Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Control of Phase-to-Phase Switching of Single-Phase Power Consumers Based on Binary Algorithms of Agent Metaheuristics

The subject of the following article is development of a new technological solution aimed at improving the quality of electrical energy in distribution networks. The authors proposed an approach based on managing the phase-to-phase switching of single-phase power consumers (SC) through the use of binary algorithms of agent metaheuristics. The purpose of using the approach is to minimize in the distribution network the values of indicators characterizing the asymmetry of currents and their total harmonic distortion. For the practical implementation of the approach, an optimization problem was formulated. A corresponding algorithm was developed and presented with a description of the details of its implementation and the results of numerical experiments obtained.
Weighted Grid Partitioning for Panel-Based Bathymetric SLAM

Bathymetric navigation enables the long-term operation of autonomous underwater vehicles by reducing navigation drift errors with no need for GPS position fixes. In the case that a bathymetric map is not available, the simultaneous localization and mapping (SLAM) algorithm is required, but this increases computational complexity and memory requirement. Panel-based bathymetric SLAM could considerably reduce the computational burden. However, it may suffers from incorrect update when the vehicle does not belong to the updated panel. This study proposes a new update method, called weighted grid partitioning, which considers the probability distribution of a vehicle's location, and is more effective in terms of the map accuracy, computational burden, and memory usage compared to standard update methods. The feasibility of the proposed algorithm is verified through simulations.
Sentiment Analysis For 2016’s SPM and STPM Leavers

Web data dump created in line with the evolution of web technologies. Users use the web to give their opinions on a subject or product, and their opinions are being used by other users as additional information for them before buying or using a product. Information sharing streams like this creating a mass dumping of information about things and products. This attracts researchers to mine, analyze and organize this information so that it can be used to help make critical decisions in many situations. Analysis of sentiment in this research is from Twitter data, one of the social media that is widely used today. Opinion from SPM and STPM leavers were mined, cleansed, and then analyzed to obtain the opinion of SPM and STPM students on their examinations, and assumptions about their future. Their opinion was then cleansed to a meaningful lexicon. Through sentiment analysis process, this lexicon is classified to positive and negative. Information like this is important for the Institute of Higher Learning to estimate student admission to their institution
A Multi-node Cooperative MAC Protocol Joint with Space-time Encoding for Underwater Acoustic Communication Networks

It is a great challenge to design effective protocols for underwater acoustic communication networks due to the specific characteristics of underwater acoustic channel, such as long propagation delay and limited available bandwidth. In order to reduce the average end-to-end delay and improve the network throughput, a multi-node cooperative MAC protocol joint with space-time encoding of physical layer for multi-user MIMO centralized network is proposed in this paper. It is receiver-initiated, where the receiver groups the reserved nodes into a series of cells according to the propagation delay between individual reserved node and itself. By scheduling the transmission time, the data packets sent by the nodes in the same cell are guaranteed to reach the receiver within a predetermined time deviation, realizing cooperative parallel transmission of multiple nodes. Simulation results show that the proposed MAC protocol achieves the shortest end-to-end delay and optimal normalized network throughput under different network sizes, traffic loads and data packet lengths compared with other three existing protocols. The performance improvement is significant especially when the data packet length is longer and the load is heavier.
Surface Water Area Mapping in Huai River Basin Over the Past Three Decades from Landsat Imagery based on Google Earth Engine

Accurate mapping of Dynamics of surface water is essential for supporting sustainable water source management and socio-economic development. To obtain a dynamic map of surface water, we obtained all available Landsat TM, ETM+, and OLI images (16760 scenes) of Huai River Basin from 1989 to 2017 based on Google Earth Engine (GEE) platform. The water surface extraction method combining vegetation index and water index was used to map the spatio-temporal pattern of the surface water body. Results show as follows: (1) the maximum, average and seasonal surface water area in the Huai River Basin shows a downward trend in the past three decades, but the minimum surface water area shows a slight upward trend; (2) both the number of maximum and the minimum surface water body show a downward trend in the past three decades; (3) the changes of numbers of surface water bodies are mainly depended on the number of small surface water bodies, and the changes in the area of water bodies are mainly controlled by larger surface water bodies area. The results of the Surface water mapping are expected to provide detailed information for optimizing water structure and water source management which are critical issues in this region.
Balance Control of Humanoid Dancing Robot ERISA while Walking on Sloped Surface using PID

This paper discussed an application of PID balance control system on humanoid dancing robot series named ERISA (EEPIS Robot Dancing Sense of Art). The robot is designed to perform one of Indonesian traditional dancing on which the robot has to move on the dancing field. The field has been designed as a flat surface. However, due to imperfect fabrication, it could have some sloped surface that makes the robot easily fall. The implementation of current balance control aims to make ERISA robot able to walk on sloped field surface in balance and not easy to fall. An analog tilt sensor located on the robot’s shoulder is used to measure the slope of the field. Using the tilt value, the control equation of robot servo mechanics is manipulated using PID, therefore, the robot autonomously moves its pose in reverse to the falling direction. The proposed balance control system is investigated when ERISA robot walking on the sloped surface. The degree of the sloped surface is set to vary from 5° to the maximum extent of robot ability. The test results show the addition of the balance control system gives ERISA robot capability of walking on the sloped surface up to 10°.
El humor como estrategia terapéutica en niños hospitalizados en unidades pediátricas en Pereira (Colombia). Reporte de una experiencia

Introduction : Pediatric patients are specially susceptible to stress. Humor has been used in many hospitals around the world as a strategy to decrease stress levels during hospitalization, specially in children. Methods : Periodic interventions were made in two intermediatecare pediatric units in a public hospital in Pereira, Colombia. The initial intervention was based on changes of the hospital environment by modifying the decoration of wards and implements used by the health workers. The groups, made up of volunteers, were trained in clown techniques, construction of characters, and personalized games . Results : 108 in-terventions were made in a period of 9 months with a mean of 12 interventions per month, during which the group worked with 648 children (72 children per month with 6 children per intervention), with ages between 2 months and 14 years. The favorable impact in hospital environment and the subjective sensation of welfare in the patients, their families and the health personnel is worth noting. Discussion and conclusions : The qualitative results of this intervention were as expected. The subjective opinion of health workers, patients and their families was satisfactory. This experience adds arguments to the discussion about the need to implement programs like this one in health institutions and to include laughter therapy in the curricula of health educational programs.
Worst-Case Reaction Time Optimization on Deterministic Multi-Core Architectures with Synchronous Languages

In this paper, we propose a new approach for the predictability and optimality of the inter-core communication and execution of tasks allocated on different cores of multicore architectures. Our approach is based on the execution of synchronous programs written in the ForeC programming language on deterministic architectures called PREcision Timed. The originality of the work resides in the time-triggered model of computation and communication that allows for a very precise control over the thread execution. Synchronization is done via configurable Time Division Multiple Access (TDMA) arbitrations where the optimal size and offset of the time slots are computed to reduce the inter-core synchronization costs. We implemented a robotic application and simulated it using MORSE, a robotic simulation environment. Results show that the model we propose guarantees time-predictable inter-core communication, the absence of concurrent accesses (without relying on hardware mechanisms), and allows for optimized execution throughput.
Development of Python-MATLAB Interface Program for Optical Communication System Simulation

This paper reports on the results of a developing Python-MATLAB interface program to implement optical wireless communication system simulation. We implement Python interface for entering input values and simulation environment variables for MATLAB. We operate MATLAB to numerical computing process about communication system simulation to generate a signal, to approximate channel characteristics and to recover the signal. Python passes input data and environment variables to MATLAB and displays output data and graphs from the simulation after MATLAB numerical computing.
Underwater three-hydrophone array passive ranging using time-dimensional Capon beamforming

The three-hydrophone array is often used for underwater passive ranging. It uses the time delay estimation (TDE) between hydrophone pairs to calculate the target range. Traditionally, the generalized cross correlation (GCC) algorithm is used for TDE. However, when the available bandwidth is limited, the accuracy of GCC decreases severely and thus, fails to meet the performance requirement. To solve this problem, we propose a new TDE algorithm based on time-dimensional Capon beamforming. The proposed method uses Capon beamforming to process sampled signals at hydrophones to obtain the time-domain output and accordingly calculates the time delays between hydrophone pairs. Specifically, we extract several subband components from GCC outputs in the frequency domian. To suppress the amplitudes fluctuation between different subbands, the subband amplitude modification and the diagonal loading (DL) algorithm are used to improve the robustness of time-dimensional Capon beamforming. Finally, we estimate the range of target using the TDE result obtained by the proposed method. What’s more, to analyze the TDE accuracy of the time-dimensional Capon beamforming, we compare the TDE variance of both GCC and the proposed method with Cramér–Rao lower bound (CRLB) under different simulation conditions. The results demonstrate that the proposed algorithm could significantly improve the performance of passive ranging in three-hydrophone array, in the case of relatively short bandwidth associated with subband amplitude fluctuations.
Wavelet Learning by Adaptive Hermite Cubic Splines applied to Bioacoustic Chirps

Acoustic monitoring is used to study marine mammals in oceans. Automated analysis for captured sound is almost essential because of the large quantity of data. The deep learning approach is an efficient method, however acoustic features are often not adapted. Convolutional Neural Net can be seen as an optimal kernel decomposition, nevertheless it requires large amount of training data to learn its kernels. An alternative using pre-imposed kernels and thus not requiring any amount of data is the scattering framework which imposes as kernels wavelet filters. Our research focuses on adaptive time-frequency decomposition of bioacoustic signal, based on cubic spline learning representation. We give the theoretical derivations of the model, and demonstrates efficient real applications of various signal, including chirps of songs of Blue Whale.
A Survey: Effect of the Number of GLCM Features on Classification Accuracy of Lasem Batik Images using K-Nearest Neighbor

Lasem batik is one of traditional Indonesian’s famous batik that has high artistic and economic value. Lasem batik can be classified into several different motifs. This study aims to analyze the number of gray level co-occurrence matrix (GLCM) features during the process of classification of Lasem batik image with K-Nearest Neighbor (KNN). KNN has advantages in overcoming probability density, able to consolidate calculations based on the number of neighbors specified, and can perform calculations with limited parameters. Feature extraction is one of the important steps before performing image classification. GLCM is one of the extraction features of a very popular texture. There are five GLCM features that are widely used, namely contrast, homogeneity, energy, correlation, and entropy. This research classifies five kinds of the famous motif of batik Lasem. The training and testing process compare the use of four and five GLCM features for each of the three experiments with different amounts of data. The test results show that the use of four and five types of GLCM features get the same accuracy in each experiment. It can be concluded that with KNN enough to use four kinds of features to speed up the calculation of the classification.
The Method of Integrating Virtual Reality with Brainwave Sensor for an Interactive Math's Game

The implementation of the Virtual Reality (VR) on game is practical for in various fields, especially in the field of Education. The implementation of a mobile based VR game is example where the players of game feel as in the real world. However, the VR game has the weakness on limited interaction of their player with the virtual environment created by the game. Currently, the interactions pass through the buttons on mobile phone and joysticks. For this reason, this research investigates the alternative media to control the virtual environment of the game using brain sensor. The prototype was created using “mindwave neurosky” as brain sensor and thingkgear as sensor drive to construct the experiment of mobile based virtual reality math game This research tests three modes signal including meditation, attention and beta signal. A meditation signal was taken when the player open and close the eye. While attention and beta signals were taken when the player focuses. The result is some model to control the VR math game with brain sensor for child five or six year old's.
Attention-Based Dual-Scale CNN In-Loop Filter for Versatile Video Coding

As the upcoming video coding standard, Versatile Video Coding (i.e., VVC) achieves up to 30% Bjøntegaard delta bit-rate (BD-rate) reduction compared with High Efficiency Video Coding (H.265/HEVC). To eliminate or alleviate different kinds of compression artifacts like blocking, ringing, blurring and contouring effects, three in-loop filters, i.e. de-blocking filter (DBF), sample adaptive offset (SAO) and adaptive loop filter (ALF), have been involved in VVC. Recently, Convolutional Neural Network (CNN) has attracted tremendous attention and shows great potential in many tasks in image processing. In this work, we design a CNN-based in-loop filter as an integrated single-model solution which is adaptive to almost any scenarios in video coding. An architecture named as ADCNN (i.e., Attention based Dual-scale CNN) with an attention based processing block is proposed to reduce artifacts of I frames and B frames, which take advantage of informative priors such as the quantization parameter (QP) and partitioning information. Different from existing CNN-based filtering methods, which are mainly designed for the luma component and may need to train different models for different QPs, the proposed filter is adapted to different QPs and different frame types, and all the components (i.e., both luma and chroma) are processed simultaneously with feature exchange and fusion between components for information supplementary. Experimental results show that the proposed ADCNN filter can achieve 6.54%, 13.27%, 15.72% BD-rate savings for Y, U, V respectively under the all intra configuration and 2.81%, 7.86%, 8.60% BD-rate savings under the random access configuration. It can be used to replace all the conventional in-loop filters and also outperforms them without increase in encoding time.
Comparison K-Nearest Neighbors (K-NN) and Artificial Neural Network (ANN) in Real Time Entrants Recognition

Face recognition is an identification system that uses face characteristics of each person to identify and differentiate a person from others. In utilizing the development of technology, face recognition has attracted much attention in the world of technology. It is used for many purposes, but most often for security and law enforcement purposes. For example, face recognition is used to prevent people from getting fake identification cards. Face recognition also can be used for entrants who will enter a particular place such as security post to help security in doing their job. However, security has limited ability to make sure that entrants are not a threat to others. In our working, face recognition is used in security post to prevent stranger people with no particular purpose come in. In this paper, we compare face recognition approach using the K-Nearest Neighbor (K-NN) algorithm and Artificial Neural Network (ANN) algorithm. K-NN and ANN serve to classify each different person in a database. The result presents K-NN has 44.101% of accuracy and ANN has 38.177% of accuracy.
Effect of Education, Performance, Position and Information Technology Competency of Information Systems to Performance of Information System

The performance of the Civil Servants information system in the Indramayu Regency Government is the work done by the employees along with the work system applied. The role of employees as information system personnel is needed to provide the best performance to superiors and customers. Competence is the knowledge and skills needed to support business processes. They represent the basis for creating value in an organization. This study has observed and measured competency factors. Competencies are used to measure the success of organizations and individuals in the field of information technology, namely technical knowledge, work coordination, problem solving and prevention, communication, and service, and accountability. This study aims to determine how strong the influence of education, mastery, position and information technology competencies on the performance of information system actors in Indramayu District Government. Descriptive research method with survey technique, hypothesis testing is done by using path analysis. The result is, simultaneous hypothesis testing with the level of education, mastery and level of position, and information technology competencies affect the effectiveness and efficiency, discipline and work initiatives of information system actors. In the partial examination of formal education does not significantly influence the effectiveness and efficiency of information system factors.
A Survey on Detection Techniques for Cryptographic Ransomware

Crypto-ransomware is a type of malware that encrypts user files, deletes the original data, and asks for a ransom to recover the hijacked documents. It is a cyber threat that targets both companies and residential users, and has spread in recent years because of its lucrative results. Several articles have presented classifications of ransomware families and their typical behaviour. These insights have stimulated the creation of detection techniques for antivirus and firewall software. However, because the ransomware scene evolves quickly and aggressively, these studies quickly become outdated. In this study, we surveyed the detection techniques that the research community has developed in recent years. We compared the different approaches and classified the algorithms based on the input data they obtain from ransomware actions, and the decision procedures they use to reach a classification decision between benign or malign applications. This is a detailed survey that focuses on detection algorithms, compared to most previous studies that offer a survey of ransomware families or isolated proposals of detection algorithms. We also compared the results of these proposals.
Static Camera Calibration for Advanced Driver Assistance System Used in Trucks - Robust Detector of Calibration Points

The paper presents selected investigation results on a static (factory) calibration procedure of the camera, for the use in Advanced Driver Assistance System (ADAS) mounted in trucks. It is a commercial project that has already been put for production. For this reason, special emphasis was put on the reliability of the proposed solutions. Additionally, the developed algorithms were optimized so that all computations are completed within an assumed period of time (requirements). One of the problems encountered during the development of the calibration procedure was the fact that various objects were located around the calibration pattern in the factory. In some situations these objects may be classified as calibration points, resulting in false outcomes of the overall calibration procedure. For this reason, special emphasis was placed on the development of appropriate robust algorithms responsible for the detection of calibration points. For example, the implemented methods allow for distinguishing the false points and removing them from the list of detected points. The overall procedure was carefully tested under various conditions, including unfavorable ones, with more than 500,000 tests.
Development of Regional Public Election Analytics Application In Indonesia Using Data from Twitter

Many social media users use Twitter to comment on political issues especially during the elections. In order to extract information from Indonesian people's tweets about the election, this research made an analytics application to analyze the prospective regional election in Indonesia. The research method using Software Reuse, which relies on Reusable Component-Based Software Engineering (CBSE). Some steps done in this research are requirement specification, component analysis, requirement modification, system design with reuse, development and integration, and system validation. In system validation, we have done functional requirement testing in the form of black box testing and non-functional requirement test in the form of performance (running time), accuracy, and usability. The results of black box testing indicate that the functional system has been run according to the required specifications that have been made. The results of the classification testing using SVM method showed its average accuracy is 90.73% in general election mode and 84.58% in the election discussing each of the candidates. The average process time of backend module is 665 seconds from 1500 data tweets. In addition, the dashboard module usability result of the user's understanding of the application based on the questionnaire results shows an average value 2.7 of the value range 0-3 of all users who populated the questioner