Reviewerly/exHarmony huggingface dataset:
Use of Synthetic Data on Object Detection Applications

It is not always possible to find enough data in Deep Learning applications, where huge amounts of data is needed. One may face difficulties while trying to perform qualified works, where data cannot be collected in rare occuring events and situations involving privacy concerns. To overcome this issue, we create a military vehicle dataset in which great amounts of Synthetic Dataset can be found. Thus, with this dataset, we bring a solution to the problem of detecting military vehicles, which is an area where collecting sufficient data is a problem. In a training with 5, 10 and 50 epochs we raise the mAP score by 0.09, 0.03, and 0.02, respectively.
THE ATTITUDE TOWARDS USE OF DIGITAL EDUCATIONAL TECHNOLOGIES DURING COVID-19 PANDEMIC IN RUSSIAN UNIVERSITY TEACHERS: PROBLEMS AND SOLUTIONS

THE ATTITUDE TOWARDS USE OF DIGITAL EDUCATIONAL TECHNOLOGIES DURING COVID-19 PANDEMIC IN RUSSIAN UNIVERSITY TEACHERS: PROBLEMS AND SOLUTIONS
Itihasa: A large-scale corpus for Sanskrit to English translation

This work introduces Itihāsa, a large-scale translation dataset containing 93,000 pairs of Sanskrit shlokas and their English translations.The shlokas are extracted from two Indian epics viz., The Rāmāyana and The Mahābhārata.We first describe the motivation behind the curation of such a dataset and follow up with empirical analysis to bring out its nuances.We then benchmark the performance of standard translation models on this corpus and show that even state-of-the-art transformer architectures perform poorly, emphasizing the complexity of the dataset. 1 मा नषाद प्र तष्ठां त्वमगमश्शाश्वतीस्समा:। यत्क्रौञ्च मथु नादे कमवधी: काममो हतम ्॥ O fowler, since you have slain one of a pair of Krauñcas, you shall never attain prosperity (respect)!
Development Of Students’ Leadership Qualities At The Contemporary University

The article dwells on the essence of leadership as a major trend in modern society.The authors study the phenomenon of leadership in the student environment, the attitude of students and teachers towards it; they search for effective technologies to develop leadership qualities in students during their studies at a university.The research results in the analysis of today's youth preferences in regards to their role in the team, to communication with the leader and the possibility of becoming a leader.The article examines the current research by Russian and foreign scientists in the field of leadership; it singles out the peculiarities of developing the professional culture of a leader and discusses the possibilities of using the university digital educational environment to develop leadership qualities in students.The authors of the article define the most important leadership qualities, namely, responsibility, creativity, sociability, emotional stability, the knowledge of a foreign language, a high level of digital literacy, the ability and willingness to motivate, to inspire, and to lead.To stimulate the leadership potential of students it is strategically important to find the most effective ways of teamwork organizing, to create conditions for expanding the digital transformation of educational content, to shift goals, objectives, and educational content towards meta-subject.The article is of interest for scientists dealing with leadership problems, as well as for project team leaders, for young people interested in self-establishment, self-development, and selfmanagement in the digital economy.
A Hybrid Approach for Region-Based Medical Image Compression with Nature-Inspired Optimization Algorithm

Medical modalities generate a massive amount of digital volumetric data to analyze and diagnose medical problems. To sure over the data quality and storage space, compression primes to be an efficient methodology in the digital world. Medical images represent the body features for a diagnostic purpose that needs to get compressed without a loss of information. Reducing the redundancies and representation in a shorter manner achieved over a region of interest area on an image solves the problem. The proposed methodology uses the region-based active contour method driven by bat algorithm to segment an image into a region of interest and non-region of interest. Region of interest area compressed by a lossless integer-based Karhunen-Loeve transforms, where the non-region of interest compressed by a lossy Karhunen-Loeve transforms. Optimum results suggest that the proposed method improvises the region segmentation of a medical image, which results in achieving a high PSNR, SSIM and a quality compressed image.
Bootstrapping the Multilayer Feedforward Propagation System for Predicting the Arrival Guest in Malaysia

Predicting arrival guest is an essential step in estimating the Malaysia economic impact, particularly in short and long-term COVID-19 crisis. Neural Network family of models has been widely used in economic and tourism. Most of the study used single layer of fed forward propagation system, it is because of less sampling variation in Neural Network. However, apparently the multilayer provides a better predicting result but its disadvantage is to deal with high sampling variation. The motivation of this study is to enhance the ability of multilayer Neural Network in predicting the arrival guest. In this study, a hybrid model based on Bootstrapping the Neural Network proposed to predict the arrival guest of Singapore in Malaysia. The weights of variables to the first hidden layer nodes are bootstrapped. The subsequent hidden layer takes the bootstrapped weights in order to obtain the Neural Network output. The prediction obtained by hybrid model has been compared with conventional multilayer Neural Network in terms of small variation. The computational results shows that the hybrid model provides better performance in predicting the arrival guest. © 2021 RIGEO. All Rights Reserved.
THE RELATIONSHIP BETWEEN CYBERBULLYING AND SEXTING

The study focuses on cyberbullying and sexting as online threats, which are often perceived as identical. They are performed through a virtual environment to threaten and harm the victim.The research focused on identifying the relationship between cyberbullying and sexting. The survey involved 250 respondents between the age of 15 and 19. Its implementation was carried out through an online questionnaire. The purpose of the study is to clarify the relationship between cyberbullying and sexting, and at the same time to perceive them as two equally dangerous online threats that can occur separately.The research has shown a generally weak relationship between cyberbullying and sexting, and at the same time we discovered a much greater number of sexting cases. Sexting has become more dominant in boys and also in girls. Furthermore, we have proven that the relationship between these threats is gaining momentum depending on the differences in age and gender. These findings will help us better understand the diversity of online threats and can lead to improved prevention effectiveness. Above all, they lead to an awareness of the fact that sexting is not part of cyberbullying but occurs independently and just as often and intensely.
Facebook Integrated Chatbot for Bulgarian Language Aiding Learning Content Delivery

The paper presents a chatbot oriented linguistic approach and a software prototype which addresses the need of delivering learning content based on queries in Bulgarian language. Two distinct database query generation approaches are presented, discussed and implemented. Pros and cons for each of them are discussed.
Data Mining Optimization Based on Particle Swarm Optimization For Diagnosis of Inflammatory Liver Disease

Inflammation of the liver is a contagious disease that is a public health problem that affects morbidity, mortality, public health status, life expectancy, and other socio-economic impacts. Early diagnosis of this disease is very important so that it can be quickly treated and treated. In this study the researchers will apply and compare several data mining and optimization classification methods with particle swarm optimization (pso), including the C4.5 algorithm, k-Nearest Neighbor, C4.5 with PSO, and k-Nearest Neighbor with PSO to diagnose inflammatory diseases. carefully, then compare which of the several of these methods is the most accurate. Based on the results of measuring the performance of the three models using the Cross Validation, Confusion Matrix and ROC Curve methods. Based on the research results, it is known that the C4.5 method with PSO is the best method with an accuracy of 79.51% and an under the curva (AUC) value of 0.950, then the k-Nearest Neighbor method with PSO has an accuracy of 75.59% and an AUC value of 0.909, then the C4.5 method with an accuracy rate of 70.99% and an AUC value of 0.950, then the k-Nearest Neighbor method with an accuracy rate of 67.19%, and an AUC value of 0.873. This proves that particle swarm optimization can improve the performance of the classification method used.
Covid-19 Images and Video Denoising Algorithms Based on Convolutional Neural Network CNNs

In this paper, the most sophisticated denoising algorithms of images and video are applied and implemented. More precisely, we study and implement the video denoising algorithms "VBM3D", "VBM4D", "DVDNet" and "FastDVDnet". Much attention is given to the latest DVDNet and FastDVDNet algorithms, which are based on CNN. We carry out a detailed quantitative and qualitative comparative study between the considered algorithms. Two assessments are adapted; the first is a qualitative comparison based on the qualityof the images / videos and the second is quantitative in terms of PSNR and running time criteria. To see the direct impact of our study on the current pandemic, and to show the importance of image and video preprocessing algorithms in the field of medical imaging; we apply the considered denoising algorithms based on CNN on our built COVID- 19 dataset and TEST_PCR videos.
INDONESIA – A GOLDMINE OF DARK TOURISM DESTINATIONS

Known for its significant potential in the tourism industry, Indonesia fails to promote dark tourism destinations as one of their main pillars of national tourism. As some countries were bold enough to create tourist attractions out of the catastrophes that hit them during their history, Indonesia holds one of the most death obsessed culture in the world, but it is not a strong dark tourism destination. The purpose of this paper is to analyze the potential of dark tourism destinations Indonesia has, the motivation of “dark tourists” consumers and the approach of Indonesians towards dark tourism. Keywords: dark tourism, macabre tourism, zombie tourism
Improved Outsourced Provable Data Possession for Secure Cloud Storage

With the advent of data outsourcing, how to efficiently verify the integrity of data stored at an untrusted cloud service provider (CSP) has become a significant problem in cloud storage. In 2019, Guo et al. proposed an outsourced dynamic provable data possession scheme with batch update for secure cloud storage. Although their scheme is very novel, we find that their proposal is not secure in this paper. The malicious cloud server has ability to forge the authentication labels, and thus it can forge or delete the user’s data but still provide a correct data possession proof. Based on the original protocol, we proposed an improved one for the auditing scheme, and our new protocol is effective yet resistant to attacks.
Continuous Fusion of Motion Data Using an Axis-Angle Rotation Representation with Uniform B-Spline

The fusion of motion data is key in the fields of robotic and automated driving. Most existing approaches are filter-based or pose-graph-based. By using filter-based approaches, parameters should be set very carefully and the motion data can usually only be fused in a time forward direction. Pose-graph-based approaches can fuse data in time forward and backward directions. However, pre-integration is needed by applying measurements from inertial measurement units. Additionally, both approaches only provide discrete fusion results. In this work, we address this problem and present a uniform B-spline-based continuous fusion approach, which can fuse motion measurements from an inertial measurement unit and pose data from other localization systems robustly, accurately and efficiently. In our continuous fusion approach, an axis-angle is applied as our rotation representation method and uniform B-spline as the back-end optimization base. Evaluation results performed on the real world data show that our approach provides accurate, robust and continuous fusion results, which again supports our continuous fusion concept.
Unbiased Gradient Estimation for Variational Auto-Encoders using Coupled Markov Chains

The variational auto-encoder (VAE) is a deep latent variable model that has two neural networks in an autoencoder-like architecture; one of them parameterizes the model's likelihood. Fitting its parameters via maximum likelihood (ML) is challenging since the computation of the marginal likelihood involves an intractable integral over the latent space; thus the VAE is trained instead by maximizing a variational lower bound. Here, we develop a ML training scheme for VAEs by introducing unbiased estimators of the log-likelihood gradient. We obtain the estimators by augmenting the latent space with a set of importance samples, similarly to the importance weighted auto-encoder (IWAE), and then constructing a Markov chain Monte Carlo coupling procedure on this augmented space. We provide the conditions under which the estimators can be computed in finite time and with finite variance. We show experimentally that VAEs fitted with unbiased estimators exhibit better predictive performance.
Semantic Knowledge Discovery for User Profiling for Location-Based Recommender Systems

This paper introduces a purposed Location-based Recommender System (LBRS) that combines sentiment analysis and topic modelling techniques to improve user profiling for enhancing recommendations of Points of Interest (POIs).Using additional feature extraction, we built user profiles from a Foursquare dataset to evaluate our model and provide recommendations based on user opinions toward venues.Our combined model performed favourably against the baseline models, with an overall improved accuracy of 0.67.The limitations were the use of one dataset and that user profiles were constructed using predicted emotions extracted as features from review data with topic modelling, rather than literal user emotions.Nevertheless, this provides a step forward in user profile and emotion scoring, contributing further to the development of LBRS in the Tourism domain.
HITMI&amp;T at SemEval-2021 Task 5: Integrating Transformer and CRF for Toxic Spans Detection

This paper introduces our system at SemEval-2021 Task 5: Toxic Spans Detection.The task aims to accurately locate toxic spans within a text.Using BIO tagging scheme, we model the task as a token-level sequence labeling task.Our system uses a single model built on the model of multi-layer bidirectional transformer encoder.And we introduce conditional random field (CRF) to make the model learn the constraints between tags.We use ERNIE as pre-trained model, which is more suitable for the task accroding to our experiments.In addition, we use adversarial training with the fast gradient method (FGM) to improve the robustness of the system.Our system obtains 69.85% F1 score, ranking 3rd for the official evaluation.
Non-named Entities – The Silent Majority

Knowledge Bases (KBs) usually contain named entities. However, the majority of entities in natural language text are not named. In this position paper, we first study the nature of these entities. Then we explain how they could be represented in KBs. Finally, we discuss open challenges for adding non-named entities systematically to KBs.
A model of selection history in visual attention

Author(s): Meibodi, Neda; Abbasi, Hossein; Schubo, Anna; Endres, Dominik M | Abstract: Attention can be biased by the previous learning and experience. We present an algorithmic-level model of this bias in visual attention that predicts quantitatively how bottom-up, top-down and selection history compete to control attention. In the model, the output of saliency maps as bottom-up guidance interacts with a history map that encodes learning effects and a top-down task control to prioritize visual features. We test the model on a reaction-time (RT) data set from the experiment presented in (Feldmann-Wustefeld, Uengoer, a Schubo, 2015). The model accurately predicts parameters of reaction time distributions from an integrated priority map that is comprised of an optimal, weighted combination of separate maps. Analysis of the weights confirms learning history effects on attention guidance.
Comparing global news sentiment using hesitant linguistic terms

Global policy makers need to maintain a pulse on the state of play of global governance. Advances in analytical tools, such as global news dashboards, can provide current information on changes to global sentiment. In particular, identifying unexpected shifts in sentiment following a major news event may better inform stakeholders’ actions. This paper defines a methodology to evaluate global sentiment for periods before, during, and after a major event. Each period's sentiment can be derived from news articles generated by news outlets. The sentiment is expressed in terms of hesitant linguistic terms to capture the range of sentiments articulated in each article. This representation is advantageous as it permits the interpretation of sentiments without conversion to numerical values. Sentiment from each article considered is aggregated into three centralized sentiments representing periods before, during, and after a particular event. This leads to a second enhancement to existing methods where the concept of a central opinion is represented in hesitant linguistic terms. Each of these sentiments is associated with a measure of consensus indicating the degree of agreement among the articles within their corresponding periods. A real case is presented for a noteworthy event in recent history. Three thousand three hundred fifty-two articles that referenced both the World Health Organization and President Trump during the 2-week period surrounding the event are analyzed. The results show that the methodology presented can detect changes in aggregated sentiment and consensus. When compared with means of aggregation based on crisp approaches, our model is more sensitive to shifts in sentiment. This type of information can better inform policy makers about public opinion as it not only detects shifts in sentiment but also discourses among citizens.
Performance Comparison of SAR Image Speckle Noise Removal Algorithms

SAR images have achieved a prominent position in the arena of remote sensing and satellite technology.This SAR images can captured in any weather either its day or night, cloudy or sunny.SAR images will find many applications in image processing, it has many application in resource management, agriculture, mineral exploration and environmental monitoring.The useful information of the SAR image also was affected with speckle noise.The noise that corrupt the SAR (Synthetic Aperture Radar) images were affects the appearances of the image is multiplicative or granular speckle noise.Accordingly, for such speckle noise kinds different speckle noise removal method were available.The most significant method that used to remove speckle noise from SAR image is filtering technique.The SAR image speckle noise is sometimes suppressed by removing a speckle noise, using removal filter algorithm on the image before display and further analysis.To do this Median, Guided Filter (GF), Lee, Box, Adaptive or Wiener filter algorithms were used and their performances were compared in PSNR, SNR and MSE and from those all used algorithms the GF achieves better performance in high PSNR value of 37.8342.
Review on Challenges in SAAS Model in Cloud Computing

Distributed processing is a quickly making industry with broad possibilities for progress. In any case, this new improvement confronted the security issue inconveniences. This condition is phenomenal. Scientists in the IT and data security fields need to inspect and manage these issues ordinarily. This article showed the dangers of passed on figuring model faced. At that, we investigated the influenced fragments of Programming as an Administration (SaaS) security illustrates.
Evaluating Universal Dependency Parser Recovery of Predicate Argument Structure via CompChain Analysis

Accurate recovery of predicate-argument structure from a Universal Dependency (UD) parse is central to downstream tasks such as extraction of semantic roles or event representations.This study introduces compchains, a categorization of the hierarchy of predicate dependency relations present within a UD parse.Accuracy of compchain classification serves as a proxy for measuring accurate recovery of predicate-argument structure from sentences with embedding.We analyzed the distribution of compchains in three UD English treebanks, EWT, GUM and LinES, revealing that these treebanks are sparse with respect to sentences with predicate-argument structure that includes predicate-argument embedding.We evaluated the CoNLL 2018 Shared Task UDPipe (v1.2) baseline (dependency parsing) models as compchain classifiers for the EWT, GUMS and LinES UD treebanks.Our results indicate that these three baseline models exhibit poorer performance on sentences with predicate-argument structure with more than one level of embedding; we used compchains to characterize the errors made by these parsers and present examples of erroneous parses produced by the parser that were identified using compchains.We also analyzed the distribution of compchains in 58 non-English UD treebanks and then used compchains to evaluate the CoNLL'18 Shared Task baseline model for each of these treebanks.Our analysis shows that performance with respect to compchain classification is only weakly correlated with the official evaluation metrics (LAS, MLAS and BLEX).We identify gaps in the distribution of compchains in several of the UD treebanks, thus providing a roadmap for how these treebanks may be supplemented.We conclude by discussing how compchains provide a new perspective on the sparsity of training data for UD parsers, as well as the accuracy of the resulting UD parses.
Prototyping an IoT-enabled Autonomous Unmanned Ground Vehicle Using SLAM

Any system that has a capability to perform any dull, difficult, dangerous, or dirty task without any support of human being, such type of system is known as Autonomous systems. In today’s era, there is a boom of such autonomous systems and in fact they are now gradually replacing human beings. In the catalogue of such autonomous systems robotic manipulators are very common and famous. This is because of their ability to pick and drop the heavy and sensitive packages from one place to another. There are also various autonomous vehicles that detect the safe and familiar paths, this detection is known as path following and tracking phenomena. These autonomous vehicles have more accuracy than an ordinary human being and with their usage one may maximize the efficiency and productivity of any industrial plant. Similarly in the field of information and communication technology, Internet of things (IoT) has been also one the trending topics. Thus, this manuscript presents the prototyping of an IoT-enabled autonomous unmanned ground vehicle (AUGV) which will not only follow the familiar path autonomously and avoid the obstacles using simultaneously localization and mapping (SLAM) but will also update the fundamental parameters of its surroundings i.e., temperature, pressure, humidity, detection of hazardous gas and exact location on provided internet protocol (IP). The reader will be able to understand the implementation of SLAM based path following and IoT-based data acquisition. These features make this prototype one of its kinds to be opted in various conditions