Reviewerly/exHarmony huggingface dataset:
PENGARUH KEPERCAYAAN DIRI DAN KEMAMPUAN KOMUNIKASI TERHADAP KEMAMPUAN PUBLIC SPEAKING MAHASISWA PGSD

This study aims to determine: (1) The effect of self-confidence on the ability of public speaking; (2) The effect of communication skills on public speaking skills; (3) The effect of self confidence and communication ability on public speaking ability of Elementary School Teacher Education Students 2019, Faculty of Teacher Training and Education, University of Mataram. This research is an ex post facto study using a quantitative data approach. The subjects in this study were students of class 2019, amounting to 345 and then taken as many as 38% of the population of around 131 students. Analysis Methods of data collection using a questionnaire. Test the validity of the instrument using Product Moment correlation and reliability testing using Alpha Cronbach. Data analysis uses descriptive statistical analysis; analysis prerequisite test consisting of normality test, linearity test, and multicollinearity test; and regression analysis consisting of a simple linear regression test and multiple linear regression test. The results showed: (1) There was a positive and significant effect of self- confidence on the public speaking ability. (2) There is a positive and significant influence of communication ability on the public speaking ability. (3) There is a positive and significant influence of self-confidence and communication ability on the public speaking ability.
Disseminating marine weather forecasts and gathering feedback from artisanal fishers in south India

Despite recent advances, uptake of climate and weather services is limited for many groups that need forecasts for safe and sustainable livelihoods such as artisanal fishers. On the Arabian Sea coast of southwestern India, for instance, they often fish in foul weather, and risk accidents during the monsoon season marked by high wind and waves. To make forecasts more easily accessible, we tested and deployed a low-cost, web-based, knowledge co-production system called Radio Monsoon. The website offers wind speed and wave height forecasts in graphics and local-language text with multiple interfaces - social media, WhatsApp, and a free phone service using voice over internet protocol. Focus groups and conversations connect the fishers with forecasters in a feedback loop. This paper examines how Radio Monsoon addresses challenges in knowledge co-production by looking at its technology deployment and societal impact.
An Accountable Data Trading Platform Based on Blockchain

In the era of big data, the sale of digital data has became a promising business. Although data trading brings a big dividend, it still has the issues of data authentic right and infringement tracking difficulty. The solutions of monitoring consumers and platform regulation introduce privacy and security issues. To overcome these challenges, a blockchain-based accountable data trading platform is proposed. The platform uses the blockchain technology to build a distributed secure and trusted environment for big data trading. Before listing datasets on the data market, a secure dataset similarity comparison method is used to detect illegal resale. The misbehavior of customers can be traced by the blockchain nodes because the key points of the data trading are recorded in the blockchain. Through secure analysis and performance evaluation, we evaluate the feasibility and effectiveness of the trading platform.
Intelligence Comes from Within—Personality as a UI Paradigm for Smart Spaces

The buzzword “smart home” promises an intelligent, helpful environment in which technology makes life easier, simpler or safer for its inhabitants. On a technical level, this is currently achieved by many networked devices interacting with each other, working on shared protocols and standards. From a user experience (UX) perspective, however, the interaction with such a collection of devices has become so complex that it currently rather stands in the way of widespread adoption and use. So far, it does not seem likely that a common user interface (UI) concept will emerge as a quasi-standard, as the desktop interface did for graphical UIs. Therefore, our research follows a different approach. Instead of many singular intelligent devices, we envision a UI concept for smart environments that integrates diverse pieces of technology in a coherent mental model of an embodied “room intelligence” (RI). RI will combine smart machinery, mobile robotic arms and mundane physical objects, thereby blurring the line between the physical and the digital world. The present paper describes our vision and emerging research questions and presents the initial steps of technical realization.
Learning to Model Another Agent’s Beliefs: A Preliminary Approach

It is often useful for one agent to predict what another agent will believe after receiving new information. In fact, in order to appear intelligent in situations involving multiple interacting agents, we fundamentally need to be able to predict how changes in the world will affect the beliefs of others. This process involves two distinct processes. First, we need to devise a model that captures the way that beliefs change in response to new information. Second, we need to observe the behaviour of individual agents to determine their specific beliefs. In the AI literature, these problems have been addressed by distinct communities. In this paper, we bring these two communities together by demonstrating how an agent can learn a model of belief change from observed behaviour. We argue that this process is essential for natural interaction in an AGI setting, but it has not been addressed to date in a unified manner.
Usability Study of a Pre-anesthesia Evaluation App in an University Hospital: Before the Revision of User Interface

A pre-anesthesia evaluation app named EVAN was developed to facilitate anesthesiologists in their daily practice. EVAN contained a well-organized interface for medical records and an evaluation entry page. The purpose of this study is to assess the usability of EVAN and to find the weakness in the user interface before the revision. Twenty participants were recruited in this study with three components: user task analysis, questionnaires, and semi-structured interview. During the user task analysis, participants were asked to complete 16 tasks under monitoring. The second component of the study is questionnaires, consisted of SUS and AttrakDiff-Short. According to the results, seven participants completed all the tasks successfully. Two tasks had the most failures (8 and 7 failures, respectively). Participants were unable to find a specific function due to inadequate clues on the interactive buttons. Results of task analysis also revealed that the current logic of data storage which aimed to prevent accidental deletion, was not intuitive for most participants. Despite the drawbacks, the gradings of SUS questionnaire was 80.6 ± 14.4, which is an above average result. Scores from the AttrakDiff-Short were all positive, for both pragmatic quality, hedonic quality, and attractiveness. The results of this study are summarized and integrated for the revision of EVAN.
Talk about the Computer Database Technology in Application of Information Management

&lt;p&gt;With the development of the society and the progress of the times, data information shows a leap growth phenomenon. Each industry has a huge amount of data information, so in every field people's demand for information management technology increasingly enhance. The development of database technology solves the problem people meet and can manage the huge data information effectively. Nowadays, database technology is widely used in every field. This paper discusses the application of computer database technology in the field of information management and discusses the relevant measures and methods to strengthen the application function of database technology.&lt;/p&gt;
Feature Selection on Noisy Twitter Short Text Messages for Language Identification

The task of written language identification involves typically the detection of the languages present in a sample of text. Moreover, a sequence of text may not belong to a single inherent language but also may be a mixture of text written in multiple languages. This kind of text is generated in large volumes from social media platforms due to its flexible and user friendly environment. Such text contains very large number of features which are essential for development of statistical, probabilistic as well as other kinds of language models. The large number of features have rich as well as irrelevant and redundant features which have diverse effect over the performance of the learning model. Therefore, feature selection methods are significant in choosing features that are most relevant for an efficient model. In this article, we consider the Hindi-English language identification task as Hindi and English are often the two most widely spoken languages of India. We apply different feature selection algorithms across various learning algorithms in order to analyze the effect of the algorithm as well as the number of selected features on the performance of the task. The methodology focuses on the word level language identification using a novel dataset of 6903 tweets extracted from Twitter. Various n-gram profiles are examined with different feature selection algorithms over many classifiers. Finally, an exhaustive comparative analysis is put forward with respect to the overall experiments conducted for the task.
Using fMRI to Predict Training Effectiveness in Visual Scene Analysis

Visual analysis of complex real-world scenes (e.g. overhead imagery) is a skill essential to many professional domains. However, little is currently known about how this skill is formed and develops with experience. The present work adopts a neuroergonomic approach to uncover the underlying mechanisms associated with the acquisition of scene expertise, and establish neurobehavioral markers for the effectiveness of training in scene imagery analysis. We conducted an intensive six-session behavioral training study combined with multiple functional MRI scans using a large set of high-resolution color images of real-world scenes varying in their viewpoint (aerial/terrestrial) and naturalness (manmade/natural). Participants were trained to categorize the scenes at a specific-subordinate level (e.g. suspension bridge). Participants categorized the same stimuli for five sessions; the sixth session consisted of a novel set of scenes. Following training, participants categorized the scenes faster and more accurately, reflecting memory-based improvement. Learning also generalized to novel scene images, demonstrating learning transfer, a hallmark of perceptual expertise. Critically, brain activity in scene-selective cortex across all sessions significantly correlated with learning transfer effects. Moreover, baseline activity (pre-training) was highly predictive of subsequent perceptual performance. Whole-brain activity following training indicated changes to scene- and object-selective cortex, as well as posterior-parietal cortex, suggesting potential involvement of top-down visuospatial-attentional networks. We conclude that scene-selective activity can be used to predict enhancement in perceptual performance following training in scene categorization and ultimately be used to reveal the point when trainees transition to an expert-user level, reducing costs and enhancing existing training paradigms.
3D Hologram Learning Kit Development for Elementary Education

This paper aims to explain the design for immersive learning resources that include 3D holographic learning content intended to complement basic textbooks. Also, it shows a development process for a portable learning kit that is accessible and universally available for collaborative learning to increase the interest and understanding of elementary school students. When it comes to designing 3D hologram learning content, we have worked with scientific content based on the national curriculum for fifth and sixth grade levels in elementary school. We used an IAP process (A Design Process based on Iterative Agile Prototyping) for developing a tangible learning kit, which consists of a reflective mirror, a tablet holder, and a height adjustment stand, providing 3D holographic images via a tablet and an app controller. We have assessed the learning kit in order to define problems and improvements for future development. As a primary test, the 3D learning content was evaluated with student users from the perspectives of immersion, and social interaction. For the secondary test, we conducted a usability analysis from teacher’s observation and interviews focusing on tangible interaction, and convenience. As a third test, the kit was evaluated by experts in field of EdTech (Education and Technology) to reveal its weaknesses and strengths in aspects of marketability with feedback and possible improvements. Through this exploratory study, we have identified the strengths and weaknesses of this 3D hologram learning kit and defined further improvements as a universal teaching resource in the future classroom.
The Basic Concepts Classification (BCC)

The Basics Concept Classification (BCC) is a “universal” scheme: it attempts to encompass all areas of human understanding. Whereas most universal schemes are organized around scholarly disciplines, the BCC is instead organized around phenomena (things), the relationships that exist among phenomena, and the properties that phenomena and relators may possess. This structure allows the BCC to apply facet analysis without requiring the use of “facet indicators.” The main motivation for the BCC was a recognition that existing classifications that are organized around disciplines serve interdisciplinary scholarship poorly. Complex concepts that might be understood quite differently across groups and individuals can generally be broken into basic concepts for which there is enough shared understanding for the purposes of classification. Documents, ideas, and objects are classified synthetically by combining entries from the schedules of phenomena, relators, and properties. The inclusion of separate schedules of-generally verb-like-relators is one of the most unusual aspects of the BCC. This (and the schedules of properties that serve as adjectives or adverbs) allows the production of sentence-like subject strings. Documents can then be classified in terms of the main arguments made in the document. BCC provides very precise descriptors of documents by combining phenomena, relators, and properties synthetically. The terminology employed in the BCC reduces terminological ambiguity. The BCC is still being developed and it needs to be fleshed out in certain respects. Yet it also needs to be applied; only in application can the feasibility and desirability of the classification be adequately assessed.
Quantum Criticism: an Analysis of Political News Reporting

In this project, we continuously collect data from the RSS feeds of traditional news sources.We apply several pre-trained implementations of named entity recognition (NER) tools, quantifying the success of each implementation.We also perform sentiment analysis of each news article at the document, paragraph and sentence level, with the goal of creating a corpus of tagged news articles that is made available to the public through a web interface.We show how the data in this corpus could be used to identify bias in news reporting, and also establish different quantifiable publishing patterns of left-leaning and right-leaning news organisations.
The Shortest Path Planning Based on Reinforcement Learning

Abstract This paper proposes a shortest path planning of agent in an environment based on reinforcement learning. This method adopts the Q-learning algorithm, which has gained increasingly using in agent path planning recently. This algorithm can be fully designed to a reasonable environment model and applied to other professional fields. Yet, reinforcement learning in path planning still needs to improve the rate of convergence. The learning process takes several iterations and spends a long time to find the final path. A local optimal problem in search process deals with other explorations that provided shortest path for agent to take optimal actions. For the agent, the reward value of inclined movement is introduced to find the shortest path. The autonomous obstacle avoidance is utilized to obtain the optimal path in the process. Finally, the model of intelligent agent movement environment has established in matlab. The effectiveness of the algorithm has proved by simulation results.
Working in Detail : How LSTM Hyperparameter Selection Inﬂuences IMDB Sentiment Analysis Results

Sentiment analysis of written customer reviews is a powerful way to generate knowledge about customer attitudes for future marketing activities. Meanwhile, Deep Learning as the most powerful machine learning method is of particular importance for sentiment analysis tasks. Due to this current relevance, an LSTM network based on a literature review to solve the challenging classification task of the IMDB LargeMovie Dataset is created. Hyperparameters are varied separately from each other to better understand their single influences on the overall model accuracy. Furthermore, we transformed variants with positive impacts into a final model in order to investigate whether the impacts can be cumulated. While preparing the amount of training data and the number of iteration steps resulted in a higher accuracy, pre-trained word vectors and higher network capacity did not work well separately. Even though implementing the variants with positive influences together raised the model´s performance, the improvement was lower than some single variants.
XGBoost Classifier to Extract Asset Mapping Features

In steep growth in the consumption of Internet, Big Data came into picture for handling enormous amount of data. However, the data that is generated through internet has high dimensional data. So, feature engineering will be performed—to extract the best feature subset from high dimensional data. Assets are the ones to keep, expand upon, and support for the one who and what is to come. Asset mapping is a positive and charming way to learn about the community. It empowers us to contemplate where individuals live and work. It also challenges us to recognize how other people see the same community. In this paper, a model is introduced to find the required assets based on the population in the area and whether the available assets are tangible are not, is identified by extracting the features from the data gathered from the government of Andhra Pradesh. The data is pre-processed by extracting the best features in it by using feature engineering methods and classifiers like XGBoost, Random Forest and ExtraTreeClassifier. The experimental results proves that XGBoost provides the most accurate results for the specified target.
BLOCKCHAIN BASED BUSINESS MODEL FOR DIGITAL ASSETS MANAGEMENT IN TRUST LESS COLLABORATIVE ENVIRONMENT

With the advancements of digitalization, an organization can only survive by working incollaboration with other organization by sharing its digital assets. Due to lack of trust among the collaborativeorganization, the management of digital assets is challenging in todays rapidly growing competitive world.Traditional approaches of digital asset management are based on centralized access control and do not suitedwell in collaborative environment. Collaborative environment does not allow any single organization to take fullcontrol over entire digital assets because of different trust level among the organizations. Further, theseapproaches suffer from single point of failure that cost very high to the businesses. These problems ofcentralized approach are the roots for the need of a decentralized approach for digital assets management. In thispaper, we discussed the need of decentralized access control for digital assets management and reviewed howBlockchain based business model emerge as a good alternate for digital assets management in a trust lesscollaborative environment.
A Comparative Study of Distributed Ledger Technologies

Distributed ledger technologies represent decentralized databases, without the requirement for a central authority to authenticate and validate transactions. Blockchain technology is one of these technologies that provides a record of digital transactions that have been executed and validated by consensus of a majority of participants in the system. It uses software that processes on the order of the newly created and connected blocks via cryptographic means to ensure security and integrity. It has a verified record of each transaction that has been executed till date, which cannot be removed from the system, thereby ensuring the integrity of information. Although Bitcoin is the most popular application of blockchain, it has been used in diverse application domains such as healthcare, logistics, and security. While blockchain enables fairness and prevention from denial-of-service attacks, it lacks in transaction processing time as compared with credit cards and other online services. Hashgraph, another distributed ledger technology, has emerged recently, which focuses on addressing this limitation, promising extraordinary improvement in transaction processing times. Although hashgraph was initially deployed in private settings, public deployment has been recently launched, enabling its adoption for wider application domains. Within this context, this chapter is focused on presenting a comparative study of these distributed ledger technologies, identifying strengths and weaknesses to highlight potential application domains.
Security Threat Analysis and Prevention towards Attack Strategies

Twentieth century is undergoing a huge change on businesses, end-users buying commodities, and even how billion-dollar deals are finalized. Digitization has seen global trends fit within the reaches of four walls of an end-user. With a major pandemic situation overseen in 2020, the need for working remotely, realizing daily KPIs online, and creating a barrier on how systems are designed has become more important than ever. Increased needs in cloud computing, introductions of General Data Protection Regulations (GDPR), usage of remote devices, etc., has influenced the needs of highly secured systems which follow a strict jurisdiction on possible threats and how to mitigate them. Threat analysis is the process of evaluating suspicious actions, breach by intelligence bodies or cybercriminals. Security threats are damaging and follow well-defined strategic moves, which leads to a breach. The prevention of attack scenarios is a study of attack strategies and the necessary steps that are taken to avoid any such breaches. This chapter looks at security threats and strategies used by cybercriminals to gain access to a system and cause damage. We will learn about threat modeling techniques and prevention measures, that are employed by organizations to ensure that data is safe and secured.