Reviewerly/exHarmony huggingface dataset:
Maximizing an Opportunity for Practice in Realizing Students’ Writing Publication through Thematic Progression Models

The competence of organizing a highly constructed writing for the students’ publication is academically required. Thus, the students are intended to familiarize with various styles to practice. The thematic progression in this case enables to create a meaningful text as it services numerous techniques to accomplish coherence and cohesion. To accommodate them, this researcher aims to guide as well as facilitate them with manual model: the thematic progression. The virtual coaching was done in two sections: 1) The orientation of essential publication for university level through thematic progression models, and 2) The independent construction of the manuscript. The participants were gained from eight semester in majoring English Education Department of UIN Walisongo Semarang in Academic Years 2019/2020. The findings qualitatively reported that the most students increasingly showed some advances in organizing texts by realizing thematic progression technique.
Adaptive filter based on Monte Carlo method to improve the target tracking in radar systems

Abstract The state estimation and tracking of random target is an attractive research problem in radar system. The information received in the radar receiver was reflected by the target, that it is received with many white and Gaussian noise due to the characteristics of the transmission channel and the radar environment. After detection and location scenarios, the radar system must track the target in real time. We aim to improve the state estimation process for too random target at the given instant in order to converge to the true target state and smooth their true path for a long time, it simplifies the process of real-time tracking. In this framework, we propose a new approach based on the numerical methods presented by MONTE CARLO (MC) counterpart the method conventionally used named Extended KALMAN Filter (EKF), we showed that the first are more successful. Keywords: Radar, Monte Carlo, Extended KALMAN Filter, Tracking, PF, Random target.
RECONCILIATION OF STUDIES AND WORK DUTIES OF STUDENTS DURING DISTANCE LEARNING

Appears in: EDULEARN21 Proceedings Publication year: 2021Pages: 3566-3571ISBN: 978-84-09-31267-2ISSN: 2340-1117doi: 10.21125/edulearn.2021.0745Conference name: 13th International Conference on Education and New Learning TechnologiesDates: 5-6 July, 2021Location: Online Conference
ABOUT THE ORGANIZATION AND USE OF THE EDUCATIONAL MULTI-AGENT SYSTEM

E. Zaytsev, E. Nurmatova, R. Khalabiya, I. Stepanova, L. BuninaMIREA - Russian Technological University (RUSSIAN FEDERATION)
Memes posted by the 'Political Editing 2.0' Instagram account

Sampling of memes and digital images posted to the 'Political Editing 2.0' Instagram account, offering a tangata whenua and takatāpui viewpoint on politics and current events in Aotearoa New Zealand. This online content was collected as part of the Library’s rapid response collecting regarding the Covid-19 pandemic and associated lockdowns/alert level changes in Aotearoa New Zealand. Viewpoints reflected in the collection do not represent the views of the Library or its staff. An algorithm chooses one image for display on the Library's website.If you are a rightsholder and have reason to believe we should not be providing access to this preserved content, please contact the Library. Title supplied by Library. Quantity: 246 digital image(s).
An Unsupervised Video Game Playstyle Metric via State Discretization

On playing video games, different players usually have their own playstyles. Recently, there have been great improvements for the video game AIs on the playing strength. However, past researches for analyzing the behaviors of players still used heuristic rules or the behavior features with the game-environment support, thus being exhausted for the developers to define the features of discriminating various playstyles. In this paper, we propose the first metric for video game playstyles directly from the game observations and actions, without any prior specification on the playstyle in the target game. Our proposed method is built upon a novel scheme of learning discrete representations that can map game observations into latent discrete states, such that playstyles can be exhibited from these discrete states. Namely, we measure the playstyle distance based on game observations aligned to the same states. We demonstrate high playstyle accuracy of our metric in experiments on some video game platforms, including TORCS, RGSK, and seven Atari games, and for different agents including rule-based AI bots, learning-based AI bots, and human players.
LibGuides: Resource Sharing/ILL at University of Richmond Libraries : Receiving Your Items

This guide summarizes uses, updates, and guidance for Resource Sharing/ILL at Boatwright Library and Parsons Music Library at the University of Richmond. How resource sharing items get to you!
Leveraging Pre-trained Checkpoints for Sequence Generation Tasks

Unsupervised pre-training of large neural models has recently revolutionized Natural Language Processing. By warm-starting from the publicly released checkpoints, NLP practitioners have pushed the state-of-the-art on multiple benchmarks while saving significant amounts of compute time. So far the focus has been mainly on the Natural Language Understanding tasks. In this paper, we demonstrate the efficacy of pre-trained checkpoints for Sequence Generation. We developed a Transformer-based sequence-to-sequence model that is compatible with publicly available pre-trained BERT, GPT-2 and RoBERTa checkpoints and conducted an extensive empirical study on the utility of initializing our model, both encoder and decoder, with these checkpoints. Our models result in new state-of-the-art results on Machine Translation, Text Summarization, Sentence Splitting, and Sentence Fusion.
Stained Scripts Image Segmentation Using Clustering Methods

Image segmentation is a process of concentrating and processing of useful information, maintaining the important effects in an image. The article presents a neural network-based algorithm which is efficient in segmenting gray-scale images. Difficulties faced in segmentation of stained images are high luminance variance, non- uniform degradation and smudges or smears. When document image is viewed from far, difficulties are overcome by decreasing foreground information. Clustering-based segmentation is effective when there is degradation. Investigational results show the capability and high functioning of proposed algorithm.
RANCANG BANGUN GAME EDUKASI 3 DIMENSI PERAWATAN ORANG UTAN BERBASIS GAME MAKER: STUDIO

Utilization of computers in modern times is increasingly sophisticated, in addition to being used as an information processing tool lately, computers are also used as entertainment media, namely games. With the ability and sophistication of computers today various computers have provided sophisticated graphical displays. The object of this research is to create a Save Orang Utan game using Game Maker: Studio Engine. Making this game starts from collecting data about ways of making games with Game Maker: Studio, how to design 3-dimensional characters in the game using Blender 3D data about games that can be made with Game Maker: Studio. The game is run only by one player, in the sense of only one user. This research has resulted in the game Save Orang Utan. This game was created as an educational tool that is entertaining and to be further developed for the better.
Analysis of Effectiveness of the Browsing Behavior of Web User using Sequence Weight Knowledge

A number of web users worldwide regularly follow the web to share their information, converse various topics, share, stay connected and obtain information. As a result, enormous amounts of data are generated by the web users and then one could employ this data to obtain useful predictions of certain web user related behavior. There are many platforms where web users could communicate and exchange information. These platforms comprises of social networks, and digital communication networks. In this research paper, the effectiveness of the browsing behavior of web user is analysed using sequence weight learning. The outcome shows that the proposed personalized recommendation framework using the Competence Scoring process for sequence weight learning able to achieve significantly higher accuracy. The investigation result of web uses also shows the personalized framework predicts future items reliably and could be employed to automatically recommended next-items to indented web users.
Applications of Matchmoving for Forensic Video Analysis of a Fatal Sprint Car Accident: Part I

The methodology used for the reconstruction of a high-profile Sprint Car accident that was captured by at least three different video recording devices is presented in two parts. Part I discusses a classical method of an accident reconstruction, and Part II discusses matchmoving technique to accurately analyze the video footage of the accident. Accidents captured on video are unlike most simple car collision evaluations and require expert knowledge from experienced professionals. Understanding the race car vehicle dynamics as it relates to recorded video footage allows a proper methodology to be followed in order to gather and process the evidence needed to provide meaningful data to the trier of fact. This paper discusses the classical process to reconstruct the accident as well as the currently acceptable scientific methodologies that were used to collect and interpolate the available scientific evidence. A visualization of the vehicles involved, Sprint Car #13 (SC#13) and Sprint Car #14 (SC#14), is shown in Figure 1.
Log-Polar Space Convolution for Convolutional Neural Networks

Convolutional neural networks use regular quadrilateral convolution kernels to extract features. Since the number of parameters increases quadratically with the size of the convolution kernel, many popular models use small convolution kernels, resulting in small local receptive fields in lower layers. This paper proposes a novel log-polar space convolution (LPSC) method, where the convolution kernel is elliptical and adaptively divides its local receptive field into different regions according to the relative directions and logarithmic distances. The local receptive field grows exponentially with the number of distance levels. Therefore, the proposed LPSC not only naturally encodes local spatial structures, but also greatly increases the single-layer receptive field while maintaining the number of parameters. We show that LPSC can be implemented with conventional convolution via log-polar space pooling and can be applied in any network architecture to replace conventional convolutions. Experiments on different tasks and datasets demonstrate the effectiveness of the proposed LPSC. Code is available at https://github.com/BingSu12/Log-Polar-Space-Convolution.
INTERACTIVE TECHNOLOGY WORKSHOP AS AN ACTIVITY FOR SOCIAL-EMOTIONAL COMPETENCE IN A POST-PANDEMIC SCENARIO

INTERACTIVE TECHNOLOGY WORKSHOP AS AN ACTIVITY FOR SOCIAL-EMOTIONAL COMPETENCE IN A POST-PANDEMIC SCENARIO
Confidence-Aware Scheduled Sampling for Neural Machine Translation

Scheduled sampling is an effective method to alleviate the exposure bias problem of neural machine translation. It simulates the inference scene by randomly replacing ground-truth target input tokens with predicted ones during training. Despite its success, its critical schedule strategies are merely based on training steps, ignoring the real-time model competence, which limits its potential performance and convergence speed. To address this issue, we propose confidence-aware scheduled sampling. Specifically, we quantify real-time model competence by the confidence of model predictions, based on which we design fine-grained schedule strategies. In this way, the model is exactly exposed to predicted tokens for high-confidence positions and still ground-truth tokens for low-confidence positions. Moreover, we observe vanilla scheduled sampling suffers from degenerating into the original teacher forcing mode since most predicted tokens are the same as ground-truth tokens. Therefore, under the above confidence-aware strategy, we further expose more noisy tokens (e.g., wordy and incorrect word order) instead of predicted ones for high-confidence token positions. We evaluate our approach on the Transformer and conduct experiments on large-scale WMT 2014 English-German, WMT 2014 English-French, and WMT 2019 Chinese-English. Results show that our approach significantly outperforms the Transformer and vanilla scheduled sampling on both translation quality and convergence speed.
Meta-Data Specification for the Description of Social Science Data Resources – Cessda Metadata Model

Finding the necessary digital objects on the internet poses increasing challenges, and one way to overcome this problem is to use metadata that describes digital objects in a specific way.This paper aims to explain the importance and role of metadata and metadata standards/schemes, with particular reference to metadata used to describe data sets that researchers collect, archive, and disseminate in the Social Sciences.The paper describes the metadata model developed by the Consortium of European Social Science Data Archives (CESSDA ERIC) for the digital archiving of data sets in the European Research Area (ERA).
EVALUATION OF SCHOOL PRINCIPALS IN RUSSIA: DEVELOPMENT OF EFFICIENCY CRITERIA FOR THE 21 CENTURY

Y. Fedorchuk , O. Gordashnikova , A. Kuznetsov Institute of Education Management of the Russian Academy of Education (RUSSIAN FEDERATION)
Shifting the Focus of the Fair Use Defense in Google LLC v. Oracle America Inc. — Analysing the differences between the majority and dissenting opinion of the U.S. Supreme Court

Article Shifting the Focus of the Fair Use Defense in Google LLC v. Oracle America Inc. — Analysing the differences between the majority and dissenting opinion of the U.S. Supreme Court was published on June 1, 2021 in the journal Computer Law Review International (volume 22, issue 3).
MemSum: Extractive Summarization of Long Documents using Multi-step Episodic Markov Decision Processes.

We introduce MemSum (Multi-step Episodic Markov decision process extractive SUMmarizer), a reinforcement-learning-based extractive summarizer enriched at any given time step with information on the current extraction history. Similar to previous models in this vein, MemSum iteratively selects sentences into the summary. Our innovation is in considering a broader information set when summarizing that would intuitively also be used by humans in this task: 1) the text content of the sentence, 2) the global text context of the rest of the document, and 3) the extraction history consisting of the set of sentences that have already been extracted. With a lightweight architecture, MemSum nonetheless obtains state-of-the-art test-set performance (ROUGE score) on long document datasets (PubMed, arXiv, and GovReport). Supporting analysis demonstrates that the added awareness of extraction history gives MemSum robustness against redundancy in the source document.
Facial emotion recognition and detection

Face and Emotion recognition presents a challenging problem in the field of image analysis and computer vision. The security of information is becoming very significant and difficult. Security cameras are presently common in airports, offices, universities, ATMs, and banks, and in any locations with a security system. Face recognition is a biometric system used to identify or verify a person from a digital image. The Face recognition system should be automatically able to detect a face and its emotions. This system would be able to perform for multiple people at a time.
Lung Disease Detection Of Diabetic Patients By Dnn Approach Using Ct Scan Images

A huge scope of lung surface disease examples can be seen in CT scan examinations. These pictures are the intermixed combination of different examples and henceforth it turns out to be extremely hard for Radiologists to separate among them and analyze the disease. One method for understanding this issue is the utilization of Diabetic Neural Network (DNN). DNN is utilized for design grouping and image recognition frameworks. They have accomplished less error on the database and the image classification utilizing DNN was shockingly quick. The CT Scan Images utilized in this investigation are authoritatively checked by the ensured Radiologist. The previous proposal discusses the identification of different levels of diabetics in a patient and continuation is described in this research. This proposed research work is aimed to design a classifier system for lung disease diagnosis of diabetic patients using Diabetic Neural Networks (DNN) when the Fundus Image of the Diabetic patient is given as input the affected range and desired disease of lungs for various level diabetic stages are detected. The classifier system described in this research is developed for the mass screening of patients related to the diabetic. This classifier system is developed to detect various lung diseases such as Asthma, pneumothorax or atelectasis, bronchitis, COPD, Lung cancer, pneumonia, etc.
A Tale Of Two Long Tails

As machine learning models are increasingly employed to assist human decision-makers, it becomes critical to communicate the uncertainty associated with these model predictions. However, the majority of work on uncertainty has focused on traditional probabilistic or ranking approaches - where the model assigns low probabilities or scores to uncertain examples. While this captures what examples are challenging for the model, it does not capture the underlying source of the uncertainty. In this work, we seek to identify examples the model is uncertain about and characterize the source of said uncertainty. We explore the benefits of designing a targeted intervention - targeted data augmentation of the examples where the model is uncertain over the course of training. We investigate whether the rate of learning in the presence of additional information differs between atypical and noisy examples? Our results show that this is indeed the case, suggesting that well-designed interventions over the course of training can be an effective way to characterize and distinguish between different sources of uncertainty.
Smart Drone for Smart Work

The aim of this project is to provide the performance evaluation and overview of a Smart Drone which would be Used for Solving the Problems like flood and the Present Pandemic Situation.The COVID_19 is a Global Pandemic That Caused more then 8,85,000 deaths an infected more Than 4 million people Worldwide, make us rethink how government, organizations and societies around the world can work with minimum or without physical contact. So, the Technologies like AI, IOT, Open CV playing to get the outbreaks of COVID-19.The COVID_19 is a Global Pandemic That Caused more then 8,85,000 death sand infected more Than 4million people Worldwide make us rethink how government organizations and societies around the world can work with minimum or without physical contact. So the Technologies like AI, IOT, Open CV playing to get the outbreaks of COVID-19.
Extra Large Sequence Transformer Model for Chinese Word Segment

Abstract Chinese word segment is widely studied in document analysis. The accuracy of the current popular word segment model, LSTM+CRF, is still not satisfactory. Models trained by the popular dataset often fails in the out-domain situation. In this paper, combining the Transformer-XL layer, the Fully-Connect layer, and the Conditional Random Field layer, the proposed model improved 3.23% in the macro-F1 score, comparing to the BERT+CRF model, on the MSR2005 Chinese word segment test dataset.
RANCANG BANGUN SISTEM INFORMASI EVENT ORGANIZER BERBASIS WEB DI ANTIQUE SALON DAN PELAMINAN

Information technology serves to help facilitate human work in all aspects of life. The Infortmation Systems is functioned for the purpose of improving the Event Organizer service. A website - based infortmation systems is needed as a transmitter of informations in increasing customer interest so that they can see details of items and packages in detail. The method of data collection is by a using obserrvation techniques, literrature studies, and interviewss. The systems diesign methode used is the waterfalls where the stagess are plannings, analysise, design, and system testings. Website designs using CodeIgniter4 framework, Bootstrap4, and MySQLto shortens the times with satisffactory result. With the developments of this informations systems, it is hoped that it can be a solutions to the problemss faced at Antique Salon and aisles in conveying detailed information regarding details of items, packages, services offered and other features.
Research on Wisdom Education of College English in the Era of Big Data

With the rapid development of modern information technology and the arrival of the era of big data, “smart education” as a new concept has been constantly explored and studied by Chinese and foreign scholars from multiple angles combined with practice. At the same time, this new teaching mode, which is endowed with new connotation and characteristics, also provides new ideas for the innovation of college English teaching and reform path. Based on this, this paper focuses on the concept and characteristics of smart education, analyzes the current situation of college English classroom teaching in China, and discusses how to reconstruct and innovate college English classroom teaching methods and implementation approaches under the background of big data technology from the aspects of classroom teaching organization, teaching and learning path optimization and the feasibility of smart education teaching mode, so as to provide a reference for the research of college English wisdom education.
On the Classification Training of Vocal Music Technique under the Pentathlon Technique

The classification of vocal music has its scientific basis. In the classification training of vocal music technology, it requires the precipitation of academic background and discipline, so as to ensure that the classification training of vocal music technology can carry out the actual operation and training under the guidance of pentathlon technology concept. This paper studies and discusses the operability of vocal music skill grading training on the basis of pentathlon technical concept.
TMEKU System for the WAT2021 Multimodal Translation Task

We introduce our TMEKU 1 system submitted to the English→Japanese Multimodal Translation Task for WAT 2021.We participated in the Flickr30kEnt-JP task and Ambiguous MSCOCO Multimodal task under the constrained condition using only the officially provided datasets.Our proposed system employs soft alignment of word-region for multimodal neural machine translation (MNMT).The experimental results evaluated on the BLEU metric provided by the WAT 2021 evaluation site show that the TMEKU system has achieved the best performance among all the participated systems.Further analysis of the case study demonstrates that leveraging wordregion alignment between the textual and visual modalities is the key to performance enhancement in our TMEKU system, which leads to better visual information use.
Соционические инструменты стимулирования

On the basis of the informational model of the psyche and Jung-Augustinavichute-Reinin dichotomies, socionics distinguishes various types of human motivation, communication styles, combinations of other psychological traits. This provides a variety of methods to stimulate human activity.
STUDY OF THEORETICAL AND PRACTICAL READINESS OF TEACHERS FOR GUIDANCE OF EDUCATION ACTIVITIES

Federal State Budgetary Educational Institution of Higher Education «Petrozavodsk State University (RUSSIAN FEDERATION)