Reviewerly/exHarmony huggingface dataset:
Are you certain that the law of gravity is really a law?

Do you have a pencil or pen in your hand? If you do and you let go of it, what would happen? Well, that’s the easiest question in this book! The pencil would fall. Why? That’s easy, too: Gravity causes it to fall. Simple, eh? But change the question a bit: Are you certain that the pencil will fall? Of course! Why are you certain? Well … because the law of gravity is a law. But, why are you certain that the law of gravity is a law?
The Impact of Indonesia’s School Operational Assistance Program on The Transition Rate from Primary to Junior Secondary School

This study investigates the impact of Indonesia’s School Operational Assistance Program (BOS) on the transition rate from primary to junior secondary school using data from the Indonesian Family Life Survey (IFLS) and the Difference-in-Differences method. The results showed that the School Operational Assistance Program (BOS) significantly increases the transition rate from Primary to Junior Secondary School. Meanwhile, the School Operational Assistance Program (BOS) is more effective in increasing the transition rate in girls compared to boys. When comparing rural and urban settings, the program is more effective in rural than urban areas. Despite results showing that the School Operational Assistance Program (BOS) has successfully increased the transition rate from primary to junior secondary school, the program still has several problems, such as inadequate management of the funds and the lack of quality control related to its usage. The three models tested in the study conclude that the School Operational Assistance Program (BOS) significantly improved the overall transition rate from primary to junior secondary school in Indonesia, particularly for girls and rural areas. Acknowledgments. This research is supported by DRPMI Universitas Padjadjaran (UNPAD) for Lecture Competency Research (RKDU) scheme. We thank our colleagues from the Department of Economics, who provided insight and expertise that greatly assisted the research and we thank “anonymous” reviewers for their so-called insights.
Data Mining Tool To Help The Scientific Community Develop Answers To Covid-19 Queries

Science has time and again proven to be one of the most powerful tools in finding solutions to the problems faced by the world.Let it be natural or man-made challenges, hard work put into finding efficient answers to tackle them has proven to safeguard the ecosystem.Sometimes the research community is put under pressure when humanity faces the challenge of survival like the Covid-19 pandemic.A great extent of published works needs to be studied to find an optimal solution to existing or new queries related to the virus.In this research work, we build an efficient data mining tool using the CORD-19 Dataset to help the community come up with answers to Covid-19 related questions.We use a combination of semantic and keyword search to reduce the solution space of our model.Our model makes use of parallelism, paraphrasing, and state-of-the-art natural language processing techniques which will serve as a time and energy-saving tool for the information need of all doctors and researchers who are trying to put an end to the pandemic and avoid future possible outbreaks.
Noise is Not Volume Alone – Ambiences and Drony Experiments in Music and Sound Art

Noise is a complex category that has been used to describe instances of disturbance and disruption in technical vocabulary and in many artistic languages. In music, and more precisely, in sound art, noise has been imbued with specific significations that operate as aesthetical signifiers that convey meaning even beyond its intensity of volume. In this article, the theoretical aspects of noise are articulated through the analysis of concerning discourse around the transformations of the concept of sound, which ultimately resulted in the designation of a genre in itself – noise. Furthermore, it is through the enhancement of a ‘sonic turn’ that the notion of listening as a generative aesthetic practice has referred to the body as the main instance of meaning construction in relation to both time and space.
1437: APPLICATION OF MACHINE LEARNING IN A LINKED MULTICENTER DATASET: CLASSIFYING MODS IN SEPTIC SHOCK

Brennan, Margaret1; Lindell, Robert2; Nishisaki, Akira3; Fitzgerald, Julie4; Dziorny, Adam5 Author Information
Hologram Technology For E-Learning Arabic Language Pre-Primary And Primary School

The objective of this study was finding the starting grade class in pre-primary and early primary school that adjustable with e-learning hologram technology for e-learning Arabic language.This experimental design research used a mixed (1) quantitative method for score of control groups- traditional learning and experimental groups-hologram e-learning of primary school (2) qualitative method for descriptive assesement reports, interview and observation of pre-primary school. The population was taken from 2 IBS with total 68 participants. The validation quantitative data was analyzed by SPSS software: t –test and else Mann–Whitney test, meta-analysis to describe result for conclusion. The qualitative uses triangulation of descriptive data for validation. The result shows that 1st grade, the sig. (2tailed) result 1.00 &gt; 0.05 and 2sc of primary school, the sig. (2tailed) result 0.843 &gt; 0.05 with hologram are indicating no significant gap with traditional learning, same effectiveness, but for pre-primary school with hologram is less-classical completeness 52.5% &lt; 85%. The conclusion is hologram technology of e-learning Arabic language subject adjustable for primary school starting 1st grade and 2sc, but yet, not for pre-primary school which lean to the content attractiveness.
A Study of Video Super-Resolution Method Using Video Coded Data as Training Data

This paper presents a study of video super resolution method by applying Deformable Convolution network to coded video. It is reported the effectiveness of using coded video as training data, and numerical and visual results of coded fine-tuned model. From those results, it is discussed the relationship between about characteristic of training data and especially in video’s framerate. There it is shown that video sequence which has similar framerate with training data can perform higher PSNR, since low framerate means the motion between frames is large and Deformable Convolution can learn that large motion.
Defining different types of assessment systems and assessment procedures in the context of historical development

Introduction. The authors investigate a wide range of assessment systems in modern education. They analyze and compare changes in educational assessment procedures, taking into account the periodization of their development and modern requirements and standards. The purpose of the article is to identify and clarify various types of assessment systems and assessment procedures in the context of historical development. Materials and Methods. This theoretical research includes analysis, comparison, and generalization of Russian and international studies on the development of assessment systems based on the concept of the historical and socio-cultural determination of education. The study follows systemic, learner-centered and activity-based approaches in order to present the dynamics of the development of educational assessment as a system comprising interconnected parts and socio-psychological factors. Results. The authors clarified the essence of the dual nature of educational assessment as a didactic and socio-psychological phenomenon. They described the relationship between assessment procedures and the quality of modern education. The study identified the main periods in the development of educational assessment based on the criteria for changing the requirements for the content of education, as well as changes in the assessment system regarding tasks, methods, forms of assessment, point scales and methods of motivation. The authors have developed and justified the historical periodization of educational assessment, which includes eight main stages. The main types of assessment including partial assessment, effective assessment, systemic assessment, standardized assessment, formative assessment, summative assessment, evaluation for management, and rating assessment have been identified and described. Conclusions. The article concludes that modern education implements eight main types of assessment, which have been developed in a historical context. At the same time, standardization of education is considered as the main trend in the formation of assessment systems.
Effects of Negation and Uncertainty Stratification on Text-Derived Patient Profile Similarity

Semantic similarity is a useful approach for comparing patient phenotypes, and holds the potential of an effective method for exploiting text-derived phenotypes for differential diagnosis, text and document classification, and outcome prediction. While approaches for context disambiguation are commonly used in text mining applications, forming a standard component of information extraction pipelines, their effects on semantic similarity calculations have not been widely explored. In this work, we evaluate how inclusion and disclusion of negated and uncertain mentions of concepts from text-derived phenotypes affects similarity of patients, and the use of those profiles to predict diagnosis. We report on the effectiveness of these approaches and report a very small, yet significant, improvement in performance when classifying primary diagnosis over MIMIC-III patient visits.
Response to Fitzgerald & Bernholtz: There is still much work to be done for digital classifiers

We thank Drs. Fitzgerald and Barenholtz for their thoughtful comments [1]. We appreciate the points that our colleagues are making about sensitivity and the importance of having accurate reference standards. There are significant challenges in developing these methods in real-world settings like major hospital systems. Ultimately, our goal here is not to demonstrate the superior performance of our classifier over other tools but instead to improve screening rates in the hospital setting. If we continue to rely on manual screening, it is our belief that large numbers of patients will continue to be missed due to resource limitations of staff and time that generally restrict how extensively these can be done. Our approach has the advantage of being a fully automated screening process. In this scenario, we would aim to show non-inferiority of our digital classifier approach to existing methods. Finally, we concur with our colleagues that a larger, multi-site trial of this digital approach against current manual screening is merited and we hope to see this come to fruition in the near term. None. None. Niranjan Karnik drafted this reponse. Hale Thompson and Majid Afshar reviewed the response and provided feedback. All authors approved the response as written.
Alberto Nocentini, Etimologi si nasce e io, modestamente, lo nacqui, Firenze, Le Monnier Università, 2021, VIII + 133 p.

Article Alberto Nocentini, Etimologi si nasce e io, modestamente, lo nacqui, Firenze, Le Monnier Università, 2021, VIII + 133 p. was published on November 1, 2021 in the journal Zeitschrift für romanische Philologie (volume 137, issue 4).
Accelerating SegNet-Based Semantic Segmentation Using a Model Post-Pruning Strategy

Accelerating deep convolutional networks has recently attracted a great deal of attention due to the demand of real-time applications.SegNet is a typical deep convolution network in the field of semantic segmentation, and also is a smaller and more memory, time efficient model.In this paper, we focus on accelerating the SegNet-based semantic segmentation by using a model post-pruning strategy.Despite the fact that several methods have been proposed for accelerating deep models including pruning and compressing the weights of each layer, these methods may cause a certain loss of segmentation accuracy by irregular pruning.To address this issue, we propose a post-pruning strategy for deep model compression, which is commonly used to essentially deal with the over-fitting problem of decision tree.Different from some existing methods that employ the irregular pruning strategy, the proposed method can significantly improve the generalization ability of the compressed model.Inspired by the post-pruning method originally used in decision tree, our method minimizes a channel pruning loss function.The compressed model is then retrained to further improve its performance of semantic segmentation.Experimental results on two segmentation datasets
TransTracking for UAV: An Autonomous Real-time Target Tracking System for UAV via Transformer Tracking

Most of the existing Siamese-type trackers usually adopt pre-defined anchor boxes or anchor-free schemes to accurately estimate the bounding box of targets. Unfortunately, they suffer from complicated hand-designed components and tedious post-processings. It is not easy to adjust parameters for unique scenes in real applications. So, we propose a new scheme by formulating visual tracking as a direct set prediction problem to alleviate this issue. The main component is a transformer attached to the Siamese-type feature extraction networks. Thus, our new framework can be summarized as Siamese Network with Transformers (SiamTFR). With a fixed small set of learned object queries, we force the final set of predictions via bipartite matching, significantly reducing hyper-parameters associated with the candidate boxes. Due to the unique predictions of this framework, we significantly ease the heavy burden of hyper-parameters search of post-processings in visual tracking. Extensive experiments on visual tracking benchmarks, including GOT-10K, demonstrate that SiamTFR achieves competitive performance and runs at 50 FPS. Specifically, SiamTFR outperforms leading anchor-based tracker SiamRPN++ in the GOT-10K benchmark, confirming its effectiveness and efficiency. Furthermore, SiamTFR is deployed on the embedded device in which the algorithm can be run at 30FPS or 54FPS with TensorRT meeting the real-time requirements. In addition, we design the complete tracking system demo that can work in the real road to narrow the gap between the academic models and industrial deployments.
Digital Archiving in the Physics Literature: Author to Archive and Beyond—The American Physical Society

The American Physical Society (APS) has long recognized as its goal the diffusion of the knowledge of physics. For the past 100 years, the society has used a paper-based, print-oriented publishing process in support of this goal. This paper describes the strategies and projects, being developed and implemented, that will enable the exploitation of the emerging Internet and electronic publishing technologies in support of the Society’s goal. The transparencies used in this presentation are available on http://publish.aps.org/RAKELLY/rakelly.html. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-342-9678. E-mail address: getinfo@haworth.com]
Twitch suffers massive breach

Twitch, the live streaming service owned by Amazon, has suffered a massive data breach that not only leaked personal information but also the platform's source code.
Augmented reality research in the 21 century pandemic situation by coronavirus disease (COVID-19): A bibliometric study

The aim of this study is to identify publications in 21 century pandemic situation by coronavirus disease (COVID-19) related to augmented reality (AR) and to describe the characteristics of those studies. The Google Scholar database was searched for publications on AR in COVID-19 research criteria between 2019 and 2021. Bibliographic coupling of the details and co-occurrences of the publication of the author keywords were examined for the selected publications. A total of 2907 publications were selected following the research criteria between 2019 and 2021. The results showed that among the most researched researchers in AR study are the names of authors with color highlights from different countries. The study addressed the pattern in research and advancement of the idea of increased pandemic reality for COVID19.
Research on convolutional neural network for video classification

Convolutional Neural Network (CNN) has recently been demonstrated as an effective model in machine learning for processing a large amount of data. In addition to its applications in natural language processing (NLP), CNN plays an important role in computer vision. This machine learning model can be applied to realize face recognition, image classification, image retrieval, object detection video classification, etc. In this paper, the author constructs an CNN model to classify videos based on the data set HMDB51. HMDB51 is a large human motion database, consisting of a total of 7,000 clips distributed in 51 action classes. Different from the 2D image, video is the three-dimensional data, including time and the width and height of each frame in the video. Therefore, it is necessary to process the video into a sequence of frames of uniform length and normalize the frames firstly. This would facilitate subsequent training and help prevent premature over-fitting as well. Besides, the impact of changing parameters in this CNN model would also be tested, such as the length of image frames, the number of sample categories, the batch size to training the data and the location of dropout layers. The results show that it is difficult to significantly improve the accuracy of recognition by adjusting these parameters alone, and those various factors need to be considered comprehensively
Semi-automatic Annotation Method of Infrared Sequence Images Based on Image Significance

At present, there are some problems in manually labeling data, such as slow speed, low efficiency and low precision. In order to improve the labeling efficiency, according to the characteristics of infrared sequential images, a semi-automatic labeling method of infrared sequential images based on image saliency is proposed. The kernel correlation filtering algorithm is used to track the infrared time series target, obtain the approximate area of the target, and realize the target position pre labeling. The saliency target detection algorithm is used to calculate the target saliency map of the pre-labeled area, and the foreground target is extracted by threshold segmentation, so as to realize the target fine labeling of the pre-labeled area. The average overlap rate with the manual annotation results is about 95%, which can accurately locate the target position and improve the annotation efficiency of sequence images.
Human-Computer Interaction Data Network Analysis of Language Online Learning under the Network Background

With the continuous development of "Internet+", online language learning has received more and more attention. However, while online education brings convenience to people, it also exposes some problems. Therefore, how to improve the interactive ability and learning effect in the design of the network learning platform is an important problem to be solved in the Web2.0 era. Based on the background of "Internet +", this paper studies and analyzes the human-computer interaction data network of language online learning. The disadvantages of traditional language learning are eliminated in online interactive learning. Students' interest points and study notes generated in the process of interacting with machines All are recorded in the data network.
Socio-cultural design as a means of moral education of schoolchildren in the conditions of modern education

The purpose of the article is to analyze the features of socio-culturпal design as a means of moral education of schoolchildren in the context of modern education. On the basis of an interdisciplinary analysis of scientific sources, the essence of socio-cultural design, its principles and role in the formation of the moral qualities of the younger generation are determined. The article presents the materials of a psychological and pedagogical experiment to determine the level of moral education of schoolchildren of different ages, as well as the pedagogical conditions identified and formulated by the authors in the form of conclusions. The proposed diagnostic tools made it possible to determine the effectiveness of the application in educational practice of the Program for the sociocultural design of schoolchildren developed by the authors of this article.
SOFIE (Solar-wind with Field-lines and Energetic-particles): A data-driven and self-consistent SEP modeling and forecasting tool

Earth and Space Science Open Archive PosterOpen AccessYou are viewing the latest version by default [v1]SOFIE (Solar-wind with Field-lines and Energetic-particles): A data-driven and self-consistent SEP modeling and forecasting toolAuthorsLuluZhaoiDIgorSokoloviDTamasGombosiiDValeriyTenisheviDZhenguangHuangGaborTothiDNishthaSachdevaiDWardManchesteriDBartvan der HolstiDSee all authors Lulu ZhaoiDCorresponding Author• Submitting AuthorUniversity of MichiganiDhttps://orcid.org/0000-0003-3936-5288view email addressThe email was not providedcopy email addressIgor SokoloviDUniversity of MichiganiDhttps://orcid.org/0000-0002-6118-0469view email addressThe email was not providedcopy email addressTamas GombosiiDUniversity of MichiganiDhttps://orcid.org/0000-0001-9360-4951view email addressThe email was not providedcopy email addressValeriy TenisheviDUniv MichiganiDhttps://orcid.org/0000-0002-0934-4550view email addressThe email was not providedcopy email addressZhenguang HuangUniversity of Michigan-AOSSview email addressThe email was not providedcopy email addressGabor TothiDUniversity of MichiganiDhttps://orcid.org/0000-0001-8459-2100view email addressThe email was not providedcopy email addressNishtha SachdevaiDUniversity of MichiganiDhttps://orcid.org/0000-0001-9114-6133view email addressThe email was not providedcopy email addressWard ManchesteriDUniversity of MichiganiDhttps://orcid.org/0000-0003-0472-9408view email addressThe email was not providedcopy email addressBart van der HolstiDUniversity of MichiganiDhttps://orcid.org/0000-0001-5260-3944view email addressThe email was not providedcopy email address
Generating Radar Track Using Cycle-consistent Generative Adversarial Network

The real radar track datasets of marine targets in complex environment are insufficient, and it is difficult to obtain a large number of datasets. To solve these issues, we propose a marine targets radar track data generative method, which is based on a cycle-consistent generative adversarial network. This method achieves the end-to-end mapping from the truth trajectory to the true error track using unpaired datasets. To make the generator better recover the details of the track, we creatively put the U-Net architecture to the generator of the network, where the U-Net skip-connection structure is used to ensure the effective transmission of features. The experimental verification was performed with a self-built dataset. The results show that the proposed method can effectively learn the error distribution of samples, generate track data with higher similarity to the true error distribution, and improve the accuracy of track classification.