Reviewerly/exHarmony huggingface dataset:
Brecha Digital: Conectividad Y Equipamiento en Instituciones de Educación Fiscal en Ecuador

The present study investigates the background of the supply of internet connectivity and technological equipment in the public educational institutions in Ecuador. The theoretical approach is based on the concept of the Digital Divide, and the methodological design is grounded on documentary research technics. The analysis of the phenomenon is conducted through the review, analysis and interpretation of documents that reflects the management by the government and the public policies made viable through MINTEL. Firstly, the causes of the increases and decreases in public investments are reviewed based on the supply of connectivity and equipment to the educational institutions. Secondly, the arguments for the limited pedagogical use of ICT in Ecuador are presented. And finally, a review conducted by the States Controllers Office of the public management is attached.
Medical Image Classification Using the Discriminant Power Analysis (DPA) of Discrete Cosine Transform (DCT) Coefficients

Medical imaging systems are very important in medicine domain. They assist specialists to make the final decision about the patient’s condition, and strongly help in early cancer detection. The classification of mammogram images represents a very important operation to identify whether the breast cancer is benign or malignant. In this chapter, we propose a new computer aided diagnostic (CAD) system, which is composed of three steps. In the first step, the input image is pre-processed to remove the noise and artifacts and also to separate the breast profile from the pectoral muscle. This operation is a difficult task that can affect the final decision. For this reason, a hybrid segmentation method using the seeded region growing (SRG) algorithm applied on a localized triangular region has been proposed. In the second step, we have proposed a features extraction method based on the discrete cosine transform (DCT), where the processed images of the breast profiles are transformed by the DCT where the part containing the highest energy value is selected. Then, in the feature’s selection step, a new most discriminative power coefficients algorithm has been proposed to select the most significant features. In the final step of the proposed system, we have used the most known classifiers in the field of the image classification for evaluation. An effective classification has been made using the Support Vector Machines (SVM), Naive Bayes (NB), Artificial Neural Network (ANN) and k-Nearest Neighbors (KNN) classifiers. To evaluate the efficiency and to measure the performances of the proposed CAD system, we have selected the mini Mammographic Image Analysis Society (MIAS) database. The obtained results show the effectiveness of the proposed algorithm over others, which are recently proposed in the literature, whereas the new CAD reached an accuracy of 100%, in certain cases, with only a small set of selected features.
Action Blocks: Making Mobile Technology Accessible for People with Cognitive Disabilities

Mobile technology has become an indispensable part of our daily lives. From home automation to digital entertainment, we rely on mobile technology to progress through our daily routines. However, mobile technology requires complex interactions and nontrivial cognitive efforts to use, and is often inaccessible to people with cognitive disabilities. With this in mind, we designed Action Blocks, an application that provides one-tap access to digital services on Android. A user and/or their caregiver can configure an Action Block with customized commands, such as calling a certain person, turning on the lights. The Action Block is associated with a memorable image (e.g., a photo of the person to call, an icon of a lightbulb) and placed on the device home screen as a one-tap button, as shown in Figure 1. Action Blocks was launched in May 2020 and received much useful feedback. In this demonstration, we report the key design considerations of Action Blocks as well as the lessons we learned from user feedback.
Intelligent Strategies for Cloud Computing Risk Management and Testing

The cloud computing uses dispersed models with access whenever requested with computing devices requiring high configuration and minimum management effort. The virtual, multitenant and complex infrastructure needs early risks identification and management. The businesses involved in this computing require reassurances as main thought for cloud services testing. The research mainly recognizes the vulnerabilities, threats and risks involved in this computing. The paper is elaborated with many risk management and risk-based testing in detail with many risk reduction strategies in many computing environments, i.e., cloud computing, artificial intelligent environments and pervasive computing. This research also emphasizes on key factors in risk-oriented testing, risk minimization, trust management, limitations and benefits in cloud computations.
Application of Machine Learning Algorithm in Anesthesia

The application of artificial intelligence in medical field is promoting medical innovations.Specifically,machine learning algorithm promotes the development of anesthesiology.This review elucidates the basic concept,research advances,application,and limitations of machine learning algorithm in anesthesiology.
Solar Radiation Estimation Using Data Mining Techniques for Remote Areas—A Case Study in Ethiopia

High quality of solar radiation data is essential for solar resource assessment. For remote areas this is a challenge, as often only satellite data with low spatial resolution are available. This paper presents an interpolation method based on topographic data in digital elevation model format to improve the resolution of solar radiation maps. The refinement is performed with a data mining method based on first-order Sugeno type Adaptive Neuro-Fuzzy Inference System. The training set contains topographic characteristics such as terrain aspect, slope and elevation which may influence the solar radiation distribution. An efficient sampling method is proposed to obtain representative training sets from digital elevation model data. The proposed geographic information system based approach makes this method reproducible and adaptable for any region. A case study is presented on the remote Amhara region in North Shewa, Ethiopia. Results are shown for interpolation of solar radiation data from 10 km × 10 km to a resolution of 1 km × 1 km and are validated with data from the PVGIS and SWERA projects.
Evaluating RNN Architectures for Handling Imbalanced Dataset in Multi-Class Text Classification in Bahasa Indonesia

COVID-19 pandemic makes students can ony continue their education through the E-Learning system.In order to fulfill the goal of E-Learning, learning center departments of educational institutes need to know what the user needs and the only way to communicate with them is through feedback.However, it is a hard and time-consuming task to extract value from a large amount of feedback.This paper aims to implement and evaluate several RNN architectures' performances including Simple RNN, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), to be able to classify feedback text to its categories via a multiclass classification approach.Furthermore, this paper uses FastText in comparison with Keras Embedding Layer to extract features along with the use of Random Oversampling and SMOTE in order to deal with imbalanced dataset problem.Based on the result, our final model could achieve a macro-averaged F1-Score of 64.35% using LSTM architectures.Furthermore, our paper shows that FastText has a poor performance in every RNN architectures and Random Oversampling has a better performance than SMOTE in handling imbalanced dataset problem.
Survey of Occluded and Unoccluded Face Recognition

Computer Vision has been widely developed in recent years as the development of AI, as people want to let AI be able to see things. In all of the aspects of computer vision, face recognition seems to be one of the most important parts as for AI. People mainly want it to help them recognize the face which may be used for the security like the brush face payment. Also, as there are more and more criminals who know shelter their faces for not let the monitor to recognize their face and face recognition by the computer may solve this issue as there is a limitation for human to recognize the face and also is exhausting. In the last decade, face recognition mainly uses some linear or nonlinear models or neural network methods, which has a low accuracy and the database for these methods are small. Moreover, some of the methods may not be able to solve the picture which is occluded or is low-quality. This paper provides a recent research trends for face recognition in unobstructed, occluded and low-quality pictures. To make it easier to understand, tables of different applications, databases, methods are provided. This paper concludes by proposing some challenges may still involve in face recognition as well as some expectations for the future research.
An Empirical Study on Initializing Centroid in K-Means Clustering for Feature Selection

One of the main problems in K-means clustering is setting of initial centroids which can cause misclustering of patterns which affects clustering accuracy. Recently, a density and distance-based technique for determining initial centroids has claimed a faster convergence of clusters. Motivated from this key idea, the authors study the impact of initial centroids on clustering accuracy for unsupervised feature selection. Three metrics are used to rank the features of a data set. The centroids of the clusters in the data sets, to be applied in K-means clustering, are initialized randomly as well as by density and distance-based approaches. Extensive experiments are performed on 15 datasets. The main significance of the paper is that the K-means clustering yields higher accuracies in majority of these datasets using proposed density and distance-based approach. As an impact of the paper, with fewer features, a good clustering accuracy can be achieved which can be useful in data mining of data sets with thousands of features.
Towards Fine-Tuning of VQA Models in Public Datasets

This paper studies the Visual Question Answering (VQA) topic, which combines Computer Vision (CV), Natural Language Processing (NLP) and Knowledge Representation & Reasoning (KR&R) in order to automatically provide natural language responses to questions asked by users over images. A review of the state of the art for this technology is initially carried out. Among the different approaches, we select the model known as Pythia to build upon it, because this approach is one of the most popularized and successful methods in the public VQA Challenge. Recently, an exhaustive breakdown was done to the Pythia code by Facebook AI Research (FAIR). We choose to use this updated framework after confirming that the two implementations had analog characteristics. We introduce the different modules of the FAIR implementation and how to train our model, proposing some improvements regarding the baseline. Different fine-tuned models are trained, obtaining an accuracy of 66.22% in the best case for the test set of the public VQA-v2 dataset. A comparison of the quantitative results for the most important experiments jointly some qualitative results are discussed. This experimentation is performed with the aim of finally applying it to eCommerce and store observation use cases for VQA in further research.
RETRACTED: Analysis of the Fusion of Mathematical Modeling Thoughts Based on Computer Technology and Teaching Practice in Colleges and Universities

Abstract High mathematics is the most important course in college mathematics, which is one of the subjects with the highest proportion of College Students’ credits. However, mathematical modeling is a method to solve practical problems with models, which has been widely used to solve a variety of practical problems, including economy, industry, medicine, society and so on. With the development of computer technology, we can use a variety of effective computer modeling software for complex mathematical modeling, such as MATLAB, one of the most common mathematical modeling software. Therefore, colleges and universities must actively use computer technology, integrate the idea of mathematical modeling into mathematical practice, which can better integrate mathematical resources and teaching media. By constantly improving the way of mathematics learning, college students will better improve the efficiency of mathematics learning, which can also improve students’ thinking consciousness of applying mathematics to work. First of all, this paper analyzes the important role of mathematical modeling based on computer technology in mathematical practice. Then, this paper puts forward some questions. Finally, some suggestions are put forward.
Discussion on the relationship between clean room and traditional software engineering methods and practices

Clean room software engineering is a formal software development method that can strictly engineer software development and eliminate defects before they can cause serious harm. The clean room software engineering model has its own advantages and disadvantages in use. To use it in traditional software engineering, it is necessary to clarify the relationship between it and traditional software engineering methods and practices. Based on the technology and principles of the clean room software process, this paper gets the advantages and disadvantages of the clean room. Corresponding the traditional method-based software process to the key technology of the clean room, making the traditional management-based software engineering method and the tailored clean room compatible, and the software engineering practice of the clean room software process is compared with the traditional software engineering practice. To discuss the clean room and traditional software engineering, in order to expand the use of clean room methods, improve the clean room software process, and improve the quality of software.
Application of Computer Technology in English Translation Network Teaching

Abstract The development of modern educational technology and computer network technology will bring translation teaching into a new era, and a new translation teaching model will emerge. Computer network technology is completely suitable for translation teaching, because the type and characteristics of translation teaching determine this applicability. The scientific and artistic nature of translation can enable the learning subject to implement a high degree of self-control and independent learning in the network environment, and actively display creativity and individuality. The teaching subject can also adopt a combination of individualized and collaborative teaching methods to achieve "double "The main teaching mode" means to play the leading role of teachers and the cognitive subject role of students at the same time. The new translation teaching model based on computer network technology will certainly arouse the general attention of the domestic translation teaching circle and be widely used.
Pelatihan Penggunaan Microsoft Office Bagi Siswa SMPS St. Yosef Maubesi

The use of learning media by students, one of which is Microsoft Office, is an ability and skill that must be possessed in the era of globalization and also in the implementation of online learning. Through community service activities (PkM) we provide training to students of SMPS St. Yosef Maubesi as a solution to their low ability to use Microsoft Office, especially Microsoft office Word, Excel and Powerpoint. The training, which is carried out in three stages and includes providing modules or teaching materials, is able to increase the confidence and ability of students in using Microsoft Office. The output is in the form of modules or teaching materials for using Microsoft Office.
User Similarity-Aware Data Deduplication Scheme for IoT Applications

As an important technology in cloud storage, deduplication is widely used to reserve network bandwidth and storage resources. While deduplication brings us convenience, there are also security risks that we have to confront. If internal data from organizations are treated in the same way of ordinary data, deduplication may lead to unexpected data leakage and other issues. A user similarity-aware data deduplication algorithm is proposed which can properly handle internal data uploaded by group users. This scheme can recognize the situation that uploaders with similar attributes hold the same data in the process of deduplication. The goal of our scheme is to ensure that the participation of group users will not change the current popularity of uploaded data. In the aspect of attribute distance calculation, we divide attribute types and introduce specific attribute distance calculation methods for each type. We determine user category by comparing the similarities of their attributes. Finally, the counting method of uploaded data is adjusted adaptively according to the current popularity status of data and user categories. This scheme can avoid potential internal data leakage caused by deduplication. Through experiment evaluation, we show that our scheme is efficient, and is of great scalability and practicability.
Theoretically Validated Complexity Metrics for UML State Machine Models

The primary purpose of software design is to reduce the complexity of software systems and to abstract the details to aid in comprehension. Design languages, such as UML, offer increased flexibility and alternatives for designers. However, there are no clear objective guidelines to help software designers select alternatives that reduce model complexity and improve their comprehensibility. Current model complexity metrics are often dependent exclusively on the number of model elements without appropriately considering the complexity of these elements and their interactions. In this paper, we introduce novel complexity metrics for UML state machines models. The proposed approach and metrics are formulated by estimating a complexity rate for each element in the state machine model. The paper presents an in-depth theoretical validity evaluation using Briand ́s theoretical property-based framework to ensure the correctness of the metrics. The proposed complexity metrics satisfy four of the five Briand’s properties.
How Much Identity Management with Blockchain Would Have Saved Us? A Longitudinal Study of Identity Theft

The use of blockchain for identity management (IdM) has been on the rise in the past decade. We present the first work to study the actual, large-scale impact of using blockchain for identity management, particularly how it can protect Personally Identifiable Information (PII) to curb identity theft and fraud. Our insight is that if blockchain-based IdM protects PII, it can reduce the number of theft and fraud cases that take advantage of such PII. At the Center for Identity at the University of Texas at Austin, we have modeled about 6,000 cases of identity theft, and PII exploited in them. We utilize this model to investigate how three real-world blockchain-based IdM solutions (Civic, ShoCard, and Authenteq) could have reduced the identity theft loss over the past 20 years if they had been universally used. We identify which PII protected by blockchain is more critical. We also suggest new PII to include in blockchain-based IdM. Our work paves the way for the design of more effective blockchain-based IdM or any other new line of IdM for that matter.
Heirloom Wearables: A Hybrid Approach to the Design of Embodied Wearable Technologies

Author(s): Dierk, Christine Marie | Advisor(s): Paulos, Eric | Abstract: Device miniaturization and new materials have enabled wearable technology to be integrated more seamlessly with the body, leveraging form factors such as clothing, jewelry, temporary tattoos, and beyond. While adopting familiar forms, many of these new technologies inherit interaction modalities and usage patterns from existing technologies (e.g. Smartphones), rather than being inspired by the rich cultural history and connotations of the form factor itself. In this thesis, I motivate the unique opportunities that arise when incorporating existing practices and cultural meaning into the design of wearable technologies. I define a new class of wearable technologies that adopt existing form factors, leveraging well-established body practices and traditions -- Heirloom Wearables. I argue that this class of wearable technologies can foster meaningful relationships with technology on the body that more closely resemble experiences with traditional body-worn artifacts than modern wearable technologies. I present a lightweight framework to facilitate the design of Heirloom Wearable technologies, and detail five exemplar prototypes designed to operationalize the framework: fingernail-worn devices, interactive hair, dynamic clothing a accessories, interactive hats, and lotion interfaces. The concept of Heirloom Wearables, the framework, and the exemplar prototypes demonstrate how the limitations and constraints of body-based technologies can be transformed into opportunities for design, and highlight how body-centric practices can inform new and embodied wearable technologies.As a result, this work contributes a design methodology towards a more diverse range of inclusive wearable technologies. This new landscape of devices blurs the distinction between modern wearable technologies and traditional body-worn artifacts, fostering meaningful and intimate relationships with technology on the body.
On the Road to Hospital Digital Transformation: Using Conceptual Modeling to Express Domain Ontology

During the COVID-19 pandemic, Israel Aerospace Industries engaged with hospitals with the goal of promoting their effective operation by means of digital transformation, and particularly by designing an information system in support of the hospital operational processes. In this short paper we report our use of conceptual modeling to capture and communicate the organizational and operational ontology of the problem domain, based on a requirements specification for the system. We discuss how the derived ontology relates to metamodeling, and discuss some practical and theoretical implications of using metamodeling to document ontology Copyright © 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.