Reviewerly/exHarmony huggingface dataset:
Analysis of Supervised Learning Methods on Artificial Neural Networks

Artificial intelligence applications are now highly developed, where hardware is given the ability to think and act like humans. One method for building artificial intelligence applications is artificial neural networks (ANN). ANN is currently highly developed in areas of application that start as diverse as classification, prediction, games, robots, IoT, object/sound recognition, and so on. Unlike the human brain, which is designed to solve many problems, ANN can only solve a specific problem. To make ANN intelligent, learning methods are needed to train ANN to be able to solve certain problems with a good degree of accuracy. There are 3 learning methods commonly used to make ANN intelligent, are perceptron, backpropagation, and extreme learning method. Perceptron is the simplest learning method for the simplest ANN structure. Backpropagation was found to answer the weaknesses of perceptron which cannot solve nonlinear problems. And finally ELM exists to overcome the problem of long computational time in perceptron and backpropagation learning. This research will conduct an analysis of the three ANN learning methods using several problems. Problems used include AND logic, OR logic, XOR logic, iris datasets and MNIST digit handwritten digits. Because the ANN learning method is based on random parameters, each case will be trained 10 times for each method. After testing it is concluded that ELM has a very good speed with a pretty good degree of accuracy.
SOCIAL NETWORKS AND EDUCATIONAL PROGRAMS OF CULTURAL INSTITUTIONS IN BULGARIA – GOOD EXAMPLES IN THE CONDITIONS OF LOCKDOWN

S. Dimitrova, S. Spasova, S. Dusheva, P. GindevUniversity of Library Studies and Information Technologies (BULGARIA)
Deep learning-based detection and prediction of trending topics from streaming data

Detecting and predicting trending topics from steaming social data has always been the point of active research area in business and research firms to take quick decisions, change marketing strategies and set new goals. Topic modelling is one of the excellent methods to analyse the contents from large collection of documents in an unsupervised manner and it is a popular method used in natural language processing, information retrieval, text processing and many other research domains. In this paper, deep learning-based topic modelling technique has been proposed to detect and predict the trending topics from streaming data. The online version of latent semantic analysis with regularisation constraints has been designed using long short-term memory network. Specifically, a problem of detecting the topics from streaming media is handled as the minimisation of quadratic loss function constrained by ℓ1 and ℓ2 regularisation. The online learning mechanism supports scalable topic modelling. For topic prediction, sequence-to-sequence long short-term memory network has been designed. Experimentally, significant results have been achieved in terms of query retrieval performance and topic relevance metrics for topic detection on our published dataset. For topic prediction, the results obtained in terms of root mean squared error are also significant.
Towards a New Approach for Building Trust and Confidence in Machine Learning Data and Models: Based on an Integration of Artificial Intelligence and Blockchain (Part 2)

Sentiments and opinions trends analysis and study are two important disciplines of Big Data exploitation. In our case related to Business Intelligence (BI), they make it possible to detect and extract relevant information from raw unstructured textual data, which we call: Textual Business Intelligence (TBI). It is in this context that we propose a new approach allowing to build trust and confidence in machine learning data and therefore in models: based on an integration and a convergence of artificial intelligence and Blockchain. This second part of the article will be dedicated to our approach and the technological integration.
MODEL MATEMATIKA SEIRD (SUSCEPTIBLE, EXPOSED, INFECTED, RECOVERED, DAN DEATH) UNTUK PENYEBARAN PENYAKIT ISPA

In the last few decades, the Upper Respiratory Tract Infection has become one of the three leading causes of death and disability in the world, both in developing countries and in developed countries. In Indonesia, the trend of this disease continues to increase throughout 2016 - 2019 and in children it has caused 1 - 4 children under five to die every hour. In this study, the spread of this disease was modeled mathematically by using the SEIRD Model (Susceptible, Exposed, Infected, Recovered, and Death). Then, the equilibrium points of the model are determined, stability analysis is performed, and the model solution is determined using the Runge Kutta Method
Motivation Level of Student After Implementation Learning Media Based on 3D-Pageflip

A learning media based on 3D-Pageflip was applied in learning chemistry to highlight student's motivation level in learning.This study used a pre-experimental with one-shot case study design, just one group was treated during the study.In total, 32 students with grade 10th from one group class at one of senior high school Yogyakarta-Indonesia participated in this study.Data were collected by the student motivation questionnaire consisting of 20 question items, with the results of the validity being in the range, 0.77 <MNSQ <1.33 and reliability were 0.75, so it was good to use.Data analysis used descriptive quantitative.The result showed that level student's motivation in learning obtained poor (3.1%), moderate (21.9%), good (71.9%), and very good (3.1%) criteria.Generally, the students had good (71.9%)level motivation in learning chemistry with learning media based on 3D-Pageflip.Thus the students' motivation to learn chemistry by using 3D-Pageflip-based learning media was in a good category.Therefore, learning media based on 3D-Pageflip can be used as an alternative media that can be used by teachers in the learning process.
DeblurRL: Image Deblurring with Deep Reinforcement Learning

Removing non-uniform blur from an image is a challenging computer vision problem. Blur can be introduced in an image by various possible ways like camera shake, no proper focus, scene depth variation, etc. Each pixel can have a different level of blurriness, which needs to be removed at a pixel level. Deep Q-network was one of the first breakthroughs in the success of Deep Reinforcement Learning (DRL). However, the applications of DRL for image processing are still emerging. DRL allows the model to go straight from raw pixel input to action, so it can be extended to several image processing tasks such as removing blurriness from an image. In this paper, we have introduced the application of deep reinforcement learning with pixel-wise rewards in which each pixel belongs to a particular agent. The agents try to manipulate each pixel value by taking a sequence of appropriate action, so as to maximize the total rewards. The proposed method achieves competitive results in terms of state-of-the-art.
BTS-Net: Bi-directional Transfer-and-Selection Network For RGB-D Salient Object Detection

Depth information has been proved beneficial in RGB-D salient object detection (SOD). However, depth maps obtained often suffer from low quality and inaccuracy. Most existing RGB-D SOD models have no cross-modal interactions or only have unidirectional interactions from depth to RGB in their encoder stages, which may lead to inaccurate encoder features when facing low quality depth. To address this limitation, we propose to conduct progressive bi-directional interactions as early in the encoder stage, yielding a novel bi-directional transfer-and-selection network named BTS-Net, which adopts a set of bi-directional transfer-and-selection (BTS) modules to purify features during encoding. Based on the resulting robust encoder features, we also design an effective light-weight group decoder to achieve accurate final saliency prediction. Comprehensive experiments on six widely used datasets demonstrate that BTS-Net surpasses 16 latest state-of-the-art approaches in terms of four key metrics.
Optimal Approximations Made Easy

The fundamental result of Li, Long, and Srinivasan on approximations of set systems has become a key tool across several communities such as learning theory, algorithms, computational geometry, combinatorics and data analysis. The goal of this paper is to give a modular, self-contained, intuitive proof of this result for finite set systems. The only ingredient we assume is the standard Chernoff's concentration bound. This makes the proof accessible to a wider audience, readers not familiar with techniques from statistical learning theory, and makes it possible to be covered in a single self-contained lecture in a geometry, algorithms or combinatorics course.
The Utilization of the Radon Transform for the Extraction of the Orientation of Linear Features in Binary Images

This paper considers the idea of using the Radon transform to extract the orientation features of lines in binary images.The Radon transform sends a line at a particular orientation in image space to a point in feature space.The ensuing set of points in feature space is called a sinogram.This computation is usually performed for a large group of angles (over the interval [0,179] degrees taken in integer increments in this paper).Therefore, linear features at specific orientations will be mapped to points having maximum value at particular angles.For angular spacing of at least 5 degrees, the peaks of the sinogram at the angles corresponding to the orientations of the lines will be clearly visible (in a bar plot of sinogram peaks) above sinogram values at other angles.The mapping from image space to feature space accomplished by the Radon transform which maps rectangular coordinates (x,y) to coordinates (range, angle) provides for the garnering of the orientation of the linear features in binary images.In particular, the coordinates (range, angle) in the sinogram allow for distinguishing between lines oriented at one angle versus lines oriented at another angle or angles.This particular property of the sinogram allows for the extraction of the orientation features of lines in an image.
Covid-19 Risk Mapping with the Fuzzy C-Means Method in DIY Province

The Fuzzy C-Means method is one of the grouping methods that can be used by the cluster model. This method can be used to classify various case data, one of which is related to Covid-19. This research is intended to determine the level of risk of Covid-19 based on several determining variables in the province of Yogyakarta Special Region (DIY) during the last 2 months. The data is processed using fuzzy c-means clustering (FCM) analysis which is a development of fuzzy clustering analysis with c participation to analyze the Covid-19 pandemic in DIY province based on several determining variables, namely the number of additional positive victims, pdp , and the frequency of recovered patients. The number of additional positive victims, pdp, odp and frequency of recovered patients were used as variables in grouping regions by district based on the level of risk to Covid-19. The best grouping results are obtained based on and Risk. Districts that are included in the High Risk group are Sleman and Yogyakarta Regencies. Meanwhile, other districts are included in the Low Risk group, namely Bantul, Kulon Progo, and Gunungkidul Regencies.
Research on path planning of mobile robot based on improved A* algorithm

Path planning plays an essential role in mobile robot system. Aiming at the low efficiency of A* algorithm in the process of robot path finding, this paper proposes two improvement methods to solve the problem of inefficient searching of A* algorithm. The first method is to improve the distance calculation formula in A* algorithm, and the second method is to perform the evaluation function in A* algorithm. Weighted processing. Experimental data shows that the comprehensively improved A* algorithm dramatically reduces the number of useless access nodes and speeds up the time-consuming search process, which reduces the search time by about 12～14% compared to the standard A* algorithm.
A Modified Convolutional Network for Auto-encoding based on Pattern Theory Growth Function

This brief paper reports the shortcoming of a variant of convolutional neural network whose components are developed based on the pattern theory framework.
Meningkatkan Kemampuan Membaca Report Text melalui Game pada Siswa Kelas IX C MtsN 1 Bengkalis Tahun Ajaran 2018/2019

The study aims to find out the students ability in reading report texts by using games (kokora and puzzle) of the ninth grade students class C at MTsN 1 Bengkalis in the academic year 2018/2019. The research was conducted of the ninth grade students class C at MTsN 1 Bengkalis. There were 36 students. This research is classroom action research through for phases; planning, implementation, observation, and reflection. Learning strategy used games (kokora and puzzle). In data collection technique used observation and test. After the implementation of the games (kokora and puzzle), the improvement can be seen from the average value of the class has risen from the result of the first cycle class average rose to 77,88 and the second cycle increased to 89,13. In the first cycle the percentage of the overall completeness of students increased to 69,4% or 25 out of 36 students, then on the second cycle increased to 91,6% or 33 out of 36 students. Based on these descriptions, it can be concluded that an increase learning achievement of students in reading report text begin cycle I and cycle II. Then the use strategy games (kokora and puzzle) can improve learning outcome in reading report text of the ninth grade students class IX C at MTsN 1 Bengkalis.
Adaptive Bitrate Streaming Over Cellular Networks: Rate Adaptation and Data Savings Strategies

Adaptive bitrate streaming (ABR) has become thede factotechnique for videostreaming over the Internet. Despite a flurry of techniques, achieving high quality ABRstreaming over cellular networks remains a tremendous challenge. First, the design ofan ABR scheme needs to balance conflicting Quality of Experience (QoE) metrics suchas video quality, quality changes, stalls and startup performance, which is even harderunder highly dynamic bandwidth in cellular network. Second, streaming providers havebeen moving towards using Variable Bitrate (VBR) encodings for the video content,which introduces new challenges for ABR streaming, whose nature and implicationsare little understood. Third, mobile video streaming consumes a lot of data. Althoughmany video and network providers currently offer data saving options, the existingpractices are suboptimal in QoE and resource usage. Last, when the audio and videotracks are stored separately, video and audio rate adaptation needs to be dynamicallycoordinated to achieve good overall streaming experience, which presents interestingchallenges while, somewhat surprisingly, has received little attention by the researchcommunity. In this dissertation, we tackle each of the above four challenges.
A Review on Software Testing Techniques

Software testing is very essential to improve the overall quality of software by reducing bugs, errors and software maintenance cost. Software testing is necessary part of Software Development Life Cycle (SDLC) and as testing is critical in post and pre development process, so it needs to be driven with some enhanced and efficient techniques and methodologies. The aim of this paper is to improve the quality of software by discussing about some existing testing technique and some improved testing technique.
Research on the Value Optimization of Online Education Platform in Universities

As China has gradually entered the network information society, all walks of life have been rapidly developed. In the education industry, network information technology has promoted the emergence of online education platforms. This is a new teaching method that has attracted the attention and recognition of the majority of teachers and students who have profited from its advantages of abundant resources, quick start, and convenience. By analyzing the connotation and management mode of online education platform, the application strategies of online education platform in colleges and universities are discussed.
SimPoE: Simulated Character Control for 3D Human Pose Estimation

Accurate estimation of 3D human motion from monocular video requires modeling both kinematics (body motion without physical forces) and dynamics (motion with physical forces). To demonstrate this, we present SimPoE, a Simulation-based approach for 3D human Pose Estimation, which integrates image-based kinematic inference and physics-based dynamics modeling. SimPoE learns a policy that takes as input the current-frame pose estimate and the next image frame to control a physically-simulated character to output the next-frame pose estimate. The policy contains a learnable kinematic pose refinement unit that uses 2D keypoints to iteratively refine its kinematic pose estimate of the next frame. Based on this refined kinematic pose, the policy learns to compute dynamics-based control (e.g., joint torques) of the character to advance the current-frame pose estimate to the pose estimate of the next frame. This design couples the kinematic pose refinement unit with the dynamics-based control generation unit, which are learned jointly with reinforcement learning to achieve accurate and physically-plausible pose estimation. Furthermore, we propose a meta-control mechanism that dynamically adjusts the character's dynamics parameters based on the character state to attain more accurate pose estimates. Experiments on large-scale motion datasets demonstrate that our approach establishes the new state of the art in pose accuracy while ensuring physical plausibility.
An Enhanced ANN-HMM based classification of video recordings with the aid of audio-visual feature extraction

INTRODUCTION: As an essential part of life, the use of the Internet has increased exponentially.This rising Internet bandwidth speed has made video data transmission a more popular and modern form of information exchange.For classification of video date files there is a requirement of human efforts.Also for reducing the rate of clutter in video data on Internet, a suitable automatic video classification method is required.OBJECTIVES: In this work, we tried to find a successful model for video classification.METHODS: To make a successful model we use different schemes of visual and audio data analysis.On the other hand we choose some music, traffic and sports videos for different analysis.The model is based on Hidden Markov model (HMM) and Artificial neural network (ANN) classifiers.In order to gather the final results, we developed an "enhanced ANN-HMM based" model.RESULTS: Our approach attained an average of 90% success rate among all three classification classes.CONCLUSION: In aim of this work is to categorize and caption the videos automatically.Here we proposed an enhanced HMM-ANN based classification of video recordings with the aid of audio visual feature extraction.
Impact of Computer Simulation Assisted Virtual Experiment Module in Learning Hydrogen Atom in Senior High School

Abstract This study aims to (1) develop virtual-experimental software based on augmented reality as an interactive learning medium, which can be used as a teaching tool to improve the students’ mastery of physics concepts, (2) test the comparative advantage of Hydrogen Atom learning with a virtual approach experiment based on augmented reality with ordinary learning models on senior high school students’ learning outcomes. The research was categorized as quasi-experimental research which used a non-equivalent pre-test post-test control group. The research instrument used was a learning outcome test. The research data were analyzed by using covariance analysis (ANACOVA). The results of this study showed that the learning outcomes of students who studied with the AR-based virtual in the experimental learning model group were higher than students who studied in the conventional learning model group.
MOVING FORWARD TOWARDS REMOTE LEARNING OF IT IN TIMES OF PANDEMIC

Background: On-line learning has been a buzzword in what concerns higher education and IT learning. However, several motives have prevented its full adoption by students and teachers. With the emergence of a pandemic situation in the second semester of the school year 2019/2020, it was necessary to implement and adopt draconian measures of confinement, that enabled the adoption of several on-line learning tools in "real time", changing (permanently?) the learning and the way teaching is performed.Purpose: The purpose of this study is to report the changes that were made in the syllabus of the "Management Information Technologies and Systems" taught to sophomore (2nd year) “general management” and “tourism management” course cohort at University X, with the introduction of remote learning tools and its alignment with the existing e-learning platforms and softwares available. Furthermore, an assessment of the student performance in each course cohort is made, when in comparison with the 2018/2019 cohorts.Methods: A qualitative and quantitative study was performed to evaluate the state of the art in how the subject of “Management Information systems and technology” is performed in leading universities, especially in those where remote learning is more advanced. A comparison between this state of the art is made when in comparison to the proposed syllabus presented in early 2020 and the changes that were made during the semester. Student uptake and use of the tutorials and tools were made, comparing the 2019 spring semester with the 2020 spring semester, in order to evaluate the changes in student behaviour and the final results obtained in each course cohort.Results: There was an overall positive impression of the students with the introduction of the changes induced by the pandemic. However, the comparison of the results obtained in each cohort with the previous year shown that a slight improvement in performance was observed between the two years analysed and within the two cohorts studied.Conclusions: In spite of the precarious conditions and the epidmiological context in which this work took place, it was possible to implement significant changes in the teaching methods and in the subjects approached, reinforcing the distance learning and chaning "on the fly" several aspects of the teaching protocol. Neverthless, and since the evaluation mechanism was similar to the one in the previous year, it was possible to compare the results that have shown a slight improvement in the final results.
KONTROL MUTU PEWARNAAN HISTOKIMIA DAN IMUNOHISTOKIMIA

Background: Tissue staining methods need controls to give best quality results. Controls is necessary to support the validity, correctness, and reliability of the staining results. Haematoxylin and Eosin (HE) and immunohistochemistry (IHC) staining require different control quality.&#x0D; Objective: To discuss the utilization of controls used in tissue staining. &#x0D; Method: This is a narrative review with literature from 2008-2016.&#x0D; Results: Routine staining usually does not require any control. Specific stainings require controls to support the validity, correctness, and reliability of the staining results. Histochemistry staining requires only positive control, while positive and negative controls must be included in IHC staining. A tissue section which expresses the protein of interest is reffered to positive control, whereas those without any target antigen expression are defined as negative control. &#x0D; Conclusion: Histochemistry and IHC staining controls are needed to obtain the validity of the tissue or cell samples.
LibGuides: Remote Library Services : Tutorials/Guides

This guide will provide useful information for off-campus users Tutorials on how to search research databases