Reviewerly/exHarmony huggingface dataset:
THE ROLE OF INTELLIGENCE AND TALENT IN THE LEARNING PROCESS

Talent and intelligence are human resources that are indispensable in solving life's problems. The development of intelligence and talent is influenced by two factors, namely internal factors and external factors. The lack of knowledge and understanding of talent and intelligence makes most of us limit talent and intelligence to only cognitive aspects. It is important for educators to understand the intelligence and talents of students and be able to create a learning process that can generate and develop the intelligence and talents of students. Because students who have intelligence and talent and get support internally and externally will produce optimal performance so as to achieve good achievements
An Exploration of Sales Policy Management Models in the Era of the Internet of Things (IoT)

The eruption of the Internet of Things (IoT) heralds a transformative era in sales policy management, intertwining technological innovation with strategic adaptation. This review elucidates the trajectory of sales policies amidst the burgeoning IoT era, delineating the implications, strategies, and challenges emergent in this digital epoch. Through a meticulous exploration of traditional versus IoT-driven sales policy management models, and tangible insights derived from diverse case studies across B2C and B2B models, this review sheds light on the tangible and potential implications of IoT within sales and marketing realms. Further, it prognosticates future trends, forecasting the integration of emerging technologies like AI and Blockchain within IoT-driven sales policies, and anticipates the ensuing challenges and solutions pertinent to data security, privacy, and organizational adaptation. Conclusively, the synthesis of IoT within sales policy management envisages a future that is not only technologically advanced but also strategically and ethically nuanced, demanding a harmonization of technology, strategy, and ethics as organizations navigate through the interconnected digital terrains of the future.
The Problems of Indicating the Sustainable Development of Education in the Digital Economy

The research aims to determine the basic problems linked with developing indicators of sustainable development of contemporary education in the digital economy. The main research methods include an analytical review of the scientific literature on the sustainable development of education, the method of dialectics, the principles of dialectics, and the activity approach. During the research, the authors concluded that it is quite difficult to develop unified and universal indicators of sustainable education development in the digital economy. The main problems faced by the developers are the problems of determining the results of educational activities, the conditions of stabilization of the education system in its development, and its direct dependence on the socio-economic level of the digital economy. The research novelty is the author’s approach to defining the main areas for developing indicators of sustainable development of digital education. The activity-based access to the study of digital education makes it possible to identify several main components that take place in any educational system as an activity and a sphere of public life: educational potential, educational activities, educational social relations, and institutions. Having its own structure, each element determines the viability of the entire education system and makes it possible to develop appropriate indicators of its sustainable development.
Developing Employee Creativity in NET TV

The aim of this study is to develop employee creativity in NET TV, through the mediating role of meaning of work on the effect of perceived organizational support and proactive personality. The study uses a quantitative approach through a questionnaire survey on 171 respondents that have met certain criteria. The sample was obtained through proportional random sampling. Then the data was analyzed with Path Analysis with SPSS and Sobel Test. All the hypotheses are proven to be accepted, meaning that perceived organizational support and proactive personality have either direct and indirect effect on employee creativity. The mediation roles of meaning of work is also indicated to be significant.This study empirically adds value on the literature of employee creativity by investigating more variables that may link to the development of creativity. It is expected that the results will serve as consideration for the management to establish policy to foster innovation.
Analisis Kapasitas Sistem Saluran Drainase (Studi Kasus Drainase Jalan Siliwangi Kota Batam)

Batam City is one of the cities in the Riau Archipelago Province with the largest population growth. Along with the development of the city and the increase in population, the need for public facilities and infrastructures also need to be developed, such as on Jalan Siliwangi. Jalan Siliwangi is one of the roads in the Batu Besar area, Nongsa sub-district in Batam city which is experiencing problems with the drainage system. Drainage is a technical measure to prevent excess water due to high rainfall intensity and drain it to a collection point. Rainfall intensity is a condition where rain is highly concentrated and occurs at a certain time. This research was conducted to know whether the Siliwangi Road drainage channel is working optimally so that it can accommodate water during high rainfall. This analysis uses the Gumbel method using extreme values. The extreme values of rain intensity that will be taken into account are several return periods, namely 2, 5, 10, 25, 50, and 100 years. The calculated channel discharge using location data is Q = 0.5637 m3/second, while the planned discharge calculated using rainfall data is Q = 2.064 m3/second. From the results of the analysis of the dimensions of the planned canal found in the field, it does not meet the design capacity so waterlogging occurs, where the height and width of the planning canal are larger. By using the frequency distribution formula of the Gumbel and Log Person Type III methods for the calculation and application of parameters related to the analysis of average data, standard deviation, and determining the frequency of rainfall for the T-year return period in the discussion above, it can be concluded that the drainage channel on Jalan Siliwangi Batam unable to prevent inundation or flooding during high rainfall.
Analisis Spasial Kenaikan Tinggi Permukaan Air Batang Sumani Menggunakan Model Permukaan Digital

The occurrence of flooding in the Batang Sumani area of ​​Solok Regency is caused by several things, including the low slope of the river, the river cross-section which is not wide enough, the topography of the plains, heavy bends along the river and silting due to sedimentation and the vulnerability to flood hazards which is quite high in the watershed area. The influence of the topography of the plains causes inundation on land managed and used by the community. In accordance with technological developments in digital mapping of the earth's surface, the earth's surface can be presented in the form of a three-dimensional map called a Digital Elevation Model (DEM). This DEM is used as a model, analysis and representation of conditions related to the earth's surface. The use of DEM in the process of analyzing inundation caused by surface runoff will help in identifying the coverage of inundation areas and water catchment areas. By using the Simulate water level rise / flooding analysis from the Global Mapper application it is possible to simulate water level rise / flooding which makes it possible to simulate the coverage or spread of water/flooding by increasing the water level to a certain depth and spatial analysis using the Geographic Information System can see the influence of the inundation area on land use managed by the community around the inundation area. So that the location and coverage of the inundation on land use can be obtained. Most of the inundation areas are in garden areas at 37.56%, rice fields at 23.83%, fields at 24.03% and residential areas at 13.67%. There was a significant increase in the water level rise of 2 meters.
Deep Learning: A Study of Pattern Recognition for Personalized Clothing

Objectives: This article aims to enhance the efficiency of clothing recognition and retrieval by implementing deep learning algorithms for personalized clothing pattern recognition. Methods: Based on the you only look once version 4 (YOLOv4) algorithm in deep learning, the CSPDarknet-53 in the original algorithm was replaced by GhostNet, and the original Leaky ReLU activation function was replaced by FMish. Then, an improved YOLOv4 algorithm was obtained. Experiments were carried out on the personalized clothing pattern set, the Fashion Mnist dataset, and the DeepFashion dataset to compare and analyze different algorithms. Findings: When replacing CSPDarknet-53 with GhostNet and the Leaky ReLU activation function with FMish, the optimized YOLOv4 algorithm performed significantly better, verifying the reliability of the YOLOv4 improvement. The optimized algorithm achieved an F1 value of 94.22% and a mAP of 95.41% on different datasets, and 39.51% and 49.56% on the DeepFashion dataset, respectively, outperforming other deep learning methods such as the faster-recurrent convolutional neural network. Furthermore, the floating-point operations per second of the optimized YOLOv4 algorithm were 8.72 G, showing a reduction of 49.71% compared to the traditional algorithm. This suggested that it had low complexity and calculation amounts. Novelty: The optimized YOLOv4 algorithm performs excellently in recognizing personalized clothing patterns, which can provide a new and reliable approach for recognition and retrieval in the field of clothing. Doi: 10.28991/HIJ-2023-04-03-03 Full Text: PDF
Crisp-DM Implementation for Elderly Social Protection

In Jakarta, social assistance is available for specific categories of older adults but is given by Conditional Cash Transfer (CCT). Technical assistance is based on a database of beneficiaries and its mining due to the regulation registry. The sub-national social government maintains a Mining database. The author uses a database in one of the districts as a case study. The author uses Crisp DM to understand this social welfare problem because it has its logic of understanding, the logic and the details of pre-processing the data. Then, to decide, used a Support Vector Machine (SVM) as a model and tested it with cross-validation. To obtain the classification accuracy formed from the data processing results. This research aims to see the predictive results of the distribution of KLJ program assistance. Then, answer the form of modeling to create a decision system for the distribution of KLJ. For this reason, SVM is used to make decisions related to social assistance issues. In the SVM, which consists of two classes, it can be interpreted that one class contains “stop” while the other “receive”. However, there are offers bias in the data processing, with the "if" condition. The data processing reaches 100% accuracy and gets SVM kernel weight.
A Convolutional Network-based Applied Approach to Frontal Facial Recognition and Solutions for Occluded Images

Facial recognition technology is constantly being used in the most diverse sectors, from facial expression analysis to measure customer satisfaction in stores to a police instrument for identifying people. To summarize, from an image obtained by a camera, the technique identifies faces contained in that photo and compares them with a database of faces previously registered in the system. Based on advances in terms of algorithms and processing of hardware obtained in recent years, it was possible to provide solutions for verification and facial recognition through modern cell phones. The present work addresses the problem of facial recognition analysis that has the potential to scale for applications based on server processing. Using Histogram of Oriented Gradients to extract face features and the FaceNet Convolutional Neural Network this study brings results in different public (Labeled Face in the Wild and CelebFaces) and private databases. This study obtained satisfactory results with an accuracy of 90% in the best cases for private databases. As part of the work, this study also sought to evaluate the effect of partial occlusion on faces from the use of face masks because of the Covid-19 pandemic scenario, obtaining satisfactory results above 80%.
Método para Reidentificação Facial

Recenlty, many face reid strategies have been developped. These strategies create vectorial spaces capable of representing data on reduced dimensions. Such representations are produced by deep learning models that learn to maximize the intra-class similarity and minimize the inter-class similarity. The method described in this article proposes a new face reid strategy based on the Facenet model. It consists of training support vector machines on the euclidean distances calculated between the embeddings of the Facenet model, notably reducing the false positive rate.
Um novo método de poda iterativo de máquinas de vetores-suporte de mínimos quadrados

Least-Square Support Vector Machines (LSSVM) are solved from a linear system, unlike support vector machines (SVM), which require the application of quadratic programming. So it is preferable in some problems to use LSSVM because the complexity of the resolution will be lower. However, unlike SVM, LSSVM generates non-sparse solutions. A significant disadvantage, given that all training patterns will be support vectors of LSSVM. To overcome such non-sparse solutions, we propose an iterative solution with pruning without causing a loss of efficiency, named as FSLM-LSSVM. The proposed method is compared with two others in the literature, P-LSSVM and IP-LSSVM. Finally, the evaluation of the proposed solution consisted of comparing the averages of the hit rates.
Síntese de uma Nova Política de Ofuscação que Garanta Diferentes Níveis de Privacidade e Utilidade

The world is increasingly connected through computer networks, which makes it necessary to develop techniques capable of guaranteeing the privacy and utility of the transmitted information. In this work we consider the problem of obfuscation of discrete event systems that guarantees different levels of both privacy and utility of the generated information. The system is modeled as a labeled transition system that has secret states. The first goal is to keep the information generated by the system useful so that legitimate users can use it for decision making. At the same time, the second goal is to hide critical information about the secret states of the system. However, unlike the previous approaches presented in the literature, we consider the case in which it is possible for the obfuscator to report the secret states. Thus, this approach increases the number of systems capable of being obfuscated and, since it keeps the public view of the system with the same behavior as the original system, it makes difficult to notice the presence of the obfuscator. To perform the synthesis of the obfuscator we used the software SynthSMV.
DESENVOLVIMENTO DE UM SISTEMA AUTOMATIZADO PARA COLETA DE DADOS DE TENSIOMETRIA UTILIZANDO IOT

This work presents the partial results obtained on the development of applied research. Nowadays, in the agricultural process there is a growing need on automating the productive process with technological support. One of the jobs is the data collection about the soil’s hydric condition that is done manually and, on large properties, may become tricky and longstanding. Once dealing with a manual conference, it may generate mistakes on reading. With these problems in mind, the authors look for creating automated systems for irrigation using Agriculture 4.0 (Agro 4.0) concepts. This part of the manuscript presents the materials and methods applied to the building of a prototype made to digitalize, send and store data about the soil’s hydric tension in a single cloud database. Furthermore, there are shown details of the interface created to view the data from the internet. Such features line up the presented system with the Internet of Things (IoT) and provide evidence that it may be used as a tool for monitoring useful sensorial information on the automated irrigation process.
Aprendizado de Máquina aplicado na previsão da geração de energia elétrica de uma usina solar fotovoltaica no Ceará

Given the growing of solar photovoltaic (PV) power in the Brazilian power mix, and considering that PV generation is intermittent, there is great need of PV generation forecasting models for better operational planning of both the PV power plant itself and the power grid. Thus, this paper proposes the application of Machine Learning models to forecast the power generation of a 160 MW PV plant located in the state of Ceará (Brazil). The aim is to forecast the daily power output of the power plant over 365 days based on the historical power data and the weather data of the PV power plant. To accomplish the task, distinct prediction models were tested, such as: sequences recognition, Artificial Neural Networks (ANN), XGBoost and hybrid approaches. The performance of the implemented models was evaluated using error metrics, and the XGBoost model achieved the most accurate results as regard to the prediction error and the execution time followed by the ANN. The Pattern Sequence-based Forecast (PSF), which is more transparent than an ANN or even XGBoost, has proved competitive, having the best performance among the models trained only with historical data of power generation.
GNSS processing using Kalman Filter on Lie Groups

This paper explores the problem of position and speed estimation of a target using satellite-based measurements in a Lie Group Kalman-like filter. The filter employs an intrinsic formulation of a nearly constant velocity model based on the Frenet-Serret frame, embedded in a Lie Group structure. It stands as a more suitable model for the kinematics of a target in space than the usual Euclidean model since it induces a banana-shaped distribution instead of the typical ellipsoidal provided by Gaussian schemes. The paper presents numeric experiments using a real application dataset to evaluate the proposed filtering scheme’s performance against the standard Euclidean representation approach. A differential GNSS solution provided by commercial software furnishes the ground truth. The results indicate a better performance of the proposed Lie Groups filtering scheme.
Transporte cooperativo de objeto por multi-robôs

The transport of object by groups of robots can be advantageous when an object is too large or too heavy to be effectively handled by a single robot alone. It requires the coordination and synchronization of pushing forces to be exercised by the robots. In this paper, we propose a new algorithm called Cooperative Transport of Object (CTO). The algorithm has four stages. The first one implements the search for object. The second stage of the algorithm start when a robot finds the object. It then recruits the other robots. During the third stage each robot first computes then goes to the position it must be located at to execute the pushing actions. When the positioning of all robots around the circular object is completed, the fourth stage commences. Therein, the robots start alternating between pushing and positioning actions to maintain the object within the expected transport trajectory. This is performed until the object is homed. The algorithm is implemented in GRITSBot swarm robots, using Robotarium. The algorithm is effective to carry out the stages and the corresponding performance is promising.
Análise de Explicabilidade de um Modelo de Aprendizado de Máquina para Aplicações Industriais

Machine learning models are used in numerous applications and, for some of them, it is desirable to be able to explain the output of the models. The search for model explainability lead to the development of Tree SHAP : a framework for tree-based models that calculates the contribution of each input feature to the predictions. In this paper, we train a regression model that predicts the yield value of seamless steel tubes at the end of the heat treatment process. We then perform a model explainability analysis to highlight the gains of such analysis in industrial applications.
Avaliação de Topologias de Redes Neurais Artificiais para Previsão de Potência e Energia Fotovoltaica

Renewable energy sources such as solar photovoltaic have grown every year. Although beneficial, the integration of photovoltaic solar generation systems can cause problems due to meteorological variations that lead to uncertainties in energy production. In this sense, this work evaluated three artificial neural networks topologies to improve the forecasting of photovoltaic power and energy. The results show that the topology with 5 neurons in the hidden layer was able to predict the photovoltaic energy with an error of less than 1.3% in relation to the measured energy.
Trajectory Optimization for a Collaborative Robot UR5 in a Scenario with Obstacles

Collaborative robots are becoming more present in various activities, inside and outside the industry. The use of these robots allows greater precision and accuracy in carrying out the tasks. However, it is important to take into account some factors to ensure the safety of the system, such as the ability to avoid obstacles that may be present in the operating environment. In this work, we propose a system for trajectory optimization of a robotic manipulator in complex environments using the algorithms Covariant Hamiltonian Optimization for Motion Planning (CHOMP) and Stochastic Trajectory Optimization for Motion Planning (STOMP), and an RGB+D sensor for obstacle detection. The entire system was implemented based on the open-source framework Robot Operating System (ROS). Performance of the algorithms was analyzed based on their success rate, planning time, and duration of the generated trajectory. Results indicate that the proposed system can generate feasible and collision-free trajectories in static environments.
Modeling of observation instruments and path planning under time-variant illumination areas for water exploration in lunar polar region

This paper proposes a novel path planning algorithm designed for efficient lunar water exploration. The proposed algorithm leverages a Bayesian network-based sensor model in combination with the RRT* algorithm. It is well-known that water is highly likely to be present in permanently shadowed regions (PSRs). However, during exploration within these regions, the rover must escape to illuminated areas within a limited time to recharge its battery. Therefore, it is crucial to develop a path planning algorithm that ensures both timely battery recharging and efficient water exploration. To address this challenge, we first introduce a sensor model based on a Bayesian network for the neutron spectrometer. Subsequently, we propose an algorithm that utilizes this sensor model and the RRT* algorithm to effectively explore water on the lunar surface. The paper provides a numerical simulation, using real data about time-variant illumination areas and time-invariant PSRs on the lunar surface, to illustrate the effectiveness of the proposed algorithm. The results reveal that the algorithm accurately and efficiently detects water under realistic illumination conditions.