Reviewerly/exHarmony huggingface dataset:
Evaluation of the conditioning to determine their thermal comfort in the educational institutions of the Puno region

This research aims to evaluate the conditioning that determines the thermal comfort in the Educational Institutions of the Puno Region.The schools of the study site lack thermal comfort due to the existing low temperatures, so the study evaluates the conditioning that allows determining the thermal comfort in the Educational Institutions of the Region.The methodology used in this context is experimental since measurements have been made in situ, obtaining results that allow comparing and demonstrating the use of passive materials in the classrooms to improve the interior conditioning, bringing; as a result, an efficiency of 85% as a conclusion we can say that the implementation of passive materials inside the classrooms, helps to improve thermal comfort.
Doctor Recommendation Chatbot: A research study

Healthcare is very important in our life. As the population increases, day by day the quality of communication between patients and doctors becomes less effective. So because of a load of patients in hospitals, the quality of treatment decreases day by day. So, there should be a system by which a patient can easily communicate with a chatbot. Then the chatbot will examine the patient's problem and at last, the chatbot will recommend a specific doctor for a patient. With the help of artificial intelligence, the proposed system will be developed using natural language processing that means the chatbot first diagnoses the patient's problem then recommends a doctor for consultation. During the COVID pandemic, the trends of virtual assistance increased, and people are interacting with robots and virtual assistants. In many countries, there are robots in hospital receptionists to solve problems of people and work like humans. Chatbots make it easy to find a particular doctor by telling them problems. In this research study, there are different literature reviews included making a healthcare chatbot.
Research on dynamic path planning method of moving single target based on visual AGV

Abstract In order to research the ability of AGV to cope with the dynamic path planning in mobile single-object dynamic environment, the dynamic path planning methods based on reduced point and non-reduced point were proposed. This paper focuses on the dynamic path planning method of non-reduced point moving targets, adopts the method of combining historical data and real-time data, establishing the NAR neural network prediction model to predict velocity of moving target, and connecting with the A * algorithm, through the example simulation experiment, the results show that the effective success rate of the algorithm reaches 77.5% in the map environment set in this paper.
Implementasi Convolutional Neural Network Untuk Deteksi Emosi Melalui Wajah

The human emotional condition can be reflected in speech, gestures, and especially facial expressions. The problem that is often faced is that humans tend to be subjective in assessing people's emotions. Humans can easily guess someone's emotions through the expressions shown, as well as computers. Computers can think like humans if they are given an algorithm for human thinking or artificial intelligence. This research will be an interaction between humans and computers in analyzing human expressions. This research was conducted to prove whether the implementation of CNN (Convolutional Neural Network) can be used in detecting human emotions or not. The material needed to conduct facial recognition research is a dataset in images of various kinds of human expressions. Based on the dataset that has been obtained, the images that have been collected are divided into two parts, namely training data and test data, where each training data and test data has seven different emotion subfolders. Each category of images is 35 thousand data which will later be trimmed to around a few thousand data to balance the dataset. According to their class, these various expressions will be classified into several emotions: angry emotions, happy emotions, fearful emotions, disgusting emotions, surprising emotions, neutral emotions, and sad emotions. The results showed that from the calculation of 40 epochs, 81.92% was obtained for training and 81.69% for testing.
The Effectiveness of Using Crossword Puzzle Games Through Student’s Vocabulary Mastery

The purpose of this research was to find out whether there was an effect of using crossword puzzle games through students’ vocabulary mastery and the significant differences before and after using crossword puzzle games through students’ vocabulary mastery. The population of this research was eleventh students of Vocational High School 1 Banyuputih. XI AKL as the experimental class consisted of 25 students. For the sampling technique, the researcher used purposive sampling. In the getting data, it was obtained by using questionnaires filled out by students and used tests that divided into pre-test and post-test. This research method used quantitative research methods and conducted a pre-experimental design as the research design of this study with data testing using SPSS 16. Based on the analysis result of this research showed that the result of the T-test formula to test the hypothesis of this research insignificance 2 degree (α) of 5% is T-test (to) &gt; T-table (table) (18.90 &gt; 1.713). Therefore, the null hypothesis (H0) is rejected and the alternative hypothesis (Ha) is accepted that there is a significant difference between students’ vocabulary mastery before and after using crossword puzzle games.
AN INTERACTIVE DRUG SUPPLY CHAIN TRACKING SYSTEM USING BLOCKCHAIN 2.0

The modern pharmaceutical supply chain is a complex process in which researches is carried out to produce drugs and according to the research made the drugs is manufactured and these drugs are distributed from the manufacturer to the pharmacies.This process involves a number of stakeholders from manufacturer, wholesaler, distributor, pharmacies and finally end-users, the patients.At each stage, inventory must be managed based on demand in pharmaceutical products.Inventory management system is vital because an excess of inventory could cost the pharmacy money and it is more complex because it needs to track the lot numbers and expiration dates of medicines.Pharmaceutical supply chain suffers from various issues such as drug shortages, temperature control of drugs, lack of visibility in shipment and storage, inventory management, lack of coordination and the most important one drug counterfeiting.The principal regulatory bodies responsible for drug quality try to solve these issues by various ways.But they are very much unregulated, expensive and fragmented.The blossoming technology, Blockchain by its inherent properties such as immutability, transparency, and distributed nature could solve the problems of pharmaceutical supply chain.This paper starts with an introduction about Drug supply chain and their problems.It elucidates how Blockchain Technology could come to rescue pharmaceutical supply chain.This paper proposes a novel drug supply chain management system along with inventory management based on Blockchain 2.0 specifically Hyperledger Fabric.The proposed system records all transactions using Blockchain, thus helping in tracking the drug along its supply chain as well as solving the issues associated with drug supply chain in an efficient manner.It also measures performance of the proposed system using the Benchmark tool called Hyperledger caliper.The system proves its efficiency in terms of success rate, throughput and transaction latency.
Unification in lax logic

In this paper, we focus on the intuitionistic propositional logic extended with a local operator [22] (also called nucleus [21]); such logic is commonly named lax logic after [9]. We prove that unification is finitary in this logic and supply algorithms for computing a basis of unifiers and for recognizing admissibility of inference rules, following analogous known results for intuitionistic logic.
MACHINE LEARNING FOR MAPPING SPATIAL DISTRIBUTION OF THICKNESS AND CARBON STOCK OF TROPICAL PEATLAND BASED ON REMOTE SENSING DATA: A CASE STUDY IN LAKE SENTARUM NATIONAL PARK, INDONESIA

Indonesia is one of the countries with the largest area of tropical peatlands in the world.These wide peatlands have a vital role in the carbon cycle and carbon storage in huge quantities, thus strict conservation in the area is necessary.One effort to carry out conservation is to understand the spatial distribution of carbon stock in peatlands.This study aims to map the spatial distribution of carbon stock based on peat thickness modeling using machine learning algorithms, i.e., Random Forest (RF), Quantile Regression Forests (QRF), and Cubist.A case study was conducted in a part of Lake Sentarum National Park, Indonesia where human interference is still limited.Digital elevation model (DEM) and synthetic aperture radar (SAR) data were included as the input variables.The results showed that RF performed the best among other models to estimate peat thickness (mean = 1.877 m) with an RMSE (root mean square error) of 0.483 m and an R 2 of 0.786.DEMs are the most important parameters in our analysis compared to SAR data.Based on the best model, we estimated the total volume of 5,112,687 m 3 in the study area, produced at 12.5 m resolution, which was then converted to a total of carbon stock at 0.337 ± 0.106 Mt carbon.
Temporal scatterplots

Visualizing high-dimensional data on a 2D canvas is generally challenging. It becomes significantly more difficult when multiple time-steps are to be presented, as the visual clutter quickly increases. Moreover, the challenge to perceive the significant temporal evolution is even greater. In this paper, we present a method to plot temporal high-dimensional data in a static scatterplot; it uses the established PCA technique to project data from multiple time-steps. The key idea is to extend each individual displacement prior to applying PCA, so as to skew the projection process, and to set a projection plane that balances the directions of temporal change and spatial variance. We present numerous examples and various visual cues to highlight the data trajectories, and demonstrate the effectiveness of the method for visualizing temporal data.
Local Path-Planning Simulation and Driving Test of Electric Unmanned Ground Vehicles for Cooperative Mission with Unmanned Aerial Vehicles

Recently, various studies related to the development of unmanned vehicles have been conducted around the world. In particular, unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) have been developed and utilized for various purposes. In this study, we developed a method for the path generation of UGVs in a system in which one operator controls many different types of unmanned vehicles. In the driving control system (DCS), it is necessary to process sensor data such as GPS/INS and LiDAR when generating a path by receiving the target waypoint from the ground control station. In addition, the DCS must upload the current location, posture, state, etc., as well as save driving log. Therefore, in order to recognize obstacles in real time and generate a path, a safe path generation algorithm with a short computation time is required. Among the various path generation methods, the potential field algorithm was selected, and the algorithm was modified to reduce the computation time. The computation time before and after modification of the algorithm was obtained and compared through simulation, and the algorithm was verified through application to an actual system by performing an obstacle avoidance experiment and a simultaneous control experiment for two UGVs.
Automatic generation of language exercises based on a universal methodology: An analysis of possibilities

The aim of the paper is to examine the possibilities for automatic generation of language learning exercises and compare them to those manually compiled by language instructors. The paper first presents a universal methodology applied in manually created exercises for learning the language for specific purposes, elaborated with examples in the field of academic English. Next, the automation of the procedure is explored through a series of steps which include creating the corpus, analysing each exercise type and the possibility of its automatic generation, automatically generating the exercise, and evaluating the end result. The results of the evaluation suggest that automatic generation of exercises can serve as a preliminary step of a two-stage process of exercises development in which each exercise, however, needs additional approval from the language expert.
Support Vector Quantile Regression for the Post-Processing of Meso-Scale Ensemble Prediction System Data in the Kanto Region: Solar Power Forecast Reducing Overestimation

Although the recent development of solar power forecasting through machine learning approaches, such as the machine learning models based on numerical weather prediction (NWP) data, has been remarkable, their extreme error requires an increase in the amount of reserve capacity procurement used for the power system safety. Hence, a reduction of the serious overestimation is necessary for efficient grid operation. However, despite the importance of the above issue, few studies have focused on the model design, suppressing serious errors, to the best of the authors’ knowledge. This study investigates a prediction model that can reduce the huge overestimation of the solar irradiance, which poses a risk to the power system. The specific approaches used are as follows: the employment of Support Vector Quantile Regression (SVQR), the utilization of Meso-scale Ensemble Prediction System (MEPS, Meso-scale EPS for the regions of Japan) data, which is based on the forecasts from Meso-scale Model (MSM) as explanatory variables, and the hyperparameter adjustment. The performance of the models is verified in the one day-ahead forecasting for surface solar irradiance at five sites in the Kanto region as the numerical simulation, where their forecasting errors are measured by the root mean square error (RMSE) and the 3σ error, which corresponds to the 99.87% quantile error of the order statistics. The test results indicate the following findings: the SVRs’ RMSE and 3σ error tend to be trade-offs in the case of varying the penalty of the regularization term; by using SVR as a post-processing tool for MSM or MEPS data, both of the score of their metrics can be improved from original data; the MEPS-based SVQR (MEPS-SVQR) could provide superior performance in both metrics in comparison with the MSM-based SVQR (MSM-SVQR) if the parameters are properly adjusted. Although the time period and the type of MEPS data used for the validation are limited, our report is expected to help the design of NWP-based machine learning models to enable short-term solar power forecasts with a low risk of overestimation.
Weight Reduction of a Drone Using Generative Design

Generative design has the potential to be optimized with different parameters using a design method based on artificial intelligence and by defining the design problem. The use of this method on a drone frame is presented with explanations of the various design phases. The goal of the optimisation was to be able to fit a battery with a larger capacity onto the Unmanned Aerial Vehicle and compensate for its increased weight by reducing the weight of the drone frame using a generative algorithm. As the production possibilities were limited, adaptation to the selected manufacturing technology was also taken into account during the optimization.
DAD: Data-free Adversarial Defense at Test Time

Deep models are highly susceptible to adversarial attacks. Such attacks are carefully crafted imperceptible noises that can fool the network and can cause severe consequences when deployed. To encounter them, the model requires training data for adversarial training or explicit regularization-based techniques. However, privacy has become an important concern, restricting access to only trained models but not the training data (e.g. biometric data). Also, data curation is expensive and companies may have proprietary rights over it. To handle such situations, we propose a completely novel problem of ‘test-time adversarial defense in absence of training data and even their statistics’. We solve it in two stages: a) detection and b) correction of adversarial samples. Our adversarial sample detection framework is initially trained on arbitrary data and is subsequently adapted to the unlabelled test data through unsupervised domain adaptation. We further correct the predictions on detected adversarial samples by transforming them in Fourier domain and obtaining their low frequency component at our proposed suitable radius for model prediction. We demonstrate the efficacy of our proposed technique via extensive experiments against several adversarial attacks and for different model architectures and datasets. For a non-robust Resnet-18 model pretrained on CIFAR-10, our detection method correctly identifies 91.42% adversaries. Also, we significantly improve the adversarial accuracy from 0% to 37.37% with a minimal drop of 0.02% in clean accuracy on state-of-the-art ‘Auto Attack’ without having to retrain the model.
Development of the teacher&amp;apos;s competence in the conditions of additional professional education

The article reveals the essence of the competency approach in the process of teachers’ qualification development. The specificity of additional professional education, its advantages in comparison with the basic training of a teacher at a university are characterized. The content of the concepts of &amp;quot;competence&amp;quot; and &amp;quot;professional competence&amp;quot; of a teacher in the innovative conditions of education development is presented, their scientific and theoretical justification is given based on the analysis of scientific and pedagogical sources. The main guidelines and pedagogical conditions of the process under study are identified; the new functions of the teacher in the aspect of the implementation of new guidelines in education are described. The necessity of updating the content and technology of the organization of the educational process within the framework of additional professional education organized on the basis of a higher educational institution is actualized. A description of the five-year work experience of the Ryazan State University named after S.A. Yesenin on the organization of advanced training of teachers in the competence paradigm in the city of Ryazan and the Ryazan region: the key objectives of the course training of teachers, the problematic thematic field of the programs of advanced training and retraining of teaching staff on popular topics are identified, the results of the experimental experience of the university on the development of the competence of certified teachers at the stage of postgraduate education are analyzed. In conclusion, the article substantiates the prospects for the teacher&amp;apos;s competence development: the development of the educational process and the quality of education.
Lexical Diversity in Statistical and Neural Machine Translation

Neural machine translation systems have revolutionized translation processes in terms of quantity and speed in recent years, and they have even been claimed to achieve human parity. However, the quality of their output has also raised serious doubts and concerns, such as loss in lexical variation, evidence of “machine translationese”, and its effect on post-editing, which results in “post-editese”. In this study, we analyze the outputs of three English to Slovenian machine translation systems in terms of lexical diversity in three different genres. Using both quantitative and qualitative methods, we analyze one statistical and two neural systems, and we compare them to a human reference translation. Our quantitative analyses based on lexical diversity metrics show diverging results; however, translation systems, particularly neural ones, mostly exhibit larger lexical diversity than their human counterparts. Nevertheless, a qualitative method shows that these quantitative results are not always a reliable tool to assess true lexical diversity and that a lot of lexical “creativity”, especially by neural translation systems, is often unreliable, inconsistent, and misguided.
A Novel Image Splicing Detection Algorithm Based on Generalized and Traditional Benford’s Law

Due to the ease of access to platforms that can be used by forgers to tamper digital documents, providing automatic tools for identifying forged images is now a hot research field in image processing. This paper presents a novel forgery detection algorithm based on variants of Benford's law. In the proposed method, Mean Absolute Deviation (MAD) feature is extracted using traditional Benford's law. Also, generalized Benford's law is used for mantissa distribution feature vector. In addition to Benford's law-based features, other statistical features are used to construct the final feature vector. Finally, support vector machine (SVM) with three different kernel functions is used to classify original and forged images. The method has been tested on two common image datasets (CASIA V1.0 and V2.0). The experimental results show that 0.27% and 0.21% improvements on CASIA V1.0 and CASIA V2.0 datasets were achieved, respectively in detection accuracy by the proposed method in comparison to best state-of-the-art methods. The proposed efficient algorithm has a simple implementation. Moreover, on the basis of Benford’s law rich features are extracted from images so that classification process is efficiently performed by a simple SVM classifier in a short time.
Utility Frequent Patterns Mining on Large Scale Data based on Appriori MapReduce Algorithm

Pattern mining is a standout amongst the majority essential responsibilities to separate significant and helpful data from unprocessed data. Here the work intends to separate itemsets are speak to a homogeneity and consistency in data. At present techniques have been produced in such manner; the developing enthusiasm for data have cause of execution of presented Pattern Mining procedures to be drop. The objective of article, to enhance new productive “PM Algorithms” to work on huge data. At this situation, a progression of techniques dependent on MapReduce structure and the hadoop environment has been proposed. Here enhancement technique is in stages, initial two algorithms Apriori MapReduce through no prune methodology are planned, and it separates any current itemset in data. Second, “Space pruning AprioriMR” and it prunes hunt space by methods for the exceptional of monotone properties are proposed.
ANALISIS KINERJA WEBSITE PEMERINTAH KOTA PEKALONGAN

The Pekalongan City Government has an official website as a form of service to the community. Website is one form of implementation of E-Government. Website maintenance and repair needs to be done regularly to improve services to the community. There are so many visitors, of course, it becomes a problem when simultaneously accessing a website. For this reason, it is necessary to carry out a work analysis from the Pekalongan City Government website. In analyzing the Pekalongan City Government website, this study used performance testing especially using load testing with several variables including Internet speed, threads (virtual users), ramp-up period, repetition. The tool used to perform the analysis is JMeter. The results of the tests carried out are: for an access speed of 1.8 MBps, the server can only serve about 16.8 requests every minute. This of course can cause the website to go down which ultimately cannot be accessed when visited by many visitors. Keywords: Performance testing, load testing, JMeter.