
STUDY OF THE MOTIVATIVE COMPONENT OF TECHNOLOGICAL COMPETENCE OF THE TEACHER OF PROFESSIONAL TRAINING

The growth in demand for blue-collar professions justified the need to improve the quality of personnel training.The state, focusing on the demand of society in the training of competitive specialists capable of carrying out professional activities in the new conditions, has subjected the system of professional education to significant changes.Institutions of secondary vocational education strive to meet the world educational standards of WorldSkills.First of all, the need for competent teachers of vocational training who owns educational technologies at a high level for the implementation of the training of workers, employees and mid-level specialists is actualized.In the formation of the technological competence of students of higher educational institutions, the motivational component is of particular importance.The article is aimed at analyzing the experience of the formation of the motivational component of the technological competence of vocational training bachelors.The technological competence of teachers of vocational training is a necessary element for the implementation of future professional activities and presupposes the bachelor's possession of innovative educational technologies, the ability to realize their capabilities in specific conditions.The study, aimed at studying the motivational component of technological competence, uses a methodology for diagnosing educational motivation of students.The results allow us to record the growth of professional and educational and cognitive motivation.
An adversarial framework for open-set human action recognition using skeleton data

Human action recognition is a fundamental problem which is applied in various domains, and it is widelystudied in the literature. Majority of the studies model action recognition as a closed-set problem. However, in real-life applications it usually arises as an open-set problem where a set of actions are not available during training butare introduced to the system during testing. In this study, we propose an open-set action recognition system, humanaction recognition and novel action detection system (HARNAD), which consists of two stages and uses only 3D skeletoninformation. In the first stage, HARNAD recognizes a given action and in the second stage it decides whether theaction really belongs to one of the a priori known classes or if it is a novel action. We evaluate the performance of thesystem experimentally both in terms of recognition and novelty detection. We also compare the system performance withstate-of-the-art open-set recognition methods. Our experiments show that HARNAD is compatible with state-of-the-art methods in novelty detection, while it is superior to those methods in recognition.
Constructive Domains with Classical Witnesses

We develop a constructive theory of continuous domains from the perspective of program extraction. Our goal that programs represent (provably correct) computation without witnesses of correctness is achieved by formulating correctness assertions classically. Technically, we start from a predomain base and construct a completion. We then investigate continuity with respect to the Scott topology, and present a construction of the function space. We then discuss our main motivating example in detail, and instantiate our theory to real numbers that we conceptualise as the total elements of the completion of the predomain of rational intervals, and prove a representation theorem that precisely delineates the class of representable continuous functions.
Smart Educational System to enhancing Students Performance through Teachers Efficiency in the United Arab Emirate

This research examines a conceptual framework of the smart educational system for improving instructors’ efficiency and student outcomes in the UAE. The study mainly focused on the precarious framework of smart learning and key features of smart learning settings through earlier studies. There was a sample of 326 learners who were examined on the academic impact of the Smart Education System. Nevertheless, the results demonstrated that the use of the smart education system has a positive effect on teachers’ efficiency and educational outcomes. The political implications of this research are that it is anticipated that smart education will steadily improve, particularly in the UAE context.
Application of remote sensing in mapping hydrothermally altered zones in a highly vegetative area - A case study of Lolgorien, Narok County, Kenya

Objective: To map areas of possible hydrothermal alteration using remote sensing technology; To map geological structures controlling mineralisation using remote sensing; To carryout field mapping to ground truth the features identified by remote sensed data.Methods: Landsat 8 Operational Land Imager (OLI) and Shuttle Radar Topography Mission (SRTM) remote sensed images were downloaded from USGS website.Landsat images were processed using band ratios, band composites, principal component analysis techniques in ArcGIS to map areas of possible hydrothermal alteration.SRTM image was analysed using hillshade analysis technique in ArcGIS to map geological structural features controlling mineralisation.Findings: The study found that in several areas especially Southern, South Eastern, Central and North Western part of Lolgorien, there is a possibility of hydrothermal alteration as spectral signatures associated with iron oxide and clay minerals were identified.It was also found that areas with possibility of hydrothermal alteration are also associated with relatively large number of lineaments.It was also found that Southern and South-Eastern part of Lolgorien are also associated with numerous artisanal mines proving the fact that indeed gold mineralisation may be found in these places.However, due to thick vegetation cover, mapping of different types of lithological units found in Lolgorien was not possible.Novelty/Application: The application of remote sensed technology helped in identification of new areas of possible mineralisation such as Central and North-Western parts of Lolgorien which despite having similar geological properties (in terms of lineament density and hydrothermal alteration) have never been exploited for gold or other minerals.
Prospects of primary gold potential in southern Siberian platform based on study results for placer gold morphological and geochemical features

Data on geological structure and minerals of the southern Siberian platform are presented. Placer and primary gold occurrences widespread in this region are briefly characterized. Based on placer gold morphological and geochemical property studies using electron microscopy, its four types are revealed. Conclusions are made about potential primary sources of various placer gold types. Using retrospective data and based on the authors’ placer gold morphological and geochemical studies, Ust-Ilimskaya, Chernorechinskaya and Buraevskaya gold areas are characterized, their prospects for discovering primary gold deposits of various genetic types are determined.
Dynamic anisotropy and non-linear geostatistics supporting short term modelling of structurally complex gold mineralization

Dynamic anisotropy
Exploring the Correlation Between Students’ Interest in Listening and Listening Comprehension in English Context

The beginning of human learning is from hearing, thus, many educational experts and educational theories claim that the first learning activity is auditory. The influence of students’ listening comprehension, such as cognitive factors and psychological aspects also involved. One of the psychological problems is students’ interest. The purpose of this research is to find out a significant correlation between students’ interest in listening and their listening comprehension on the eleventh-grade students of SMKN 3 Tanah Putih, Rokan Hilir. The sample of this research was 26 students. The technique used in this research was correlational. In collecting the data, this research was distributing questionnaires to the respondents to assess students’ interests. Aside from assessing students’ listening comprehension, this research conducted a listening test for the students. In analysing the data, the questionnaires were assessed by Likert’s scale rating. The result of this research showed that the score of Sig. (2-tailed) gathered from SPSS 22, it was found that Sig. (2-tailed) the result was 0.001. Because of Sig. (2-tailed) was smaller than 0.05, it can be concluded that the null hypothesis (Ho) was rejected, and the alternative hypothesis (Ha) was accepted, which states there is a significant correlation between students’ interest in listening and their listening comprehension. In conclusion, there is a positive correlation between students’ interest in listening and their listening comprehension. It means that students with higher interest will get better listening comprehension than the lower one. In other words, the higher the interest of students, the better listening comprehension can be achieved.
Analysis of the cores of these Improvements of Online Teaching System and Model－Based on the Evaluation and Feedback on the Online Teaching Model and Teaching Platform

In recent years, MOOC, SPOC and other online teaching modes have attracted widespread attention. Online teaching platforms such as Tencent Classroom and MOOC of Chinese universities have emerged in an endless stream. During the 2020 outbreak of COVID-19, schools at all levels actively engaged in online teaching. The problems and challenges faced in the course of this teaching process will push the research hotspots of modern educational technology to construct the online teaching model that meets the needs of colleges and universities in the context of "Internet + Education". This study collected the evaluation and feedback of college students from different universities and different majors on online teaching mode and teaching platform, conducted a quantitative study of SPSS samples, and analyzed the influence of learners' and teachers' participation on online teaching effect. Results show that: there is a positive correlation between learners' attitude towards online teaching and the effect of online teaching, and between learners' participation and the effect of online teaching. There is also a positive correlation between teacher’s participation and effect of online teaching. There is no clear correlation between learners' use of equipment and online teaching effectiveness. Through the interview, learners reported low self-evaluation in online learning, and there are some problems in the teaching process, such as lack of teaching experience, poor platform interaction ability, and low supervision ability of managers. This study argues that in the development of online teaching, learners' and platform users' sense of experience and teachers' participation should be further improved and perfected.
Penggunaan ZnO/zeolit sebagai katalis dalam degradasi tartrazin secara ozonolisis

An investigation on modification of natural zeolite with ZnO for the degradation of tartrazine. In this study, ZnO as a semiconductor is modified into a natural zeolite as support to form ZnO/zeolite that can increase the efficiency degradation of tartrazine. Further, the formed catalyst was added to tartrazine by determining the variation in ozonolysis time, the amount of addition of the catalyst, and the addition of a catalyst time. The results of degradation were determined by UV-Vis spectrophotometer at 424 nm. The result showed that the percentage of degradation obtained on each catalyst in the degradation. The resulted percent degradation of 20 ml of tartrazine at concentration of 15 mg/L using 20 mg ZnO/zeolite was 56.80%, while using 0.77 mg ZnO was 42.25%, and with the addition of 19.23 mg of Zeolite was 31.18%, all of that condition was proceeded by 40 minutes of ozonolysis. Thus, the result indicates that the ZnO/zeolite catalyst can increase percentages of tartrazine degradation by ozonolysis. It is known that the catalyst ZnO/zeolite is very effective in increasing the degradation of tartrazine. Analysis of tartrazine compounds using fourier-transform infrared spectroscopy (FTIR) after degradation changes in wavenumber indicates that there is a breaking of the bonds of tartrazine compounds. Characterization of ZnO/zeolite catalyst using FTIR, X-Ray diffraction (XRD) and scanning electron microscope (SEM), in each spectrum there was no shift, indicating that there is no change in ZnO/zeolite structure
About the confusion-matrix-based assessment of the results of imbalanced data classification

When applying classifiers in real applications, the data imbalance often occurs when the number of elements of one class is greater than another. The article examines the estimates of the classification results for this type of data. The paper provides answers to three questions: which term is a more accurate translation of the phrase "confusion matrix", how preferable to represent data in this matrix, and what functions to be better used to evaluate the results of classification by such a matrix. The paper demonstrates on real data that the popular accuracy function cannot correctly estimate the classification errors for imbalanced data. It is also impossible to compare the values of this function, calculated by matrices with absolute quantitative results of classification and normalized by classes. If the data is imbalanced, the accuracy calculated from the confusion matrix with normalized values will usually have lower values, since it is calculated by a different formula. The same conclusion is made for most of the classification accuracy functions used in the literature for estimation of classification results. It is shown that to represent confusion matrices it is better to use absolute values of object distribution by classes instead of relative ones, since they give an idea of the amount of data tested for each class and their imbalance. When constructing classifiers, it is recommended to evaluate errors by functions that do not depend on the data imbalance, that allows to hope for more correct classification results for real data.
MP2020: Visual quality assessment database for macro photography images

With the development of mobile phone camera technology, mobile phones can take a large number of macro photography images that previously could only be taken by professional cameras. Therefore, it is of great significance to study the quality of macro photography images. For this reason, a macro photography image visual quality evaluation database is established and it is named as MP2020. The database contains 100 reference images and 800 distorted images of four distortion types, including 200 distorted images of JPEG 2000, 200 distorted images of JPEG, 200 distorted images of white noise, and 200 distorted images of Gaussian blur. The DMOS values in the database were calculated from 48000 data which are provided by 60 subjects. Ten classical image quality assessment algorithms were tested on the MP2020 database. The experimental results show that the existing image quality assessment algorithms, which are widely used, are not applicable to the macro photography images. Therefore, MP2020 would contribute to the improvement of existing algorithms and the development of new algorithms. MP2020 has been uploaded to GitHub for download.
Enhancement of Target-Oriented Opinion Words Extraction with Multiview-Trained Machine Reading Comprehension Model

Target-oriented opinion words extraction (TOWE) seeks to identify opinion expressions oriented to a specific target, and it is a crucial step toward fine-grained opinion mining. Recent neural networks have achieved significant success in this task by building target-aware representations. However, there are still two limitations of these methods that hinder the progress of TOWE. Mainstream approaches typically utilize position indicators to mark the given target, which is a naive strategy and lacks task-specific semantic meaning. Meanwhile, the annotated target-opinion pairs contain rich latent structural knowledge from multiple perspectives, but existing methods only exploit the TOWE view. To tackle these issues, we formulate the TOWE task as a question answering (QA) problem and leverage a machine reading comprehension (MRC) model trained with a multiview paradigm to extract targeted opinions. Specifically, we introduce a template-based pseudo-question generation method and utilize deep attention interaction to build target-aware context representations and extract related opinion words. To take advantage of latent structural correlations, we further cast the opinion-target structure into three distinct yet correlated views and leverage meta-learning to aggregate common knowledge among them to enhance the TOWE task. We evaluate the proposed model on four benchmark datasets, and our method achieves new state-of-the-art results. Extensional experiments have shown that the pipeline method with our approach could surpass existing opinion pair extraction models, including joint methods that are usually believed to work better.
Performance Examination of “Scopus” Publication Activity with Selected Webometrics Indicators for Leading Russian Universities

The paper is based on a comparative analysis of a joint set of Russian federal, national research and global universities (43 universities) in terms of their “Scopus” publication activity along with the Webometrics Openness and Excellence indicators (January 2018). The authors proposed a rough 5-level classification of the quality characteristics of Google Scholar Citation and Scopus profiles, studied the consistency of these characteristics across the entire sample of the universities, and carried out a regression analysis between the “Sopus” publication activity and the Openness and Excellence indicator ranks for the universities under study. The study showed that comparing these indicator ranks with the “Scopus” publication activity of the universities made it possible to see the effect of their integration into the Open Access movement.
Image encryption using block based transformation algorithm

Digital images are widely communicated over the Internet. The security of digital images is an essential and challenging task on shared communication channel. Various techniques are used to secure the digital image, such as encryption, steganography and watermarking. These are the methods for the security of digital images to achieve security goals, i.e. confidentiality, integrity and availability. Individually, these procedures are not quite sufficient for the security of digital images. This research presents a novel approach of encrypting the images. It comprises of three key components. The original image has been encrypted using large secret key by rotating pixel bits to right through XOR operation. For steganography, encrypted image has been altered by least significant bits (LSBs) of the cover image and obtained stego image, then stego image has been watermarked in the time domain and frequency domain to ensure the ownership. The proposed approach is efficient, simpler and secured; it provides significant security against threats and attacks. The results have been evaluated in terms of PSNR and MSE and comparative analysis with other popular existing methods has been performed.
STRATEGIES TO SOLVE WRITING DIFFICULTIES ON RECOUNT TEXT FOR INCLUSION STUDENTS

The objective of this study is to find out the teachers’ strategies to solve the students’ difficulties in writing recount text. This study was conducted in the eighth grade of an inclusion class in Yogyakarta. The study belongs to a qualitative research. The participant of this study was three teachers of English. The study investigation was done by interviewing the three teachers of English at Junior High School. The data collecting technique were interview and recording. There were several steps to analyse the data: first, analysing the students’ difficulties, then interviewing the teachers and the last step is descriptive analysis. The result of the study shows that the teachers’ strategies to solve the difficulties are: (a) in content aspect, the teachers used picture series as the learning media and guided writing; (b) in organization aspect, the teachers used guided writing; (c) in grammar aspect, the teachers used random word, modelling of text and mini dictionary product; (d) in mechanic aspect, the teachers recommended to improve reading habit of students; (e) in generic structure of recount text aspect, the teachers used semi guided and supported by picture series as learning media.
Analysis of Student's Creativity Value and Process Skills through Learning Strategies Guided Inquiry

This study aims to analyze the effect of learning with guided inquiry strategies on creativity and science process skills at SMA Negeri 1 Bandar Baru. This research uses a quantitative descriptive approach with quasi experimental research techniques. The design used in this study was a non-equivalent control group design. Data collection was grouped into 2 groups, namely the experimental group and the control group. The number of samples in this study were 50 students for the experimental class and 52 students for the control class. The research instruments were creativity tests and science process skills tests. The value of the students' creativity test results at a significance level of 0.05 obtained that the sig value was 5.965&gt; 1.98 (tcount&gt; ttable) and Sig (2-tailed) 0.000 &lt;0.05 indicating that the data was significant or significantly different between the experimental class and the class control. Hypothesis testing with the SPSS version 23.0 for windows program at a significant level of 0.05 was obtained; The value of students 'science process skills shows that t-count&gt; t-table at a significant level of 0.05, namely 6.055&gt;1.98, indicates that there is an effect of guided inquiry strategies on students' science process skills. This shows that the science process skills of students who have a high level of creativity with students who have a low level of creativity, where students who have a high level of creativity obtain higher process skills than students who have a low level of creativity and between students' creativity and science process skills.