Reviewerly/exHarmony huggingface dataset:
Literary education in the context of STEAM approach (based on western research)

The development of the STEAM-approach is one of the main trends in global education. It integrates Sciences (S), Тechnology (T), Engineering (E), Arts (A), and Mathematics (M). Future Specialists need comprehensive training in the exact Sciences, Biology, Engineering, and Design – and this future is being prepared today. STEAM education is introduced in Western schools from an early age. The most important theoretical position of this approach is the statement that a student who knows the artistic beginnings of life (literature, painting, music, art design), achieves more in mathematics, and in engineering, and in Sciences. STEAM-literature curriculum is also based on an interdisciplinary and applied approach. The purpose of a lesson on the analysis of a literary text, as a rule, is associated with the creation of a material object (model) by students, in which their knowledge of all the listed disciplines is comprehensively invested. The main tool for interpreting a literary text in this approach is design. Not just design tasks are solved in the classroom with its help – design is one of the basic forms of modern visual art. The main question, which the author of the article faces, is whether it is possible to implement such the technologies at school without losses for the studied work of art. The analysis of western models leads to the conclusion that the significance of literature, as an independent subject, changes within the STEAM-approach – part of its autonomy is delegated to other disciplines. At the same time, a new, unexpected configuration of subjects appears in the classroom: literature is now integrated with biology, design, and mathematics. Thus, the interdisciplinary basis of STEAM takes the teaching of literature to a new interdisciplinary level. At the same time, it raises some questions about the degree of presence of the literary text itself in this approach, and the laws of its creation and the depth of its interpretation by students.
Features' value range approach to enhance the throughput of texture classification

The definition of an image's category from a database with huge texture categories needs massive computation and time cost. Existing texture classification works focus on texture representation to improve the accuracy and efficiency of classification. This research wants to reduce the categories of the main classifier to decrease the comparison time of classification. To overcome computation time, a features' value range (FR) approach to enhance the throughput of texture classification is proposed. The proposed approach decreases the number of candidate categories as a pre-classifier in a two-step serial classification. With the decrease in the number of candidates, the main classifier can work on a few categories to find the final category. Here, configuration parameters are defined and some criteria are proposed for evaluating the FR approach. The performance of the FR is evaluated in the presence of different levels of Gaussian noise. Finally, it is shown that using effective features (EF) and hardware implementation approaches can extend the applicability of the FR approach. Experimental results depicted that the throughput of the final decision increased up to 14.85× with considerable reliability.
Development of a VR-based Manipulation System for Dual-arm Robots

Following its applications on reception, space exploration, health care, and others, the dual-arm robot is employed for industrial automation more intensively these days. With a human-like two-arm structure of high degrees of freedom, it should be a strong competitor for industrial tasks of high complexity. However, up to date, its deployment is still far behind the traditional single-arm robot manipulator. It may be partially due to its higher price and larger installation space. Meanwhile, the lack of proper methods or devices to deal with task planning and teaching that usually involves robot motions of more than 10 DOF is also influential. Motivated by it, in this paper, we propose a novel manipulation system for the industrial dual-arm robot manipulator using virtual reality (VR). Especially, we propose taking advantage of the similarity between the dual-arm robot and human arm and come up with a human-like path planner, with an intention to incorporate that of human into robot path planning. In addition, we also furnish the system with several assistive tools, including that for physical behavior emulation and self-collision detection. Simulations based on realistic environments are conducted to demonstrate the effectiveness of the proposed manipulation system, along with the questionnaires to evaluate user's responses.
Research on Intelligent Fault Diagnosis of Board Circuit Based on Expert Case Reasoning

Aiming at the matching conflict problem in the inference engine of traditional expert system, a method of applying expert case reasoning to board level circuit fault diagnosis is proposed. This method focuses on the index strategy and matching principle of expert case reasoning, and gives the method of case representation, organization and storage, and maintenance measures of case library. According to the research, the combination of hierarchical multi-level indexing mechanism and k-nearest neighbor matching principle and the use of expert case-based reasoning for fault diagnosis of board-level circuits can solve the bottleneck problems such as matching conflicts. Taking the failure of power supply circuit in airborne electronic equipment as an example, it is concluded that expert case reasoning has the advantages of fast diagnosis speed and high accuracy.
An Integrated Approach for Extraction of Carbonate Rocks Based on Landsat OLI Data: a Case Study around Stone Forest, Southwest China – Upgrading for Land-use Planning Purposes

Abstract Geological remote sensing information extraction of the lithology has always been the hotspot and difficulty in remote sensing applications. Carbonate rocks covered more than 3.4 million km 2 in China. The study area is located around Stone Forest, which is World Natural Heritage. Sedimentary rocks distributes mainly in the area, and their spectral characteristics are complex and difficult to be recognized by remote sensing. Landsat OLI data was selected as the information source. The false color synthesis, principal component change, band ratio and other processing technologies of OLI data were applied to the lithological enhancement identification of carbonate rocks. Selected information will be used to improve the quality of the land-use planning.
A construction involving inverse limits

Inverse limits appear in several branches of topology including theory of continua, general topology and set-theoretic topology. We will present a new construction using inverse limits. It will be shown that every compact space is an irreducible image of a compact F-space of the minimal weight.
Near-Optimal Concentric Circles Layout

The majority of graph visualization algorithms emphasize improving the readability of graphs by focusing on various vertex and edge rendering techniques. However, revealing the global connectivity structure of a graph by identifying significant vertices is an important and useful part of any graph analytics system. Centrality measures reveal the “most important” vertices of a graph, commonly referred to as central or influential vertices. Hence, a centrality-oriented visualization may highlight these important vertices and give deep insights into graph data. This paper proposes a mathematical optimization-based clustered graph layout called Near-Optimal Concentric Circles (NOCC) layout to visualize medium to large scale-free graphs. We cluster the vertices by their betweenness values and optimally place them on concentric circles to reveal the extensive connectivity structure of the graph while achieving aesthetically pleasing layouts. Besides, we incorporate different edge rendering techniques to improve graph readability and interaction.
Infrared Image Derivation Method for Generative Adversarial Network with Object Constraint

The generative adversarial network provides a method to derive infrared images from visible images, but the contour and details of the object of the generated infrared images are inaccurate. to solve this problem, we investigate how to improve the framework of the generative adversarial network, four different constraints are designed in this paper. Namely, the sequential cascade generation model of the subject in the segmentation map(GA), the sequential cascade generation model of the visible subject(GB), the superimposed generation model(GC), and the previous convolution feature cascade generation model(GD). In GB, the first three channels is the main body, and their inputs are the target segmentation map of visible images, which directly cascade Object segmentation graph in the last three channels. The experimental results show that the infrared image generated by GB has more intact target outlines, clearer target details, and is better on the whole.
MobiVision: A Novel Energy-Efficient Mobile Deep Learning Framework for Computer Vision

The development of mobile devices, such as smartphones, drones and augmented-reality headsets, have greatly promoted processing the convolutional neural network (CNN) model on them. One popular research topic is designing CNN models for image classification task on mobile terminals because these equipments would produce many videos or images data everyday generally. However, these pre-trained models usually possess complex structure and plenty of parameters so that they are difficult to be implemented on resource-limited mobile terminals for their serious time delay and energy consumption. A common solution is compressing neural networks to make them adapt to limited computation and memory resource in mobile devices, but it is not the best idea for pruned models always sacrificing accuracy. In this paper, We propose MobiVision, a novel neural network framework that conclude two main stages, which is defined as partitioning solution space and judging class for an image input. The former, utilizing deep learning-based clustering method, focuses on distinguishing which small solution space an image belongs to, while the latter calls a light-weight neural network associated to that solution space to recognize certain class of input. Series of experiments have proved that MobiVision achieves better performance than most of existing models serving for mobile devices because energy MobiVision consumed is little as well as accuracy of the model is equivalent to others meanwhile.
What Visual Attributes Make Mascot Characters Memorable? A Case Study on “Yuru-Charas”

Yuru-charas are mascot characters created to promote local tourism or organizations, which have been developed especially in Japan. The visual design of each yuru-chara can be an important feature influencing its popularity. This paper investigates the visual attributes that make mascot characters memorable. Specifically, we conducted a Visual Memory Game, which evaluates whether participants could have short-term memory of a target yuru-chara. The experiment showed that memorability scores of yuru-charas were different from one another. We manually assigned visual attributes to each yuru-chara image and then applied Wilcoxon rank sum test to analyze the attributes that contribute to making a yuru-chara image memorable. The result implied that memorability scores of yuru-charas whose designs are relatively simple were statistically significant than visually complex yuru-charas.
A Theoretical Persuasive Framework for Supporting and Evolving an Individual’s Sustainable Mobile Traveling Attitude

This paper proposes an innovative theoretical framework for the development of a persuasive prototype for supporting and evaluating an individual's traveling attitude through the use of well-designed persuasive smartphone applications for sustainable urban mobility purposes. The newly introduced framework emphasizes the importance of engaging and committing users to improve their travel habits through continuous motivation and evaluation of both how their traveling habits affect the environment and how they contribute to better future mobility planning decisions. It consists of five stages; it takes into consideration various elements from existing frameworks and introduces a critical stage, that of the users' rewarded assessment/feedback, based on the daily evaluation of the application effectiveness to persuade the user to move in an eco-friendlier way. For each stage of the proposed framework, a detailed description of the strategies that could be applied, and the related target group is provided.
A Hybrid Approach for Detecting Prerequisite Relations in Multi-Modal Food Recipes

Modeling the structure of culinary recipes is the core of recipe representation learning. Current approaches mostly focus on extracting the workflow graph from recipes based on text descriptions. Process images, which constitute an important part of cooking recipes, has rarely been investigated in recipe structure modeling. We study this recipe structure problem from a multi-modal learning perspective, by proposing a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">prerequisite tree</i> to represent recipes with cooking images at a step-level granularity. We propose a simple-yet-effective two-stage framework to automatically construct the prerequisite tree for a recipe by (1) utilizing a trained classifier to detect pairwise prerequisite relations that fuses multi-modal features as input; then (2) applying different strategies (greedy method, maximum weight, and beam search) to build the tree structure. Experiments on the MM-ReS dataset demonstrates the advantages of introducing process images for recipe structure modeling. Also, compared with neural methods which require large numbers of training data, we show that our two-stage pipeline can achieve promising results using only 400 labeled prerequisite trees as training data.
An Unsupervised Approach to User Characterization in Online Learning and Social Platforms

Making sense of data that is automatically collected from online platforms such as online social media or e-learning platforms is a challenging task: the data is massive, multidimensional, noisy, and heterogeneous (composed of differently behaving individuals). In this chapter we focus on a central task common to all on-line social platforms and that is the task of user characterization. For example, automatically identify a spammer or a bot on Twitter, or a disengaged student in an e-learning platform.
Local Adaptive Thresholding Techniques for Binarizing Scanned Lampung Aksara Document Images

Lampung characters are one of the region-heritage characters from Indonesia. However, the current trend makes these characters becoming unknown. The concern about the extinction of these characters makes several researchers tried to digitized the documents which contained Lampung Character. Nevertheless, the process of digitalization is not free from noise. This problem makes us want to handle the noise from the scanned documents using binarization and noise removal techniques, especially in Lampung characters' documents. In this paper, we implemented local adaptive thresholding using the Niblack method and the Sauvola method for thresholding value. We also implemented Adaptive Wavelet Thresholding for Bayes Shrink for removing salt and pepper noise from the binarization process. The result showed that the Sauvola thresholding gives better results compared to Niblack thresholding. Our contribution in this paper is the implementation of both processes in Lampung Characters document
STGauntlet: Recognizing Hand Gestures over Multiple Hand-Worn Motion Sensors

Hand gesture recognition with wearables typically focuses on the characteristics of a single point on hand, but ignores the diversity of motion information over hand skeleton. As a result, current methods suffer from two key challenges to manage multiple hand joints: displacement detection and motion representation. This leads us to define a spatio-temporal framework, named STGauntlet, that explicitly characterizes the hand motion context of spatio-temporal relations among multiple joints and detects hand gestures in real-time. The framework introduces the Lie algebra to capture the inherent structural varieties of hand motions with spatio-temporal dependencies among multiple joints. In addition, we developed a hand-worn prototype with multiple motion sensors respectively attached to various joints on hand and collected 7000 samples of seven gestures from nine subjects. Our in-lab study shows that STGauntlet is capable of detecting gesture types together with their 3D tracking trajectory with 97.35% and 95.17% accuracies for subject dependent and independent recognition, respectively.
Reliable (Secure, Trusted, and Privacy Preserved) Cross-Blockchain Ecosystems for Developing and Non-Developing Countries

The chapter suggests an iterative social system in which individuals and totals use a development, watch its arranged and unintended outcomes, and after that, build new improvements. Blockchain development has the potential to construct productivity, capability, straight imposition, and disintermediation in shared worth or information exchange. This chapter proposes how the blockchain will be implemented in developing and non-developing countries. These countries can use the blockchain for financial services, transportation, healthcare, e-marketplace, etc. And what is the risk and danger of using blockchain in non-developed countries?
Creating a culture of baseline control: Change management on development projects

Collectively, the astronomical community is responsible for building phenomenal, cutting edge observatories and cameras; machines that have revolutionized humankind’s understanding of the Universe. It is the nature of these complex projects to be inherently difficult to fully scope a-priori, leading to cost and schedule overruns. While we should not expect to eliminate this risk entirely, more can be done to manage change while executing our programs. By creating a culture of baseline control, the entire team can be enlisted to help identify and mitigate programmatic impacts in the best manner for the system. Practical baseline management habits and practices will be presented, with an emphasis on how to implement a robust change control process and culture amongst your program team.
Deep Subspace Clustering with Block Diagonal Constraint

The deep subspace clustering method, which adopts deep neural networks to learn a representation matrix for subspace clustering, has shown good performance. However, this representation matrix ignores the structural constraint when it is applied to subspace clustering. It is known that samples from different classes can be taken as embedding in independent subspaces. Thus, the representation matrix should have a block diagonal structure. This paper presents the Deep Subspace Clustering with Block Diagonal Constraint (DSC-BDC), a model which constrains the representation matrix with block diagonal structure and gives a block diagonal regularizer for learning a suitable representation. Furthermore, to enhance the representation capacity, DSC-BDC reforms the block-diagonal structure constraint by performing a separation strategy on the representation matrix. Specifically, the separation strategy ensures that the most compact samples are selected to the represent data. An alternative optimization algorithm is designed for our model. Extensive experiments on four public and real-world databases demonstrate the effectiveness and superiority of our proposed model.
Using Active Learning to Understand the Videoconference Experience: A Case Study

Videoconferencing is becoming ubiquitous, especially so during the COVID-19 pandemic. However, user experience of a videoconference call can be variable. To better understand and classify the performance of videoconference call systems, this paper reports a case study in which active learning - an interactive form of machine learning in which system engineers provide labels for instances of feature data - is applied to videoconference call logs. Investigations reveal that although system engineers have differing videoconference domain knowledge and so provide a wide range of labels, the active learning approach produces promising results in terms of model scale, accuracy and confidence reflecting the subjectivity of engineers’ experience.
Fostering critical thinking skills among future teachers

Today, a person who thinks critically is able to conduct a dialogue, determine the nature of any problem and find alternative solutions, and distinguish fact from assumption. This person is not afraid to question established opinions and judgments. That is why such kind of people are considered to be competitive and in demand on the labor market. The article, based on modern scientific positions, reveals the meaning of “critical thinking”. The development of critical thinking is based on observation skills; on the ability to ask questions and find the necessary resources; on the ability to check beliefs, assumptions and opinions, contrary to the evidence; on the ability to distinguish and identify a problem; on the ability to assess the validity of statements and arguments; and on the ability to find and make smart decisions, and understand the logic and logical arguments. The authors of the article found that the process of solving pedagogical problems was the basic unit of critical thinking skills among future teachers. The availability of critical thinking skills among future teachers is determined by using a critical thinking test, developed by Lauren Starkey, an American author (Starkey, 2004). The analysis of the survey results shows that the majority of respondents have an average level of critical thinking skills. The average level of critical thinking skills indicates that an average future teacher’s forms of thinking are unevenly developed. That is why it is difficult for a future teacher to make important decisions. We propose to foster critical thinking skills by solving pedagogical problems, which involves the use of modern interactive forms, methods and techniques of teaching.
A Statistical Approach Towards Fraud Detection in the Horse Racing

With the inception of online betting in S. Korea, various foreigner professional gambling groups have exploited the betting regulations. This phenomenon has occurred mainly in Asia, because the regulations on gambling in these countries are complex and robust. Our study focuses on the horse racing in S. Korea, which is operated under the government funding. The foreigner gambling groups tried unlimited betting by modifying the official IoT (Internet of Things) based APP arbitrarily. We have checked that some abnormal transactions can occur by modifying this application. Our study proposes a fraud detection method that can help detecting abnormal activities and prevent them. Currently, the Korea Racing Authority (KRA) has been criticized for being ill-equipped to detect abnormal activities with the Walkerhill Incident. Our study presents a new anomaly detection model that uses a flexible window. In this study, we propose an idea that aims to detect abnormal betting transactions.