Reviewerly/exHarmony huggingface dataset:
An E-Health system for data stream analysis

E-Health technologies arose as a suitable approach to support diseases diagnostics and treatment decisions since the Internet of Things devices can monitor humans over a long period. Most of the E-Health technologies are based on machine learning to analyze and classify patients data, returning a possible diagnosis for health professionals as fast and accurately as possible. However, machine learning techniques have high computational complexity, limiting their usage to meet the real time requirements of E-Health systems. Within this context, this paper proposes an E-Health system to analyze and to classify diseases in patients with biological exams on data streams, using machine learning techniques. The applied data stream approach enables the online training of the classifiers, as well as a suitable performance for data processing. The experiments performed were based on a database of real patients. The results (considering 19 different anomalies) suggest the feasibility of the proposed E-Health system, reaching 96%, 94.21%, 92.14%, and 92.53% of accuracy, precision, sensitivity, and cover index, respectively, overcoming the existing solutions.
Designing and Implementing Social Information Processing Model of Learning Speaking Using Communicative Approach

The communicative approach based on the understanding of language means functional expression through linguistic, discourse, sociocultural, and acronymical competencies. The design model of the social information processing in learning speaking with this communicative approach with stages referring to Borg and Gall. The implementation of this model was carried out at one stage, that is in an extensive testing in Indonesia high school. The model of social information processing in learning speaking with communicative approach emphasizes linguistic skills and communicative competence in Javanese practice that students are able to form meaning by obtaining, processing data, and producing appropriate solutions to problems, and developing language concepts in interaction using communication language in accordance with its function, as well as increasing learning activities plus productive social democracy.
Vehicle-Human Interactive Behaviors in Emergency: Data Extraction from Traffic Accident Videos

Currently, studying the vehicle-human interactive behavior in the emergency needs a large amount of datasets in the actual emergent situations that are almost unavailable. Existing public data sources on autonomous vehicles (AVs) mainly focus either on the normal driving scenarios or on emergency situations without human involvement. To fill this gap and facilitate related research, this paper provides a new yet convenient way to extract the interactive behavior data (i.e., the trajectories of vehicles and humans) from actual accident videos that were captured by both the surveillance cameras and driving recorders. The main challenge for data extraction from real-time accident video lies in the fact that the recording cameras are un-calibrated and the angles of surveillance are unknown. The approach proposed in this paper employs image processing to obtain a new perspective which is different from the original video's perspective. Meanwhile, we manually detect and mark object feature points in each image frame. In order to acquire a gradient of reference ratios, a geometric model is implemented in the analysis of reference pixel value, and the feature points are then scaled to the object trajectory based on the gradient of ratios. The generated trajectories not only restore the object movements completely but also reflect changes in vehicle velocity and rotation based on the feature points distributions.
Python Teaching Research and Practice from the Perspective of Computational Ecology

In view of the current teaching practice of Python, combined with the characteristics of Python language, this paper expounds the teaching concept of Python language from the perspective of computational ecology and based on the theory of learning motivation. Under the guidance of the teaching concept, it organizes “useful” + “interesting” teaching cases, from the initial understanding of Python ecology, in-depth understanding of Python ecology to the application of Python ecology, and pays attention to the gradual progress of knowledge. It is a contemporary tool to stimulate students’ learning motivation and cultivate students’ habits of calculation thinking and engineering thinking, and explore a new perspective of the application of computer theory.
Blockchain Solutions for Book Publishing

This article assesses the opportunities for the publishing industry to utilize blockchain technologies to improve current processes in the book and journal publishing communities. It explains blockchain as a concept and then outlines scenarios and examples where blockchain could be used or is already being used in publishing. The article outlines opportunities that blockchain presents for new business models and the areas of revenue distribution, contract, rights, and royalties, and potential improvements it could bring to the supply chain of content via better workflow, sales, production, and collaboration processes.
Piecewise-Linear Path Following for a Unicycle using Transverse Feedback Linearization

Path planning and following together constitute a critical part of the decision-making hierarchy in autonomous ground vehicles. One of the simplest instances of this architecture is when the path planner generates waypoints that define a sequence of collision free line segments from a start location to goal destination and when the vehicle's kinematic model is taken to be Dubin's vehicle. The low level feedback controller can then be design by treating the path following problem as a set stabilization problem; one such approach is called transverse feedback linearization (TFL). However, for a Dubin's vehicle with only one input, the direction of traversal along the path is completely determined by the vehicle's initial condition. In this paper we provide easily certifiable sufficient conditions and a systematic design procedure that guarantees the robot satisfies the initial condition requirements at transitions between line segments of the path. Our analysis relies on geometric properties of the path; as a result we construct a formal connection between the feasible motions generated by the planner and the path following controller's convergence properties.
Trust and Reliability Management in Large-Scale Cloud Computing Environments

Trust is a firm belief over a person or a thing in distributed environment based on its feedback on review based on its performance by others. Similarly, in cloud, trust models play an important role in solving various open challenges in cloud environment. This chapter showcases all such issues that can be solved by trust management techniques. This work discourses various trust management models and its categorization. The work discourses existing work using trust models from the field of grid computing, cloud computing, and web services because all these domains are sub child of each other. The work provides an abstract view over all trust models and find the suitable one for cloud and its future prospects.
Grading Homeworks, Verifying Code: How Thorough Should the Feedback Be?

In the ideal world, we should assign many homeworks and give a thorough feedback for each homework. However, in reality, the instructor’s time is limited, so we can either assign few homeworks and give a detailed feed back for all of them, or we can assign many homeworks, but give a less thorough feedback. What is the optimal thoroughness? A similar question can be raised for code verification: what is the optimal amount of feedback that should be provided to each programmer? In this paper, we provide answers to these questions.
Bequest of RETE Algorithm for Rule Assessment in Context Database

In the current era, since software automation is buzzing everywhere and it is creating huge opportunities to pay heed on generating rules and reasoning. In the same direction, our research work has been carried out. If any system to be context-aware its database should be in a position to provide few facilities to its system. So, this research work elucidates different contexts and its dimensions through the context dimension tree. The novelty of this research work is, it has used context data for generating the rules and reasoning using RETE rule-based algorithm, Rule assessment against facts/data and ordering of statement is been simplified using a particular algorithm.
An interactive application based on augmented reality and rules-based reasoning to support educational activities of high school students

According to the latest estimates of the United Nations Educational, Scientific and Cultural Organization (UNESCO), in 2017, more than 617 million children and adolescents were not achieving minimum proficiency levels regarding reading and mathematics. In this line, it is essential for children and youth developing mainstays for solving problems and adequately understanding the information around them. These mainstays are strongly related to logical and mathematical skills. Therefore, in this paper, we present an educational tool that uses augmented reality to provide interactive exercises for the following areas: functions, areas, volumes, angles, and the Pythagorean theorem. Similarly, our proposal incorporates a decision support system that suggests to a given student what activities and exercises must solve. The validation process of the tools was carried out with the support of 179 volunteers.
A comparative analysis of Bayesian network structure learning algorithms applied to crime data

The theories about crime and correction have their inception in the eighteenth century, highly influenced by the anthropological thoughts emerging during the age of Enlightenment. Throughout the decades, the criminological studies observed their sociological essence encompassing practices from othe r scientific fields to explain the more contemporary questions, becoming Criminology an inherently interdisciplinary science as a result. The adoption of concepts from Exact Sciences is a recent moving, originating it a novel research area, called Computational Criminology, which employs procedures from Applied Mathematics, Statistics and Computer Science to provide original or enhanced solutions to such questions. One of the most prominent tasks brought by this rising field is crime prediction, which attempts to uncover potential targets for future police intervention and also help solving already committed offenses. The present comparative analysis thus investigates the employment of statistical inference by means of Bayesian network for predictive policing, using the openly accessible registers from Chicago Police Department. Numerous algorithms are available to learn the structure for a Bayesian network purely from data and a comparative examination about them is hence described, with the purpose to establish the most precise and efficient one, according to the attributes of the said criminal dataset, for the implementation of the intended inference.
Developing an Immersive Virtual Classroom

As virtual reality (VR) technologies continue to improve and become more accessible, educators are increasingly incorporating VR learning experiences in teacher education contexts. This chapter is a case study of TeachLivE™, a virtual classroom platform designed for practicing teaching in a safe virtual space. This chapter describes the system, development, and challenges faced when incorporating immersive VR technologies. Recommendations are provided for future research, development, use, and facilitation of immersive VR learning experiences.
IMPLEMENTATION OF ICT BASED PEDIATRIC TELEHEALTH CARE POSYANDU AS AUTOMATIC MONITOR AND IDENTIFICATION OF INFANT’S GROWTH AND DEVELOPMENT

Background: Posyandu is one of the Indonesian government’s attempt in order to monitor and improve the health and life quality of the community, especially infant. However, the implementation of Posyandu is facing some issues such as low effectiveness and low accuracy during the data collecting process of the infant’s growth and development. Purpose: This study aims to develop an automatic telehealth care product in order to help to increase the effectivity and accuracy in the implementation of Posyandu. Methods: (1) Development of the Telehealth Care Posyandu Application, (2) Implementation of the application in the form of social service program. Result: (1) “Toddler” Telehealth Care Application based in Android and ICT was buith with artificial intelligence of Decision Tree and Random Forest method. Program testing was done with 97.89% accuration score from total 85 infant’s growth data. While from 47 questionnaire data of infant’s development, accuracy score of 83.33% was obtained. (2) Target’s respond on the Telehealth Care Posyandu Application shown the status of “Very Satistified” based on the score of 81% from the satisfaction survey. The satisfaction survey covered three aspects which are: System, User, and Interaction. Conclusion: “Toddler” Telehealth Care Posyandu Application was proven to has high accuracy, sensitivity, and sensitivity score and also resulted in “Very Satisfied” user respond.
Panel: Latin American Computing Education Landscape and the Relation with the ACM/IEEE-CS Computing Curricula

Computing Curricula 2005 (CC2005) and the developing 2020 version (CC2020) are the international referents of undergraduate computing education. ACM and IEEE-CS efforts to offer a global classification of computing curricula programs recommending subjects based on a competency-based curricula design. Latin America (LA) is an active region in computing education. However, it is difficult to understand how LA computing programs relate to the CC2020 competency-based approach. This panel seeks to provide the current landscape of undergraduate LA computing curricula in comparison to the CC2020. The intention is to highlight differences and commonalities in nomenclature, types of computing programs, curricula management, and accreditation. The panel will raise the discussion concerning the challenges for computing programs in the LA region surrounding curricula transformation towards a better understanding of programs at international level. Audience involvement and panelist interactions promise to generate a shared understanding of undergraduate computing programs in LA and their global perspective.
Interval valued fuzzy gamma near-algebra over interval valued fuzzy field

In this paper, we introduced an interval valued fuzzy gamma near-algebra over interval valued fuzzy field. Using this notion we have described some basic properties. An attempt has been made for homomorphism of near-algebra in terms of fuzzy near-algebra.
Underwater Image Processing using Graphics Processing Unit (GPU)

Image processing is a method used for enhancement of an image or to extract some useful information from the image. It is a type of signal processing in which input is an image and output may be an image or any characteristics/features associated with that image. In this paper we will be focusing on a specific type of Image Processing i.e. Underwater Image Processing. Underwater Image Processing has always faced the problem of imbalance in colour distribution and this problem can be tackled by the simplest algorithm for colour balancing. We will be proceeding with the assumption that the highest values of R, G, B observed in the image corresponds to white and the lowest values corresponds to darkness. The underwater images are majorly saturated by blue colour because of its short wavelength and in this paper, we aim to enhance the image. We proposed a colour balancing algorithm for normalizing the image. The entire process will first be carried out on a CPU followed by a GPU. We will then compare the speedup obtained. Speedup is an important parameter in the field on image processing since a better speedup can help reduce the computation time significantly while maintaining a higher efficiency.
User Behavioral Preference Analysis Based on Location Information

According to the characteristics of users' location, the double-layer XGBoost algorithm is improved to infer the user's location information and realize the analysis of users' behavioral preference. Firstly, the outlier processing is proposed for the basic data such as shop information, user transaction records, etc.; secondly, the original shop location, average price and other information as well as the location information data such as GPS and WiFi of users' mobile devices are used to optimize the alternative set of precise location and build positive and negative sample. Finally, the improved XGBoost algorithm constructs the multi-dimensional features of multi information fusion to predict user behavior. The experimental results show that the double-layer XGBoost can achieve an average prediction accuracy of 92.65% on the user transaction record data set, which is better than SVM and KNN algorithm. Therefore, the double-layer XGBoost algorithm can achieve high accuracy and fast user behavioral preference analysis.
TCEC Cup 5

This is the report of the 'TCEC Cup 5' computational experiment, the 5th TCEC knockout event and the second major part of TCEC’s 17th season. It started on 24th April 2020 and featured the best 16 available engines of the ‘TCEC 17’ league event. The games were held at Rapid tempo, the different format calling for different optimal settings in the engines. This enabled a comparison with performance at slower tempi although both are at super-GM level. STOCKFISH defeated LEELA CHESS ZERO in the Final. All decisive games in the supplementary data have been played out by FRITZ17 at search depth 24 to enable and to benchmark endgame practice.
How Indonesian Junior High School Students Comprehend the Reading Text? A Digital Media Literacy Utilisation

Digital media is currently very familiar with the students’daily activities. However, as one of school policies, most Junior High Schools in Klaten, including SMPN 5 Klaten, prohibit their students from using digital media, specifically Smartphones, in the learning process. The objective of this present study was to describe the improvement of students’ ability in comprehending the reading text through digital media literacy. This research applied Classroom Action Research with its cycles. Each cycle was executed through sets of activities consisting of planning, acting, observing, and reflecting. By involving the eighth graders of SMPN 5 in Klaten, tests, observations, interviews, and document studies were used to obtain the data, and the data were qualitatively and quantitatively analysed. The results showed that the use of digital media literacy could improve the students’ ability in comprehending the texts. The improvement was shown by the increase of the percentage of the students obtaining the mastery criteria stated by the school by 22% in pre-cycle, increased to 63% in the first cycle, and 100% in the second cycle. How their ability in comprehending texts improved by the use of digital media literacy is also discussed.
Graph embedded incomplete multi‐view clustering method with proximity relation estimation

More and more importance has been attached to multi-view clustering due to its outstanding performance against single-view clustering. Existing studies are on the basis of the assumption that every sample exists in all views. However, in reality, this is often not the case and the real data are always with incomplete views, which lead to the failure of the conventional methods. To address the issue, the authors present a novel method, referred to as incomplete multi-view spectral clustering with proximity relation estimation (IMSC_PRE), which exploits the proximity relations of samples in different views to complete their affinity graphs. Different from existing methods, IMSC_PRE fully excavates the manifold structures of all views to estimate the proximity relations of missing samples in each view, which presents a brand-new strategy to exploit the proximity information lying in the whole samples with good interpretation. Experimental results on four datasets demonstrate the advantages of the proposed method in comparison with the state-of-the-art methods.
A Collaborative Filtering Framework Based on Variational Autoencoders and Generative Adversarial Networks

The generation models such as Variational Autoencoders (VAE) and Generative Adversarial Networks (GAN) have been demonstrated to be of high effectiveness in standard collaborative filtering applications. However, the conventional VAE can't capture the data distribution well when the data is sparse or the auxiliary information is added, resulting in low recommendation accuracy. In this paper, we propose a novel VAE-GAN-based collaborative filtering (CF) framework, named CF-VAE-GAN, to provide higher accuracy in recommendation. First, auxiliary information such as user comments and item multimedia features are added to VAE. And then, we use the discriminator of GAN to improve the reconstruction objective of VAE. Finally, we design two CF-VAE-GAN models of users and items, respectively. Empirical results indicate that our method outperforms state-of-the-art methods in terms of Recall and NDCG.
Start-Up Community Using Blockchain

In India, 90% start-ups fail within the initial five years due to lack of huge capital and innovations. So, our idea is to develop a blockchain-based social network for start-ups, which will allow the collaboration between different start-ups and entrenched organizations who can provide some social and financial support to the start-ups. A token will be required to access this network, the user will earn the token through the Proof-of-Value protocol. In the proof-of-Value approach, the user needs to attach proof of their skills the subject matter expertise will verify it while stopping the spread of fake data. Thus, this will persuade the other member within the community and also the venture capitalist that the users are trustworthy and resourceful. In addition, the token will allow the users to get access to posting, commenting, and voting. The blockchain technology will ensure that the verification process to endorse skills is decentralized. The venture capitalist or a stranger will be able to trust the user more with this all-encompassing platform. And no second-guessing is required. Or delve into your social media profile to discover what sort of individual you are outside of work.
Analyzing goal variability in cyber-physical system networks

Networks of collaborative cyber-physical systems can achieve goals individual systems are incapable of achieving on their own. However, which goals such a network can achieve depends, in part, on the networks current configuration, i.e. its composition of partaking individual systems. As networks of collaborative cyber-physical systems are of a dynamic nature, the composition of such a network can change during runtime, leading to a plethora of often similar, albeit slightly different configurations. Due to the huge number of possible configurations and their various dependencies to the different goals of the network, it is infeasible to handle this amount of information manually. Hence, to provide support for reasoning about dependencies between different configurations and the goals they can achieve, this paper contributes an automated model-based reasoning approach using view generations. Our approach allows for exploring which goals can be fulfilled by which configurations and which goals cannot be fulfilled by these configurations. We evaluated the approach using an industrial case study which shows the applicability of the approach and a controlled experiment which shows the benefits of the approach.