Reviewerly/exHarmony huggingface dataset:
Design Research

Smart textiles are able to interact with the environment and perform dynamic changes over time. They open up innovative potential for the design of responsive environments, as they are able of sensing and responding to stimuli and they are an active element in shaping the environment. The phenomenon approached in this research focuses on the design possibilities of smart textiles to filter light that passes through them, creating responsive lighting scenarios according to textiles physical and dynamic qualities. This interaction was explored through color and shape thermo-responsive textiles, towards changing light transmittance without acting upon the light source: Dynamic Light Filters.
Research on College English Teaching Model Based on Information Technology of Artificial Intelligence

The traditional college English teaching model cannot fully meet students' individualized learning needs in terms of teaching methods and teaching evaluation, while the AI-assisted college English teaching can effectively solve the limitations in teaching period, teaching space and evaluation methods, etc. Based on the application of artificial intelligence technology in college English reading, listening, speaking, and writing teaching, this paper discusses the tendency of the traditional college English teaching mode in the era of AI, which offers possibilities of the improvement in college English teaching, and the establishment of a new teaching mode, especially in the natural language processing(NLP) field. Related researches have demonstrated that it will provide a new train of thought and entry point for the reform of teaching mode to apply AI into college English teaching, and it will be as well as significant for achieving teaching goals. Relevant studies show that the application of artificial intelligence to college English education will provide a new train of thought for the reform of teaching mode, and is of great significance to achieve teaching objectives.
Endpoint Prediction of Copper Converter Blowing Based on Artificial Bee Colony Algorithm and Regularized Extreme Learning Machine

Endpoint prediction of copper converter blowing is significant to entire copper pyrometallurgical process. It provides effective information for controlling production and the prevention of over-blowing and under-blowing is essential to ensure product quality. To address the multivariate coupling, nonlinearity and uncertainty characteristics caused by the intermittent cyclic operation of copper converter blowing, we propose the FA-ABC-RELM model to achieve accurate prediction of copper converter blowing endpoints. Factor analysis (FA) is used to reduce the raw data dimension and extract key information. Then, regularized extreme learning machine (RELM) model optimized by artificial bee colony (ABC) algorithm is used to predict blowing endpoint. It improves the model stability, prediction accuracy and generalization ability through the introduction of regular terms and parameter optimization. Simulation experiments are conducted with the production data of copper plant, and the proposed method is comprehensively analyzed in combination with the back propagation (BP) neural network model. The experimental results show that the FA-ABC-RELM model has higher fitting degree and smaller prediction error. Therefore, the developed method can be implemented as a promising endpoint prediction method for copper converter blowing, and it provides a guiding significance in quality control and correction of copper smelting process.
Quantum Algorithms

In this chapter we shall look at a number of quantum algorithms. We are going to compare their performance, in terms of number of steps, to classical algorithms that accomplish the same task.
What a line can say : Investigating the semiotic potential of the connecting line in data visualizations

The line is a graphical element widely used in data visualizations, its purpose often being to signal a connection between other visual elements. Based on social semiotic theory, this article investigates what semiotic functions connecting lines can have and how these functions can be related to variations in form. The results show that, in addition to the basic function of connecting elements, such lines can also indicate the level of certainty, direct the viewer to read the information either as a narrative or a conceptual claim, indicate patterns of cohesion, and regulate the viewer’s position. These findings allow for further empirical research on the formation of visual conventions.
Pirzi Doll”: A Method for Increasing Children’s Knowledge and Attitude in Balanced Nutrition

Nutrition is one of the main problems in the world, where the number of malnourished patients reaches less than 104 million children and one third of all causes of child mortality worldwide are still caused by malnutrition. Nutrition knowledge in school-aged children is one of the factors determining patterns food consumption and nutritional status. Nutrition education in school-aged children can improve children's nutritional knowledge and play a role in food selection and eating habits. Nutrition education should start from an early age. Nutrition and health education began to be directed at kindergarten and elementary school students, since this age group has a habit of attitude that is relatively easy to be formed. This study aims to determine the effect of nutrition education by story’s telling with pirzi doll media to increase knowledge and attitude of children about balanced nutrition. This study used quasi-experimental design with two group pre and post-test design sampling was carried out with a systematic random sampling. Sample of research were children enrolled in Kindergarten IT Rabbani and IT Menara Fitrah in Ogan Ilir District. There were 37 samples on control group dan 39 samples on experimental group. The results of statistical analysis using Mann-Whitney test. The mean score of knowledge after being given a nutritional education was 26.15 ± 3.10 in the experimental group and in the control group was 26.05± 3.16 (p-value=0.817). The mean score of attitudes after being given a nutritional education was 13.69 ± 1.97 in the experimental group and in the control group was 11.24 ± 3.67 (p-value=0.002). There was no difference in mean score of knowledge between the experimental and control groups after being given a nutritional education. There was a difference in mean values of attitudes between the experimental and control groups after being given a nutritional education.
Multimodal Human Aerobotic Interaction

This chapter discusses HCI interfaces used in controlling aerial robotic systems (otherwise known as aerobots). The autonomy control level of aerobot is also discussed. However, due to the limitations of existing models, a novel classification model of autonomy, specifically designed for multirotor aerial robots, called the navigation control autonomy (nCA) model is also developed. Unlike the existing models such as the AFRL and ONR, this model is presented in tiers and has a two-dimensional pyramidal structure. This model is able to identify the control void existing beyond tier-one autonomy components modes and to map the upper and lower limits of control interfaces. Two solutions are suggested for dealing with the existing control void and the limitations of the RC joystick controller –the multimodal HHI-like interface and the unimodal BCI interface. In addition to these, some human factors based performance measurement is recommended, and the plans for further works presented.
Vertex Finding

Abstract Vertex finding is the search for clusters of tracks that originate at the same point in space. The chapter discusses a variety of methods for finding primary vertices, first in one and then in three dimensions. Details are given on model-based clustering, the EM algorithm and clustering by deterministic annealing in 1D, and greedy clustering, iterated estimators, topological vertex finding, and a vertex finder based on medical imaging in 3D.
The Internet of Things (IoT)

As the Internet of Things (IoT) opens up new business opportunities, firms are trying to understand the impacts of the IoT revolution on their supply chain. Despite the tremendous interest by supply chain managers to leverage the IoT, there are still a paucity of studies that integrate the management side of supply chain with the technical side of the IoT. This chapter attempts to fill the current gap in research. This chapter discusses capabilities of the IoT-based supply chain applications. Then, it presents a conceptual framework of the IoT applications essential for smart supply chain. Finally, this chapter discusses technical and managerial challenges faced by supply chain managers in implementing the IoT.
Trace-class operators and Fourier transform

A. A weighted Lebesgue-Banach convolution algebra is constructed.On this algebra, a Fourier transform related to trace-class operators is defined.Convergence properties are obtained.Also a Fourier transform of a system, modeled by an operator, is defined and basic properties are studied.
Blockchain Based Service Providers Payment Methodology

Following quite a while of huge improvement and research in advanced cash, the most acclaimed Bitcoin industry chain has been bit by bit finished including mining, trade, money trade, ATM, unavoidable computerized wallet structure, etc. Particularly, its blockchain innovation has become FinTech associations' rising business and research bearings, additionally been applied in the interdisciplinary medicinal science, inventory network and Internet of things. Digitizing cash can tackle numerous issues in physical money, for example, the wild fake banknotes. Consequently, not just the straightforwardness can be cost-adequately safeguarded in store's record, yet in addition client's privileges and interests can be ensured while utilizing the advanced wallet. For the administration, the guideline and reviewing of monetary exchange can be made less complex and progressively helpful for charge assortment issue. Digitized exchange subtleties can be a lot simpler to review by any registering gadget than physically review the regular record books. Moreover, to make Service Providers to utilize digitized money a lot simpler, in this paper, we propose and send a Blockchain based Payment framework for clients and Service Providers
Software Architectures—Views and Documentation

Software architecture is the foundation for automotive software design. Being a high-level design view of the system it combines multiple views on the software system, and provides the project teams with the possibility to communicate and make technical decisions about the organization of the functionality of the entire software system. It allows also us to understand and to predict the performance of the system before it is even designed. In this chapter we introduce the definitions related to software architectures which we will use in the remainder of the book. We discuss the views used during the process of architectural design and discuss their practical implications.
Digital Musical Instruments as Probes: How Computation Changes the Mode-of-Being of Musical Instruments

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Unsupervised Segmentation of Remote Sensing Images Using FD Based Texture Analysis Model and ISODATA

In this paper, an unsupervised segmentation methodology is proposed for remotely sensed images by using Fractional Differential (FD) based texture analysis model and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA). Essentially, image segmentation is used to assign unique class labels to different regions of an image. In this work, it is transformed into texture segmentation by signifying each class label as a unique texture class. The FD based texture analysis model is suggested for texture feature extraction from images and ISODATA is used for segmentation. The proposed methodology was first implemented on artificial target images and then on remote sensing images from Google Earth. The results of the proposed methodology are compared with those of the other texture analysis methods such as LBP (Local Binary Pattern) and NBP (Neighbors based Binary Pattern) by visual inspection as well as using classification measures derived from confusion matrix. It is justified that the proposed methodology outperforms LBP and NBP methods.
Learners-Mobile Interaction

The increasing demand for education in African continent has motivated more research in mobile learning to describe, explain and predict changes and chances in leveraging mobile technology to learn anytime and anywhere. Mobile technologies provide learning beyond the classroom walls where learners can have access to resources according to their needs without involving teachers. In developing countries like Africa, mobile learning is still gaining its ground and remains under-studied. With technological advancements and high usage of mobile phones in Africa, the educational horizon has broadened. Mobile learning plays a major role in delivering education in Africa. The rapid use of mobile devices in mobile learning projects in Africa confirm that mobile learning is transforming the traditional way of learning, teaching and delivery of education by integrating social media and advanced mobile technologies in education. After a critical look at the current status as well as the latest development in mobile learning, this chapter presents a road map of m-learning to guide future action and thinking of learners, teachers and institutions in Africa. It also tracks trends and their impact, key theories and key findings of mobile learning. The current chapter ends with a critical analysis of technologies and key theories that can contribute to the sustenance of mobile learning in the African continent.
Plagiarism Detection Algorithm for Source Code in Computer Science Education

Nowadays, computer programming is getting more necessary in the course of program design in college education. However, the trick of plagiarizing plus a little modification exists among some students' home works. It's not easy for teachers to judge if there's plagiarizing in source code or not. Traditional detection algorithms cannot fit this condition. The author designed an effective and complete method to detect source code plagiarizing according to the popular way of students' plagiarizing. There are two basic concepts of the algorithm. One is to standardize the source code via filtration against to remove the majority noises intentionally blended by plagiarists. The other one is an improved Longest Common Subsequence algorithm for text matching, using statement as the unit for matching. The authors also designed an appropriate HASH function to increase the efficiency of matching. Based on the algorithm, a system was designed and proved to be practical and sufficient, which runs well and meet the practical requirement in application.
Analysis on Falling Risk of Elderly Workers when Mowing on a Slope via Motion Capture

Due to the aging of rural population, in Japan's terraced field areas, many elderly people have to do the mowing works of paddy levees manually. However, it takes the high risk of falling when mowing on slops, and many accidents are caused by the unstable postures. The analysis of their body motion becomes necessary. Based on our previous study, the personal factors could be important on affecting the mowing behaviors. Therefore, in this paper, we focus more on the mowing workers' personal factors (such as age and physique) and try to analyze the effect on their falling risk via a high precision motion capture device. A set of experiments was conducted in September 2020, and four new mowing workers over 60 years old were invited to participate in the experiments. Their joints angles data and related personal factor data were used to analyze the factors that have the greatest influence on body stability during slop mowing. The results of stepwise regression showed that the mowing workers' age and physique have significant effects on the stability of their body motion. The results of this study can clarify the factors that prevent accidents in the fall detection systems and offer useful insights for the training of new mowing workers.
The Filter Representation of Wavelets

We saw in the previous chapter that the wavelet kernels G and H had some repeating structure in the rows and columns, similar to the circular Toeplitz structure in filters. Clearly the matrices are not filters, however. Nevertheless, we will now prove that wavelet kernels can be implemented easily in terms of filters. but that several filters are needed in the computation. Each of these filters will have an interpretation in terms of how the wavelet transform treats different frequencies. Much has been done in establishing efficient implementations of filters, and by expressing a wavelet transform in terms of filters we can take advantage of this.
Financial Crisis, Financial Globalization and Financial Development in Africa

This study unites two streams of research by simultaneously focusing on the impact of financial globalization on financial development and pre- and post-crisis dynamics of the investigated relationship. The empirical evidence is based on 53 African countries for the period 2004-2011 and Generalized Method of Moments. The following findings are established. First, whereas marginal effects from financial globalization are positive on financial dynamics of activity and size, corresponding net effects (positive thresholds) are negative (within range). Second, while decreasing financial globalization returns are apparent to financial dynamics of depth and efficiency, corresponding net effects (negative thresholds) are positive (not within range). Third, financial development dynamics are more weakly stationary and strongly convergent in the pre-crisis period. Fourth, the net effect from the: pre-crisis period is lower on money supply and banking system efficiency; post-crisis period is positive on financial system efficiency and pre-crisis period is positive on financial size.
Expanding Bloom's Two-Sigma Tutoring Theory Using Intelligent Agents

This article describes how management education is engaged in significant programmatic reforms in response to the business community's call for web-savvy, problem-solving graduates. Web-based intelligent tutors provide a readily accessible vehicle for enhancing business students' learning performance as well as prepare them for the rigors of the global marketplace. A primary goal of these AI-based systems is to approach Bloom's two-sigma learning performance standard. Bloom found that average students tutored one-to-one with mastery learning techniques performed two standard deviations better than students who learned via conventional teaching methods. Intelligent tutors can also be used to identify students at risk, to formulate appropriate intervention plans, and to support team learning. The purpose of this article is to highlight the growing potential for using intelligent tutors to enhance student and team learning opportunities and outcomes and to outline strategies for implementing this revolutionary process throughout the management education community of practice.
Data parallelism

This chapter examines MapReduce and stream programming, two approaches to shared-memory data-parallel programming, that is, to implementing parallel algorithms that process large amounts of data. It explains each approach, describes a simple generic framework for developing applications with that approach, and demonstrates how to use it to implement WordCount and KMeans, two simple data-parallel applications. Finally, it shows how to implement the framework.
Dynamic Template Generation

A test blueprint/test template, also known as the table of specifications, represents the structure of a test. It has been highly recommended in assessment textbook to carry out the preparation of a test with a test blueprint. This chapter focuses on modeling a dynamic test paper template using multi-objective optimization algorithm and makes use of the template in dynamic generation of examination test paper. Multi-objective optimization-based models are realistic models for many complex optimization problems. Modeling a dynamic test paper template, similar to many real-life problems, includes solving multiple conflicting objectives satisfying the template specifications.
Quality Measures for Semantic Web Application

The Semantic Web is a standard of Common Data Formats on WWW with aim to convert the current web data of unstructured and semi-structured documents into a common framework that allows data to be shared and reused across applications, enterprises. The main purpose of the Semantic Web is driving the evolution of the current Web by enabling users to find, share, and combine information more easily. Humans are capable of using the Web to carry out tasks such as searching for the lowest price for a LAPTOP. However, machines cannot accomplish all of these tasks without human direction, because web pages are designed to be read by people, not machines. The semantic web is a vision of information that can be readily interpreted by machines, so machines can perform more of the tedious work involved in finding, combining, and acting upon information on the web. The chapter presents the architecture of semantic web, its challenging issues and also data quality principles. These principles provide a better decision making within organization and will maximize long term data integration and interoperability.
Multi-Robot Cooperative Hunting Based on Virtual Reality Technology

Multi-robot cooperative rounding up is one of the important methods to test the intelligence level of multi-robot system. In order to test the intelligence of multi-robot system, a multi-robot cooperative rounding up system is established in this paper. In order to realize the rounding up system, this paper uses finite state machine to simplify the rounding up behavior of multi-robot. At the same time, in order to observe the multi-robot system more intuitively, this paper uses Vega software to realize virtual reality technology and applies it to the rounding up system established in this paper. Experiments show that the virtual reality technology based on VEGA can effectively simulate the multi-robot roundup, so that observers can observe the system more intuitively and effectively, so as to find out whether there are problems in the system.
Face Recognition Based on Higher Order Neural Network Group-Based Adaptive Tolerance Trees

Recent artificial higher order neural network research has focused on simple models, but such models have not been very successful in describing complex systems (such as face recognition). This chapter presents the artificial higher order neural network group-based adaptive tolerance (HONNGAT) tree model for translation-invariant face recognition. Moreover, face perception classification, detection of front faces with glasses and/or beards models of using HONNGAT trees are presented. The artificial higher order neural network group-based adaptive tolerance tree model is an open box model and can be used to describe complex systems.