Reviewerly/exHarmony huggingface dataset:
Numerical Cognition Based on Precise Counting with a Single Spiking Neuron

(iScience 23, 100852-1–100852-12; February 21, 2020) In the originally published article, labels of Figures 1B-1D and 2A-2C contain spelling errors in important technical terms. Specifically, Figures 1B-1D, 2A and 2B state "Mometum" whereas the correct term is "Momentum," and the y-axis label of Figure 2C says "variacne" instead of "variance". Additionally, the title of Figures 2A and 2B have been corrected to proper spellings of "Homogeneous" and "Inhomogeneous." The corrections of these typos do not affect any of the reported results or conclusions of the paper but nevertheless should be stated correctly. The authors sincerely apologize to the readers for this unintended lack of thoroughness and any confusion that may have resulted from these typos.Figure 1. Comparison of Training Convergence for Momentum and Adaptive Learning under Different Background Noise Conditions (original)View Large Image Figure ViewerDownload Hi-res image Download (PPT)Figure 2. Training Convergence Properties of Momentum and Adaptive Learning (corrected)View Large Image Figure ViewerDownload Hi-res image Download (PPT)Figure 2. Training Convergence Properties of Momentum and Adaptive Learning (original)View Large Image Figure ViewerDownload Hi-res image Download (PPT) Numerical Cognition Based on Precise Counting with a Single Spiking NeuronRapp et al.iScienceJanuary 21, 2020In BriefNeuroscience; Cognitive Neuroscience; In Silico Biology Full-Text PDF Open Access
Clout Chasing for the Sake of Content Monetization: Gaming Algorithmic Architectures with Self-Moderation Strategies

This short discussion paper addresses how controversy is monetized online by reflecting on a new iteration of the shock value in media production, identified on social media as the ‘clout chasing’ phenomenon. We first exemplify controversial behavior, and subsequently proceed to defining clout chasing, which we discuss this concept in relation to existing frameworks for the understanding of controversy on social media. We then outline what clout chasing entails as a content monetization strategy, and address the risks associated with this approach. In doing so, we introduce the concept of ‘content self-moderation,’ which encompasses how creators use content moderation as a way to hedge monetization risks arising out of their reliance on controversy for economic growth. This concept is discussed in the context of the automated content governance entailed by algorithmic platform architectures, to contribute to existing scholarship on platform governance.
Unreal Collaboration: Exploring the Use of Formal Collaborative Learning Strategy in Games Development Coursework

s research focuses on gaming, simulation and computer programming education and how these technologies can more effectively address
Experimental analysis and comparative study of secure data outsourcing schemes in cloud

When customers are transferring their private data to any third party, then there is much responsibility of both security and compliance. The outsourcing process is computationally secure if it is performed without unveiling to the other external agent or cloud, either the original data or the actual solution to the computations. Secure multiparty computation computes a certain function without revealing their private secret information. Homomorphic encryption is another solution that can deal with this situation. In homomorphic encryption, ciphertext (data in encrypted format) should be sent to the cloud, the computations are made on the ciphertext, and the result of this computation is a ciphertext form itself. If the result of the computation is decrypted, then the correct plaintext result must be obtained. In this paper, we summarised our proposed secure outsourcing computing protocols. As a cloud application, our proposed protocols encrypt data by different users and further are transformed to cloud. By utilising our protocols, we performed experimental analysis on virtual cloud to examine the novelty in terms of computational and communication complexity. Our implementations involve both CPU and GPU-based simulation results. We improved our adopted procedure to achieve better speed-up and security.
ANN Based Block Chain Security Threat Mechanism

Blockchain was first introduced to the world in a form of crypto currency. Since then it’s grabbing popularity day by day and has already started changing lifestyle and business process in some areas. The Blockchain is a data structure, and consists of time-stamped list of blocks, used to create a digitally secured, transactional ledger that, facilitates the process of recording transactions and tracking assets in a distributed business network instead of resting with a single provider. It brings a paradigm shift from the conventional way of storing transactional data, exchanging value, assets decentralizing and sharing data across a large network of untrusted participants without intervention of any centralized trusted agency, but still building a trusted system without compromising data integrity, security and reliability. This paper is based on survey of different consensus mechanisms already available so far and an introduce our own approach to tack one of the security threats by introducing a Neural Network to the consensus mechanism of Blockchain.
PENGUKURAN KINERJA JURNAL ILMIAH TERAKREDITASI DI BATAN (ANALISIS BIBLIOMETRIK)

Measurement of the performance of scientific journals needs to be done to evaluate and improve the quality, quantity and scientific impact of R&amp;D practitioners. This study aims to evaluate the performance of accredited scientific journals in BATAN to improve the quality of journal management more optimally. The method used is bibliometric analysis. Data sources were obtained from the journal portal at http://jurnal.batan.go.id. Data processing and analysis using Microsoft Excel, Publish or Perish Software (PoP) and VOSviewer. The results of the study show that all journals have a good degree of scale. There are 4 journal titles that have not been indexed in DOAJ. The most productive writer is Wisnu Ari Adi, and the most cited are S. Hartanto and Ratnawati. Journal metric analysis results show the scientific impact measured by the number of articles cited included in the high category. The most productive and cited journal is J. Sains Materi Indonesia, but the journal that has the highest ratio of the number of cytities is Urania at 1,44. Visualization of collaboration shows the collaboration between authors obtained from eight accredited journals.
Asymmetric key Cryptography using Laplace Transform

This paper presents a method of Asymmetric key cryptography using Laplace transform and inverse Laplace transform respectively on Maclaurin’s series to attain information and network Security. The public key and private key are used to encrypt and decrypt data in Asymmetric cryptography. Public and private key are generated using Encryption and Decryption algorithms with a numerical example. Frequency allocations of characters in plain text file and cipher text file with proposed algorithm are analyzed using bar diagrams. It has been observed that the repeated character in encipher file has same frequency while running ElGamal and RSA encryption algorithms but differ in proposed algorithm. Time complexity of each algorithm is tested for distinct file size and is presented in a suitable table. Statistical analysis for the proposed algorithm is performed using coefficient correlation and compared with ElGamal, RSA algorithms. All these tests ensure that the proposed algorithm provide network security and key recovery attack.
Reflections on Online Delivered Lessons, During COVID-19 Induced Restrictions, for Students of Informatics in a Vocational Lyceum of Greece

During the pandemic outbreak of COVID-19, coinciding with school year 2019-2020, house confinement measures were taken throughout Greece. Conventional face-to-face schooling presented a threat to public health. As a result the Greek Ministry of Education proceeded to suspend school operations for a while and sponsor online lessons of various shapes and forms as a temporary measure soon after. The lessons, hastily designed to address extraordinary conditions and delivered in an ad hoc manner utilizing teachers’ personal experiences and preferences to fill a gap created by house confinement, created pressures on existing infrastructures on one hand, and a corpus of diverse experiences, contradicting evidence and on occasion reports on the other. A discussion on that corpus was inevitable and took part originally among teachers of the same school either unofficially or during sessions of the teachers associations of each school. The discussion should and will expand beyond that especially in reference to cases that constitute good practices or where data were collected. To this purpose, this paper presents the case of two theoretical lessons taught at senior high-school students of the Information Applications’ Technician specialty in a Vocational Lyceum. The lessons operated surprisingly well, in comparison to similar lessons, concerning student attendance, student engagement level and continuance. Some data were collected with a questionnaire and their processing later informed discussions during reflection plenary sessions of the online class. An attempt is made to explain the divergence from the general case and reflection on results is used to draw conclusions that may benefit further attempts.
STATE NATIONAL PROGRAM FOR THE DEVELOPMENT OF SCHOOL EDUCATION FOR 2004–2009 YEARS: (SCIENTIFIC AND HISTORICAL ANALYSIS).

The article from a historical and scientific point of view analyzes the problems and reforms, as well as the concrete measures implemented to solve them in the public education system of Uzbekistan in the framework of the state National program for the development of school education for 2004-2009 years.
PERANCANGAN APLIKASI PEMANTAUAN KINERJA KARYAWAN BERBASIS ANDROID DI PT. SALESTRADE CORP. INDONESIA

The challenges facing the organization oriented profit and non profit currently is more complex. PT. Salestrade Corp. Indonesia is a national private company which is engaged in the electrical distributor of various companies in several countries in the world. However, in terms of administration, there is still much that needs to be improve, including in the case of handwritten work reports, attendance has not been recorded properly, there is no control regarding work in the field and there is no system that monitors employee performance. The implementation of this system is not effective and efficient. Therefore, a company needs a computerized system and requires a database to facilitate data storage and access data if needed at any time in order to produce useful information for the company. For that we need an application that functions to monitor employee performance and organize administration and company reports using an Android-based application. With this application it is hopefully that it can help make it easier for companies and employees to make work and attendance reports and make it easier for management to monitor employee performance so that administratively it becomes tidier.
Rosetta Stone CALL Software as A Vocabulary Teaching Media at Indonesian High Schools

Vocabulary is the basic aspect of language. The ability to master vocabulary holds an important part in learning a language, including English. That becomes one of the researcher's considerations to conduct this research when observing at one of Indonesia's high schools since the students show difficulties in vocabulary mastery. Modifying the teaching media can become a problem solving for students' lack of vocabulary mastery. In this case, using Rosetta Stone CALL software is suggested to overcome the problem. The research's objective was to determine whether or not the use of Rosetta Stone CALL software can improve the tenth-grade students' vocabulary mastery. The researcher applied a quasi-experimental method, with two group pre-test and post-test design. The research sample was forty students from two classes taken from the population of the 10th-grade students of SMA Negeri 2 Barru. The data analysis result showed that the pre-test's mean score in the experimental class was 2.21, and the post-test was 3.04. It proves that the tenth-grade students' vocabulary mastery improved using Rosetta Stone CALL Software. After analyzing the data using SPSS version 21, the probability value in the post-test was 0.01, and the significance value was 0.05. It means that the probability value (0.01) was lower than the significance value (0.05). It was indicated that H0 was rejected and H1 was accepted. It revealed that the vocabulary mastery of the tenth-grade students of SMA Negeri 2 Barru for the experimental and control groups was significantly different.
A Novel CBIR-Based System using Texture Fused LBP Variants and GLCM Features

This paper presents Multiple-features extraction and reduction for Content-Based Image Retrieval (CBIR). At first, discrete wavelet transform (DWT) is applied on the R, G and B channels to get multi-level decomposition of the image in which approximation and detailed coefficients are extracted. Approximation coefficients contain the main content of the image while detailed coefficients provide the local noise variation in the image. Further computationally efficient and rotation- invariant Dominant-Rotated Local Binary Pattern called a texture descriptor is applied on all approximation and detailed coefficients. By calculating the descriptor relative to reference in a local neighbor patch, a rotation invariance feature image is obtained. The proposed methodology contains the complete structural information extracted by the Local Binary Patterns and also extract the extra information using the information of magnitude, thus attaining extra discriminative power. Then, by getting the Dominant Rotated Local Binary Pattern image, concept of GLCM has been used to extract the statistical characteristics for the classification of texture images. GLCM directly works with the intensity of the images and also provides the spatial relationship of the pixels in the image by calculating frequency of occurring of similar patterns in different directions which makes it useful for the extraction of texture characteristics. GLCM has been improvised into a generalized co-occurrence matrix that extracts significant spatial properties from the distribution of local maxima. Further Median Robust Extended Local Binary Pattern is extracted out of the approximation and detailed coefficients and also the histogram is calculated out of them. Unlike the traditional LBP method and several variants of the LBP, Median Robust Extended Local Binary Pattern compares the local median of images instead of intensities of the raw images. It is a multiscale LBP-type descriptor that proficiently compares the image medians with novel sampling schemes, capable of capturing both microstructure and macrostructure. Further feature concatenation is applied in which GLCM features of DRLBP and histogram of MRELBP are combined for getting a large feature vector. Further, we applied a mutual information concept to sort out the most differentiable features for all categories of CORAL dataset which are further fed to particle swarm optimization-based feature selector which reduced the number of features that can be used in the classification phase. PSO uses the SVM classifier in evaluating its objective function which is average precision value for the selected features. PSO tries to increase precision value for all the categories and provides a feature vector with a large precision value when classify by SVM. Further three classifiers are trained ad tested named SVM, KNN and decision tree in which SVM gives high accuracy and precision rates of classification. Experimental results show above 94% accuracy and .80 to .90 precision values for most of the categories of CORAL dataset.
Application of K-Means Clustering Algorithm for Determination of Fire-Prone Areas Utilizing Hotspots in West Kalimantan Province

Forest and land fires are disasters that often occur in Indonesia. In 2007, 2012 and 2015 forest fires that occurred in Sumatra and Kalimantan attracted global attention because they brought smog pollution to neighboring countries. One of the regions that has the highest fire hotspots is West Kalimantan Province. Forest and land fires have an impact on health, especially on the communities around the scene, as well as on the economic and social aspects. This must be overcome, one of them is by knowing the location of the area of ??fire and can analyze the causes of forest and land fires. With the impact caused by forest and land fires, the purpose of this study is to apply the clustering method using the k-means algorithm to be able to determine the hotspot prone areas in West Kalimantan Province. And evaluate the results of the cluster that has been obtained from the clustering method using the k-means algorithm. Data mining is a suitable method to be able to find out information on hotspot areas. The data mining method used is clustering because this method can process hotspot data into information that can inform areas prone to hotspots. This clustering uses k-means algorithm which is grouping data based on similar characteristics. The hotspots data obtained are grouped into 3 clusters with the results obtained for cluster 0 as many as 284 hotspots including hazardous areas, 215 hotspots including non-prone areas and 129 points that belong to very vulnerable areas. Then the clustering results were evaluated using the Davies-Bouldin Index (DBI) method with a value of 3.112 which indicates that the clustering results of 3 clusters were not optimal.
Cartoon Character Generation using Generative Adversarial Network

Animated faces show up in cartoons, comics and games. They are broadly utilized as profile pictures in online life stages, for example, Facebook and Instagram. Drawing an animated face is work intense. Not just it requires proficient skills but also its time consuming. A lot of time is wasted in creating a cartoon character from scratch, and most of the cases ends up in creating an awkward character having very low polygon intensity. Generative adversarial network (GAN) framework can be trained with a collection of cartoon images. GANs comprises of a generator network and a discriminator network. Because of the ability of deep networks and the competitive training algorithm, GANs produce realistic images, and have great potential in the field of image processing. This method turns out to be a surprisingly handy tool in enhancing blurry images. The underlying idea behind GAN is that it contains two neural networks that compete with each other in a zero-sum game, which constitutes of generator and a discriminator.
Deep Learning based Dynamic Hand Gesture Recognition with Leap Motion Controller

Dynamic hand gesture recognition (DHGR) is an important yet difficult thing in the pattern recognizing groups and research communities.The latest establishment of new methods for acquisitions like Leap Motion and the Kinect permits deriving a vital data about hand pose which could be utilized for precisely recognizing the gestures.The previous system designed a Hidden Conditional Neural Field (HCNF) classifier with Leap Motion Controller (LMC) for DHGR.The descriptive information about the gestures by hand is obtained with the assistance of LMC.It keeps a note of the movement of hands and fingers in a digitalized manner and provides some points related to every gesture.Training and recognition is done with the help of this.However, HCNF has issue with overfitting problem due to the great expressive power, especially when trained on a small corpus.It reduces the classification accuracy.To solve this problem the proposed system designed a deep learning approach for DHGR.This system has 2 main phases such as feature extraction and classification.Initially, the acquired data from LMC is taken as an input and perform feature extraction from it.The given feature vector single-finger features and double-finger features.Based on extracted feature vector, Weighted Bias Mean based Convolutional Neural Network (WBMCNN) is utilized for DHGR.The demonstrated technique is analyzed on 2 sets of data of dynamic hand gesture accompanied by frames got with a Leap Motion Controller.The LeapMotion-Gesture3D recognizes with an accuracy of92.8% and Handicraft-Gesture dataset recognizes to an accuracy of 96.7%.This method can be specifically used for DHGR.The experimental result shows that it has a better capability in comparison with the previous methods in terms of accuracy and execution time.
An Ontology Model to Assess the Agro-Climatic and Edaphic Feasibility of a Location for Rubber Cultivation

One of the important commercial agricultural products is rubber. It is collected from the latex of the tree Hevea Brasiliensis. Because of the rubber’s invaluable qualities such as non-conduction of electricity, elasticity and resistance to water it is used for different purposes. During the last five decades, natural rubber has achieved the highest growth among major crops in the country in terms of expansion area, production and productivity. Sowing, tapping and processing are some common methods of rubber cultivation.Rubber cultivation is considered as an important source of income for rural communities. Various agro-climatic factors such as temperature, latitude, water availability, altitude, wind and edaphic factors such as soil drainage, slope, soil type, soil depth, soil water table and pH have to be considered to find the feasibility of an area for rubber cultivation. In this paper, ontology is constructed to assess the edaphic and agro-climatic feasibility of an area for rubber cultivation. Carefully-collected knowledge that is very domain-sensitive is required for constructing ontology. Ontology contains domain specific knowledge to increase the powerfulness of a particular search engine. Based on the constructed ontology, the edaphic and agro-climatic feasibility of an area is assessed for rubber cultivation.
Dual Axis Tracker for Photovoltaic Panel

Main challenge of solar-tracking systems are the sunlight detection, providing position and delay of PV movement, designing control module for low consumption dc servo motor(s). Objective of this paper is designing and implementing automatic control for detecting maximum solar light to a solar panel. The two-axis solar tracker is used for optimizing conversion of solar energy into electrical energy, at cost of the mechanical and the maintenance need, for the best efficiency. The hardware development, two dc servomotors are adjusted which is controlled by drive module moving panel by using four Light-Dependent Resistor (LDR) to provide the analog signals; the signals are processed by ATMEGA328P micro-controller with Arduino.
Can Universities Improve their Competitiveness using Information Technology

Rapid changes in information technology happen in today's era of Society 5.0 and the Industrial Revolution 4.0. Universities are expected to adapt to these changes to maintain their existence. This study examines the relationship between information technology use and organizational competitiveness in higher education. Quantitative data were collected from 29 private universities in Indonesia. Data analysis was performed using PLS-SEM. The results revealed that information technology, consisting of Information Quality, System Quality and Service Quality, had significant positive impact on the competitiveness of universities in Indonesia. This implies that it is necessary for universities to optimize the application of information technology in their business processes in order to improve competitiveness in the era of Society 5.0 and the Industrial Revolution 4.0.