Reviewerly/exHarmony huggingface dataset:
Self-Organizing Maps hybrid Implementation Based on Stochastic Computing

Internet of Things (IoT) applications and mobile systems are more and more dependent on Machine Learning based solutions, thus requiring a big computational power with a low cost in terms of power consumption. This fact has revived the interest in nonconventional hardware computing methods capable to implement complex functions in a simple way in contrast with the conventional ones. This work proposes a novel hardware/software hybrid Self-Organizing Map (SOM) implementation using stochastic computing. In turn, to support this development, several stochastic block designs are presented as the squared Euclidian distance, and the Winner-Take-All (WTA) similarity check. The capabilities and performance of the methodology is tested over a well-known classification task as the Iris flower benchmark, archiving the same classification performance than the software solutions. The proposed solution presents a low-cost methodology in terms of hardware resources and power, due to its inherent capacity to implement complex functions in a simple way. This enables the methodology to implement large self-learning classifiers based on SOM with low hardware requirements.
ANALISIS TINGKAT KERENTANAN PENCEMARAN AIR TANAH PADA WILAYAH PENAMBANGAN DAN PENGOLAHAN EMAS RAKYAT DESA PANCURENDANG, KABUPATEN BANYUMAS

The people's gold mining and processing activities have an impact on the surrounding environment. Some residents immediately dispose of waste from gold processing into the environment. Therefore, analysis of the level of vulnerability of pollution is needed to determine the size of the level of difficulty and ease of contaminated substances to influence water quality, both surface water and groundwater. The purpose of this study was to analyze the level of vulnerability of groundwater pollution around the study area.&#x0D; The method used in this research is survey method and field mapping, sampling method: purposive sampling, laboratory analysis method, mathematical method, and descriptive evaluation method. The calculation to determine the level of vulnerability of pollution is the DRASTIC method for groundwater vulnerability with 7 parameters, namely groundwater depth, rainfall, aquifer type, soil texture, slope, slope, unsaturated zone type, and hydraulic conductivity.&#x0D; Based on the results of the study it was known that the study area had 2 classifications of the level of vulnerability of groundwater pollution, namely the moderate and high classification. Areas that are located in the direction of groundwater flow and are lower than pollutant sources will potentially be more polluted.&#x0D; &#x0D; Keywords : DRASTIC, Pollution, Groundwater, Gold Mine
Schema2QA: Answering Complex Queries on the Structured Web with a Neural Model

Building a question-answering agent currently requires large annotated datasets, which are prohibitively expensive. This paper proposes Schema2QA, an open-source toolkit that can generate a Q&A system from a database schema augmented with a few annotations for each field. The key concept is to cover the space of possible compound queries on the database with a large number of in-domain questions synthesized with the help of a corpus of generic query templates. The synthesized data and a small paraphrase set are used to train a novel neural network based on the BERT pretrained model. We use Schema2QA to generate Q&A systems for five this http URL domains, restaurants, people, movies, books and music, and obtain an overall accuracy between 64% and 75% on crowdsourced questions for these domains. Once annotations and paraphrases are obtained for a this http URL schema, no additional manual effort is needed to create a Q&A agent for any website that uses the same schema. Furthermore, we demonstrate that learning can be transferred from the restaurant to the hotel domain, obtaining a 64% accuracy on crowdsourced questions with no manual effort. Schema2QA achieves an accuracy of 60% on popular restaurant questions that can be answered using this http URL. Its performance is comparable to Google Assistant, 7% lower than Siri, and 15% higher than Alexa. It outperforms all these assistants by at least 18% on more complex, long-tail questions.
A Smoothing Motion Method for High-Speed Cable-Driven Parallel Camera Robots

This study discusses trajectory planning based on a higher-order polynomial interpolation and the smoothing motion control method of high speed cabledriven parallel camera robots.High speed and high manoeuverability of cable-driven parallel robots make it difficult to achieve stable flexible starting and fast reversing motion.In the existing literature, smoothing motion methods for high-speed cable-driven parallel robots are not adequately considered.For this purpose, first, the transition trajectories of start-stop and turning of the cable-driven parallel robots are planned based on a higher order polynomial interpolation.Further, on the basis of the system dynamic model and the PD feed-forward controller, a disturbance observer is applied in order to inhibit the disturbances from trajectory mutation.Then the stability of the hybrid control strategy is proved.Finally, the main factors which influence the smoothing motion are discussed using numerical simulations and experimental tests.The results indicate that the smoothing motion method consisting of higher-order polynomial interpolation and the disturbance observer are both effective and feasible for engineering applications.
Obtaining accurate estimated action values in categorical distributional reinforcement learning

Categorical Distributional Reinforcement Learning (CDRL) uses a categorical distribution with evenly spaced outcomes to model the entire distribution of returns and produces state-of-the-art empirical performance. However, using inappropriate bounds with CDRL may generate inaccurate estimated action values, which affect the policy update step and the final performance. In CDRL, the bounds of the distribution indicate the range of the action values that the agent can obtain in one task, without considering the policy’s performance and state–action pairs. The action values that the agent obtains are often far from the bounds, and this reduces the accuracy of the estimated action values. This paper describes a method of obtaining more accurate estimated action values for CDRL using adaptive bounds. This approach enables the bounds of the distribution to be adjusted automatically based on the policy and state–action pairs. To achieve this, we save the weights of the critic network over a fixed number of time steps, and then apply a bootstrapping method. In this way, we can obtain confidence intervals for the upper and lower bound, and then use the upper and lower bound of these intervals as the new bounds of the distribution. The new bounds are more appropriate for the agent and provide a more accurate estimated action value. To further correct the estimated action values, a distributional target policy is proposed as a smoothing method. Experiments show that our method outperforms many state-of-the-art methods on the OpenAI gym tasks.
Development Study and Effectiveness of Online Data Based Scientific Writing Model Using Endnote Application for MGMP Chemistry Teachers Padang Panjang City

In writing scientific work, a work system is needed online (online) so that the search process is fast and accurate. The online data-based writing model with endnote application is an alternative model of scientific writing that can be applied to school teacher teachers and lecturers. The purpose of this study was to develop a model of scientific writing and measure the effectiveness of its application to MGMP Chemistry teachers in Padang Panjang City. The method used is the development of the ADDIE model and the success test with CV (Coefficient of Variations). Implementation is carried out on all Chemistry teacher teachers who are members of the Padangpanjang City Chemical MGMP. Each teacher will receive training and assistance in creating an Online Library as a database. The training continued with the application of the preparation of scientific works based on student learning outcomes data in each school. From the results of the study, it was found that the level of ease in literacy and compilation of scientific work reached 70% SS and 30% S. The level of interest of Master in compiling scientific work with Endnote reached 30%. SS san 70% S. The desire of participants to start compiling scientific work reaches 20% US, 60% S and 20% SS. The training effectiveness test found that the coefficient of variation had decreased from 2.75% to 1.63%, so it could be concluded that the model for the preparation of scientific work was successfully applied to the Teachers of the Padangpanjang Chemical MGMP.
512 bit-SHA3 design approach and implementation on field programmable gate arrays

&lt;p&gt;In this work, the authors consider the newly selected Hash Secure (SHA-3) algorithm on FPGA Gateway. The design is logically optimized for zone efficiency by combining the Rho steps and the one-pass algorithm. Logically recording these three steps registers leads to usage 16% of the logical resources for all implementations. This in turn reduces the latency and increases the maximum operating frequency of the project. It uses only 240 sections and has a frequency of 301.02 MHz compared to the design results with the previous FPGA implementation described in SHA3-512, the design shows the Throughput-Per-Slice (TPS) ratio of 30, 1.&lt;/p&gt;
Usable Disk Space Control Based on Hadoop Job Features

Hadoop is an open-source platform for big data processing. In the case of analyzing extremely big data, hard disk drives are usually used. Hard disk drives have different performance depending on where data is placed on the disk. In other published work, an approach for improving Hadoop I/O performance by using features of Hadoop jobs to decide on the location of data placement was proposed. This approach separates the entire hard disk drive space into two areas, which are outer and inner areas. However, this approach did not actively utilize the fastest available zones in each area. In this paper, we propose a new method for improving this method by active usage of the fastest available zones in each area. Our evaluation of the proposed method with a popular Hadoop benchmark shows that the new method can improve job performance by 9% when compared with existing approach.
Concept drift in bias and sensationalism detection

Due to easy dissemination of news in social media and the Web, there has been an increasing rise of disinformation on important political issues like elections in recent years. Computational solutions for automatic bias and sensationalism detection for news articles can have tremendous impact if used in the right way. Because news is an ever-shifting domain, concept drift is an issue that must be dealt with in any real-world computational news classification system that relies on features and trained machine learning models. Yet, an empirical study of concept drift in such systems, especially popular systems released recently as open-source and used within organizations, has been lacking thus far. This short paper reports results on an empirical study specifically designed to assess concept drift, using an open-source, popular computational news classification system, on real news data crawled from the Web. We find that even a gap of two years (2017 vs. 2019) can lead to significant concept drift, a far narrower gap than observed in traditional machine learning domains, making deployment of pre-trained or openly available computational news classification models an ethically suspect issue.
Media Podcast terhadap Kemampuan Menyimak

The objective of this research was to determine the effect of podcast media on the listening comprehension on subjects Bahasa Indonesia in class IV SD Negeri Parepare. This research uses a quantitative approach to the type of experimental research and uses the Quasy Experimental design in the form of The Nonequivalent Pretest-Posttest Control Group Design. The sampling technique used was purposive sampling with a sample of 46 students. Data collection techniques used in this research are tests in the form of multiple choices and documentation. Data analysis used descriptive statistical analysis techniques and inferential statistical analysis techniques. In testing the hypothesis in inferential statistical analysis using the Paired Sample T-test. The result is that podcast media affects the increase in listening comprehension on subjects Bahasa Indonesia in class IV SD Negeri 55 Parepare.
An Efficient Algorithm to Find the Height of a Text Line and Overcome Overlapped and Broken Line Problem during Segmentation

Line segmentation is a critical phase of the Optical Character Recognition (OCR) which separates the individual lines from the image documents.The accuracy rate of the OCR tool is directly proportional to the line segmentation accuracy followed by the word/character segmentation.In this context, an algorithm, named height_based_segmentation is proposed for the text line segmentation of printed Odia documents.The proposed algorithm finds the average height of a text line and it helps to minimize the overlapped text line cases.The algorithm also includes post-processing steps to combine the modifier zone with the base zone.The performance of the algorithm is evaluated through the ground truth and also by comparing it with the existing segmentation approaches.
NUMERICAL ALGORITHM FOR FINDING OPTIMAL INITIAL CONCENTRATIONS OF CHEMICAL REACTIONS

This article is devoted to the problem of developing methods for mathematical modelling in the sphere of optimal planning in a chemical experiment. In the article, the problem of finding the optimal ratio for initial concentrations of substances is formulated in general terms and an algorithm for solving this problem is constructed basing on the method of artificial immune systems. The developed algorithm for finding the optimal initial concentrations of substances allows solving the problem of experiment planning in chemistry at the computational experiment stage. In this case, the solution of the optimization problem found with its help does not depend on the choice of the initial approximation. The algorithm was tested for the industrially meaningful process of benzilidenebenzilamine synthesis for which the optimum values of the initial concentrations were calculated in order to obtain maximum yield of the reaction product.&#x0D; ABSTRAK: Artikel ini ditujukan untuk masalah mengembangkan kaedah pemodelan matematik dalam bidang perancangan optimum dalam percubaan kimia. Dalam artikel itu, masalah mencari nisbah optimum untuk konsentrasi awal bahan digubal secara umum dan algoritma untuk menyelesaikan masalah ini dibina berdasarkan kaedah sistem imun buatan. Algoritma yang dibangunkan untuk mencari kandungan konsentrasi awal yang optimum membolehkan menyelesaikan masalah perancangan percubaan dalam kimia pada peringkat percubaan pengkomputeran. Dalam kes ini, penyelesaian masalah pengoptimuman yang didapati dengan bantuannya tidak bergantung pada pilihan permulaan awal. Algoritma ini telah diuji untuk proses sintesis benzilidenebenzilamin yang bermakna industri yang mana nilai-nilai optimum kepekatan awal dikira untuk mendapatkan hasil maksimum produk reaksin.
Pengujian Black Box pada Aplikasi Sistem Penunjang Keputusan Seleksi Calon Pegawai Negeri Sipil Menggunakan Teknik Equivalence Partitions

Support System for Decision on the Selection of Prospective Civil Servants is still an error when making changes to the data that has been entered, so that it can cause data can not be updated. So it needs to be tested to find problems with this software. Tests conducted on this software use the Black Box method based on Equivalence Partition as a whole with regard to the use, benefits, and results found from the use of the software. Equivalence Partition-based Black Box method tests the quality of the application that will be carried out by software testing documentation with the discovery of errors in each form that is divided into three error models, including errors in Functions, Data Structures and Interfaces. This test provides results where we will find out whether the software This has an error or not, and this test can guarantee the quality of this software.
Fast Convergent Gravitational Search Algorithm

Gravitational search algorithm (GSA) is modeled on the theory of Newton’s law of gravity and motion. It is a well-known swarm intelligence (SI)-based heuristic optimization technique. GSA also experienced the problem of slow convergence like other SI-dependent algorithms. Hence, to improve the convergence and exploitation capability, a variant of GSA is introduced. The proposed algorithm is specified as fast convergent gravitational search algorithm (FCGSA). In FCGSA, a sigmoidal function is used that controls the number of agents that employ the force to another agent in the search region, to enhance the convergence speed. Further, in the location modification process of FCGSA, the step size is exponentially computed, leads to improve exploitation capability. The intended algorithm is compared with basic gravitational search algorithm (GSA), spider monkey optimization (SMO), biogeography optimization algorithm (BBO), and one of its recent variant fitness-based gravitational search algorithm (FBGSA) over 15 benchmark functions and the obtained outcomes prove the performance of the introduced algorithm.
TRANSFORMATIONAL AND TRANSACTIONAL LEADERSHIP STYLES TOWARDS ORGANIZATIONAL COMMITMENT IN THE HOTEL INDUSTRY

In the hotel industry, high service quality has become a concern to the hotel service providers as a means for gaining a competitive advantage since hotels provide similar services. Apart from that, hotels need to have committed staff towards improving the quality of service. In this context, the organizational commitment of operational employees is necessary for providing better services for the guest. The leadership styles in the hotel industry can provide different motivators that lead to organizational commitment. Therefore, the purpose of the study is to examine the perception of the relationship between leadership styles (transformational and transactional) and organizational commitment. The quantitative approach was applied in this study by using questionnaires through a survey. The data analysis was tested by using the SmartPLS 3.0 software. The results suggested that transactional leadership style and transformational leadership styles have a positive relationship with organizational commitment.
Hand ControlAR: An Augmented Reality Application for Learning 3D Geometry

The traditional way of learning geometry cannot provide a great support for novice students since the geometric figures are 2D on the blackboard or the book. In consideration that Augmented Reality(AR) provides an intuitive way to learn geometry, an interactive AR system that enables students to naturally and directly manipulating 3D objects through hand gesture-based interactions and intuitively explore the spatial relationship between spheres and polyhedrons is proposed in this paper. The proposed gesture-based interaction enables the user manipulate AR objects in the real 3D space instead of 2D space. We design three levels of study to enable students to learn the geometric concepts as well as an experiment to evaluate the effectiveness of the AR system. Analysis of experimental results showed that the proposed system is easy to use, attractive, and helpful for students.
Energy-Efficient Path Planning: Designed Software Implementation

The paper continues developing the method of a real-time quasi-optimal trajectory planning for anthropomorphic manipulator.Our goal includes software implementation of previously designed methods and algorithms of the quasi-optimal trajectory for anthropomorphic manipulator.The general structure and interrelation of the developed methods and algorithms is described, as well as their significance for obtaining the final result.Component diagrams and communication diagrams are presented, on the basis of which the interfaces of software components are built.Their structure and composition of each software component are given.The description of the interfaces of various components of the system allows the use of design components as part of other software systems, for example, designed to control the manipulators of an anthropomorphic robot or as part of other modeling systems.The illustration of the developed software implementation is given.For the software implementation performed by the author, with standard input data, the necessary variable memory capacity is about 212 Kbytes, which allows using the proposed software implementation on the computer systems of existing manipulators in real time.
Tweet Sentiment Analysis Regarding the Brazilian Stock Market

This paper describes a methodology for analyzing sentiments and for knowledge discovery in tweets regarding the Brazilian stock market. The proposed methodology starts by preprocessing and characterizing tweets to obtain an associated vector-space model. After that, a dimensionality reduction is em- ployed by using Principal Component Analysis and t-Stochastic Neighbor Embedding. Sentiment analysis of stock market tweets is performed by considering the tasks of sentiment classification, topic modeling and clustering, along with a visual analysis process. Experiments results showed satisfactory performances in single and multi-label sentiment classification scenarios. The visual analysis process also revealed interesting relationships among topics and clusters.
Building Custom HAAR-Cascade Classifier for face Detection

There are superior pre-trained HAAR-Cascade classifiers available on the Internet whose detection accuracy is quite impressive for the task of face detection in the presence of different illuminations conditions and different poses of the face.But the drawback to use such pre-trained classifies for any detection task is we never know how training of such classifiers can be done, how to prepare dataset for a particular detection task and how to use different parameters of the classifiers while training.In this paper we build our own Custom HAAR-Cascade Classifier using "Cascade Trainer GUI (a tool designed by Amin Ahmadi) to detect face/faces in any given image/images.We also create dataset which include positive and negative samples to use during training purpose.We also demonstrate how to retrain the classifier after analyzing error matrix after each detection stage and how to increase accuracy of the classifier in detection work.