Reviewerly/exHarmony huggingface dataset:
School textbook as an object of pedagogical research

The article deals with investigations of scientists in the field of school textbook theory. The material has been structured taking into account the main components of this field of knowledge: purpose (the dual essence of this type of educational literature has been revealed – it is a carrier of educational content and teaching aids; position of the textbook as a model of integral learning process has been substantiated (systematization, consolidation and control, self-education, integrating, coordinating, developmental-educational), structure (general didactic structure of the school textbook covers the text and non-text components – the apparatus of mastering, illustrative material, orientation apparatus), methods of analysis and evaluation (distinguish traditional and theoretical) – analytical and formalized methods; the structural-functional approach to the textbook analysis has been proved). The peculiarities of textbooks for elementary school have been analyzed: the most complete definition of this phenomenon has been formulated; the leading functions of the textbook addressed to junior students have been defined – informational, developmental, educational, motivational; the features of structural components (taking into account the age of students) have been specified; methods of analysis and evaluation of the textbook have been classified: methods of analysis of the manuscript, methods of evaluation of the textbook itself, diagnostic evaluation ex post facto; a comprehensive approach to the analysis of textbooks for elementary school has been proposed; methods of analysis of certain aspects of the textbook (motivational component, developmental orientation, etc.) have been offered.
Machine learning architecture and framework

Machine Learning (ML) is a branch of Artificial Intelligence that enables computer systems to learn from past experiences and improve accordingly without the direct intervention of the programmer. ML enables machines to behave very similarly to human beings. In order to extract the required information from the huge amount of data, ML can be used to design algorithms based on the trends of data and relationships among the data. ML can be applied in various fields such as intrusion detection, bioinformatics, health care, marketing, game playing, and so on. It enables the computers or the machines to make data-driven decisions rather than being explicitly programmed for carrying out a certain task. These programs or algorithms are designed in a way that they learn and improve over time when they are exposed to new or unseen data. Due to the huge amount of data, the significance of ML can be seen in various sections of the society. Especially in industries, ML is assisting exploration of the hidden patterns of the data, and through this the overall performance of the business can be improved. It is cost-effective, affordable, and simple computing techniques allow the analysis and handling of a huge amount of complex data. ML is not only helping to understand and identify the hidden patterns of a diverse set of data but also encourages automation in analysis in place of humans. Also, ML is helping industries to avail of the opportunities and make it profitable in future endeavors. In this chapter, we first review the fundamental concepts of machine learning such as feature assessment, unsupervised versus supervised learning, and types of classification. Then, details of the ML architecture and framework are discussed.
Who's Who in Mineral Names: John Innes (1948–1993)

John Innes was born in Brisbane, Queensland, Australia, 22 August 1948, the only child of William “Bill” and Eleanor Sarah Innes. Bill Innes was a builder and had hoped that his son would follow hi...
Real Time Fencing Move Classification and Detection at Touch Time during a Fencing Match

Fencingis a fast-paced sport played with swords which are Épée, Foil, and Sabre. However, such fast-pace can cause referees to make wrong decisions. Review of slow-motion camera footage in tournaments helps referees' decision-making, but it interrupts the match and may not be available for every organisation. Motivated by the need for better decision-making, analysis and availability, we introduce the first fully-automated deep learning classification and detection system for fencing body moves at the moment a touch is made. This is an important step towards creating a fencing analysis system, with player profiling and decision tools that will benefit the fencing community. The proposed architecture combines You Only Look Once version three (YOLOv3) with a ResNet-34 classifier, trained on ImageNet settings, to obtain 83.0 % test accuracy on the fencing moves. These results are exciting development in the sport, providing immediate feedback and analysis along with accessibility, hence making it a valuable tool for trainers and fencing match referees.
Weighted automata are compact and actively learnable

We show that weighted automata over the field of two elements can be exponentially more compact than non-deterministic finite state automata. To show this, we combine ideas from automata theory and communication complexity. However, weighted automata are also efficiently learnable in Angluin's minimal adequate teacher model in a number of queries that is polynomial in the size of the minimal weighted automaton. We include an algorithm for learning WAs over any field based on a linear algebraic generalization of the Angluin-Schapire algorithm. Together, this produces a surprising result: weighted automata over fields are structured enough that even though they can be very compact, they are still efficiently learnable.
Study on visualization of forest fire spread based on ArcGIS

The unique geographical features and changeable climatic of Yunnan Province make the forest fire prone and frequent. Aiming at the problem of forest fire that may be suffered by overhead transmission lines and power grid equipment in Yunnan Province, the research on forest fire spread was carried out, and the method of combining Rothermel model and Huygens principle was mainly used to simulate forest fire spread. On this basis, the powerful graphic processing capabilities and spatial analysis capabilities of ArcGIS are used to graphically display the spread process to improve usability and visibility.
Video Episode Boundary Detection with Joint Episode-Topic Model

Social online video has emerged as one of the most popular application, where “bullet screen comment” is one of the favorite features of Asian users. User behavior report finds that most people are used to quickly navigate and locate his concerned video clip according to its corresponding video labels. Traditional scene segmentation algorithms are mostly based on the analysis of frames, which cannot automatically generate labels. Since time-synchronized comments can reflect the episode of current moment, this paper proposed an unsupervised video episode boundary detection model (VEBD) for bullet screen comment video. It could not only automatically identify each episode boundary, but also detect the topic for video tagging. Specifically, a Joint Episode-Topic model is first constructed to detect the hidden topic in initial partitioned time slices. Then, based on the detected topics, temporal and semantic relevancy between adjacent time slices are measured to refine the boundary detection accuracy. Experiments based on real data show that our model outperforms the existing algorithms in both boundary detection and semantic tagging quality.
Adversarial Metric Knowledge Distillation

Knowledge distillation is dedicated to improving the performance of light weight networks by transferring knowledge during the training process. Meanwhile, it is important to apply knowledge distillation on different situations. The previous knowledge distillation method with adversarial samples uses a traditional knowledge distillation loss to let the student learn a good decision boundary. In this paper, we propose a novel method named Adversarial Metric Knowledge Distillation (AMKD), which utilizes adversarial samples to transfer the dark knowledge from the teacher to student. We select adversarial samples which are close to the decision boundary of two classes to metric the distance with negative class samples employing triplet loss constraint. The method guarantees the student network learning relationships among samples by quantitative metric learning. Therefore, we not only transfer information of the decision boundary but also ensure the student network can always maintain a proper distance from other negative classes. This can be another good exploration for knowledge distillation with adversarial samples. The experiments on CIFAR-10, CIFAR-100 and Tiny ImageNet datasets verify that the proposed knowledge distillation method works effectively on improving the student network performance.
CLIF: Cross-Layer Information Fusion for Stereo Matching and its Hardware Implementation

The rapid advancement of intelligent systems, especially robotics and autonomous driving, is highly reliant on low-complexity and high-accuracy stereo matching algorithms. However, the performance of state-of-the-art stereo matching algorithms still has great space for improvement by gaining awareness of the implicit information hidden in the cost volume layers. In this paper, we propose a low-complexity local stereo matching algorithm named Cross-Layer Information Fusion (CLIF), to improve the matching accuracy by exploring the hidden information. First, we analyze and extract the hidden information into an auxiliary extractor using a novel fusion method. Second, we propose an information sharing strategy that transforms the extractor into a regularization term on each cost volume layer. Then we improve the design by re-constructing the information extractor between the adjacent cost volume layers and form a pipelined hardware architecture on the FPGA platform. Experimental results show that the proposed CLIF algorithm improves 6.53% average accuracy incurring negligible resources and performance impacts, compared to the state-of-the-art solutions.
A new stemming algorithm dedicated for Arabic documents Classification

Stemming is the process of mapping several words related semantically but different morphologically to one word root. Therefore it is very important preprocessing step before perform any natural language processing task such as text classification for reducing and getting accurate features. Arabic language possess spatial morphological characteristics make stemming process difficult. Therefore many light and root stemmers proposed for handling this problem but unfortunately most of them appeared confusion between the original letters and affixes may attached with root of the words. This paper introduced A new stemming algorithm dedicated for Arabic documents classification (NSAAD) it is dedicated for stemming Arabic words using predefine patterns and affixes lists. Experiments performed on lists of words related in their root but different in morphological form. Comparing with Information Science Research Institute's (ISRI) stemmer, proposed technique introduced better results.
Information Retrieval and Sentimental Analysis with Databricks

With the rapid development of cloud computing, the information increases rapidly. Cheap cloud storage and computing power accelerate the development of massive data, and make the large data information collection and knowledge retrieval become necessary. The percentage of unstructured big data is more than 50%, so it is stored in the form of a file for the most part. Big data is split into many blocks that are stored within the server with some corresponding metadata of storage on the master server. How to collect the big data and keywords and retrieve the information are discussed in this paper. The proposed methodology is the information retrieval and sentimental analysis mechanism to improve text information retrieval and opinion mining using databricks. Information retrieval and analysis become more popular research fields within the world. Big data is a collection of heterogeneous structured, unstructured and semi-unstructured data. The basic aim of this paper is to present a broad picture of massive Data and to point out how information are often retrieved using evolutionary computation techniques i.e. databricks that help in the information retrieval process in a better way compared to traditional retrieval techniques. This paper also covered the setup and configuration of databricks.
Air-writing recognition using reverse time ordered stroke context

Air-writing is a new human and smart device communication approach, permits users to write inputs in a natural and relentless way. This touch-less way can prevent users from virus infection such as COVID-19. Compared with other methods, air writing is more challenging due to its unique characteristics such as redundant lifting strokes, multiplicity (different writing styles from various users), and confusion (different character types written in air are similar). Without the need of any starting trigger, a novel reverse time-ordered algorithm is proposed in this paper to efficiently filter out unnecessary lifting strokes, and thus simplifies the matching procedure. As to the second and third issues, a tiered arrangement structure is proposed by sampling the air-writing results with various sampling rates to solve the multiplicity and confusion problems. Analyzed with other recently proposed air writing algorithms, the proposed approach reaches satisfactory recognition accuracy (above 94%) without any starting triggers.
A Hidden Markov Model and Fuzzy Logic Forecasting Approach for Solar Geyser Water Heating

Time-based smart home controllers govern their environment with a predefined routine, without knowing if this is the most efficient way. Finding a suitable model to predict energy consumption could prove to be an optimal method to manage the electricity usage. The work presented in this paper outlines the development of a prediction model that controls electricity consumption in a home, adapting to external environmental conditions and occupation. A backup geyser element in a solar geyser solution is identified as a metric for more efficient control than a time-based controller. The system is able to record multiple remote sensor readings from Internet of Things devices, built and based on an ESP8266 microcontroller, to a central SQL database that includes the hot water usage and heating patterns. Official weather predictions replace physical sensors, to provide the data for the environmental conditions. Fuzzification categorises the warm water usage from the multiple sensor recordings into four linguistic terms (None, Low, Medium and High). Partitioning clustering determines the relationship patterns between weather predictions and solar heating efficiency. Next, a hidden Markov model predicts solar heating efficiency, with the Viterbi algorithm calculating the geyser heating predictions, and the Baum–Welch algorithm for training the system. Warm water usage and solar heating efficiency predictions are used to calculate the optimal time periods to heat the water through electrical energy. Simulations with historical data are used for the evaluation and validation of the approach, by comparing the algorithm efficiency against time-based heating. In a simulation, the intelligent controller is 19.9% more efficient than a time-based controller, with higher warm water temperatures during the day. Furthermore, it is demonstrated that a controller, with knowledge of external conditions, can be switched on 728 times less than a time-based controller.
A neuro-diversified benchmark generator for black box optimization

No Free Lunch Theorem presents a dilemma in the evaluation of emerging evolutionary algorithms in terms of handling various real world problems and their unknown internal structures, since the performances of these algorithms are related to the corresponding benchmarks. Although white and black box schemes have made impressive progress in overcoming this dilemma, such as clear property definition and basis function composition, the evaluation of algorithms on sophisticated suites remains insufficient on account of the limited quantity and diversity of such benchmarks, which can induce bias in a narrow problem domain. Therefore, this study proposes a novel framework for randomly generating diversified benchmark functions to comprehensively evaluate evolutionary algorithms in a black box scenario. The proposed approach adopts a recurrent neural network with various activation functions to produce test problems with important characteristics such as ruggedness and multi-funnels. In addition, the proposed framework can generate virtually limitless chaotic benchmarks by using random weights. The experimental results demonstrate a distinct difference among the performance of the tested optimizers on the proposed problems and the well-known BBOB and CEC problems, which implies the necessity of the proposed benchmarks when facilitating a more comprehensive evaluation.
Automatic Segmentation of Bone Canals in Histological Images

The literature provides many works that focused on cell nuclei segmentation in histological images. However, automatic segmentation of bone canals is still a less explored field. In this sense, this paper presents a method for automatic segmentation approach to assist specialists in the analysis of the bone vascular network. We evaluated the method on an image set through sensitivity, specificity and accuracy metrics and the Dice coefficient. We compared the results with other automatic segmentation methods (neighborhood valley emphasis (NVE), valley emphasis (VE) and Otsu). Results show that our approach is proved to be more efficient than comparable methods and a feasible alternative to analyze the bone vascular network.
Collaborative Human-Agent Planning for Resilience

Intelligent agents assist people in complex scenarios, such as managing teams of semi-autonomous vehicles. However, planning models may be incomplete, leading to plans that do not adequately meet the stated objectives, especially in unpredicted situations. Humans, who are apt at identifying and adapting to unusual situations, may be able to assist planning agents in these situations by encoding their knowledge into a planner at run-time. We investigate whether people can collaborate with agents by providing their knowledge to an agent using linear temporal logic (LTL) at run-time without changing the agent’s domain model. We presented 24 participants with baseline plans for situations in which a planner had limitations and asked the participants for workarounds for these limitations. We encoded these workarounds as LTL constraints. Results show that participants’ constraints improved the expected return of the plans by 10% ( $$p < 0.05$$ ) relative to baseline plans, demonstrating that human insight can be used in collaborative planning for resilience. However, participants used more declarative than control constraints over time, but declarative constraints produced plans less similar to the participants’ expectations, which could lead to potential trust issues.
Contrast Image Quality Assessment Algorithm Based on Probability Density Functions Features

Recently, the existing image quality Assessment algorithms (IQAs) works focusing on distorted images by compression, noise and blurring. Reduced-reference Image Quality Metric for Contrast-changed images (RIQMC) and No Reference-Image Quality Assessment (NR-IQA) for Contrast-Distorted Images (NR-IQA-CDI) have been created for CDI. For each of the five global feature that used in NR-IQA-CDI, The statistical model or the Probability Density Function (PDF) was determined using a Sun2012 database which containing a wide variety of natural scene images. NR-IQA-CDI showed poor performance in two out of three image databases, where the Pearson Correlation Coefficient (PLCC) were only 0.5739 and 0.7623 in TID2013 and CSIQ database, respectively. For this reason, we present the NR-IQA-CDI based on Monotonic Probability Density Functions (PDFs) (NR-IQA-CDI-MPCF) to address the problem of the existing bell-curve-like PDF of contrast features that cannot reflect the monotonic relation between contrast feature values and perceptual image quality. The findings indicate that the NR-IQA-CDI-MPCF outperforms the current NR-IQA-CDI, especially in the TID2013 database.
Cryptocurrency Exchanges: Predicting Which Markets Will Remain Active

About 99 percent of cryptocurrency trades occur on organised exchanges and many investors subsequently keep their digital assets in accounts with cryptocurrency markets. This generates exposure to the risk of exchange closures. We construct a database containing eight key characteristics on 238 cryptocurrency exchanges and employ machine learning techniques to predict whether a cryptocurrency market will remain active or whether it will go out of business. Both in-sample and out-of-sample measures of forecasting performance are computed and ranked for four popular machine learning algorithms. While all four models produce satisfactory classification accuracy, our best model is a random forest classifier. It reaches accuracy of 90.4 percent on training data and 86.1 percent on test data. From the list of predictors we find that exchange lifetime, transacted volume and cyber security measures such as security audit, cold storage and bug bounty programs rank high in terms of feature importance across multiple algorithms. On the other hand, whether an exchange has previously experienced a security breach does not rank highly according to its contribution to classification accuracy.
Saudi education postgraduates’ (Trainee teachers’) perspectives on distance education

The purpose of this exploratory study undertaken in Saudi Arabia is to understand how in-service and new teachers perceive distance education. The study involved nineteen male and female postgraduates who were pursuing an education master’s degree during the COVID-19 pandemic. This is a qualitative research asking participants to provide a 300-word response to the open-ended question: “How will you implement what you have learnt in your studies so that distance education can be effectively provided and convince others of the merit of this approach?” The task was presented in Arabic and the complete assignments were returned electronically. The 11,181-word corpus underwent content and thematic analyses yielding eight themes. Participants addressed issues pertinent to teaching and learning, equity in technological access and digital literacy. To make DE in Saudi Arabia successful, thorough preparation of involved parties, including educators and learners, and their digital teaching and learning skills should be mastered.&#x0D; Keywords: Distance education, e-learning, education postgraduates, Saudi Arabia, “Vision 2030”
Modern Analysis of Hutchinson's Trace Estimator

The paper establishes the new state-of-art in the accuracy analysis of Hutchinson's estimator for the matrix trace. Leveraging tools that have not been previously used in this context, particularly hypercontractive inequalities and results on concentration of sub-gamma distributions, we offer an elegant and modular analysis, as well as numerically superior bounds. Besides the improvements, this work aims to better popularize the aforementioned techniques within the CS community. Leveraging tools that have not been previously used in this context, particularly hypercontractive inequalities and results on concentration of sub-gamma distributions, we offer an elegant and modular analysis, as well as numerically superior bounds. Besides the improvements, this work aims to better popularize the aforementioned techniques within the CS community. Besides the improvements, this work aims to better popularize the aforementioned techniques within the CS community.