Reviewerly/exHarmony huggingface dataset:
RG/E Derivation of Relativistic First-Order Fluid Dynamics

We derive the relativistic fluid dynamic equation as describing the asymptotic slow dynamics of the relativistic Boltzmann equation with quantum statistics by applying the RG/E method, as a natural extension of the analysis done for the non-relativistic case done in Chap. 8 : The resultant equation is the relativistic first-order fluid dynamic equation, i.e., a relativistic analogue of the Navier-Stokes equation in the non-relativistic case. The derivation is based on a faithful solution of the Boltzmann equation based on the perturbation theory and is free from any ad hoc assumptions. The resultant equation is found to uniquely coincides with that in the energy frame. A proof is also given that our equation possesses the linear stability, irrespective of the properties of the collision term, in the sense that the equilibrium solution is stable to any linear disturbances.
Test Sample Selection for Handwriting Recognition Through Language Modeling

When it comes the need to automatically recognize handwritten information, there may be a scenario in which the capture device (such as the camera of a mobile phone) retrieves several samples—a burst—of the same content. Unlike general classification scenarios, combining different inputs is not straightforward for state-of-the-art handwriting recognition approaches based on neural end-to-end formulations. Moreover, since not all images within the burst may depict enough quality for contributing in the overall recognition task, it is hence necessary to select the appropriate subset. In this work, we propose a pilot study which addresses this scenario in which there exists a burst of pictures of the same manuscript to transcribe. Within this context, we present a straightforward strategy to select a single candidate out of the elements of the burst which achieves the best possible transcription. Our hypothesis is that the best source image is that whose transcription most likely matches a Language Model (LM) estimated on the application domain—in this work implemented by means of n-grams. Our strategy, therefore, processes all the images of the burst and selects the transcription with the highest a priori probability according to the LM. Our experiments recreate this scenario in two typical benchmarks of Handwritten Text Recognition (HTR) and Handwritten Music Recognition (HMR), validating the goodness of our proposal and leaving room for promising future work.
THE EXPERIENCE OF THE FORMATION AND DEVELOPMENT OF A SCIENTIFIC SCHOOL

The article analyzes the genesis of the formation and development of the scientific school «Continuous development of human potential». The modern problems in the activity of the department as the core of the scientific school are identified. The vectors of advanced formation of the competencies of future management specialists in accordance with the new realities are outlined. A conceptual approach to the continuous improvement of the scientific and methodological process of forming and developing the human potential of specialists is proposed
Spotlight on Artificial Intelligence and Freedom of Expression: A Policy Manual

This publication is part of the project “Spotlight on Artificial Intelligence and Freedom of Expression” (SAIFE)
An Iterative Centroid Approach for Diffeomorphic Online Atlasing

Online atlasing, i.e., incrementing an atlas with new images as they are acquired, is key when performing studies on very large, or still being gathered, databases. Regular approaches to atlasing however do not focus on this aspect and impose a complete reconstruction of the atlas when adding images. We propose instead a diffeomorphic online atlasing method that allows gradual updates to an atlas. In this iterative centroid approach, we integrate new subjects in the atlas in an iterative manner, gradually moving the centroid of the images towards its final position. This leads to a computationally cheap approach since it only necessitates one additional registration per new subject added. We validate our approach on several experiments with three main goals: 1- to evaluate atlas image quality of the obtained atlases with sharpness and overlap measures, 2- to assess the deviation in terms of transformations with respect to a conventional atlasing method and 3- to compare its computational time with regular approaches of the literature. We demonstrate that the transformations divergence with respect to a state-of-the-art atlas construction method is small and reaches a plateau, that the two construction methods have the same ability to map subject homologous regions onto a common space and produce images of equivalent quality. The computational time of our approach is also drastically reduced for regular updates. Finally, we also present a direct extension of our method to update spatio-temporal atlases, especially useful for developmental studies.
ZoomNet for Topic-Oriented Fragment Recognition in Long Documents

This work introduces a new information extraction task called <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Topic-Oriented Fragment Recognition (TOFR)</b> , whose goal is to recognize information related to a specific topic in long documents from professional fields. In this paper, we introduce two TOFR datasets to study the problems of processing long documents. We propose a novel neural framework named <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Zooming Network (ZoomNet)</b> , which overcomes the challenge of combining information over long distances with limited computing resources by flexibly switching between skimming and intensive reading in processing long documents. In general, ZoomNet first establishes a hierarchical representation aligned to the text structure, which relieves the conflict between local information and extensive contextual information. Then, it synthesizes different levels of information to assign tags via multi-scale actions. We combine supervised and reinforcement learning methods to train our model. Experiments show that the proposed model outperforms several state-of-the-art sequence labeling models, including BiLSTM-CRF, BERT, XLNET, RoBERTa, and ELECTRA, on both TOFR datasets with big margins.
DRL-Based and Bsld-Aware Job Scheduling for Apache Spark Cluster in Hybrid Cloud Computing Environments

The explosive growth of data volume has forced enterprises to use public clouds to extend the service capacity of their original private clouds. spark, as one of the most important big data computing engines, has been favored by academia and industry for its low latency, simplicity of use, and versatility. When Spark is deployed in a hybrid cloud environment, its default job scheduling policy leads to performance degradation and cluster usage cost increase as it ignores the characteristics of the job and hybrid cloud environment. To address these issues, we propose a deep reinforcement learning-based Spark job scheduler to improve the performance of Spark clusters and reduce the total cost of cluster usage in hybrid cloud environments . Specifically, an actor network is trained to schedule Spark jobs and a critical network is trained to evaluate the actions made by the actor. The proposed proximal policy optimization (PPO) agent can adaptively learn the characteristics of different types of jobs and the characteristics of hybrid cloud environments to schedule Spark jobs rationally, thus reducing the total usage cost of clustered virtual machines (VMs) and the average bounded slowdown (bsld) of jobs. Experiments show that the DRL-based scheduling algorithm reduces the total usage cost of the cluster by 23.3% and improves the performance of the cluster by 4.4% compared to the baseline scheduling algorithm in job burst arrival mode.
Vacation Advisor Based on Dynamic Ratings

This work proposes a recommender system that helps the tourists to describe their experiences with various tourist attractions and acts as a source for sentiment analysis. By employing a long short-term memory network, the reviews provided by various users are rated on a scale of five. Real-world recommender systems are being successfully used. However, the systems considering the current location of the user and the reviews provided by various users are sparse. This paper proposes a recommender system that can automatically consider a user’s geographical location and recommend the neighboring places which satisfy the user given criteria. The method performs web scraping to determine the current location of the user and considers an R tree spatial data structure for representing the neighboring places. It trains a long short-term memory network to analyze the reviews provided by users to rate the places. The method will recommend close by places to the users which satisfy their constraints and will act as a platform which allows them to provide their reviews for different places.
On Combining Ontologies and Rules

Ontology languages, based on Description Logics, and nonmonotonic rule languages are two major formalisms for the representation of expressive knowledge and reasoning with it, that build on fundamentally different ideas and formal underpinnings. Within the Semantic Web initiative, driven by the World Wide Web Consortium, standardized languages for these formalisms have been developed that allow their usage in knowledge-intensive applications integrating increasing amounts of data on the Web. Often, such applications require the advantages of both these formalisms, but due to their inherent differences, the integration is a challenging task. In this course, we review the two formalisms and their characteristics and show different ways of achieving their integration. We also discuss an available tool based on one such integration with favorable properties, such as polynomial data complexity for query answering when standard inference is polynomial in the used ontology language.
Learning to Drive Fast on a DuckieTown Highway

We train a small Nvidia AI JetRacer to follow the road on a small DuckieTown highway. In the real-world, roads do not always have the same appearance, so the system should not be trained on lane markings alone but on the complete view of the front camera. To make this possible, the system is trained in simulation using a recent reinforcement learning approach in an end-to-end fashion. This driving experience is then transferred to the circumstances encountered on a real track. Transfer learning is surprisingly successful, although this method is very sensitive to the details of the vehicle dynamics. We trained multiple models at different speeds and evaluated their performance both in simulation and in the real world. Increasing the velocity proves difficult, as the learned policy breaks down at higher speeds. The result is a small Nvidia AI JetRacer, which is able to drive around a DuckieTown highway, based on simulated experiences.
Implementation Of The School Literature Movement (Slm) During The Covid-19 Pandemic In Junior High School In West Java Province, Indonesia

The low literacy skills of students in primary and secondary schools in the Republic of Indonesia (RI), since 2015 the government has implemented the School Literacy Movement (SLM) program. It is only that in early 2020 the program was disrupted due to the COVID-19 pandemic. This research is to describe how the SLM program was implemented during the COVID-19 pandemic in a secondary school in West Java Province, Indonesia. This descriptive qualitative research uses the literature study method. The main reference sources are literacy movement manuals published by the Indonesian government, activity programs compiled by the school, and other secondary sources. The results showed that with the active collaboration of school residents, SMP ASBP KBB and SMP NKB West Java Province, Indonesia, continued to implement the SLM program even though during a pandemic emergency. Various initiatives made schools more adaptive to the presence of internet technology, making activities more meaningful, integrated, and appropriate. parameters have been able to create a Literate School Ecosystem (LSE) as the main goal of program success.
Seamless Experience of Learning across Contexts for Chinese Vocabulary Learning: A Pilot Study

This paper delineates a pilot study on a seamless language learning design that emphasises on the seamless Chinese vocabulary learning experiences across a multitude of contexts of "at home, on campus, in the classroom, at apartment and other places".A systematic seamless learning approach was introduced.In addition, DingTalk, a technology-enhanced learning platform in tandem with social media was adopted to enhance respondents' Chinese vocabulary learning.Eight international students of Chinese as a foreign language (CFL) participated in this study for eight weeks, where they were permitted to utilise their mobile devices to capture vivid images and create artifacts on DingTalk, the social media platform, in conjunction with conducting a series of peer-to-peer discussions regarding the artifacts.Through the findings of the post-study reflections, interviews, and the data obtained from students upon the conclusion of the study, an overall positive influence on the student's ability to diligently leverage their spectrum of vocabulary in a multitude of contexts was evident.Furthermore, the notion of seamless language learning design has empowered students to inculcate the habit of autonomous learning and translate in-class language learning to practical real-life scenarios through their seamless learning experience across various contexts, which strongly correlates with their educational competence both in and out of the classroom.
TICs y el aprendizaje en el Área de Comunicación de Estudiantes de Sexto Grado en el I.E. Nicanor Rivera Cáceres de Barranco, 2021.

La investigación tuvo como objetivo identificar en qué medida se utilizan las TIC y el aprendizaje, en el área de comunicación, de los estudiantes de sexto grado en el I. E. Nicanor Rivera Cáceres, de Barranco (2021). El software CmapTools solo se empleó con el grupo experimental pero la evaluación de pretest y el postest se aplicó a ambos grupos. El nivel de investigación fue descriptivo correlacional; asimismo, se obtuvo la confiabilidad del instrumento de aprendizaje colaborativo mediante la prueba KR-20, que arrojó como resultado 0,857. Por tanto, se concluyó que el uso del software CmapTools mejora notablemente el aprendizaje colaborativo. Abstract The aim of this research was establishing the relationship between Information and Communication Technologies (ICT) and learning in the area of communication among sixth grade students from Nicanor Rivera Caceres Educational Institute (I.E. Nicanor Rivera Cáceres) de Barranco, Peru. To gather the information a quantitative approach methodology was applied, with a correlational scope and a non-experimental and cross-sectional design. The survey was used as a technique and the questionnaire as a research instrument. The population and sample consisted of 49 sixth grade students, from which the inferential result was that there existed a relationship between the ICT variable and learning with a Pearson value of 0.454 and a p-value of 0.002. It was concluded that when using ICTs continuously among students, learning is achieved much faster, more productively and more beneficial for them.
Implementation of Sentimental Analysis Using Twitter Data

Online media are becoming extra attention those days. Public and personal evaluation on a extensive collection of topics are communicated and unfold always by diverse on line media. Twitter is one of the on line media this is obtaining prominence. Twitter gives association’s short and compelling method to research clients' viewpoints closer to the primary to accomplishment within side the industrial Centre. Building up a software for belief research is a manner to cope with be applied to computationally degree clients' insights. This paper investigates the plan of a end examination, setting apart a massive degree of tweets. Prototyping is applied on this flip of events. Results set up clients' factor of view thru tweets into fantastic and negative, that is addressed in a pie define and html page. Nonetheless, this system has desired to create on an internet utility framework, but due to restrict of Django which may be bartered with a Linux employee or LAMP, for added this system ought to be finished.
Central banking for inclusive development: Through a new lens

Compared with fiscal policy, the role of central banking in supporting inclusive development is less frequently discussed in general as well as in the context of the COVID-19 pandemic. Until the 1980s, many central banks worldwide had developmental mandates, such as using credit allocation and special financing schemes, to promote such priority economic sectors as manufacturing activities and exports (Dafe and Volz, 2015). Since then, the spread of neoliberal economic policies and the perceived failure of developmental central banking prompted many central banks in developing countries to abandon such policies and adopt an inflation-targeting approach. Such an approach primarily narrows the core mandate of central banks to maintaining price stability, which supports inclusiveness only implicitly. More recently, many central banks, particularly of advanced economies, have been criticized for launching unconventional monetary policy measures in response to the 2007–2008 global financial and economic crisis (Volz, 2017). Examples of unconventional measures include purchase of toxic assets of the corporate sector and injection of substantial liquidly into the system to support banks and spur economic activity. Such experiences have led central banks to become extra cautious or discouraged when exploring any potential additional role that may be deemed non-traditional.
Mixing and Spatializing Future Generation Audio Content

Effective immersive sound design includes the fundamental application of localization, spatialization and advance audio production practices (AAAP). This chapter will present principles and methods for creating three-dimensional soundfields using audio objects, spatializer tools, 3D panning, advance simulation techniques and higher order ambisonics for localization and spatialization.
Improving the efficiency of mining internal resources at information center - Library of the University of Transport Ho Chi Minh City

Information Center - Library is the unit that connects, collects and processes documents to better teaching and learning activities of the University's staff, lecturers and students. One of the typical and outstanding types of document in the Center is the endogenous ones. These materials are always up-to-date and flexible, which really have high value in science, especially they are not published in the commercial market. This paper aims to work on the exploitation and use of endogenous materials at the Ho Chi Minh City University of Transport. On that basis, some solutions are proposed to improve the efficiency of exploiting this resource to serve scientific research as well as teaching and learning activities in the University.
TECHNOLOGIES FOR THE DEVELOPMENT OF MOBILE APPLICATIONS USING BACK-END PLATFORM

The work considers the modern backend platform QuickBlox, which provides a ready infrastructure for the development of mobile applications. The most important functions and modules supported by this platform are analyzed, the main advantages and disadvantages of using QuickBlox are highlighted.
EDUCATIONAL ADVANCES ACROSS ALL LANGUAGE DOMAINS: RESULTS AND EXTENSIONS FROM THE DYNAMIC TRICKY MIX MODEL

This paper demonstrates how Dynamic Systems Theory (DST) can generate powerful educational interventions. Our multiple well-controlled studies include typically-developing children between 2 and 8 years of age as well as children with variations of language disorders and with ages between 4 and 12 years. Despite the wide variation in participant characteristics, we argue that our results demonstrate again and again a core conclusion: Rapid progress in language, literacy, and narrative skills only occurs when there is a favorable dynamic convergence of cognitive readiness, scaffolding partner strategies, positive emotional engagement by child and by partner, high attention, and freedom from distraction or anxiety. We term such favorable dynamic convergences Dynamic Tricky Mixes. Under such Dynamic Tricky Mix conditions children displayed significant advances in literacy, oral language, narrative, and sign language. Other labs have shown similar advances for second language learning. Moreover, and quite surprising, under rigorous equation of Learning Condition Mixes during intervention, children with prior histories of very poor learning learned at rates matching that of children with no prior learning disabilities. This result held for deaf, autistic, dyslexic, and language-delayed children. These excellent learning rates by the children with severe learning disabilities will aid in planning more ambitious reforms in the language-and literacy-facilitating procedures of educators, special educators, and speech language pathologists. In addition, across all educational domains Dynamic Tricky Mix strategies are powerful catalysts for moving any child from a "stuck" position toward truly rapid learning. We draw further implications from our rapid vocabulary acquisition intervention work with ordinary 4-year-olds. In this case we demonstrated that with twice-weekly sessions vocabulary growth across 5 months leaped forward compared with matched control children. Many children learned at the astonishing rate of 20 new lexical items per hour. By the use of pretest/posttest comparison data on cognitive processes we further demonstrate that the experimentally-caused leap in vocabulary had cascading effects on improved memory and related cognitive skills. Thus, rapid gains by the intervention children dynamically fed into their becoming better prepared for further rapid gains in language acquisition. In turn, this set of findings enriches accounts at the theoretical level of how so much language learning usually can be achieved in the preschool years. Likewise, in evolutionary terms these kinds of mutual enhancements between language progress and cognitive processing power may help elucidate periods when there are explosive rates of changes in Hominin cultures and in brain size and capacity.
Spatial data processing meets RDF graph exploration

Efficient processing of RDF data is a basic requirement for querying RDF knowledge graphs, which are today a centerpiece in the semantic processing of information on the Web. In this paper, we investigate the issue of efficient processing of RDF spatial data. Our aim is to extend the query evaluation strategy that relies on graph exploration to support spatial processing. We propose several methods to achieve this goal. We shown the drawbacks and advantages of each method via a few sample queries. Our first results show that the proposal that combines graph exploration and spatial indexing can give very good and promising results compared to existing approaches.