Reviewerly/exHarmony huggingface dataset:
Visualisasi dan Analisa Data Penyebaran Covid-19 dengan Metode Klasifikasi Naïve Bayes

The covid-19 virus became a pandemic in 2020. The spread of covid cases has hit the whole world, reaching 63 million cases in 190 countries as of November 2020. Information regarding the spread of covid is necessary for the general public. This research will produce a system that can provide information on the geographic distribution of covid cases. The data on the distribution of covid cases in this study were also used to analyze the classification using the Naive Bayes Classifier method. The Naive Bayes Classifier method works by using probability calculations so that this research can be used to classify the covid status in an area. The results of this study have succeeded in providing information on the status of the covid pandemic based on data on covid cases that have occurred around the world. Covid case data becomes training data for the analysis of the Naive Bayes classifier method so that it can determine the status of the Covid pandemic based on test data provided by system users. This research has succeeded in helping users to know the status of the Covid pandemic in an area well because it has reliable training data.Keywords:System, Covid, Naïve Bayes Classifier.
Path-planning in 3D space using butterfly optimization algorithm

The Butterfly Optimization Algorithm is one of the most recent nature-inspired algorithms that mimic the butterflies' behavior in mating and finding food, for solving the global optimization problems.The algorithm utilizes the sense of butterflies of smelling for determining the location of nectar and find mates, which is based on the foraging strategy of those insects.This paper represents a method of using the BOA algorithm for solving the problem of path planning in three-dimensional space.The proposed method finds a path from a particular starting point to any chosen goal, where the generated final path is completely safe and collisionfree.The algorithm is based on 3 phases: the initial phase, the iteration phase, and the final phase.The movement of butterflies is based on two search moves, one of them is Local random search; where the butterfly moves randomly within the swarm, and the other is Global search; where the butterfly moves towards the best-fitted butterfly in the current population.The proposed method in this paper is able to find a collision-free path from the start point to the goal in all of the presented test environments in proximately well performance and the results were computed in terms of execution time and path length.
Novel technique for broadcast footage overlay text recognition

Living in a constant news cycle creates the need for automated tracking of events as they happen. This can be achieved through the investigation of broadcast overlay textual content. There exists a great amount of information to be deciphered via these means before further processing, with applications spanning from politics to sports. We utilize image processing to create mean cropping masks based on binary slice clustering from intelligent retrieval to identify areas of interest. This data is handed off to CEIR, based on the connectionist text proposal network (CTPN) to fine-tune the text locations and an advanced convolutional recurrent neural networks (CRNN) system to carry out text recognition to recognize the text strings. In order to improve the accuracy and reduce processing time, this novel approach utilizes a preprocessing mask identification and cropping module to reduce the amount of data being processed by the more finely tuned neural network.
Implementasi DenseNet Untuk Mengidentifikasi Kanker Kulit Melanoma

Skin is a part of a human body that covers the entire body and protect the lower layer from direct sunlight and another microorganism. Because of that, skin cells are always changing and could be changed because of genetic mutation that causes skin cancer. In general, skin cancer is divided into three groups, namely : skin cancer Basal cell carcinoma, skin cancer Squamous cell carcinoma, and skin cancer Melanoma. Melanoma skin cancer is caused by abnormal growth in melanocyte cells. Several methods are proposed to predict Melanoma skin cancer using ResNet, LeNet, and Support Vector Machine. System performance is measured based on the value of accuracy, precision, recall, and f-measure. This experiment is conducted using a Melanoma skin cancer dataset that obtained the average value in terms of accuracy, precision, recall, and f-measure are 0.94, 0.95, 0.92, and 0.94 respectively. Based on that result, the proposed DenseNet121 performs better with 0.94 accuracy, compared with ResNet, LeNet, and Support Vector Machine method.&#x0D; &#x0D; Keywords— Convolutional Neural Network; Image Classification; Melanoma Classification; DenseNet121.
Convenient Access to Archived Predictions: Canada’s CaSPAr Platform

ydrologic, land surface, and other environmental models require meteorological input data-for example, precipitation and temperature to estimate discharge and soil moisture.In a hindcast these data are usually provided by ground observations, but in forecast mode, they require forecasted meteorological inputs from numerical weather prediction (NWP) models.Archived numerical predictions are required to evaluate or improve such forecasting systems.However, archives of NWP data are rare.Furthermore, while data are usually disseminated by the agencies producing forecasts-including the European Centre for Medium-Range Weather Forecasts (ECMWF), NOAA, and Environment and Climate Change Canada (ECCC)users of existing archives often need to download the full dataset since a spatial, temporal, and/or variable selection is not possible.There are exceptions, such as NOAA's National Operational Model Archive and Distribution System (NOMADS).In this repository of weather model outputs and inputs, and a limited subset of climate model datasets generated by NOAA, the user can select variables and specific time periods.But the download options, FTP and HTTP(S), are limited to very short archives of a few Convenient
MARKEDNESS OF THEME IN EVERYTHING, EVERYTHING MOVIE DIALOG

This study entitled Markedness Realization in Everything, Everything Movie Script. The research is meant to analyze how the Theme markedness realized in the clauses of the movie script. The theory that is used in this research is Systemic Functional Linguistics (SFL) proposed by Halliday (1994), regarded with textual function that observes clause as a message and analyzes it from the thematic structure in the term of Theme-Rheme. He states that Theme can be divided into two, unmarked Theme is an element that occupies the point of departure position of the clause, conflating with the grammatical subject and marked Theme is an element other than occupies the point of departure position of the clause, but does not conflates with the grammatical subject. The method that is applied in this research is descriptive qualitative method. The source of data were one of the most viewed romantic movies in 2017 script, Everything, Everything, taken from Springfield Springfield site. Data of the research were selected, based on the second character dialog of this movie script. The purpose of this research is to indicate the four types of markedness and to describe the realization of the Theme markedness in the movie script. The research findings showed that the dominant type of markedness realization is simple unmarked Theme with 69.4% percentage. This type of markedness dominated other kinds of markedness realization in addition to simple marked Theme with 2.4% percentage, multiple unmarked Theme with 27.1% percentage, and multiple marked Theme with 0.9% percentage.
Generative Text Modeling through Short Run Inference

Latent variable models for text, when trained successfully, accurately model the data distribution and capture global semantic and syntactic features of sentences.The prominent approach to train such models is variational autoencoders (VAE).It is nevertheless challenging to train and often results in a trivial local optimum where the latent variable is ignored and its posterior collapses into the prior, an issue known as posterior collapse.Various techniques have been proposed to mitigate this issue.Most of them focus on improving the inference model to yield latent codes of higher quality.The present work proposes a short run dynamics for inference.It is initialized from the prior distribution of the latent variable and then runs a small number (e.g., 20) of Langevin dynamics steps guided by its posterior distribution.The major advantage of our method is that it does not require a separate inference model or assume simple geometry of the posterior distribution, thus rendering an automatic, natural and flexible inference engine.We show that the models trained with short run dynamics more accurately model the data, compared to strong language model and VAE baselines, and exhibit no sign of posterior collapse.Analyses of the latent space show that interpolation in the latent space is able to generate coherent sentences with smooth transition and demonstrate improved classification over strong baselines with latent features from unsupervised pretraining.These results together expose a well-structured latent space of our generative model.
Artificial intelligence and challenges for copyright law

Abstract The question of who should own the copyright of a creative work by an artificial intelligence (Al) is as yet largely unanswered. Due to the author’s increased distance from the works being created, the granting of copyright protection to AI has not been forthcoming. This article assesses the prevalence of AI- and computer-generated works in which, beyond the initial input, the works produced involve more artificial than human contribution, and the impact this has on authorship. While the main geographical focus of this article is Australia, it makes comparisons with the UK and New Zealand (NZ) to explore how Australian copyright law can be adapted to incorporate positive aspects of UK and NZ law. This article explains how the statutory provisions in both the UK and NZ are undeniably far-sighted in the modern world, in which computer-generated programmes are increasingly used. This article argues that the case-by-case basis is most suitable when deciding the human entity who is the fortunate receiver of copyright protection in works that are essentially made by an advanced non-human entity.
Confidentiality assurance in Multi-agent Robotic System

In this paper, the authors consider the feasibility of using quantum encryption in a multi-agent robotic system. The authors consider examples of such systems, describe the functional structure of the system in question, describe the scheme for ensuring information security. Finally, the authors see the use of quantum cryptographic algorithms appropriate for resolving the issue of confidentiality in multi-agent robotic systems and believe that this method has the potential for further scientific developments in the field of security in multi-agent robotic systems.
Evaluation of multimodal semantic segmentation using RGB-D data

Our goal is to develop stable, accurate, and robust semantic scene understanding methods for wide-area scene perception and understanding, especially in challenging outdoor environments. To achieve this, we are exploring and evaluating a range of related technology and solutions, including AI-driven multimodal scene perception, fusion, processing, and understanding. This work reports our efforts on the evaluation of a state-of-the-art approach for semantic segmentation with multiple RGB and depth sensing data. We employ four large datasets composed of diverse urban and terrain scenes and design various experimental methods and metrics. In addition, we also develop new strategies of multi-datasets learning to improve the detection and recognition of unseen objects. Extensive experiments, implementations, and results are reported in the paper.
Prediction Tourist Visits With Multiple Linear Regressions in Artificial Neural Networks

Tourist visit is a topic of discussion that has been much researched by previous researchers in conducting a prediction process. Many prediction models have been produced that refers to the use of several methods to obtain output in the form of information that is needed by the tour manager. Judging from the results of the study, it is still only focused on the discussion in producing output without testing the correlation of variables used as predictors. The problem in this study is how to predict the number of tourist visits by using Multiple Linear Regression (MLR) as a correlation test predictor variable and Artificial Neural Network (ANN) as a calculating machine in making predictions. The implementation of these two methods is very suitable to be used in terms of prediction, where the MLR method test results show the correlation of predictor variables used namely xxx correlation. Then in the prediction process that has been done to produce output with an accuracy value of xx%, the value of MSE xx% and RMSE is xx. Therefore, this research will be useful for managers of the tourism sector so that the goal achieved from this research is to assist the tourism office in seeing how many visits will occur in the next period.
ModifiedCritical Incident (mCIT): AMeans to Uncover Experience of Mobile Social Messaging System Using Informant-SpecificCues

Part of Human-computer interaction (HCI)researchevolves around understanding of the experiential aspects of users -their what, why and how of technology use in the domestic setting which is always privy to the users. The process of understanding is often not straightforward and a reliable means to develop thisHCI knowledge is deemed necessary. This article describes a study whereCritical Incident Technique (CIT) was adapted to HCI research and applied in the context of social messaging system use in understanding technology-mediated familial bonding enactment.CIT often involves the recalled critical incidents – or significant instances of a specific activity as experienced by research participants to recognize similarities, differences, and patterns, and to seek insights into how and why people engage in the activity of interest.However, such approach may contaminate the reliability and trustworthiness of the findings. Thus, our adapted version of CIT utilizes the procedures and logic of CIT, but differs in two ways: 1) Relying on situated and informants’ genuine critical incidents to elicit experience of interaction and 2) ‘uncritical’ incidents were also included as complementary to critical incidents to cueing informants on the phenomenon of interest. This uncritical incident represents negative evidence to counter the drawback of existing technique.Considering both critical and uncritical incidents in the studyhas provided a bigger picture and rich descriptions of the technology-mediated familialbonding enactment from both lens ofgenuine incidents and informants.
Anomaly detection with noisy and missing data using a deep learning architecture

Risk-based and automatic security systems require to monitor passengers’ whereabouts in a terminal discretely to allow timely detection of suspicious behaviors and preventing malicious actions. In a series of two papers Thomopoulos et al. have introduced a methodology providing real-time risk assessment for airport passengers based on their trajectories. The proposed methodology implements a deep learning architecture. It is fully automated, reducing the workload of the video surveillance operators leading to less error-prone conclusions. Furthermore, the proposed methodology has been integrated with the OCULUS Command &amp; Control (C2) System and the i-Crowd Simulator, a crowd simulation platform developed in the Integrated Systems Lab (ISL) of the Institute of Informatics and Telecommunications at NCSR “Demokritos.” In this paper we extend our previous work by introducing noise in both training and testing data used for tracking passengers and detecting anomalies in their tracks. Extensive testing of the anomaly detection system in the presence of noise demonstrates that the system is extremely resilient in noise. Furthermore, we consider the case of missing data in both training and testing data in order to model a realistic scenario of tracking with cameras with gaps in the passengers tracks from camera to camera due to missing data from transmission delays and/or data overflow. Extensive testing with the i- Crowd simulator demonstrates considerable robustness in the performance of the anomaly detection system in both noisy and missing data. The experimental results indicate that the proposed anomaly detection system is robust to both noisy and missing data and thus a very promising risk assessment scheme that can reliably be used for risk-based security under realistic operational conditions.
01 Using tablets in a prekindergarten classroom to foster phonological awareness

Today’s young children are exposed to a variety of digital technologies in their home andschool environments. While the presence of these technologies is increasing in USprekindergarten classrooms, teachers must critically analyze the role these tools will play inthe quest to provide early learners with developmentally appropriate practice. The benefitof using tablet-based applications, compared to traditional concrete materials, to teachphonological awareness skills is the focus of this study conducted in 2014. The sampleconsisted of 27 four- and five-year-old children, who attended a half-day early childhoodprogram, 5 days a week, in a Midwestern university laboratory school in the US. Childrenparticipated in similar small-group instruction once or twice a week for 4 months, fromSeptember to December, targeting phonological awareness skills. A comparison was madebetween those who were instructed using the traditional materials and those who usedtablet-based applications. Both groups were found to make progress toward mastering thetested skills. There was no significant difference between the two groups’ acquisition ofthese skills. Students were able to achieve targeted phonological awareness skills usingeither method of instruction.
A Combined Approach of Feature Selection and Machine Learning Technique for Handwritten Character Recognition

Handwritten Character Recognition has proven its worth in most challenging research areas in the various application based domains like bank cheques, signature verification, aids for blind persons, etc. The role of alphabetical characters is quite focused to recognize the scanned text that can be further deployed for sentimental analysis. In this research paper, we have deployed a promising approach to recognize handwritten English alphabets. We have mainly aimed in the digits recognition. The features of Handwritten Numerals are extracted using geometrical and gradient-based measures. The redundancy of feature set is reduced using feature selection method, and machine learning approach is deployed to generalize the decision boundary. The experimental outcome proves its promising worth comparable to existing arts.
Low-Rank Riemannian Optimization for Graph-Based Clustering Applications

With the abundance of data, machine learning applications engaged increased attention in the last decade.An attractive approach to robustify the statistical analysis is to preprocess the data through clustering.This paper develops a low-complexity Riemannian optimization framework for solving optimization problems on the set of positive semidefinite stochastic matrices.The low-complexity feature of the proposed algorithms stems from the factorization of the optimization variable X = YY T and deriving conditions on the number of columns of Y under which the factorization yields a satisfactory solution.The paper further investigates the embedded and quotient geometries of the resulting Riemannian manifolds.In particular, the paper explicitly derives the tangent space, Riemannian gradients and Hessians, and a retraction operator allowing the design of efficient first and second-order optimization methods for the graph-based clustering applications of interest.The numerical results reveal that the resulting algorithms present a clear complexity advantage as compared with state-of-the-art Euclidean and Riemannian approaches for graph clustering applications.
IMPLEMENTASI PENGEMBANGAN ENERGI BARU TERBARUKAN PEMBANGKIT LISTRIK TENAGA MIKRO HIDRO (PLTMH) OLEH DINAS ENERGI DAN SUMBER DAYA MINERAL PROVINSI SUMATERA BARAT DI SOLOK SELATAN

This study aims to determine the Renewable Energy Development Implementation in South Solok Regency. New and Renewable Energy is clean energy that is rich in diversity and quantity such as solar energy, wind, natural gas, ocean waves, garbage, wood, biomass, nuclear and water (microhydro). New and Renewable Energy was developed by the West Sumatra Provincial ESDM Office in the South Solok area since 2013, however, in implementing this development there were still some obstacles which was faced by the West Sumatra Provincial ESDM Office as the community which has not felt the impact of developing the benefits of PLTMH. It was because there were some PLTMH which were not functioning or have damaged caused by natural disasters such as floods and landslides, there was still a lack of technicians to repair the damage to the PLTMH, there was still a lack of costs obtained from PLTMH customers who aimed to repair damage to PLTMH generators and turbines. This study was a qualitative research which used descriptive method. The research respondents were selected by using a purposive sampling technique. For the data collection, this study used interview guidelines, observation, and documentation of the research as the instrumentation. The validation technique used the triangulation method. Furthermore, the data analysis technique was reducing data, displaying data, and drawing the conclusions of the study which has been conducted. The findings showed that the Renewable Energy Development implementation for Micro Hydro Power Plants (PLTMH) by the ESDM Office of West Sumatra Province in South Solok was still not running as it should be, because there were still some obstacles which were faced in developing of New Renewable Energy of PLTMH that the results was not being implemented properly. Supporting factors for the implementation of this PLTMH development were the data and information, communication, bureaucratic attitudes, and organizational structure. While communication, organizational characteristics, economic environment and human resources were the inhibiting factors. The Effort to overcome these obstacles was the government has signed several commitments related to the development of Renewable Energy. Beside that, to fill the conditions of the national electricity, the government has also involved the private sector
Blended learning – innovative approach for effective learning in the digital educational era

The article addresses the concept of blended learning, as an innovative opportunity for effective learning in the age of digital education. Definitions, existing models, foreign experience of implementing blended learning in the educational process are presented. To achieve this successfully, the author examines organizational strategies, information and communication technologies that allow the achievement of blended learning.