Reviewerly/exHarmony huggingface dataset:
Factors Influencing Smart Device Acceptance in Korea: Application of a Modified Technology Acceptance Model

The recent advances in digital media technologies have been applied to smart media that are becoming the main tools for human communication and life throughout the world.They have raised an academic interest in what are the deciding factors for acceptance of new media technologies.This study proposed and analyzed a modified technology acceptance model (TAM) in order to explore determining factors for acceptance of smart devices in Korea.More specifically, the study empirically explored relationships among three categories of variables (external, TAM, and acceptance variables) which were fundamental elements of Davis' TAM model that described and explained the process of smart device acceptance.The external variables included enjoyment, income, and social pressure.The TAM variables were perceived usefulness and ease of use, and the acceptance variables consisted of the degree of acceptance and further acceptance intention.Face-to-face survey interviews were conducted using a sample of 691 respondents who were randomly selected proportionately by controlling the sex, age, and income.After a path analysis of a structural equation model, the following results were found.First, enjoyment was positively related to all the TAM variables and all the acceptance variables.Second, social pressure was positively associated with all the TAM variables and further acceptance intention.Third, income level had a positive relationship with the degree of acceptance.Finally, the TAM variables were still considered to be essential in the model.In conclusion, practitioners and marketers need to emphasize enjoyable traits and social pressure messages to increase the new device acceptance level.In addition, more possible external variables need to be developed and used in future relevant studies..
A Rule-based Information Extraction System

Designing intelligent expert systems capable of answering different human queries is a challenging and emerging area of research. A huge amount of web data is available online and majority of which are in the form of unstructured documents covering articles, online news, corporate reports, medical records, social media communication data, etc. A user in need of certain information has to assess all the relevant documents to obtain the exact answer of their queries which is a time consuming and tedious work. Also, sometimes it becomes quite difficult to obtain the exact information from a list of documents quickly as and when required unless the whole document is read. This paper presents a rule-based information extraction system for unstructured web data that access the document contents quickly and provides the relevant answers to the user queries in a structured format. A number of tests were conducted to determine the overall performance of the proposed model and the results obtained in all the experiments performed shows the effectiveness of the model in providing required answers to different user queries quickly.
Raspberry Pi based automatic temperature measurement and logging system for solar thermal purposes

For automatic temperature measuring and logging purposes in solar thermal experiments, a single board computer - Raspberry Pi based and sensor system is proposed. Different data sets are observed for sensors, thermometers, and thermocouples and they are compared. The consistency of the automated temperature measurement system is also verified.
Hyperledger F abric Block chain for data S ecurity in IOT D evices

Data security for IOT devices is very import aspect these days as the world is moving towards digitalization. Consider a smart energy meter which provides a way to monitor the energy consumption at home, data security in such smart meter reading is very important. If the Power reading signals are tampered, then it may cause serious economic loss for the authorities. The personal information infringement of user can occur at the database and may fall in the hands of unethical persons. In order to address these issues in this paper we propose to use a permissioned blockchain network. Blockchain maintains time stamped ledger records that are very hard to tamper. Every transaction is recorded and distributed across many participant nodes, these records are immutable because they have blocks of data which are linked to each other with strong cryptographic hash. The blockchain network is built using hyperledger fabric, where all the participant nodes are registered and only registered nodes involve in consensus process of transaction. In fabric, MSP (membership service provider) identifies the identity of the participant nodes through X.509 digital certificates issued by certificate authority. Along with creation of blockchain network for the application, a mobile client, a web client, an Arduino client and web server is created. The Arduino client is the hardware module that has an energy meter (SDM120) measuring the energy consumption of the user and sends this information serially to NODEMCU. NODEMCU POSTs the read energy details to the web server at particular api, web server POSTs the details to the Blockchain Network, where transactions undergoes consensus to add this information to blockchain ledger. Now data is decentralized and every peer node has the local copy of ledger. The updated information can be queried and seen on the web Client and Mobile client user interfaces. Anonymity-enhanced blockchain has been implemented to avoid the disclosure of personal information or data. Also performance analysis of the application is carried out for number of sequential requests and concurrent requests from many users using different tools.
Three-Dimensional Image Reconstruction for Virtual Talent Training Scene

In real training, the training conditions are often undesirable, and the use of equipment is severely limited. These problems can be solved by virtual practical training, which breaks the limit of space, lowers the training cost, while ensuring the training quality. However, the existing methods work poorly in image reconstruction, because they fail to consider the fact that the environmental perception of actual scene is strongly regular by nature. Therefore, this paper investigates the three-dimensional (3D) image reconstruction for virtual talent training scene. Specifically, a fusion network model was deigned, and the deep-seated correlation between target detection and semantic segmentation was discussed for images shot in two-dimensional (2D) scenes, in order to enhance the extraction effect of image features. Next, the vertical and horizontal parallaxes of the scene were solved, and the depth-based virtual talent training scene was reconstructed three dimensionally, based on the continuity of scene depth. Finally, the proposed algorithm was proved effective through experiments.
Sentiment Analysis for Twitter Data

With the advancements in web technology and its growth, there's an incredible volume of information present everywhere on the net for internet users and plenty more data is generated on a daily basis. Internet emerged as place for exchanging ideas, sharing opinions, online learning and political views. Social networking sites such as Facebook, Twitter, are rapidly growing as the users are allowed to post and revel their views on various topics, and can discussion with different groups and communities, or post messages across the world. In the area of sentiment analysis large numbers of researchers are working. The main focus is on twitter data for sentiment analysis, that's helpful to research the info within the tweets,where opinions are heterogeneous, highly unstructured, and are either positive,or negative, or neutral.in many cases. In this paper, we provide a study and comparative analysis of existing techniques used for opinion mining through machine learning approach. Naive Bayes &amp; Support Vector Machine, we provide research on twitter data.
Application of the Improved Grey Wolf Algorithm in Spacecraft Maneuvering Path Planning

In many space missions, spacecraft are required to have the ability to avoid various obstacles and finally reach the target point. In this paper, the path planning of spacecraft attitude maneuver under boundary constraints and pointing constraints is studied. The boundary constraints and orientation constraints are constructed as finite functions of path evaluation. From the point of view of optimal time and shortest path, the constrained attitude maneuver problem is reduced to optimal time and path solving problem. To address this problem, a metaheuristic maneuver path planning method is proposed (cross-mutation grey wolf algorithm (CMGWO)). In the CMGWO method, we use angular velocity and control torque coding to model attitude maneuver, which increases the difficulty of solving the problem. In order to deal with this problem, the grey wolf algorithm is used for mutation and evolution, so as to reduce the difficulty of solving the problem and shorten the convergence time. Finally, simulation analysis is carried out under different conditions, and the feasibility and effectiveness of the method are verified by numerical simulation.
Pedagogical Support of Socio-Professional Self-Determination of Students

Recently, the prevailing trend is that graduates do not work in their profession; the purpose of the article is to study ways to improve the effectiveness of social and professional self-determination of students. The authors conducted an empirical study of the causes of this phenomenon and identified the main ones (the choice was made for the student by his parents; in the process of studying, the student became disillusioned with the chosen profession; the chosen profession does not bring the necessary earnings; did not find a job in the speciality). The article noted that the percentage of work in the profession depends on how long ago the specialist graduated from the university. The authors identified what causes could be eliminated in the process of teaching students. Having analyzed the existing pedagogical methods for students' more confident social and professional self-determination, the authors proposed the conceptual foundations of pedagogical support for students' social and professional self-determination. These recommendations will make it possible to correct students' social and professional self-determination in their speciality. The authors proposed an algorithm that allows to visually and transparently determine the student's attitude to the chosen speciality and ways to persuade the student to look for a job and work in the profession and the necessary tools for this. The proposed dual approach to adjusting the socio-professional self-determination of students will allow them to identify the problem at an early stage, track it and fix it with adjustment. Research has shown that there are several main reasons why a student does not work in a profession. All these reasons, in our opinion, are subject to correction in the learning process. The main feature of success is the desire of the university to identify such students at an early stage, a developed adjustment mechanism and constant control and monitoring. A further promising direction of research is the development of questionnaires that can identify this problem and a mechanism for attracting artificial intelligence that can guide each student individually and signal when the university's intervention is necessary.
MOVE-STEP STRUCTURE OF THE RESULTS AND DISCUSSION SECTION OF ELECTRONIC ENGINEERING RESEARCH ARTICLES WRITTEN BY CHINESE AND THAI RESEARCHERS

This study explored the move-step structure of the Results and Discussion (R&amp;D) section of electronic engineering research articles (EERAs) written by Chinese and Thai researchers. Two corpora, with each containing 12 R&amp;D sections, were compiled for analysis with reference to the three frameworks of Kanoksilapatham (2005, 2015) and Maswana et al. (2015). In addition, variations in terms of move-step structure between the two corpora were examined. Findings firstly demonstrated a newly proposed framework of 3 moves and 12 steps. What’s more, all of the three moves were found obligatory, and the variations between the two corpora mainly existed in the steps under each move category. Findings of the present study could provide insights into EERA construction for novice writers. Moreover, generic variations that are acceptable within the discipline might expand RA genre knowledge for both EE researchers and genre practitioners.
Smart Logistics using Internet of Things (IoT)- Study

Smart logistics is one of the most important advantages offered by Internet of Things technology. Logistics services seek to automate their work to reduce manual labour costs and take advantage of available technical services. In this research, we present the details of the administrative processes in the logistical fields, in addition to the details of the types of logistical services in general to clarify what the logistical processes are and how they are used. What is new is to mention that we will explain how the Internet of Things is used in the field of smart logistics, in addition to clarifying the most important studies in smart logistics services in the field of smart transportation, delivery and storage of products using the Internet of Things. Moreover, we discuss the most important developments in smart logistics services in the Kingdom of Saudi Arabia that it seeks to achieve in Vision 2030. In addition to discussing the most important technical components that the Kingdom of Saudi Arabia possesses for the success of smart logistics projects.
Formalization of the software module development using matrix forms

The article considers the formalized approach to develop a multi-user document exchange system based on a matrix form of data representation.The multi-stage development of modular software by several participants is presented as basic process.The algorithmic implementation of information flows in the context of software development is presented.The document life cycle model is described using the UML diagrams.The considered algorithm contains a hierarchical document flow structure with the subordination of some documents to others.According document flow the participant interaction model of software development process is considered.The model of administrative document flow includes the following roles (owners): the product development manager; the customer representative; the developer; the tester.As a result, the presented matrices fully describe the graph of the considered document flow process.The formalized approach based on the matrix form of representation can be used as input data for multi-user systems' algorithmization in solving modular software design problems.
Research on Sports Training Action Image Recognition Based on SDN

Current image recognition methods cannot combine the transmission of image data with the interaction of image features, so the steps of image recognition are too independent, and the traditional methods take longer time and cannot complete the image denoising. Therefore, a recognition method of sports training action image based on software defined network (SDN) architecture is proposed. The SDN architecture is used to integrate the image data transmission and interactive process and to optimize the image processing centralization. The network architecture is composed of application layer, control layer, and infrastructure layer. Based on this, the dimension of image sample set is reduced, and the edge detection operator in any direction is constructed. The image edge filter is realized by calculating the response and threshold of image edge by using lag threshold and nonmaximum suppression (NMS). The Hough transform algorithm is improved to optimize the detection range. Extracting the neighborhood feature of sports training action, the recognition of sports training action image based on SDN architecture is completed. Simulation results show that the proposed method takes less time and the image denoising effect is better. In addition, the F1 test results of the proposed method are higher than those of the literature, and the convergence is better. Therefore, the performance of the proposed method is better.
Estimating Distributions of Parameters in Nonlinear State Space Models with Replica Exchange Particle Marginal Metropolis–Hastings Method

Extracting latent nonlinear dynamics from observed time-series data is important for understanding a dynamic system against the background of the observed data. A state space model is a probabilistic graphical model for time-series data, which describes the probabilistic dependence between latent variables at subsequent times and between latent variables and observations. Since, in many situations, the values of the parameters in the state space model are unknown, estimating the parameters from observations is an important task. The particle marginal Metropolis-Hastings (PMMH) method is a method for estimating the marginal posterior distribution of parameters obtained by marginalization over the distribution of latent variables in the state space model. Although, in principle, we can estimate the marginal posterior distribution of parameters by iterating this method infinitely, the estimated result depends on the initial values for a finite number of times in practice. In this paper, we propose a replica exchange particle marginal Metropolis-Hastings (REPMMH) method as a method to improve this problem by combining the PMMH method with the replica exchange method. By using the proposed method, we simultaneously realize a global search at a high temperature and a local fine search at a low temperature. We evaluate the proposed method using simulated data obtained from the Izhikevich neuron model and Lévy-driven stochastic volatility model, and we show that the proposed REPMMH method improves the problem of the initial value dependence in the PMMH method, and realizes efficient sampling of parameters in the state space models compared with existing methods.
Evaluation of The Website ‘Aisyiyah Surakarta of University Performance Based on Search Engine Optimization Using Automated Software Testing Gtmetrix

Each college would have definitely have a website, because in the world of Education, the website is very important in addition to as one of the supporting facilities for the needs of the information, the website also as a media promotion for each college. Therefore the performance of the website on higher education should be the most important thing due to one important role of a website in the support activities in a higher education. ’Aisyiyah Surakarta of University is one of the new university after previously named Stikes Aisyiyah Surakarta, therefore, the evaluation of the quality of the website conducted to determine the quality of the website using GTMetrix. The purpose of this study is to determine the performance of the website ‘Aisyiyah Surakarta of University as well as provide recommendations for improvement for the management of the website. Based on the test results obtained by the website located at grade E.This The results of testing the research evaluation of the performance of the website ‘Aisyiyah Surakarta of University can be concluded that the Performance Score with Page Speed in level E (56%) and Yslow at the level of E (53%).
A Novel Vehicle Detection Framework Based on Parallel Vision

Autonomous driving has become a prevalent research topic in recent years, arousing the attention of many academic universities and commercial companies. As human drivers rely on visual information to discern road conditions and make driving decisions, autonomous driving calls for vision systems such as vehicle detection models. These vision models require a large amount of labeled data while collecting and annotating the real traffic data are time-consuming and costly. Therefore, we present a novel vehicle detection framework based on the parallel vision to tackle the above issue, using the specially designed virtual data to help train the vehicle detection model. We also propose a method to construct large-scale artificial scenes and generate the virtual data for the vision-based autonomous driving schemes. Experimental results verify the effectiveness of our proposed framework, demonstrating that the combination of virtual and real data has better performance for training the vehicle detection model than the only use of real data.
Visual Dynamic Simulation Model of Unstructured Data in Social Networks

Social networks contain a large amount of unstructured data. To ensure the stability of unstructured big data, this study proposes a method for visual dynamic simulation model of unstructured data in social networks. This study uses the Hadoop platform and data visualization technology to establish a univariate linear regression model according to the time correlation between data, estimates and approximates perceptual data, and collects unstructured data of social networks. Then, the unstructured data collected from the original social network are processed, and an adaptive threshold is designed to filter out the influence of noise. The unstructured data of social network after feature analysis are processed to extract its visual features. Finally, this study carries out the Hadoop cluster design, implements data persistence by HDFS, uses MapReduce to extract data clusters for distributed computing, builds a visual dynamic simulation model of unstructured data in social network, and realizes the display of unstructured data in social network. The experimental results show that this method has a good visualization effect on unstructured data in social networks and can effectively improve the stability and efficiency of unstructured data visualization in social networks.
Rast: A Language for Resource-Aware Session Types

Traditional session types prescribe bidirectional communication protocols for concurrent computations, where well-typed programs are guaranteed to adhere to the protocols. However, simple session types cannot capture properties beyond the basic type of the exchanged messages. In response, recent work has extended session types with refinements from linear arithmetic, capturing intrinsic attributes of processes and data. These refinements then play a central role in describing sequential and parallel complexity bounds on session-typed programs. The Rast language provides an open-source implementation of session-typed concurrent programs extended with arithmetic refinements as well as ergometric and temporal types to capture work and span of program execution. To further support generic programming, Rast also enhances arithmetically refined session types with recently developed nested parametric polymorphism. Type checking relies on Cooper's algorithm for quantifier elimination in Presburger arithmetic with a few significant optimizations, and a heuristic extension to nonlinear constraints. Rast furthermore includes a reconstruction engine so that most program constructs pertaining the layers of refinements and resources are inserted automatically. We provide a variety of examples to demonstrate the expressivity of the language.