Reviewerly/exHarmony huggingface dataset:
Augmented Reality for Integer Learning: Investigating its potential on students’ critical thinking

Abstract Research on critical thinking has been carried out by researchers. Critical thinking is one of the four skills students must have before the 21st century. In recent years Augmented Reality (AR) technology has provided opportunities by making learning more interesting, that able to visualizing mathematical objects in the real world. This research was conducted in a remote area of Bawean Island after the mathematics teachers participated in Augmented Reality-based mathematics learning media development program. The sample of the study was 95 students of 7th grade junior high school from five schools on Bawean Island. Investigation of the potential use of Augmented Reality technology to critical thinking skills, carried out by providing students with pretest and posttest critical thinking skills. Also, student responses were considered in this study. The results of our analysis, the ability of student’s critical thinking skills after the use of AR-based media is better than before the use of AR-based media, and students give a very good response to the use of AR-based media in this integer learning.
An Assessment of Sentence Simplification Methods in Extractive Text Summarization

The unprecedented growth of textual content on the Web made essential the development of automatic or semi-automatic techniques to help people to find valuable information in such a huge heap of text data. Automatic text summarization is one of such techniques that is being pointed out as offering a viable solution in such a chaotic scenario. Extractive text summarization, in particular, selects a set of sentences from a text according to specific criteria. Strategies for extractive summarization can benefit from preprocessing techniques that emphasize the relevance or infor-mativeness of sentences with respect to the selection criteria. This paper tests such a hypothesis using sentence simplification methods. Four methods are used to simplify a corpus of news articles in English: a rule-based method, an optimization method, a supervised deep learning model and an unsupervised deep learning model. The simplified outputs are summarized using 14 sentence selection strategies. The combinations of simplification and summarization methods are compared with the baseline --- the summarized corpus without previous simplification --- with a quantitative analysis, which suggests sentence compression with restrictions and models learned from large parallel corpora tend to perform better and yield gains over summarization without prior simplification.
Physical Activity Recommendation for Exergame Player Modeling using Machine Learning Approach

Exergames are effective tools to motivate and promote daily physical activities. However, previous studies indicated that many people who start any type of exercise drop out of the program before establishing new habits. Research has shown that personalization is key to effective game-based interventions. Player modeling and recommender systems are used for personalizing contents and services in many applications. In exergames, we believe it is important to continuously recommend personalized and appropriate types of physical activity and contents in order to improve the effectiveness of the game. In this paper, we proposed and validated the design of a personalized physical activity recommender system for exergames based on a study of participant's preferred activities. The proposed approach resulted in more accurate recommendations when comparing to an existing model in predicting users' preference toward physical activity types.
Cloud Computing-based Big Data mining Connotation and Solution

According to the contrastive perspective between traditional data mining and big data mining, this article discusses the connotation of big data mining and proposes the system frame of big data mining about cloud computing and mining service. Taking the multifunctional Hadoop big data mining platform as an example, this article analyses the internal workflow of big data mining and its advantages as well as challenges, which can provide a reference for users to cognize and apply the big data mining.
Universal-to-Specific Framework for Complex Action Recognition

Video-based action recognition has recently attracted much attention in the field of computer vision. To solve more complex recognition tasks, it has become necessary to distinguish different levels of interclass variations. Inspired by a common flowchart based on the human decision-making process that first narrows down the probable classes and then applies a "rethinking" process for finer-level recognition, we propose an effective universal-to-specific (U2S) framework for complex action recognition. The U2S framework is composed of three subnetworks: a universal network, a category-specific network, and a mask network. The universal network first learns universal feature representations. The mask network then generates attention masks for confusing classes through category regularization based on the output of the universal network. The mask is further used to guide the category-specific network for class-specific feature representations. The entire framework is optimized in an end-to-end manner. Experiments on a variety of benchmark datasets, e.g., the Something-Something, UCF101, and HMDB51 datasets, demonstrate the effectiveness of the U2S framework; i.e., U2S can focus on discriminative spatiotemporal regions for confusing categories. We further visualize the relationship between different classes, showing that U2S indeed improves the discriminability of learned features. Moreover, the proposed U2S model is a general framework and may adopt any base recognition network.
WBBA-KM: A Hybrid Weight-Based Bat Algorithm with K-Means Algorithm For Cluster Analysis

Data clustering is an unsupervised classification method used to classify unlabeled objects into clusters. The clustering is performed by partitioning clustering, hierarchical clustering, fuzzy clustering, and density-based clustering methods. However, the center of the clusters is updated according to local searches with these traditional methods, and finding the best clusters center affects the clustering performance positively. In this study, a variant bat algorithm called weight-based bat algorithm (WBBA) is proposed and the proposed WBBA hybridized with the k-means clustering method (WBBA-KM) to determine the optimal centers of the clusters. The performance of the proposed WBBA-KM has been evaluated by using six different benchmark datasets from the UCI repository and the obtained results are compared with FCM, IFCM, KFCM, KIFCM, PSO-IFCM, GA-IFCM, ABC-IFCM, PSO-KIFCM, GA-KIFCM, ABC-KIFCM, and BA-KM clustering methods in the literature. According to the experimental results, the proposed WBBA-KM clustering method performed better performance from all other clustering methods in 4 of 6 benchmark datasets and achieved better performance from the BA-KM clustering method in all benchmark datasets.
Application of Blockchain for Trusted Coordination in Collaborative Software Development

The coordination of developing various complex and large-scale projects using computers has been well established and is the so-called computer-supported cooperative work (CSCW). Collaborative software development requires similar technologies and tools to handle the coordination among participating teams. Development of complex and large-scale software systems can be largely improved by effective collaboration among participating software development teams at both component and system levels, which depends on trusted coordination among the participating teams for sharing, processing, and managing information on various participating teams, which are often operating in a distributed environment, even if they are in the same organization. Existing approaches for coordination in collaborative software development are based on using centralized repository and tools to store, process, and retrieve information on participating software development teams during the development. These approaches use centralized authority, have a single point of failure and restricted rights to own data and software. Although there are approaches for collaborative software development using blockchain, they only verify blockchain transactions using customized agreement techniques, and do not address the coordination in collaborative software development. In this paper, it is shown how private blockchain is used to provide trusted coordination in collaborative software development using smart contracts. This is due to the properties of immutability, auditability, and transparency of the blockchain.
Unification of Machine Learning Features

In the Information Age, Machine learning (ML) provides a competitive advantage to any business. Machine learning applications are not limited to driverless cars or online recommendations but are widely used in healthcare, social services, government systems, telecommunications, and so on. As many enterprises are trying to step up machine learning applications, it is critical to have a long-term strategy. Most of the enterprises are not able to truly realize the fruits of ML capabilities due to its complexity. It is easier to access a variety of data today due to data democratization, distributed storage, technological advancements, and big data applications. Despite easier data access and recent advancements in ML, developers still spend most of the time in data cleansing, data preparation, and data modeling for ML applications. These steps are often repeated and result in identical features. As identical features can have inconsistent processing while testing and training, more issues pop up at later stages in ML application development. The unification of ML features is an effective way to address these issues. This paper presents details about numerous methods to achieve ML features unification.
Turkish Text Detection System from Videos Using Machine Learning and Deep Learning Techniques

With the advancement in smart devices and high-speed internet, a continual increase in videos demands an efficient and automatic video indexing and retrieval system. To accomplish it, content-based video indexing is an optimal solution by detecting text in videos. In this study, we proposed a text detection system based on machine learning approaches. We compared conventional machine learning approaches with deep learning method. For deep learning, we implemented Convolutional Neural Network (CNN), while Logistic Regression (LR) and Support Vector Machine (SVM) are employed as conventional machine learning techniques to predict the outcome as text or non-text data. We evaluated the proposed systems on our own dataset obtained from various Turkish videos. LR obtained an overall accuracy of 95.0%, whereas SVM achieved 98.7% while CNN secured 99.8% accuracy. The experimental results show that CNN (deep learning approach) was more effective for our Turkish text dataset as compared to LR and SVM.
Persona Design in Participatory Agile Software Development

Personas are a popular method of user centered design (UCD) in technical product development and have become indispensable in software development. In agile software development, which is nowadays predominantly used in modern projects, personas do not have a role of their own. User needs are usually introduced into development via the Product Owner role by means of user stories. Especially when software is developed for user groups with special needs, it is often difficult for developers to understand the needs of the users based on common user stories.The study presented in this paper was conducted as part of the EU-funded research and development project Easy Reading. It demonstrates how personas for users with learning difficulties can be designed in inclusive research teams and how they can be successfully embedded into the agile development process.
Speaker Clustering by Co-Optimizing Deep Representation Learning and Cluster Estimation

Speaker clustering is a task to merge speech segments uttered by the same speaker into a single cluster, which is an effective tool for alleviating the management of massive amount of audio documents. In this paper, we present a work for co-optimizing the two main steps of speaker clustering, namely, feature learning and cluster estimation. In our method, the deep representation feature is learned by a deep convolutional autoencoder network (DCAN), while the cluster estimation is realized by a softmax layer that is combined with the DCAN. We devise an integrated loss function to simultaneously minimize the reconstruction loss (for deep representation learning) and the clustering loss (for cluster estimation). Many state-of-the-art audio features and clustering methods are evaluated on experimental datasets selected from two publicly available speech corpora (the AISHELL-2 and the VoxCeleb1). The results show that the proposed method exceeds other speaker clustering methods in regard to the normalized mutual information (NMI) and the clustering accuracy (CA). Additionally, the proposed deep representation feature outperforms other features that were widely used in previous works, in terms of both NMI and CA.
The Cognitive and Mathematical Foundations of Analytic Epidemiology

Analytic epidemiology is a transdisciplinary study on the cognitive, theoretical, and mathematical models of COVID-19 and other contagious diseases. It is recognized that analytic epidemiology may be better studied by big data explorations at the macro level rather than merely biological analyses at the micro level in order to not lose the forest for the trees. This paper presents a basic research on analytic epidemiology underpinned by sciences of cognition, computer, big data, information, AI, mathematics, epidemiology, and systems. It introduces a novel Causal Probability Theory (CPT) for explaining the Dynamic Pandemic Transmission Model (DPTM) of analytic epidemiology. It reveals how the fundamental reproductive rate $(R_{0})$ may be rigorously calibrated based on big data of COVID-19. A theoretical framework of analytic epidemiology is developed to elaborating the insights of pandemic mechanisms in general and COVID-19 in particular. Robust and accurate predictions on key attributes of COVID-19, including $R_{0}(t)$, forecasted infectives/resources, and the expected date of pandemic termination, are derived via rigorous experiments on worldwide big data of epidemiology.
Deep Learning-Based Recommender Systems

The term “information overload” has gained popularity over the last few years. It defines the difficulties people face in finding what they want from a huge volume of available information. Recommender systems have been recognized to be an effective solution to such issues, such that suggestions are made based on users’ preferences. This chapter introduces an application of deep learning techniques in the domain of recommender systems. Generally, collaborative filtering approaches, and Matrix Factorization (MF) techniques in particular, are widely known for their convincing performance in recommender systems. We introduce a Collaborative Attentive Autoencoder (CATA) that improves the matrix factorization performance by leveraging an item’s contextual data. Specifically, CATA learns the proper features from scientific articles through the attention mechanism that can capture the most pertinent parts of information in order to make better recommendations. The learned features are then incorporated into the learning process of MF. Comprehensive experiments on three real-world datasets have shown our method performs better than other state-of-the-art methods according to various evaluation metrics. The source code of our model is available at: https://github.com/jianlin-cheng/CATA.
Towards Artificial Intelligence: Concepts, Applications, and Innovations

Artificial intelligence (AI) is a set of theories and techniques implemented in order to achieve solutions capable of simulating intelligence. However, the quick progress in AI raises many questions regarding the benefits and risks of this technology, which can be used in many areas, drawing on advanced software and computers. The AI has also become more popular and especially used for modeling the complex behavior of most life solutions because it shows superior predictive power compared to traditional methods. This document presents the AI technology with a focus on strengths and weaknesses in its various applications to extract a list of the pros and cons of AI technology and reminds of some obstacles that can be faced during the completion of their projects. The contributions presented in this document reveal the high potential of AI methods as tools for predicting and optimizing different applications. In addition, challenges and directions for future research in the area of the use of AI techniques are presented and discussed.
On the influence of model fragment properties on a machine learning-based approach for feature location

Leveraging machine learning techniques to address feature location on models has been gaining attention. Machine learning techniques empower software product companies to take advantage of the knowledge and the experience to improve the performance of the feature location process. Most of the machine learning-based works for feature location on models report the machine learning techniques and the tuning parameters in detail. However, these works focus on the size and the distribution of the data sets, neglecting the properties of their contents. In this paper, we analyze the influence of three model fragment properties (density, multiplicity, and dispersion) on a machine learning-based approach for feature location. The analysis of these properties is based on an industrial case provided by CAF, a worldwide provider of railway solutions. The test cases were evaluated through a machine learning technique that uses different subsets of a knowledge base to learn how to locate unknown features. Results show that the density and dispersion properties have a direct impact on the results. In our case study, the model fragments with extra-small density values achieve results with up to 43% more precision, 41% more recall, 42% more F-measure, and 0.53 more Matthews Correlation Coefficient (MCC) than the model fragments with other density values. On the other hand, the model fragments with extra-small and small dispersion values achieve results with up to 53% more precision, 52% more recall, 52% more F-measure, and 0.57 more MCC than the model fragments with other dispersion values. The analysis of the results shows that both density and dispersion properties significantly influence the results. These results can serve not only to improve the reports by means of the model fragment properties, but also to be able to compare machine learning-based feature location approaches fairly improving the feature location results.
Agile Transformation: How Employees Experience and Cope with Transformative Change

Abstract Modern manufacturing is highly competitive, requiring that organizations reduce lead times and achieve greater organizational flexibility, for example by implementing agile ways of working. However, studies show that incumbent firms have persistent problems with adopting and scaling such practices. In this paper, we present an empirical account of agile transformation in a large manufacturing company that has adopted the SAFe framework. Based on interviews, focus groups, and observation data, we identify three themes for understanding how employees experience and cope with transformative change by: 1) making sense of the new, 2) practicing with peers and 3) letting go of legacy. Key findings are that initially employees are more concerned with making sense of the new rather than with the implementation of agile itself and that implementation of agile happens very gradually over time rather than through major breakthroughs. Thus, it takes time for employees to weather change, become acquainted with the new way of working and stabilize how they work together in the agile teams and across the ARTs (Agile Release Trains). We contribute to extant literature with insight into the human implications of agile transformation.
Moroccan Dialect Speech Recognition System Based on CMU SphinxTools

The main aim of an Automatic Speech Recognition system (ASR) is to produce a system that is able to simulate the human listener based on the learning approach and speech data of a studied language. In this paper, we describe the Darija Moroccan Dialect speech recognition system that is implemented to recognize the ten first Arabic digits spoken in Moroccan dialect (Darija) collected from 20 speakers including both males and females. This system is designed based on the CMU Sphinx tools through the ASR Hidden Markov Model method with small data and the Mel frequency spectral coefficients (MFCCs) that are used in the feature extraction phase. Our best-obtained accuracy is 96.27 % found with 8 GMMs.
A Transparent Digital Election Campaign? The Insights and Significance of Political Advertising Archives for Debates on Electoral Regulation

Throughout the last decade, digital technology has become increasingly important in election campaigns. Each new election has seen growing coverage of the role played by websites, social media pages and online advertising in securing electoral victory. At the UK's 2019 general election, this narrative recurred, but while in the past digital technology has been a supplement to offline campaigning, digital was now an integral component of electoral activity. Whether measured in terms of spending on digital campaigning, activity on online platforms or media coverage of digital content, the online sphere was prominent as never before. While recognising the new status of digital campaigning it is, however, important to note that this activity is by no means uncontentious. Indeed, this election raised numerous questions about what is and is not permissible online and how problematic practices should be curtailed. We explore what we know about digital campaigning at the UK's 2019 general election, presenting the first academic analysis of newly available transparency data from the Facebook and Google advertising archives. Reviewing this data, we diagnose the need to revisit existing systems of electoral regulation and oversight with a view to questions of transparency.
dg2pix: Pixel-Based Visual Analysis of Dynamic Graphs

Figure 1: dg2pix provides an overview of temporal and structural changes in dynamic graphs.The example presents a synthetic dynamic graph (200-time steps) using graph2vec [35].The x-axis presents the temporal dimension, and the y-axis displays for each time step a graph embedding as a pixel-bar.The reoccurring states (A-C) have for each time step the same amount of nodes (2500) and edges (350000) with a different number of clusters.Each state (20-time steps) was generated with SBM [25] with slight variations for the density of edges between clusters.The graphs (A-C) display a sample graph for each state.dg2pix enables us to explore and identify temporal changes, outlier graphs, and reoccurring graph structures at multiple temporal scales.
Artificially intelligent chatbots for health professions education

Chatbots are computer software designed to have conversations with humans. Chatbots are widely used in the customer service industry, where frequently asked questions or basic information is answered by the computer. Chatbots may serve useful functions for supporting health professions trainees across geographically distributed sites, as well as reducing faculty burnout. This chapter explores the potential roles of chatbots to help with scaling faculty and student time within fast-moving health professions programs. The possibilities for chatbots are organized with Bloom’s taxonomy to gain an appreciation for the simple through advanced applications. Building upon these ideas, the chapter also introduces the VoxScholar project to address how artificially intelligent chatbots might coach to teach expertise or academic success.