Reviewerly/exHarmony huggingface dataset:
Elastic distributed training with fast convergence and efficient resource utilization

Distributed learning is now routinely conducted on cloud as well as dedicated clusters. Training with elastic resources brings new challenges and design choices. Prior studies focus on runtime performance and assume a static algorithmic behavior. In this work, by analyzing the impact of of resource scaling on convergence, we introduce schedules for synchronous stochastic gradient descent that proactively adapt the number of learners to reduce training time and improve convergence. Our approach no longer assumes a constant number of processors throughout training. In our experiment, distributed stochastic gradient descent with dynamic schedules and reduction momentum achieves better convergence and significant speedups over prior static ones. Numerous distributed training jobs running on cloud may benefit from our approach.
Robust System for Crowd Counting and Estimation Using Multi-column Filters on Unmanned Platforms

The automatic estimation combined with the use of sensors is increasingly useful for various applications that improve the quality of life of human beings and their environment. These developments become more relevant if they are used in complex scenarios, such as those generated from images and videos obtained from aerial images, from optical sensors installed in unmanned systems. In this research, an algorithm with a Multicolumn Gaussian Filter was implemented, which, from images and videos obtained in real-time, with flights at altitudes above 100 m, can estimate the number of people that make up conglomerate crowds in certain sectors. A particularity of the contribution of this research is the ability to perform estimation in real-time, with a consequent multicast transmission, whose low-cost architecture allows the information to be transmitted to geographically distant locations with minimal latency. The results obtained in the research, generate high reliability, to estimation, detection, and counting of people in complex scenarios. The calculation of the error in the training phase shows 6.37% MAE error in estimation accuracy and 15.33% MSE error, these measures verify robustness in the estimate. Also, the solution implemented shows an accuracy rate of 92.39% in the classification, in addition, the field tests using UAVs show high performance in real application situations. Finally, a compensation factor has been introduced, which has made it possible to avoid dispersion in the estimation of the automatic crowd count in real employment situations.
Smartphone-based CNN-GRU Framework for Human Activity Recognition

Human activity recognition has been a widely explored topic for the past several years due to its application in mankind such as smart homes, health assistance, intrusion detection, behavioral recognition, and so on. Deep learning has been widely exploited for recognition of human activities especially CNN and its hybridization with Recurrent Neural Network (RNN). In this article, a hybrid deep learning model with the combination of Convolutional Neural Network and Gated Recurrent Unit has been proposed for human activity recognition. UCI-HAR smartphone dataset is taken for performance evaluation of this proposed model and it is found that the framework performed very well with the mean accuracy of 92.53%.
Views of Representatives of the Academic School of Russia in the Second half of the 18th Century on the Future Generation of the Russian Intelligentsia: the Position of Teachers of the Russian School in the Projects of the Staff of the St. Petersburg Academy of Sciences

In the history of the Russian intelligentsia there are still many controversial and poorly studied issues. One of the numerous groups of the intelligentsia in Russia has always been its pedagogical component. This independent social group, represented by teachers and teachers, began to form in the second half of the 18 th century, which became possible thanks to the educational reform of Catherine II. Initially, the Empress attracted a wide range of interested persons to discuss the future of Russian education. As a result, she received various proposals and projects on the establishment of a comprehensive school in Russia. In this article, special attention of researchers is drawn to two projects, the authors of which were employees of the St. Petersburg Academy of Sciences.
CIS Publication Spotlight [Publication Spotlight]

<i>A Survey of Stochastic Computing Neural Networks for Machine Learning Applications</i>, by Y. Liu, S. Liu, Y. Wang, F. Lombardi, and J. Han, <i>IEEE Transactions on Neural Networks and Learning Systems</i>, Vol. 32, No. 7, July 2021, pp. 2809&#x2013;2824.
GEOLOGICAL, GEOCHEMICAL, AND PETROGENETIC ASPECTS OF LATE NEOPROTEROZOIC YOUNGER GRANITES AT WADI UM SIDRA-WADI UM ASMER AREA, NORTH EASTERN DESERT, EGYPT.

The late Neoproterozioc younger granites in the study area are classified into monzogranites, syenogranites and alkali feldspar granites according to the field, petrography and geochemical investigations.Monzogranites are mainly exposed along W. Um Sidra at G. Abu Mesaid in the eastern parts of the study area, intruding the Dokhan volcanic with sharp contacts.Syenogranites are exposed at G. El Shagola, G. Abu Harba, G Abu Mesaid, and some parts along W. Um Sidra intrude the metavolcanics and Dokhan volcanics with sharp contacts and contain enclaves of them.Alkali feldspar granites are exposed at G. El Shaylah, G. Urf El
The Application of Multi-modal Discourse Annotation and Analysis Tool ELAN in the Construction of Cross-border Endangered Chinese Dialect-Dungan Corpus

Dungan Language is unique among the languages in the world.It is unique in the sense of linguistic typology and has been paid more and more attention by the academic circle at domestic and overseas.This paper takes ELAN, a multimodal discourse annotation and analysis tool, as the research tool, and the Dungan acoustic data as the research object, and introduces in detail how to build a Dungan voiced corpus based on ELAN, in order to provide data support and language information for Dungan research at home and abroad, and promote the level of Dungan Language research.
The current status of guaranteed right to play for children appearing on YouTube

연구목적:유튜브출연아동의놀이권보장현황을분석하였다. 방법:2020년1월부터12월까지유튜브100위안에드는아동출연채널에서1년간업 로드한9개의채널788개의동영상을모니터링하고, 내용분석을실시하였다. 결과:첫째, 유튜브출연아동의놀이특성을분석한결과, 가장많이보장되고있는놀이 특성은‘적절한장소와시간’으로788개중79개(10.03%)만이해당되었으며, 다음으로 ‘아동주도성’ 40개(4.95%), ‘놀이촉진성’ 22개(2.79%), ‘무목적성’ 6개(0.76%) 순으로 나타나유튜브출연아동의놀이권보장은거의이루어지지않는것으로확인되었다. 둘 째, 아동출연유튜브동영상에서네가지의놀이특성이모두존재하는동영상은단한 건도없는것으로확인되어유튜브출연아동의놀이는‘놀이노동’ 형태를띠는것으로 확인되었다. 셋째, 유튜브출연아동동영상업로드횟수와동영상재생시간을확인한 결과, 출연아동은한달동안평균7.30개, 최대12.75개의동영상을촬영하는것으로 나타나촬영준비와연습시간, 편집된영상까지고려했을때출연아동이유튜브촬영에 소요하는시간은상당할것으로확인되었다. 결론:유튜브출연아동의놀이노동에대한사회적합의와함께아동의수익보호를위한 법적근거마련, 노동착취및권리훼손으로부터의아동보호를위한국가의적극적인개입 등을제언하였다.
Infrafacing

Questions for Erin Manning and Brian Massumi sent by e-mail from Jonas Fritsch, Gregory Seigworth, Bodil Marie Stavning Thomsen. Q: In this interview we’re interested in speculations on how interfaces activate or intensify affects and how or if interface-affects might eventuate change—or indeed respond to ideas of becoming related to artistic creation. We’d like to […]
Improving Learning Outcomes through Learning Media in Making Financial Reports at the Hospitality Board

Inadequate learning media used in hospitality accounting learning has an impact on the difficulty of understanding the learning material. This study examines the effectiveness of learning media in making hospitality financial reports through a spreadsheet application. The research design in this activity will use the development of learning tools, according to Sugiono. This research method uses a learning media development model which includes steps, namely product testing, product revision, and uses trials. The research subjects were students of the hospitality accounting study program. The instrument used consisted of observation guidelines, interviews, questionnaires, and learning outcomes tests. The participants of the product trial were 15 students of programming courses in hospitality accounting, and 34 people in one class were tested for use. The data analysis technique used in this research is descriptive by measuring the results of student learning tests seen from the scores obtained by students in learning the financial statements of hospitality companies. The assessment is carried out based on the scores obtained from the learning outcomes of attitude and participation assessment, assignment of assignments, and an assessment of the resulting product. The results of the evaluation of the use of media in making financial reports through spreadsheets show that student learning outcomes are excellent so that this media meets the requirements for the effectiveness of media learning so that this media can be used in educational accounting learning.
Lasting Changes on Etextbook Acquisitions for Textbook Affordability due to COVID-19

When the University of Texas Rio Grande Valley University Library started purchasing etextbooks for required courses in 2018, we were one of the few in the UT System who had an explicit program. Now, it’s becoming a more common practice for libraries to put funds usually reserved for course reserves or other faculty requests into ebooks. As COVID began to shut down our physical operations we began to receive a large influx of etextbook requests from faculty. Luckily, the library had a student savings tracking process in place for required etextbook purchases, and a fund designated for these purchases. The strong relationship between Collection Development and Scholarly Communications prior to the influx of new requests made adapting to this influx relatively orderly.
Asymptotic Enumeration of Hypergraphs by Degree Sequence

We prove an asymptotic formula for the number of $k$-uniform hypergraphs with a given degree sequence, for a wide range of parameters. In particular, we find a formula that is asymptotically equal to the number of $d$-regular $k$-uniform hypergraphs on $n$ vertices provided that $dn\le c\binom{n}{k}$ for a constant $c>0$, and $3 \leq k < n^C$ for any $C<1/9.$ Our results relate the degree sequence of a random $k$-uniform hypergraph to a simple model of nearly independent binomial random variables, thus extending the recent results for graphs due to the second and third author.
Interdisciplinary Approach to Teaching Petrochemical Engineers

The rapid development of new industrial technologies has created a demand for professional engineers with interdisciplinary skills in engineering, digital manufacturing technologies, management, marketing and communication. The oil and gas industry is the key sector of the Russian economy. Nowadays, its industrial development focuses on technological transformations, international integration, introduction of digital twins, and environmental friendly processes. This complex task requires engineering personnel capable of performing various labor functions from different professional areas. The purpose of the study is to develop educational process and conditions for efficient interdisciplinary training of petrochemical engineers at the technological university. The paper presents the interdisciplinary approach to developing the set of integrated skills for students majoring in Petrochemical Engineering. These skills include the abilities to develop digital projects, to control the reliability of technical objects, to solve economical and management problems, to protect intellectual property, to communicate in English for professional and academic purposes, to work in teams both at Russian and international levels. To develop these skills, the special system for interdisciplinary training of students was organized at Kazan National Research Technological University. This system includes various interdisciplinary programs and courses developed for their implementation in lifelong education system based on the cooperation between the University and secondary schools.
The Development of Berbakti: Elder Caring Mobile Application in Indonesia

Children must care their parent as their devotion to their parent. In Indonesia, that kind condition is a common situation. But, to handling this situation in this global era is more difficult because many children choose going to another city or another region to do some activity like taking a job or going to college. It gives an impact to their parent especially when their parent is too old and needs to be cared. This motivation in this paper is based on this kind problem. The development of application uses Waterfall. The system must meet the requirement so not just technichal development is performed, social study must be conducted in the process. We use several testings such blackbox testing, server testing, and usefulness identity. Commonly, we got unsatisfied result based on testing, so some repairement must be conducted.
Mitigating the CACHEKIT attack

Through thorough research and experimentation, we arrive at the optimal defense mechanism that we call CACHELIGHT, a lightweight approach for preventing malicious abuse of cache -locking mechanisms. This novel solution leverages both the TZ and virtualization extensions in the ARM architecture to address the vulnerability identified by CACHEKIT. In this chapter, we cover the design, implementation, and implications of CACHELIGHT and how it is used to allow legitimate users to continue to utilize cache locking while giving SW the power to ensure system security by controlling and verifying the use of said mechanisms.
Reaching Understanding

Chapter 10 covered the first component of a conversational system: speech-to-text (STT). Now it's time for step 2: natural language processing (NLP) and natural language understanding (NLU). If Chapter 10 focused on recognizing and understanding what a user said, this chapter focuses on what those words mean, as NLU takes the STT representation and interprets the intended meaning behind those words to determine how to respond. Here, you learn what “meaning” means to a voice system, why it’s not a solved topic, and how to reach the best result anyway.
Multiple Random Variables

Up to now we have mainly focussed on the analysis of a single variable. We have discussed probability density functions (PDFs), probability mass functions (PMFs), and distribution functions (CDFs) as descriptions of the population values for such a single variable and connected these functions to a single random variable. These probability functions were functions with a single argument $$x\in \mathbb {R}$$ . For instance, the CDF $$F_{\theta }(x)$$ was defined for all $$x\in \mathbb {R}$$ . In this chapter we will extend the concept of probability functions to multiple arguments, say (x, y), that would represent multiple random variables.
Weaponising AI for Natural Language Processing: Novel Perspectives

Natural Language Processing is a subset of Artificial Intelligence. The field of natural language processing (NLP) is concerned with the understanding and translation of human discourse into machine-readable language. NLP allows machines to interpret and accomplish tasks based on written or spoken content. Natural language processing (NLP) has garnered a lot of attention recently because of its computational representation and analysis of human discourse. It has spread its applications in Medical and Business such as Sentiment Analysis, Language Translation, Text Extraction and Chatbot etc. In this we critically overtook on History and Methodologies of Natural Language Processing.
Operational Security in the Railway - The Challenge

Information and Communication Technologies (ICT) in the railway has replaced the legacy systems with modern IP communication networks designed for open-market sectors. This adoption and convergence of Information Technology (IT) and Operational Technology (OT) in railway has brought significant benefits in reliability, operational efficiency, capacity as well as improvements in passenger experience but also increases the vulnerability towards cyber-attacks from individuals, organizations, and governments. This paper proposes a methodology on how to deals with OT security in the railway signalling using failure mode, effects and criticality analysis (FMECA) and ISA/IEC 62443 security risk assessment methodologies.
KrakenBox: Deep Learning-Based Error Detector for Industrial Cyber-Physical Systems

Abstract Online error detection helps to reduce the risk of failure of safety-critical systems. However, due to the increasing complexity of modern Cyber-Physical Systems and the sophisticated interaction of their heterogeneous components, it becomes harder to apply traditional error detection methods. Nowadays, the popularity of Deep Learning-based error detection snowballs. DL-based methods achieved significant progress along with better results. This paper introduces the KrakenBox, a deep learning-based error detector for industrial Cyber-Physical Systems (CPS). It provides conceptual and technical details of the KrakenBox hardware, software, and a case study. The KrakenBox hardware is based on NVIDIA Jetson AGX Xavier, designed to empower the deep learning-based application and the extended alarm module. The KrakenBox software consists of several programs capable of collecting, processing, storing, and analyzing time-series data. The KrakenBox can be connected to the networked automation system either via Ethernet or wirelessly. The paper presents the KrakenBox architecture and results of experiments that allow the evaluation of the error detection performance for varying error magnitude. The results of these experiments demonstrate that the KrakenBox is able to improve the safety of a networked automation system.
Introduction

The underwater tunnel crossing the underwater strata of rivers or straits has the advantages of less land occupation, less influence on navigation, stable traffic capacity and large carrying capacity, which is an important means of crossing the water area. China has a vast territory and a large area of rivers, lakes and seas, so it has excellent conditions for the construction of underwater tunnels.
PENGEMBANGAN BUKU AJAR ALJABAR LINIER BERBASIS MATLAB MOBILE UNTUK MENUNJANG PEMBELAJARAN DARING

Learning mathematics online, especially in Linear Algebra courses requires mathematical software-based teaching materials, to strengthen the understanding of numerical concepts. The purpose of this research is to produce MATLAB Mobile-based textbooks that are suitable to support online learning. This development research uses a 4-D model containing 4 stages, namely Define, Design, Develop, and Disseminate. This research was conducted at the Mathematics Education Study Program, the University of PGRI Madiun with 20 students as the subject of the fourth semester. The research instrument and data collection in this study used validation sheets, response questionnaires, and learning outcomes tests. Data analysis of the validation sheet and questionnaire used the Likert scale and the Guttman scale, while the data analysis of the learning outcomes test used the Normalized Gain Score (n-Gain). This study produced a Linear Algebra textbook based on MATLAB Mobile to support online learning during the Covid 19 pandemic with a validity percentage of 86,5% (valid), practicality percentage 83,4% (practical), and an n-Gain score of 46,7 (moderately effectiveness). So that the textbooks developed are suitable to support online learning.
Toward Blockchain-Based Spoofing Defense for Controlled Optimization of Phases in Traffic Signal System

Controlled optimization of phases (COP) is a core implementation in the future intelligent traffic signal system (I-SIG), which has been deployed and tested in countries including the U.S. and China. In such a system design, optimal signal control depends on dynamic traffic situation awareness via connected vehicles. Unfortunately, I-SIG suffers data spoofing from any hacked vehicle; in particular, the spoofing of the last vehicle can break the system and cause severe traffic congestion. Specifically, coordinated attacks on multiple intersections may even bring cascading failure of the road traffic network. To mitigate this security issue, a blockchain-based multi-intersection joint defense mechanism upon COP planning is designed. The major contributions of this paper are the following. 1) A blockchain network constituted by road-side units at multiple intersections, which are originally distributed and decentralized, is proposed to obtain accurate and reliable spoofing detection. 2) COP-oriented smart contract is implemented and utilized to ensure the credibility of spoofing vehicle detection. Thus, an I-SIG can automatically execute a signal planning scheme according to traffic information without spoofing data. Security analysis for the data spoofing attack is carried out to demonstrate the security. Meanwhile, experiments on the simulation platform VISSIM and Hyperledger Fabric show the efficiency and practicality of the blockchain-based defense mechanism.
A MODEL OF CONSEQUENCES OF AMBIGUOUS REQUIREMENTS

A key objective in the requirements elicitation process is to ensure a common understanding of requirements between customers and users and software developers from the other. The traditional practices for preventing ambiguous requirements focused on writing requirements clearly without guidelines that help practitioners elicit requirements from customers and users unambiguously. Since multiple studies underlined the prevalence of ambiguous requirements in software projects, the key purpose of this article is to construct a conceptual model of the consequences of ambiguous requirements. The novelty of this model manifests itself in explaining the possible negative effects resultant from eliciting ambiguous requirements from customers and users across software development processes. Accordingly, this model can increase the awareness of practitioners' and academicians' potential threat of ambiguous requirements and act as a trigger to find the guidelines to prevent ambiguous requirements during requirements elicitation.
Survey Paper on Crypto-Dive: Understanding the Future Aspects of Cryptocurrencies

Cryptocurrency is now a major digital currency all over the globe and many countries are officially accepting this money as it is not regulated by any bank or government still some people find it difficult to understand and some find it risky to invest. Even in India a lot of people are unaware of this currency. This project is to create a website that will create awareness and will help new user who want to invest, sell, buy crypto currency by giving them all the historic data as well as latest live data and detailed statistic, with help of Rapid API prediction of market rate as well as keep them updated by providing latest live news on each crypto currency.
To Every Thing There Is a Season, and a Time to Every Purpose under Heaven

Between 1989 and 2002 Norman Ross (Figure 1) published tens of thousands of microfilms and microfiches, created in Russia and Eastern Europe for his company, Norman Ross Publishing. He also published 33 reference books, Over the course of 25 visits to Russia, as well as on frequent trips to Ukraine, Poland, Hungary, Czech Republic, Austria, Bulgaria, Latvia, Lithuania, and Estonia, Ross developed microfilm and book projects with national libraries and local academic institutions and organized the local filming of major current newspapers. In these endeavors he was assisted by local representatives, a multi-lingual in-house staff in New York, and guidance from Edward Kasinec, the late Robert T. Whittaker, and others. This was the most rewarding segment of owning his own publishing company, which began in 1972 and continues nearly 50 years later.