Reviewerly/exHarmony huggingface dataset:
Low-Level Image Features in Orthographic Projection Snapshot to 3D Coordinate Retrieval

Human can comprehend an engineering drawing and its contents by interpreting extracted information from the image. Hence, for the computer to perform the same task, such information in an image about the semantic of its contents is required. In particular, this information is usually derived from the low-level features. In this paper, the procedure of essential feature extraction from the drawing is discussed. Finally, the approach for 3D coordinate retrieval from obtained data is explained and the respective 3D model is reconstructed.
Real-time Obstacle Avoidance in Robotic Manipulation Using Imitation Learning

We propose a novel trajectory planning algorithm to avoid obstacles in robotic manipulation by using imitation learning method. It focuses on how to plan feasible trajectories in the manipulation environment by imitating human experience. The main components of our algorithm include a path point prediction network and a trajectory generation strategy. The network is primarily composed of several Long Short-Term Memory (LSTM) layers and a Mixture Density Network (MDN) layer with Gaussian functions, thus it can cope with sequential information and fit the multimodal dataset well. To improve the smoothness of the trajectories generated by the networks, trajectory points are sampled from the Gaussian function which has the minimal change in the configuration space. Besides, multiple trajectories are generated for a given input and the best one can be selected to accomplish the task by improving the precision of the planning algorithm. Finally, simulation experiments conducted in Gazebo simulator verify that our planning algorithm has good performance in robotic manipulation with obstacle avoidance.
Quantum Key Distribution and Quantum Networks Standardization Efforts

The keys that are used for the crypto algorithms in order to provide information security have a critical importance. In this work, the standardization efforts for quantum key distribution systems and quantum networks that are used for key distribution will be mentioned.
UNT Dallas Library: Kanopy and Streaming Video Services: Home

Kanopy is a streaming video service. You may see the current Kanopy videos that the library has available to view.
Graph-Based Semi-Supervised and Supervised Approaches for Detecting Pathogenic Social Media Accounts

In this chapter, we adopt the causal inference framework described previously along with graph-based metrics to distinguish PSMs from normal users within a short time of their activities. We propose both supervised and semi-supervised approaches without taking the network information and content into account. Results on the ISIS-A dataset demonstrate the advantage of our proposed frameworks. We show our approach achieves 0.28 improvement in F1 score over existing approaches with the precision of 0.90 and F1 score of 0.63.
L-RDFDiversity: Distributed De-Identification for Large RDF Data with Spark

Privacy protection issues for RDFs (resource description framework) have emerged with the using to public government open data, healthcare data for individuals. As that data may include personal information, it must go through a de-identification pro-cess that deletes or replaces part of the original data. To enable these protections, a method have been developed to apply k-anonymization for RDF data. However, sensitive RDF information anonymized using k-anonymization is not entirely secure and is vulnerable to attacks. In this paper, we use an l-diversity Anatomy de-identification method that can overcome the limitations of k-anonymity and guarantee stronger privacy protection than k-anonymization. Since this process for anonymization of data requires a lot of computational time, we use Spark distribution computing to provide rapid de-identification to enhance its utility.
Efficient analysis of digital systems' supplied data

Nowadays, when digital systems supply a lot of information, it is impossible for human operators to analyze it and to extract conclusions without using automation mechanisms. Thus, advances in data processing became the aim of many entities both in Industry and Academia. In order to supply reliable reports to customers and target public, each institution must take into account two important aspects: which are peculiarities of managed data and which algorithms can be used to extract most of information from it. The proficiency to extract correct trends from collected data makes entities to gain market share in lot of domains (e.g. statistics, forecasting activities). For this reason, a lot of efforts are directed in scientific research for analysis of data coming from digital devices, using also a concept which achieved good results in many branches of industry: machine learning.
Reinforcement Learning for Physics-Based Competitive Games

Physics-based games are vast in terms of possible state spaces. There are many strategies that can be implemented in competitive games such as playing passively and waiting for the opponent to make a mistake, playing aggressively to force mistakes from the opponent, or even using environmental objects to an agent's advantage. The vastness of possibilities makes it difficult for a programmer to account for all these situations and create a rule-based intelligent and believable hard-coded AI agent. This project seeks to take advantage of reinforcement learning to create agents that can adapt to dynamically changing physics-based environments such as the example of competitive vehicular soccer games. It seeks to produce believable agents that perform intrinsic behaviors such as defending their goal and attacking the ball using reward functions. Through trial-and-error, the reward function is modified to progressively form behavioral patterns that improve in performance. The performance tests prove that a reward function that considers different state space parameters can produce better performing agents compared to ones with a less defined reward function and state space. Moreover, the final agent trained through the experiments has proved to be believable and hard to distinguish from a human player.
Visual Steganography and Encryption Tachniques with Hardware Implementation

The visual steganography is used to hide secret message within an image and extracting it from destination image. The secure forms of steganography are visual steganography which is implemented in image files. For securing data encryption method is used with mathematical concepts and achieves higher level of secured communication. Both the techniques visual steganography and encryption technique are combined to achieve two layer of security. Instead of sequential search, the thread based parallel search is implemented to increase the retrieval speed at high rate of concurrent search which can be implemented on any real-time architecture.
Design and Implementation of a Voice-Based Conversational Agent for the Continuous Training and Learning of Pharmaceutical Sales Representatives

The increasing popularity and enhanced capabilities of intelligent personal assistants, such as Google Assistant, Siri, and Alexa, have allowed them to disrupt in numerous fields, some of which are: customer service, banking, and tourism. Notwithstanding, the application of intelligent personal assistants for training and learning of professional workers has been limited and not well researched. The current paper will explore a solution for these purposes applied to pharmaceutical sales representatives by using a voice-based conversational agent. The pharmaceutical sales representatives will be able to ask the agent about the properties of the medicaments and to take frequent exams about the information available to verify their knowledge. The agent is implemented in Dialogflow and is available to all devices compatible with Google Assistant.
Research and Course Guides: Library Guide for Clemson Science Majors: Home

This guide will help science majors identify and use key library resources in their disciplines.
THE EFFECT OF ANAGRAM GAME ON THE EIGHTH GRADE STUDENTS’ VOCABULARY MASTERY AT SMPN 3 BALARAJA

This research was aimed to know the effect of Anagram Game on students’ vocabulary mastery. This research used quantitative method, with quasi-experimental design. This research was conducted in SMPN 3 Balaraja. The number of the sample of the research was 70 students of eighth grade. The instrument in this research was divided into two steps of test, namely pre-test and post-test. Based on the criteria of hypothesis test that if tcount &lt; ttable then Ho is accepted, it means that there is no significant difference students’ result of learning vocabulary mastery between students in experiment class who were taught by using Anagram Game and the students in control class who were not taught by using Anagram Game. Otherwise, if tcount &gt; ttable then H1 is accepted, it means there is any significant difference of students’ result of learning vocabulary mastery between students in experiment class who were taught by using Anagram Game and students in control who were taught by using Anagram Game. The result of the calculation was t – count of ɑ = 5% was 2.04, and for t – table was 1.99. It proved that Anagram Game gave effect on students’ vocabulary mastery.
Low-Resolution LiDAR Upsampling Using Weighted Median Filter

This paper presents a 3D LiDAR data upsampling method to obtain dense 3D depth data from a low-resolution LiDAR, a vision camera, and the Weighted Median Filter (WMF) algorithm. Recently, LiDAR is widely used in the field of Computer Vision since it can obtain accurate 3D data. However, data acquisition from the LiDAR is expensive due to the high cost of the LiDAR. We address how to obtain large amounts of 3D data from a low-channel LiDAR. In this paper, we acquire LiDAR data and color images from a calibrated multi-sensor platform. We first begin the upsampling steps from linear interpolation of a depth image. And then, we use an WMF algorithm to complement the first interpolation image. We use the upsampling algorithm to create dense 3D depth map from the existing sparse LiDAR data. And we generated a high-density 3D map using the ICP matching of multiple depth data acquired by a moving robot system.
An application of geometric aspects of variational autoencoder model to forgery detection of scanned documents

The paper proposes an approach for matching of digitized copies of business documents. This task arises when comparing two versions of the same document, genuine and forgery, to find possible modifications, for example in the banking sector during the conclusion of contracts in paper form to avoid possible fraud. The matching method of two documents based on comparison images of text lines using Variational Autoencoder (VAE) trained on genuine images and calculation Fisher information metric to find modifications. Experiments were conducted on the public Payslips dataset (in French). The results show the high quality and reliability of finding document forgeries and are compared to the results of the method which applies OCR and image matching.
Self-Similarity Matrix and View Invariant Features Assisted Multi-View Human Action Recognition

This paper presents a novel Multi-View Human Action Recognition Framework which relies on Self Similarity Matrix (SSM), View and Rotation invariant features of human actions. In this paper, we accomplish the Self-Similarity between frames of an input video to extract only important frames. The self-similarity evaluation helps in the reduction of total number of training frames for every view. And further to extract scale and rotation invariant features, in this work we incorporate Gabor filter with varying scales and rotations. These features are learned through Support Vector Machine algorithm to recognize human actions under multiple views. Simulations are carried out over IXMAS multi-view dataset and the results are outperforming compared to the state-of-art methods.
Study on Business Strategy Quantification Using Topic Modeling and Word Embedding: Focusing on ‘Virtual Reality’ and ‘Augmented Reality’

This study analyzed articles related to “Virtual Reality” and “Augmented Reality,” the main elements of the Fourth Industrial Revolution, and reviewed the views that the media has by using text mining techniques. For this, 14,443 news articles on “Virtual Reality” and “Augment Reality” from 2011 to 2020 were collected from the BigKinds of Korea Press Foundation. For the analysis of collected datasets, the LDA algorithm-based topical modeling techniques implemented in Python3 languages were used. The number of topics suitable for the collected dataset using coherence was determined to be 14. The calculated topic was given the title of the topic through expert survey, and word vector was created using Word2vec, one of the word-embedding techniques. This word vector was then used to quantify the analyzed topic. Through this, quantitative expressions of strategic perspectives were made and changes in topics around the year 2016 were visualized when the issue of the “fourth industrial revolution” began to be discussed.
Modules and Techniques for Motion Planning: An Industrial Perspective

Research on autonomous cars has become one of the main research paths in the automotive industry, with many critical issues that remain to be explored while considering the overall methodology and its practical applicability. In this paper, we present an industrial experience in which we build a complete autonomous driving system, from the sensor units to the car control equipment, and we describe its adoption and testing phase on the field. We report how we organize data fusion and map manipulation to represent the required reality. We focus on the communication and synchronization issues between the data-fusion device and the path-planner, between the CPU and the GPU units, and among different CUDA kernels implementing the core local planner module. In these frameworks, we propose simple representation strategies and approximation techniques which guarantee almost no penalty in terms of accuracy and large savings in terms of memory occupation and memory transfer times. We show how we adopt a recent implementation on parallel many-core devices, such as CUDA-based GPGPU, to reduce the computational burden of rapidly exploring random trees to explore the state space along with a given reference path. We report on our use of the controller and the vehicle simulator. We run experiments on several real scenarios, and we report the paths generated with the different settings, with their relative errors and computation times. We prove that our approach can generate reasonable paths on a multitude of standard maneuvers in real time.
Полевые исследования памятников наскального искусства. Топографическая съемка и индексация петроглифов

Field research of petroglyphs is the most important stage in the process of studying rock art. The article is devoted to the issues of indexing and topographic survey of objects. Topolandscape characteristics of petroglyphs at different levels of their localization largely determine their semantics. Analysis of the height and spatial parameters of planes with petroglyphs helps to solve the issues of dating images. This method does not require sophisticated equipment; effective for exploration and stationary research of rock art objects. The main principles are fixing the horizontal contour and vertical profiles of the rock, linking petroglyphs with them. The proposed methods can be used for research purposes and in the preparation of documentation for registration in the State Catalog of Cultural Heritage Objects of the Russian Federation.
Disaster Response Simulation as a Testbed for Multi-Agent Systems

We introduce a novel two-dimensional simulator for disaster response on maps of real cities. Our simulator deals with logistics and coordination problems and allows to plug-in almost any approach developed for simulated environments. In addition, it (1) offers functionalities for further developing and benchmarking, and (2) provides metrics that help the analysis of the performance of a team of agents during the disaster. Our simulator is based on software made available by the multi-agent programming contest, which over the years has provided challenging problems to be solved by intelligent agents. We evaluate the performance of our simulator in terms of processing time and memory usage, message exchange, and response time. We apply this analysis to two different approaches for dealing with the mining dam disaster that occurred in Brazil in 2019. Our results show that our simulator is robust and can work with a reasonable number of agents.
ANALYSIS OF CONTENT VALIDITY IN ENGLISH EXAMINATION TEST ON PUBLIC HEALTH’S STUDENTS

The objective of this research is to find out the empirical evidence of the English examination test content validity made by the English lecturer for the even semester of the first grade of Public Health’s students administered at the University of Faletehan Serang. The total number of the test items was 70 items. In this study, the writer used qualitative research in which the writer collected data by asking the English final paper test and syllabus to the lecturer. Then the writer analyzed the test items, whether the final test has content validity. This study has not done yet in this institution. Also, this study is categorized as descriptive analysis in which to describe the conformity and in-conformity of the Public Healths’s English summative test with the syllabus and learning achievement. The instrument of this study was the final English test of the Public Health Department in the first semester of the first grade in the 2019/2020 academic year. The respondent used was 112 students.
DVAE-SR: denoiser variational auto-encoder and super-resolution to counter adversarial attacks

Recently, adversarial examples become one of the most dangerous risks in deep learning, which affects applications of real world such as robotics, cyber-security and computer vision. In image classification, adversarial attacks showed the ability to fool classifiers with small imperceptible perturbations added to the input. In this paper, we present an efficient defense mechanism, we call DVAE-SR that combine variational autoencoder and super-resolution to eliminate adversarial perturbation from image input before feeding it to the CNN classifier. The DVAE-SR can successfully defend against both white-box and black-box attacks without retraining CNN classifier and it recovers better accuracy than Defense-GAN and Defense-VAE.
Tests of hypotheses on quantiles of distributions of components in a mixture

The problem of testing statistical hypotheses concerning quantiles of different distributions of components in a mixture with varying concentrations is considered. Homogeneity of medians or that of interquartile ranges in different components are two possible examples of such kind of hypotheses. Estimators for quantiles in a model of mixtures with varying concentrations are presented. Sufficient conditions for the asymptotic normality of these estimators are obtained. Asymptotic confidence ellipsoids and tests for linear hypotheses based on these estimators are constructed. The quality of procedures proposed in this paper is checked for samples of finite sizes with the help of simulation results.
The Influence of Social Media on Arabic Writing Skills among Jordanian 10th Graders'

This study aimed to investigate the influence of using social media on the 10 th graders' progress in writing skills, and their attitudes towards writing at Naour District in Amman schools, and the effect of gender, academic qualifications, educational and professional experiences.A five-point questionnaire, and a standardized writing test were sophisticated.It included three composition questions to get three paragraphs based on writing.The sample of the randomized study consisted of (1064) students of the 10 th grade.The study showed, there were no significant differences in the effect of social media towards writing skills for 10 th graders' due to academic qualification on third domain, whereas there were significant differences on the effect of social media towards Arabic writing proficiency due to academic qualification in the first and second domains.There were also statistically significant differences on the effect of social media towards writing proficiency for 10 th graders'.
Peningkatan keaktifan belajar dalam pembelajaran ips melalui penerapan model pembelajaran guided note taking pada peserta didik kelas iv sekolah dasar

The objective of the reserach is to describe how to apply the Guided Note Taking (GNT) learning model that can improve learning activeness in social learning for fourth grade students of Ngadipuro Elementary School in Blora Regency. The type of research is Classroom Action Research conducted in two cycles. The subjects of the research consisted of fourth grade teachers and all students of fourth grade totaling 21 students. Data collection techniques used tests, interviews, observations, and documentation. Source triangulation techniques and data collection triangulation techniques used for data validity. Data analysis techniques used interactive analysis techniques with the stages are data reduction, presentation of research data, and drawing conclusions. As results, students' learning activeness increased in five indicators of the pre-action increasing in cycle I, and again increasing in cycle II. The five indicators of learning activeness in this study include paying attention and writing material, asking questions, expressing opinions or ideas, discussing classical problem solving, and presenting learning outcomes. Application of the GNT model can increase student learning outcomes marked by in classical completeness in pre-action by 61.90% increased in cycle 1 to 76.19%, and increased in cycle II to 85.71%. The conclusion of this research is the application of the GNT model can increase the learning activity of fourth grade students of Ngadipurwo Elementary School in social learning.
Reciprocal determination and the unity of distinct determinations in The Primal Structure of Emanuele Severino

This is the final version. Available on open access from Pensa Multimedia via the DOI in this record
PERCEPTIONS OF PRINCIPALS, TEACHERS, PARENTS AND STUDENTS REGARDING ONLINE BUSINESS AS ECONOMIC ADJUSTMENT OF THE STUDENTS IN SOCIETY

It is highly essential for modern businessmen to get awareness and expertise about online business and e-trading in this technological era. Study was cross-sectional. Stakeholders of study were students, parents, teachers and principals of all Deeni Madaris and public and private secondary schools in Southern districts of KP, Pakistan. The main objective of the study was to compare public, private secondary schools and Deeni Madaris students’ perceptions regarding online business as economic adjustment in society. Out of the (N=405957 respondents, (n=1500) respondents (principals, parents, teachers and students were taken as samples of the study over L.R. Gay size rule of thumb by using the convenient, stratified simple random and disproportionate sampling techniques. A questionnaire of six points Likert scale was developed for the data collection from. Data was analyzed through frequency and One-Way ANOVA with the help of SPSS that provide significant information for stakeholders as well as policymakers. It was revealed that teachers and public schools were highly in favor of students’ online business to make more money and economically adjust in society.