Reviewerly/exHarmony huggingface dataset:
Use of Educational Games to Improve Science Learning Outcomes of Elementary School Students

This study aims to analyze the use of educational games to improve students learning outcomes of natural science students of class VI in MI Muhammadiyah 2 Kedungbanteng. This study uses quantitative method. By collecting data using observation, documentation, tests, and questionnaires. Data analysis technique used T test. Based on the results of data analysis shows that the effectiveness of the use of educational games on learning outcomes of natural science class VI, it can be seen from the results of the T test (2-tailed) = 0.000 &lt; 0.05, the hypothesis of this research is H0 is rejected and Ha is accepted. . There is a change after the use of educational games can be seen based on the average result before the use of educational games is 63.8 while the average value after the use of educational games is 85. The use of educational games is quite efficient in increasing the learning outcomes of natural science students in grade VI MI Muhammadiyah 2 Kedungbanteng.&#x0D; Highlights:&#x0D; &#x0D; Educational games and learning outcomes: Highlighting the use of educational games to improve the learning outcomes of natural science students in class VI.&#x0D; Quantitative method and data collection: Noting the use of a quantitative approach and data collection techniques such as observation, documentation, tests, and questionnaires.&#x0D; Effectiveness of educational games: Stating the significant improvement in learning outcomes as indicated by the T-test results and the average scores before and after using educational games.&#x0D; &#x0D; Keywords: educational games, learning outcomes, natural science, quantitative method, data analysis
Speculative Vulnerability

Pandemic-tracking apps may form a future infrastructure for public health surveillance. Yet, there has been relatively little exploration of the potential societal implications of such an infrastructure. In semi-structured interviews with 23 participants from India, the Middle East and North Africa (MENA), and the United States, we discussed attitudes and preferences regarding the deployment of apps that support contact tracing to contain the spread of COVID-19. Through interpretive analysis, we examined the relationship between persistent discomfort and vulnerability when using such apps. Such an examination yielded three temporal forms of vulnerability: real, anticipatory, and speculative. By identifying and defining the temporalities of vulnerability through an analysis of people's pandemic-related thoughts and experiences, we develop the overlapping discourses of humanistic infrastructure studies and infrastructural speculation. In doing so, we explore the concept of vulnerability itself and present implications for the study of vulnerability in Human-Computer Interaction (HCI) and for the oversight of app-based public health surveillance.
Virtualization of Prolog Laboratory for the Engineering of Methods and Laboratory of the Catholic University of Colombia with A View to Inclusive Education

The objective of this article is to present the results obtained from the virtualization of the laboratories applied in the Prolog plant of the subject of engineering methods and laboratory of the Catholic University of Colombia that had been carried out in an it is only possible to continue applying the methodology as a tool that contributes to inclusive education. The results show that in general after the students adapted to the change, they achieved the expected results in their academic process, Thus, it is generally concluded that it is favorable to use in education a modality where presence can be combined with virtuality.
Comprehensive Information Retrieval Using Fine-Tuned Bert Model and Topic-Assisted Query Expansion

Retrieving relevant information covering different aspects of user information needs and ranking them based on their diverse nature are some of the important problems in the information retrieval domain. Identifying a document content covering multiple aspects of information pertaining to a query is of interest to users who wish to see everything about the query. The specific portions (information nuggets) of such documents may talk about specific aspects, and similar aspects of information can be seen across top k retrieved documents. We have proposed an information retrieval framework using the fine-tuned BERT model that identifies such aspects across top k documents and identifies such aspect based information in the form of information nuggets. Similar information nuggets are clustered based on their contextual relevance to specific aspects of the query. This work also applies topic-assisted query expansion to prune the final retrieved set of information nuggets, and the final retrieved set of information covers diverse aspects of user information needs. The experiment results done on three dataset, including COVID-19 dataset, shows that the proposed topic-assisted fine-tuned BERT model shows a better performance in comparison with the standard Vector Space Model.
State Surveillance and Corporate Surveillance

This Research Paper focuses on and deals with the various aspects of Surveillance in India and Internationally. The paper mainly focuses on the State of Surveillance in India and the laws revolving around it as well as the evolution of the Fundamental Right to Privacy in India. The concept of surveillance is divided into two sections of State Surveillance and Corporate Surveillance. It discusses the duty and the Liability of each concerning the Right to Privacy. The Paper also compares India’s Privacy and Surveillance policy with The United States of America and The United Arab Emirates. The United States of America dramatically changed its Surveillance Policy after the terrorist attack of 9/11. The United Arab Emirates envisages a more liberal and comprehensive approach towards Surveillance in their State. It gives the detailed comparison between the laws of these three nations and the vices and virtues of these laws of each nation. The advantages and the disadvantages of surveillance are analysed on various aspects and with an international perspective. This paper attempt to give a comprehensive idea of Surveillance and its impact on the people as well as in the international level due to the fast-pacing globalization.
A Differential Privacy Budget Allocation Algorithm Based on Out-of-Bag Estimation in Random Forest

The issue of how to improve the usability of data publishing under differential privacy has become one of the top questions in the field of machine learning privacy protection, and the key to solving this problem is to allocate a reasonable privacy protection budget. To solve this problem, we design a privacy budget allocation algorithm based on out-of-bag estimation in random forest. The algorithm firstly calculates the decision tree weights and feature weights by the out-of-bag data under differential privacy protection. Secondly, statistical methods are introduced to classify features into best feature set, pruned feature set, and removable feature set. Then, pruning is performed using the pruned feature set to avoid decision trees over-fitting when constructing an ϵ-differential privacy random forest. Finally, the privacy budget is allocated proportionally based on the decision tree weights and feature weights in the random forest. We conducted experimental comparisons with real data sets from Adult and Mushroom to demonstrate that this algorithm not only protects data security and privacy, but also improves model classification accuracy and data availability.
Deciphering Latent Health Information in Social Media Using a Mixed-Methods Design

Natural language processing techniques have increased the volume and variety of text data that can be analyzed. The aim of this study was to identify the positive and negative topical sentiments among diet, diabetes, exercise, and obesity tweets. Using a sequential explanatory mixed-method design for our analytical framework, we analyzed a data corpus of 1.7 million diet, diabetes, exercise, and obesity (DDEO)-related tweets collected over 12 months. Sentiment analysis and topic modeling were used to analyze the data. The results show that overall, 29% of the tweets were positive, and 17% were negative. Using sentiment analysis and latent Dirichlet allocation (LDA) topic modeling, we analyzed 800 positive and negative DDEO topics. From the 800 LDA topics-after the qualitative and computational removal of incoherent topics-473 topics were characterized as coherent. Obesity was the only query health topic with a higher percentage of negative tweets. The use of social media by public health practitioners should focus not only on the dissemination of health information based on the topics discovered but also consider what they can do for the health consumer as a result of the interaction in digital spaces such as social media. Future studies will benefit from using multiclass sentiment analysis methods associated with other novel topic modeling approaches.
Local AdaGrad-type algorithm for stochastic convex-concave optimization

Large scale convex-concave minimax problems arise in numerous applications, including game theory, robust training, and training of generative adversarial networks. Despite their wide applicability, solving such problems efficiently and effectively is challenging in the presence of large amounts of data using existing stochastic minimax methods. We study a class of stochastic minimax methods and develop a communication-efficient distributed stochastic extragradient algorithm, LocalAdaSEG, with an adaptive learning rate suitable for solving convex-concave minimax problems in the Parameter-Server model. LocalAdaSEG has three main features: (1) a periodic communication strategy that reduces the communication cost between workers and the server; (2) an adaptive learning rate that is computed locally and allows for tuning-free implementation; and (3) theoretically, a nearly linear speed-up with respect to the dominant variance term, arising from the estimation of the stochastic gradient, is proven in both the smooth and nonsmooth convex-concave settings. LocalAdaSEG is used to solve a stochastic bilinear game, and train a generative adversarial network. We compare LocalAdaSEG against several existing optimizers for minimax problems and demonstrate its efficacy through several experiments in both homogeneous and heterogeneous settings.
An online self-adaptive RBF network algorithm based on the Levenberg-Marquardt algorithm

Aiming at the problem that the Levenberg-Marquardt (LM) algorithm can not train online radial basis function (RBF) neural network and the deficiency in the RBF network structure design methods, this paper proposes an online self-adaptive algorithm for constructing RBF neural network (OSA-RBFNN) based on LM algorithm. Thus, the ideas of the sliding window method and online structure optimization methods are adopted to solve the proposed problems. On the one hand, the sliding window method enables the RBF network to be trained online by the LM algorithm making the RBF network more robust to the changes in the learning parameters and faster convergence compared with the other investigated algorithms. On the other hand, online structure optimization can adjust the structure of the RBF network based on the information of training errors and hidden nodes to track the non-linear time-varying systems, which helps to maintain a compact network and satisfactory generalization ability. Finally, verified by simulation analysis, it is demonstrated that OSA-RBFNN exhibits a compact RBF network.
A transfer learning approach based on random forest with application to breast cancer prediction in underrepresented populations

Despite the high-quality, data-rich samples collected by recent large-scale biobanks, the underrepresentation of participants from minority and disadvantaged groups has limited the use of biobank data for developing disease risk prediction models that can be generalized to diverse populations, which may exacerbate existing health disparities. This study addresses this critical challenge by proposing a transfer learning framework based on random forest models (TransRF). TransRF can incorporate risk prediction models trained in a source population to improve the prediction performance in a target underrepresented population with limited sample size. TransRF is based on an ensemble of multiple transfer learning approaches, each covering a particular type of similarity between the source and the target populations, which is shown to be robust and applicable in a broad spectrum of scenarios. Using extensive simulation studies, we demonstrate the superior performance of TransRF compared with several benchmark approaches across different data generating mechanisms. We illustrate the feasibility of TransRF by applying it to build breast cancer risk assessment models for African-ancestry women and South Asian women, respectively, with UK biobank data.
“Imagine this smart speaker to have a body”: An analysis of the external appearances and the characteristics that people associate with voice assistants

Introduction Modern digital devices, such as conversational agents, simulate human–human interactions to an increasing extent. However, their outward appearance remains distinctly technological. While research revealed that mental representations of technology shape users' expectations and experiences, research on technology sending ambiguous cues is rare. Methods To bridge this gap, this study analyzes drawings of the outward appearance participants associate with voice assistants (Amazon Echo or Google Home). Results Human beings and (humanoid) robots were the most frequent associations, which were rated to be rather trustworthy, conscientious, agreeable, and intelligent. Drawings of the Amazon Echos and Google Homes differed marginally, but “human,” “robotic,” and “other” associations differed with respect to the ascribed humanness, consciousness, intellect, affinity to technology, and innovation ability. Discussion This study aims to further elaborate on the rather unconscious cognitive and emotional processes elicited by technology and discusses the implications of this perspective for developers, users, and researchers.
ZAEBUC design and annotation

In this chapter, we present the ZAEBUC corpus annotations used by the remaining chapters in this book. In addition to rich metadata for all the texts in ZAEBUC, we discuss the various guidelines and pipeline processes we followed to create the annotations and quality check them. The annotations include spelling and grammar correction, morphological tokenization, Part-of-Speech tagging, lemmatization, and Common European Framework of Reference (CEFR) ratings. All of the annotations are done on both Arabic and English texts using consistent guidelines as much as possible. We also tracked the alignments within the different annotations, and with the original raw texts. For all annotations, we use existing automatic annotation tools followed by manual correction, except for CEFR ratings, which are only manual. We also present various measurements and correlations with preliminary insights drawn from the data and annotations. The ZAEBUC corpus annotations are intended to be the stepping stones for additional annotations. Some of the book chapters use the annotations directly, and some extend them through additional manual and automatic annotations.
Scale Decision Network for Correlation Filter-based Tracking

In this paper, we present a new correlation-filter-based tracking framework with a scale-decision network to improve the performance of the correlation-filter-based tracking technique while preserving high-speed. The bounding box is fitted to the target using the relevant scale-changing actions determined by the scale-decision network. Because we can concentrate on a single-scale sample instead of the present approach's requirement for multi-scale sampling, our approach is computationally efficient. On the benchmark datasets, the proposed tracker outperforms all real-time trackers with a computation speed of over 100 frames per second.
Promoting Teacher Confidence With Technology Through Risk Taking and Organisational Changes: A Welsh Perspective

There is no doubt that technology is a powerful pedagogical tool, playing an important role in learning within and outside of the curriculum at varying levels of education. In Wales, the recent curriculum changes, and the emergence of the Digital Competence Framework (DCF) means that primary and secondary schools and teachers across are now responsible
An Efficient Multimodal Nature‐Inspired Unmanned Aerial Vehicle Capable of Agile Maneuvers

Herein, the development of a multimodal, nature‐inspired unmanned aerial vehicle (UAV) that operates in three different flight regimes (rotor wing, tailsitter, cruise), unlike typical UAVs which only consist of two, is discussed. The platform uses a nature‐inspired method to achieve efficient hover, yet possesses the flexibility to enter a more agile state via additional tailsitter and cruise modes. Both the mechanical configuration and software/control architecture used to achieve three‐mode capability are documented in detail. A sigmoid blending control is implemented as the transition control strategy, consisting of transition coordinators that adjust the weight of each individual controller on the actuator outputs. To improve the transition sequence based on performance goals such as reduced altitude variation and throttle usage, an optimization routine is performed to obtain the optimized blending parameters. The optimized parameters are then experimentally verified on a physical prototype in lab conditions and shown to exhibit at least a 20% improvement for altitude variation and at least 5% less throttle usage across 4 consecutive transitions compared with baseline. Three‐mode capability outdoors in real‐world conditions are also demonstrated in multiple flights. Power draw in rotor‐wing mode is found to be 30% less than in tailsitter mode.
Variational deep embedding-based active learning for the diagnosis of pneumonia

Machine learning works similar to the way humans train their brains. In general, previous experiences prepared the brain by firing specific nerve cells in the brain and increasing the weight of the links between them. Machine learning also completes the classification task by constantly changing the weights in the model through training on the training set. It can conduct a much more significant amount of training and achieve higher recognition accuracy in specific fields than the human brain. In this paper, we proposed an active learning framework called variational deep embedding-based active learning (VaDEAL) as a human-centric computing method to improve the accuracy of diagnosing pneumonia. Because active learning (AL) realizes label-efficient learning by labeling the most valuable queries, we propose a new AL strategy that incorporates clustering to improve the sampling quality. Our framework consists of a VaDE module, a task learner, and a sampling calculator. First, the VaDE performs unsupervised reduction and clustering of dimension over the entire data set. The end-to-end task learner obtains the embedding representations of the VaDE-processed sample while training the target classifier of the model. The sampling calculator will calculate the representativeness of the samples by VaDE, the uncertainty of the samples through task learning, and ensure the overall diversity of the samples by calculating the similarity constraints between the current and previous samples. With our novel design, the combination of uncertainty, representativeness, and diversity scores allows us to select the most informative samples for labeling, thus improving overall performance. With extensive experiments and evaluations performed on a large dataset, we demonstrate that our proposed method is superior to the state-of-the-art methods and has the highest accuracy in the diagnosis of pneumonia.
AN ONTOLOGY FOR EDUCATIONAL AND CAREER PROFILING BASED ON THE ROMANIAN OCCUPATION CLASSIFICATION FRAMEWORK: DESCRIPTION AND SCENARIOS OF UTILISATION

AN ONTOLOGY FOR EDUCATIONAL AND CAREER PROFILING BASED ON THE ROMANIAN OCCUPATION CLASSIFICATION FRAMEWORK: DESCRIPTION AND SCENARIOS OF UTILISATION
Dangerous exploration robot based on ROS system

Based on the exploration or rescue needs of complex areas such as mines and caves, in view of the problems of narrow information detection range, short detection distance, single function, and no obstacle-crossing ability of existing detection robots, an exploration and rescue robot based on ROS system is designed. The surrounding environment information is obtained by 2D lidar, the attitude and acceleration information of the robot is obtained by the attitude sensor (IMU), the autonomous positioning and mapping of the robot is realized by the Gmapping algorithm, and the motor speed is calculated by the path planning algorithm to control the movement of the robot. The test results show that the robot has good motion performance, small size, light weight, strong obstacle-surmounting ability, and strong scalability. It provides first-hand information for the decision-making of the ground command center, and has certain value in the exploration of danger.
Solving resource forecasting in WiFi Network by NeuralProphet

Time series forecasting needs several approaches such as data pretreatment, model construction, etc. During the covid 19 outbreak, the data is very dynamic, therefore data processing and appropriate modeling are worried. Identifying patterns, recognizing abnormal data points, is one of the first stages to enhancing forecast outcomes. A point is considered an anomalous point when it is far distant from the mean of the data series. In this research, we deploy an automated anomaly detection approach that incorporates data preparation of neuralprophet library. After that, we design a model via neuralprophet to predict data after preprocessing data. The strategy is evaluated on a dataset of the times that public wifi was used every day with the purpose of forecasting the value of the following 30 days. The anticipated outcome is compared with that of Prophet, hybrid AR-LSTM, consequently indicating that the suggested technique in the study offers the best outcomes.
HS-Solver: Spreadsheet based harmony search algorithm solver for various optimization problems

There has been an advance toward the development of many metaheuristic optimization algorithms inspired by various natural and artificial phenomena. While any algorithm should be coded to each particular optimization problem, an increase in interest arises for the development of software that can generally and effectively function. HS-Solver is an open-source software added in Microsoft Excel spreadsheet in order to solve various optimization problems including linear, nonlinear, and discrete models using a music-inspired harmony search algorithm. This paper shows the basic structure of HS-Solver and its functionality in optimization examples.