Reviewerly/exHarmony huggingface dataset:
Human-Computer Interaction in Education: Keyword and Discipline Network in 20 Years

Human-Computer Interaction (HCI) education covers diverse human-oriented design approaches, which are Human Factors (HF), Human-Centered Design (HCD), User-Centered Design (UCD), and User Experiences (UX) [5]. However, the relationships among those approaches are unclear. To better understand and develop HCI pedagogy, understanding which approach is more involved than another and how it is associated with each other within HCI is significant. Therefore, the purpose of this research is to identify the relationship to the four human-oriented design approaches using the keyword network analysis method to answer the following questions: 1) What descriptors, author-chosen subject headings, related to HCI, HF, HCD, UCD, and UX have been studied in the Education field? 2) How does HCI connect to human-related design approaches? and 3) What discipline in higher education has been studying more about HCI? To find the answers, this research analyzed a total of 684 peer-reviewed journal articles’ abstracts published between 2001 and 2020 from the Educational Resource Information Center (ERIC) database. Gephi 0.9.2 was utilized to visualize frequencies and relationships of the keywords. The findings presented that the top five most frequently mentioned descriptors in HCI were “Man Machine Systems,” “Educational Technology,” “Foreign Countries,” “Interaction,” and “Teaching Methods.” The number of articles with the four human-oriented design approaches have increased over the last 20 years. The top five most identified disciplines in the HCI-related articles were “Educational Technology,” “HCI,” “Information Science,” “Computer and Society,” and “Information Systems.” The findings of this research would be valuable resources in understanding the current research trend of HCI education, and the relationship among the four human-related design approaches and developing curricula for HCI related subjects.
Scaling Adaptive Instructional System (AIS) Architectures in Low-Adaptive Training Ecosystems

This paper reviews horizontal and vertical scaling methodologies for adaptive instructional system (AIS) software architectures. The term AIS refers to any instructional approach that accommodates individual differences to facilitate and optimize the acquisition of knowledge and/or skills. The authors propose a variety of scaling methods to enhance the interaction between AISs and low-adaptive training ecosystems with the goal of increasing adaptivity and thereby increasing learning and performance. Typically, low-adaptive training systems only accommodate differences in the learner’s in-situ performance during training and do not consider the impact of other factors (e.g., emotions, prior knowledge, goal-orientation, or motivation) that influence learning. AIS architectures such as the Generalize Intelligent Framework for Tutoring (GIFT) can accommodate individual differences and interact with low-adaptive training ecosystems to model a common operational picture of the training relative. These capabilities enable AISs to track progress toward learning objectives and to intervene and adapt the training ecosystem to needs and capabilities of each learner. Finding new methods to interface AISs with a greater number of low-adaptive training ecosystems will result in more efficient and effective instruction.
Multiscale Diversity Index for RUL Analysis with Bernstein Polynomial Neural Networks

This paper employs multiscale feature extraction based on Simpson's diversity index for predicting remaining useful life (RUL) of bearings. Being a measure of variety of elements in the given time series, Simpson's diversity index (SDI) acts as a feature which is assumed to be different for time series of different quality. Thus, RUL is considered to be function of multiscale SDI in this paper. Features are mapped to RUL with modified Tensor product Bernstein polynomial (TPBP) network. The aim of this paper is to test SDI based feature extraction together with modified TPBP network for in the context of RUL analysis.
The Correlation of Motivation With Student Learning Outcomes Fourth Grade Elementary School

This study aims to determine the relationship between motivation and learning outcomes of fourth grade students at SDN Juwet Kenongo Porong Sidoarjo. This research uses quantitative research. The method used is ex post facto. The design used in this study is poduct moment correlation. The instrument used in the study was a questionnaire. The samples in this study were all fourth grade students of SDN Juwet Kenongo Porong Sidoarjo using saturated samples. The data analysis technique used in this research is validity, reliability, and hypothesis testing. The results showed that motivation has a relationship with student learning outcomes.
Immersive and Interactive Digital Stage Design Based on Computer Automatic Virtual Environment and Performance Experience Innovation

In recent years, it has been the forefront of the performing arts industry and academic research to integrate VR into the physical space of stage, so that the audience can get sensory immersive and interactive experience in the scene of mutual creation between the factual and the imaginary. This study combines the principle of Computer Automatic Virtual Environment (CAVE), a kind of immersive VR systems with theater stage space and develops an immersive interactive digital performance stage (VR-CAVE stage) that can be viewed by the audience with naked eyes, then applies it to the design and construction of an experimental small theater. Through the first experimental drama performance of the theater, it can be seen that the integration of immersive VR helps the stage to realize the new performance effect of four-dimensional space, the body represent and drive the scene, and deep immersion. Finally, the application prospect and development direction are pointed out.
A Specific Measurable Model

In the development process of video games, both the iteration of original designs and the verification of new designs are quite frequent, therefore a large number of tests are of great importance. Sketches of designers are usually linear and static, while the interactive prototypes developed from them are non-linear and dynamic. Under this circumstance, the same design, using a design manuscript and an interactive prototype to make the player understand the game’s interaction strategy, can lead to different test results. A controlled experiment has been conducted: for the same interaction design, the design manuscript as well as the interactive prototype were respectively demonstrated to two groups of players, so as to verify the impact of these two on the test results. In the meantime, this paper will provide game developers with a measurable model, which can help them make a choice between interactive prototype and design manuscript in testing. For players, this model can also be used to measure whether the interaction design actually affects their feelings because of the design itself, or because of some static and dynamic effects.
Research on the method of data architecture construction for joint intelligent simulation

Abstract In view of the data problems restricting the joint intelligent simulation, this paper constructs a data classification system. By mining and sorting the data, rules and models in cases and experiences, and combining with the basic data and ability data of the relevant system simulation model, it carries on the digital and systematic annotation, constructs the conceptual model, establishes the segmentation and part of speech standards, and adopts the methods of concept mining, attribute mining and instance mining. The data system of joint intelligent simulation is established by mining and instance mapping, and the multi-layer logical template and intelligent matching rules are used to solve the data problems of joint intelligent simulation.
Closed-Loop Vision-Based Ball Balancer

This paper presents the mathematical modeling, design, and implementation of a ball balancer. The aim is to balance the ball at a desired position on the base plate. Ball balancer with two degrees of freedom uses machine vision as feedback and DC servomotors as actuators. Mathematical modeling of ball balancer system is done based on Lagrange–Euler equation, processing of the real-time image, and finding the co-ordinates of the ball is done using Open Source Computer Vision Library software. The co-ordinates of the ball are sent to Arduino through universal serial bus (USB) to transistor—transistor logic (TTL) converter. Perfect balancing of the ball is achieved using a proportional—derivative (PD) controller and pole placement technique. Stability of the designed ball balancer system is verified by its time response analysis using MATLAB/Simulink.
Improving technologic educational activities with the help of creative thinking

This article focuses on teaching students to think creatively in the training of future teachers of technology in higher education. To this end, the views of pedagogues and psychologists on creative thinking are given, and questions and situations that teach students to think creatively in the teaching of “Working with individual details and combinations of clothing” are discussed. In the process of “Technology Education Workshop”, which is taught in the training of future teachers of technology in higher education, students have more or less different abilities and inclinations for creative thinking. Each student has certain deposits needed to find creative ideas, and the teacher must fully use their pedagogical skills in this area.
Fuzzy Metrics and Its Applications in Image Processing

This paper is a review of recent research, in which fuzzy metrics and their applications in image processing are studied. The notions of the fuzzy T-metric and the fuzzy S-metric are presented, after which, examples of known fuzzy metrics are provided, along with theorems that enable algorithms to develop new metrics. Two applications of fuzzy metrics in image processing are illustrated: Image filtering and Copy-move forgery detection. Image filtering reduces the amount of noise, while maintaining satisfactory image quality. The aim was to improve the sharpness and quality of the image, measured by the image quality indices UIQI and CPBD. It is illustrated that the image filtered with this modified algorithm has better quality and greater sharpness than images filtered with the median filter. The fuzzy metric parameters that produce images with the best quality and sharpness are determined experimentally. The digital image falsification obtained by copying and pasting part of the original image into another part of the same image is also considered. Copy-move forgery detection (CMFD) is one of the methods used to detect such forgeries in images. A cluster algorithm that successfully solves this problem is presented.
Designing BookClub: Technologically Mediated Reading and Distant Interactions to Promote Well-Being

This poster introduces BookClub, a design proposal of technology to promote well-being through reading and social interaction. Here we describe the early stages of our process, including our initial ideas and graphic documentation of our prototyping process. We also discuss a preliminary qualitative study with potential users that helped us identify the areas of opportunity in this design and future research work.
Social Competencies for Pre-professional Socialization of the Student’s Personality in the Context of Sustainable Development

Modern schools continue to emphasize the nurturing of a socially oriented student, which should contribute to social maturity. As a consequence, there is a need to focus education on the development of a specific personality of the student, coordinated with the needs of society, the environment. The aim of the article is to develop and substantiate social competencies in education, which are a prerequisite for the successful socialization of students. The description of social competencies of a student’s personality is based on the analysis, systematization, and generalization of scientific experience on the research problem. The article introduces the concept of socialization, presents social competencies, characterizes them, and shows specificity and behavioral indicators of social competencies. The author substantiates the relevance of using new social competencies, shows their features, which are formed not only in the interests of the student, his personal meanings but also in the interests of social development, which results in self-development, personal fulfillment in life, taking into account the features of society functioning at a certain period of its development. The educational environment provides motivation for learning and working activities, the ability to adapt to new socio-economic conditions. The author identifies social competencies that contribute to the successful socialization of students in the learning process, which can be used in the practical activities of educational institutions under the conditions of educational transformation.
Lazy interworking of compiled and interpreted code for sandboxing and distributed systems

More and more applications rely on the safe execution of code unknown at compile-time, for example in the implementation of web browsers and plugin systems. Furthermore, these applications usually require some form of communication between the added code and its embedder, and hence a communication channel must be set up in which values are serialized and deserialized. This paper shows that in a functional programming language we can solve these two problems at once, if we realize that the execution of extra code is nothing more than the deserialization of a value which happens to be a function. To demonstrate this, we describe the implementation of a serialization library for the language Clean, which internally uses an interpreter to evaluate added code in a separate, sandboxed environment. Remarkable is that despite the conceptual asymmetry between "host" and "interpreter", lazy interworking must be implemented in a highly symmetric fashion, much akin to distributed systems. The library interworks on a low level with the native Clean program, but has been implemented without any changes to the native runtime system. It can therefore easily be ported to other programming languages.
The Construction Mechanism of Lifelong Education Network Gold Course from the Perspective of Artificial Intelligence

Artificial intelligence and informatization have led to accelerated changes in learning methods, and people’s learning pathways have gradually expanded. The in-depth integration of lifelong learning concepts with artificial intelligence technology has led to a wider range of learning, and people’s demands for lifelong education network quality courses are more urgent. This article uses qualitative research methods to deeply analyze the theoretical source and development context of the network “golden course”, explore the application prospects of artificial intelligence technology in the lifelong education network “golden course”, and build a lifelong education network “golden course” based on artificial intelligence technology, from the four aspects of building an artificial intelligence lifelong education platform, constructing a precise teaching model, reshaping personalized learning methods, and creating a community-integrated learning environment, it proposes the construction mechanism of a lifelong education network “golden course” from the perspective of artificial intelligence.
Classification of FPGA Based Network Traffic Using Machine Learning

Abstract To successfully control infrastructure, the actual identification of internet activity is important. Including faster response times, classifiers automated segmentation strategies have demonstrated favourable performance. Throughout this document, utilizing regression analysis and decision tree features collection, the box and hydraulic model’s appropriateness is checked. Besides, the appropriate number of shipments found in a flow when obtaining flow-level characteristics is calculated by the tests conducted, showing that 46% of movement packages are a reasonable balance that guarantees high efficiency in the minimum time consumed. The tests’ findings show that randomized forests outperform other architectures with a peak precision of 35%. However, since software-driven classification algorithms do not fulfil the expected real-time specifications, we propose a statistical learning device based on the Field-Programmable Gate Array (FPGA) that uses an incredibly parallel architecture simplify the mechanism in this way. The proposed architecture achieves better performance, exceeding the recorded hardware-based classifier outputs using equivalent methods, which guarantees realistic driving management consistency in cluttered computer servers.
Learning of Monocular Camera Depth Estimation Using Point-of-view Shots

Depth estimation using RGB images plays an important role in many computer vision applications, such as pose tracking for navigation, computational photography, and 3D reconstruction. Depth estimation using a single camera has relatively low accuracy compared to that using a conventional stereo camera system and time-of-flight (ToF) sensor. Nowadays, cameras in smartphone have the optical image stabilization system and can rotate to improve image quality such as image stabilization and image deblur. In this paper, we propose a novel depth estimation technique using a single camera equipped with tilting mechanism. Instead of motion compensation for optical image stabilization, a typical usage of tilting mechanism, we exploit it to capture multi-view images from a single viewpoint, by rotating the camera module in varying angles. The captured images have inside-out views, which is more challenging in depth estimation than out-side in views. We train a network based on structure-from-motion algorithm for depth estimation of a scene to achieve high accuracy depth maps. A synthetic dataset is created from 3D indoor models to train and validate the network. Evaluation results demonstrate that we achieve state-of-the-art performance in depth estimation using a single camera.
Secure Cloud Backup for Data Sources Based on Blockchain

Abstract Users can upload, save and download their data with cloud storage. The cloud does however handle the user’s generation of data, as well as devices created by the fast Internet development of stuff, including such webcam, and IoT and edges cameras. This growth in outsourced data volume has posed two main problems in cloud infrastructure cloud management: optimum data storage and data protection. Cloud storage employs a deductibility strategy to prevent redundant data to solve the former problem and uses cryptography to solve the latter problem. Both of these questions however are orthogonal. The convergent encryption is concurrently tackles the two problems. The conventional systems rely on web server, however which put some confidence in a private entity that may malfunction in one stage. In this paper we propose a new, transparent, authenticated online storage framework, which removes data replication from the security of the file and enhances the credibility of blockchain software. Two different approaches to data deduplication secrecy are discussed, in particular: double hazing is used and symmetrical encryption is used. We have implemented the suggested architecture in order to show stability and accessibility in spite of the increase request throughput requirements for cloud data processing.\
The Influence of Icons on the Visual Search Performance of APP List Menu

In the current app list menu, in the face of a large number of list information, users need to quickly find the function to search. Designers make the page more beautiful by adding icons on the left side of the text message, and hope that this way can help users better search the text content. This study explored whether the existence of icon and the type of icon will affect the search speed, accuracy and cognitive resources utilization of the target in the list menu bar in the process of visual search. The experiment uses eye tracking method to simulate the process of users looking for the target function in app. There are three main variables in the experiment, including the number of items in the list, whether there is an icon, and the sign type of the icon. The search time, accuracy and eye movement data were recorded. The experimental results show that there are little difference between the text-only and icon-text on the reaction time and accuracy, but the presence of icons can affect the cognitive process; meaningless circle icons can make it easier for users to find the target; symbol signs and icon signs can make users understand the target more quickly. The research results provide theoretical guidance for the design of mobile phone interface and icon in human-computer interface, and help to improve the visual search speed of users and the user experience of human-computer interaction.
Hand Gesture to Control a Low Cost 3-D Printed Arm using Android Application

Abstract In this era, the technology on computer usage is highly increasing and demanding especially in engineering, business and communication sector. Many applications are built based on a smartphone technology. The idea of this paper is to develop a hand gesture system based on Android application in order to control the 3-D printed arm. A MIT application invertor was used in developing an algorithm of the hand gesture controller. The app inventor can be used and accessed with accelerometer sensor that already built-in the smartphone via a Bluetooth connection. The Android applications was used as a communication tool between the Arduino Uno and the 3-D printed arm. The accelerometer sensor was acted as an input signal to control the hand gesture of the 3-D printed arm by the change or tilt of smartphones position. The output of this system is a servo motor that was mounted on the arm and used to drive the fingers. In this project, there system was classified by two; hardware part that consists of 3D-printed arm, Arduino, electrical component, and smartphone, meanwhile, the software part consists of MIT application inverter. The result shows the 3-D printed arm was able to be implemented with the wireless and Android application.
Permissioned Blockchain Model to Strengthen Food Supply Chain during pandemic (Covid-19)

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Invertible Grayscale with Sparsity Enforcing Priors

Color dimensionality reduction is believed as a non-invertible process, as re-colorization results in perceptually noticeable and unrecoverable distortion. In this article, we propose to convert a color image into a grayscale image that can fully recover its original colors, and more importantly, the encoded information is discriminative and sparse, which saves storage capacity. Particularly, we design an invertible deep neural network for color encoding and decoding purposes. This network learns to generate a residual image that encodes color information, and it is then combined with a base grayscale image for color recovering. In this way, the non-differentiable compression process (e.g., JPEG) of the base grayscale image can be integrated into the network in an end-to-end manner. To further reduce the size of the residual image, we present a specific layer to enhance Sparsity Enforcing Priors (SEP), thus leading to negligible storage space. The proposed method allows color embedding on a sparse residual image while keeping a high, 35dB PSNR on average. Extensive experiments demonstrate that the proposed method outperforms state-of-the-arts in terms of image quality and tolerability to compression.