Reviewerly/exHarmony huggingface dataset:
Multidisciplinaridade em cuidados cardiológicos: o que mostram os estudos? / Multidisciplinarity in cardiology care: what do the studies show?

Patients who come to health services with cardiologic complaints have a high potential for severity, which leads to an increase in the number of cases submitted to surgical interventions, thus increasing the risks of complications.Thus, it is essential that the teams make use of care strategies aligned to a multidisciplinary therapeutic plan through safe interventions.The study is an Integrative Literature Review, carried out using the Latin American and Caribbean System on Health Sciences Information (LILACS), Scientific Electronic Library Online (SCIELO) and the Nursing Database (BDENF) databases.Through the discussion we saw the need to bring to the care practice of cardiac patients a vision based on safe processes that reduce the risk of adverse events and favor the work of the multidisciplinary team.It is concluded that health teams play a key role in strengthening the assistance provided for the safety of patients, based on the care provided by professionals who make up the multidisciplinary team, working together and interconnected, so that there is an effective provision of services.
Reinforcement Learning for Neural Collaborative Filtering

Artificial Intelligence (AI) has become an integral part of many modern technologies, with significant advances in real-world applications. With the rise of deep learning methods in the past decade, systems that utilize artificial neural networks have produced remarkable results in a variety of fields, such as computer vision, natural language processing and voice recognition, with performance exceeding that of humans in many cases. Examples of practical applications include self-driving cars, state-of-the-art text translators and generators, and robust object detection algorithms. The field of Recommender Systems has also taken advantage of this progress, with a plethora of novel neural networks being proposed that achieve significant improvements in providing automatic recommendations regarding the preferences of users. Aiming to further explore this area and the capabilities of different deep neural networks, we train top-performing neural collaborative filtering recommender systems under a reinforcement learning setting, which has been largely unexplored in favor of supervised learning for these models. Experimental evaluation on the MovieLens-1m dataset showcases the behavior of different neural architectures under this setting, and how the introduction of sophisticated components contributes to improved performance.
Pelatihan Model Pembelajaran Menggunakan Microsoft Teams di SD Muhammadiyah 2 Denpasar

SD Muhammadiyah 2 Denpasar is a partner of this service activity. The number of teachers who teach at this school is 42 people. SD Muhammadiyah 2 Denpasar seeks to provide services in an innovative, creative and fun learning process, and refers to the student center, which is expected to increase and develop the potential of students. During the Covid-19 Pandemic, schools held online learning. Learning is done by the teacher giving assignments by asking students to study the material in the book through Whatsapp media. Discussion of learning materials is also carried out via whatsapp. The school also uses the zoom app which is not paid for. This causes problems because of the time limit for using zoom and the number of participants. There is limited time to use zoom so that the learning process is not complete in completing one learning topic. Likewise, the limitation of 100 participants also creates obstacles in the learning process because there are subjects that are attended by more than 100 students. To overcome these problems, Microsoft Teams will be used as a learning media in this school. The first phase of activity, namely providing training on Microsoft Teams learning media, has been carried out for two days by the proposing team. The training was attended by homeroom teachers for grades 4, 5 and 6. The training participants were very enthusiastic about participating in the training activities. Participants are able to practice directly the training material provided. The second phase of service activities was continued by evaluating and monitoring the use of MsTeams by participants who had received training in previous service activities. The results of the evaluation showed that this training activity gave satisfaction to the participants because the training topics provided provided an alternative to the use of digital media for learning for students at SD Muhammadiyah 2 Denpasar.
Cataloging at the Library of Congress in the Digital Age

The Library of Congress is participating in a number of initiatives to explore issues surrounding description and cataloging of digital materials. Additionally, programs have been written by LC staff which enable electronic enhancements in cataloging activities, such as the receipt of online information from publishers in the Electronic CIP Program. This paper is both an overview of the status of cataloging-related digital projects at LC and a description of software which facilitates productivity through the provision of advanced search capabilities and the elimination of repetitive keying. [Article copies available from The Haworth Document Delivery Service: 1-800-342-9678. E-mail address: getinfo@haworth.com]
Research on Application of Big Data Technology Based on Two-Dimensional Code in Coal Mine Safety

Aiming at the current situation of coal mine safety in China is still severe and the efficiency of supervision needs to be improved, a three-level networking platform for coal mine safety supervision based on big data has been designed and implemented. The platform adopts a hierarchical architecture design method, formulates and complies with data exchange standards and specifications, builds a three-level network of coal mines, districts, counties and regions, and realizes the effective integration of multiple coal mine heterogeneous system data sources. At the same time, we use the non-spatial attribute spatial clustering method and big data analysis method to mine coal mine safety information. The use of simulated data and actual data to verify that the spatial clustering method of non-spatial attributes can find the spatial clusters of arbitrary shape and density change of the QR code, and can well identify the noise points of the coal mine safety information system based on the QR code.
Research on Intelligent Course Management System Based on Information Digitization and Database

This system uses software engineering ideas as a whole, uses My Eclipse as a development environment, Java EE as a development standard, integrates Struts, Hibernate, and MVC to design the architecture and various modules, and uses MySQL database as the basis for the system modules Management of various types of data. This technology not only guarantees the performance of the system, but also covers the scope of general teaching management work. By dividing user roles and realizing hierarchical management, the work of teaching administrators at all levels is fast, convenient, accurate, and safe, and it also provides a good Resource sharing platform.
Sentiment Analysis Using Learning Techniques

This paper depicts sentiment analysis classification as an efficient process for analysing textual data coming from various internet resources. One of the most prominent research subjects in recent years has been sentiment analysis. It is a critical challenge for both companies and users to provide an accurate and useful overview of the network, which requires a large amount of data in terms of configuration, usage and content; hence, the sentiment analysis principle is proposed to address this problem. Methods of sentiment analysis seek to reveal any feelings, subjectivity and opinions in the text. It is virtually difficult to analyse such a vast number of reviews manually. As a result, SA is used for extracting the general polarity or sentiment of opinions from documents. Sentiment research is used in Twitter, movie reviews, blogs and consumer comments, among other places. For sentiment analysis, three methods are commonly used: Machine Learning-based techniques, lexicon-based techniques and hybrid techniques, but the Machine Learning approach is more efficient and accurate. Many variations and extensions of machine learning methods and software have recently been available in recent times. This paper presents a brief introduction to the sentiment analysis, its classification and levels of sentiment analysis. Further, Machine learning techniques for sentiment analysis are also mentioned in detail in this paper. This paper aims to provide a brief knowledge of the sentiment analysis process, including standard SA methods, from the viewpoint of ML methods, in which machines interpret and identify human sentiments conveyed in speech and text.
Analysis of the Web Sites Use as Learning Resources

This article deals with an analysis of the web sites use by the students for gaining the electronic study sources and discovering of associations in the data by selected algorithm. Association rules associate a particular conclusion with a set of conditions. The Generalized Rule Induction was applied. Models were designed in SPSS Clementine.
Attribute-Based Secure Keyword Search for Cloud Computing

The attribute-based keyword search (ABKS) provides the feasibility that simultaneously achieves data searching and fine-grained access control over encrypted data, which is very applied to the cloud computing environment characterized by data storage and sharing. Recently, a number of ABKS schemes were proposed by combining attribute-based encryption (ABE) and searchable encryption (SE) primitives. In this chapter, we first briefly introduce the key techniques to achieve ABKS scheme such as necessary components used in the attributed-based encryption. Then, by several existing ABKS schemes, we describe how to design a practical and efficient ABKS construction in the cloud computing environment. Further, we show some interesting experiment results to explain the key factors of affecting the search complexity in ABKS schemes and present some ideas to design a truly practical and high- performance ABKS scheme. Also, we present future directions in this research field.
Analysis of the Coupling and Coordination Relationship between the Evolution of Enterprise Spatial Structure and Economic Development Based on the Deep Learning Model

This study is based on the unsupervised learning-based enterprise spatial structure evolution and economic coupling coordination relationship situation assessment method. Pattern recognition has high-precision characteristics, but it is necessary to train the evaluation model for the enterprise spatial structure evolution in advance and then carry out economic coupling coordination based on the trained model. The conclusions are as follows: (1) through the RC1, RC2, RC3, RC4, RC5, and RC6 evaluation indicators to evaluate the situation evaluation method based on the unsupervised learning of the evolution of the enterprise spatial structure and the economic coupling and coordination relationship, it is found that the main component characteristics as a whole meet the standard. The optimal RC is RC6: profit = −0.0885, highest = −0.0809, lowest = −0.0932, WR.WR2 = 0.0038, MA.MA3 = −0.0782, MTM.MTM = −0.0427, OSC.OSC = −0.0355, ROC.MAROC = 0.0105, SKDJ.D = −0.0268, BIAS-QL.BIAS = −0.01, WIDTH.WIDTH = 0.2408, CYD.CYDN = −0.0961, FSL.SWL = −0.0868ADTM.ADTM = −0.0379, ATR.ATR = −0.0278, DMA.DIFMA = −0.0358, DMI.ADX = 0.8516, DMI.ADXR = 0.854, EMV.EMV = −0.0942, VMACD.DIF = 0.3312, and UOS.MAUOS = −0.0846.2. Based on the deep learning model of the coupling and coordination relationship between the evolution of the spatial structure of the enterprise, the time-dependent matrix comparison experiment is divided into directed + self, directed, undirected + self, and undirected time for comparison. The experimental results on directed + self are the best; with various indicators, the upward improvement is above 10%: CP = 0.8611, CR = 0.9353, C–F1 = 0.8967, EP = 0.8865, ER = 0.857, E–F1 = 0.917, OP = 0.856, OR = 0.9845, and O–F1 = 0.99.3. The time cost, profit, and transaction volume data of the company are collected for a certain period of time, and simulation experiments are conducted to get a small difference between the predicted result and the actual data. The January data are closest to the true value: cost = 30.78, profit = 30.11, highest = 30.1, lowest = 29.7, WR.WR1 = 81.21, WR.WR2 = 45.62, AMV.AMV2 = 32.67, AMV.AMV3 = 34.95, and MCST. MCST = 36.08.4. In the model score, the best performance of LSTM data is CP = 0.3829, CR = 0.3664, C–F1 = 0.3744, EP = 0.3726, ER = 0.3004, E–F1 = 0.3326, OP = 0.9155, OR = 0.9316, and O–F1 = 0.9234, which is better than the BiLSTM model with CP = 0.3648, CR = 0.3319, C–F1 = 0.3392, EP = 0.4402, ER = 0.391, E–F1 = 0.4145, OP = 0.9215, OR = 0.9318, and O–F1 = 0.9266.
Signal Instructed Coordination in Cooperative Multi-agent Reinforcement Learning

In many real-world problems, a team of agents need to collaborate to maximize the common reward. Although existing works formulate this problem into a centralized learning with decentralized execution framework, their decentralized execution paradigm limits the agents’ capability to coordinate. Inspired by the concept of correlated equilibrium, we propose to introduce a coordination signal to address this limitation, and theoretically show that following mild conditions, decentralized agents with the signal can coordinate their individual policies as manipulated by a centralized controller. To encourage agents to learn to exploit the coordination signal, we propose Signal Instructed Coordination (SIC), a novel coordination module that can be integrated with most existing MARL frameworks. Our experiments show that SIC consistently improves performance in both matrix games and popular testbeds with high-dimensional strategy space.
Self-regulated learning and personality in university students

The present investigation analyzes the relationship between the self-regulation of learning and the personality characteristics of university students belonging to the Minuto de Dios University Corporation - UNIMINUTO of Bogotá - Colombia the sample consisted of 75 men and women between 18 and 45 years of age. From the application of the instruments: Gordon's personality profile and inventory (ancestry, responsibility, emotional stability, sociability, caution, originality, commitment, vitality, and self-esteem) and the self-report questionnaire - self-regulation of learning, the correlational analysis was carried out. Using Spearman's Rho (significance level p&lt;0.05). The results suggest that students who know and apply learning self-regulation techniques tend to be dominant (ancestry) independent, so they take responsibility for their personal goals (commitment), emotionally stable, and friendly. On the other hand, students who are cautious, original, and oriented towards understanding others tend to give little importance or use, to a lesser extent, the use of self-regulated learning strategies.
Research on Feature Matching of an Improved ORB Algorithm

Aiming at the problems of ORB (Oriented FAST and Rotated BRIEF) algorithm in complex environments, such as low matching accuracy and poor real-time performance. An improved AGAST feature point detection fusion ORB algorithm is proposed. First, build Gaussian pyramid to generate scale-invariant AGAST, then use gray-scale centroid method to generate directional feature point descriptors, and finally use Hamming distance to complete the matching, and combine RANSAC to optimize the matching results; at the same time, this article focuses on the AGAST algorithm The fixed threshold is improved in the middle, and an adaptive threshold method is proposed. The experimental results show that, compared with the original ORB algorithm, the algorithm proposed in this paper improves the time by 15.41%, and has a good matching accuracy under the environment of varying illumination and rotation scale.
Word Embedding Distribution Propagation Graph Network for Few-Shot Learning

Few-shot learning (FSL) is of great significance to the field of machine learning. The ability to learn and generalize using a small number of samples is an obvious distinction between artificial intelligence and humans. In the FSL domain, most graph neural networks (GNNs) focus on transferring labeled sample information to an unlabeled query sample, ignoring the important role of semantic information during the classification process. Our proposed method embeds semantic information of classes into a GNN, creating a word embedding distribution propagation graph network (WPGN) for FSL. We merge the attention mechanism with our backbone network, use the Mahalanobis distance to calculate the similarity of classes, select the Funnel ReLU (FReLU) function as the activation function of the Transform layer, and update the point graph and word embedding distribution graph. In extensive experiments on FSL benchmarks, compared with the baseline model, the accuracy of the WPGN on the 5-way-1/2/5 shot tasks increased by 9.03, 4.56, and 4.15%, respectively.
Triple Loss Based Framework for Generalized Zero-Shot Learning

A triple loss based framework for generalized zero-shot learning is presented in this letter. The approach learns a shared latent space for image features and attributes by using aligned variational autoencoders and variants of triplet loss. Then we train a classifier in the latent space. The experimental results demonstrate that the proposed framework achieves great improvement.
Image processing procedure for remote recording of the Gambusia sp. introduced into a water for anti-malaria

The object of research is the procedure for processing digital images for remote registration of Gambusia sp., introduced into water bodies to combat malaria, which threatens not only the African region, but also other latitudes of the world. One of the most problematic areas of research is the elimination of the masking effect of a biological object under conditions of interference (for example, water turbidity) that make it difficult to recognize Gambusia sp. on digital images taken from aboard light drones. In the course of the study, approaches were used that allow dividing a digital image into segments and sub-segments, followed by determining the ratio of the colorimetric parameters of the RGB model of the bottom section. Dispersion and correlation analysis of mean values and mean square deviation values of the RGB model parameters were used. The standard deviation was considered as the degree of diversity of colorimetric parameters in the color of a biological object. The proposed procedure made it possible to reveal a moderate negative correlation between the predominance of green and yellow-orange-red phytopigments in the dynamics of the Margalef model of phytocenosis succession in the places of introduction and habitation of Gambusia sp. This is due to the fact that the shielding of phytocenosis areas by Gambusia sp. is reflected in the nature of the relationship of the colorimetric parameters of the RGB model of the bottom area, namely, they affect the correlation between the average values of the parameters G/(R+G+B) and R/G or between the mean value and the standard deviation of the parameter G/(R+G+B). This makes it possible to use Gambusia sp. in regions affected by malaria, a wide range of light drones with remote photofixation of relatively low quality. The implementation of these possibilities will require much less material costs and a small number of personnel than underwater video filming and other known methods for studying the ichthyofauna of small water bodies in conditions of interference. It is about the registration of the results of the introduction of Gambusia sp. in such water bodies to fight malaria.
Increasing the Students’ Ability in Writing Skill by Using Board Game at the Tenth Grade of SMKS BNKP Daro-Daro Balaekha Lahusa

Writing is a transaction with words whereby someone free his self from what he presently think, feel and perceive. In learning writing, most of the students could not achieve the MCC. There were some problems faced by the students in studying writing descriptive text at the tenth grade of SMK Swasta BNKP Daro-daro Balaekha Lahusa in 2020/2021 namely: the students were lack of vocabularies. The students were unable to describe the object. The students were unable to identify topic, main idea, content from descriptive text. Therefore, to overcome the problems, the researcher conducted a research by using Board Game at the Tenth Grade of SMK Swasta BNKP Daro-daro Balaekha Lahusa in 2020/2021. The purpose of the research is to increase the students’ ability in writing descriptive text. In increasing the students’ writing descriptive text, the researcher used Board Game and applied as Classroom Action Research (CAR). The research has been conducted in two cycles, which each cycle consisted of two meetings, there were four steps in conducting the research namely (1) Planning, (2) Action, (3) Observation (4) Reflection. The result of the data in Cycle I that the average score was 44.64, the maximum score was 7.14 and the minimum score was 50. While in Cycle II the average score was 82.14; the maximum score was 72 and the minimum score was 28. From the result of the research, It can be concluded that Board Game Increase the Students’ Ability Writing Descriptive Text at the Tenth Grade of SMK Swasta BNKP Daro-daro Balaekha Lahusa in 2020/2021.
Fast discrimination of fake video manipulation

&lt;span&gt;Deepfakes have become possible using artificial intelligence techniques, replacing one person’s face with another person’s face (primarily a public figure), making the latter do or say things he would not have done. Therefore, contributing to a solution for video credibility has become a critical goal that we will address in this paper. Our work exploits the visible artifacts (blur inconsistencies) which are generated by the manipulation process. We analyze focus quality and its ability to detect these artifacts. Focus measure operators in this paper include image Laplacian and image gradient groups, which are very fast to compute and do not need a large dataset for training. The results showed that i) the Laplacian group operators, as a value, may be lower or higher in the fake video than its value in the real video, depending on the quality of the fake video, so we cannot use them for deepfake detection and ii) the gradient-based measure (GRA7) decreases its value in the fake video in all cases, whether the fake video is of high or low quality and can help detect deepfake.&lt;/span&gt;
Development of Cloud Computing Platform Based on Neural Network

Aiming at the problems of small data storage, small platform throughput, and high energy consumption in the development of existing cloud computing platforms, this paper develops a cloud computing platform based on neural networks. In the design of the platform, firstly, the functions of the cloud computing platform are determined. Based on the function design, the hardware and software of the cloud computing platform are designed. In the hardware design, the topology of cloud computing platform, data acquisition module, single-chip microcomputer, node deployment, and other types of functional hardware are designed. In the software design, the neural network is mainly used to remove the redundancy of data stored in a cloud computing platform, design the data processing flow of cloud computing platform, and complete the development of cloud computing platform based on neural network. The experimental results show that the cloud computing platform based on the neural network designed in this paper runs faster, the throughput of platform data has been significantly improved, and the operating energy consumption of the platform is low.