Reviewerly/exHarmony huggingface dataset:
Beyond the Keyboard and Sequencer: Strategies for Interaction between Parametrically-­Dense Motion Sensing Devices and Robotic Musical Instruments

The proliferation and ubiquity of sensor, actuator and microcontroller technology in recent years have pro-pelled contemporary robotic musical instruments (RMIs) and digital music controllers to become more para-metrically dense than their predecessors.Prior projects have focused on creating interaction strategies for relatively low degrees--of--freedom input and output schemes.Drawing upon prior research, this paper ex-plores schemes for interaction between parametrically--dense motion--based control devices and contempo-rary parametrically--dense robotic musical instruments.The details of two interaction schemes are presented: those consisting of one--to--one control (allowing the actions of a performer to directly affect an instrument) and those consisting of a recognition system wherein user--created gestures result in output patterns from the robotic musical instrument.The implementation of the interaction schemes is described, and a performance utilizing these schemes is presented.
Problems of Subject Retrieval in the Electronic Catalogue of Scientific Library: Review of Studies

Scientific libraries use different approaches and tools for subject retrieval. Issues related to electronic subject retrieval are widely discussed in the library community. The purpose of this review is to analyse publications of recent years on the aspects and problems related to subject retrieval in electronic catalogue (EC). For its preparation, the author used publications (and references to them) taken from the national bibliographic database — the Russian Science Citation Index (RSCI); the time coverage is 2003—2019. Particular attention in the studies is focused on the need to combine different information retrieval languages (IRL), thesauruses, subject headings and keywords to ensure effective and multi-aspect search. Since the domestic libraries have accumulated a lot of experience in indexing and retrieval through hierarchical classifications (UDC and LBC), it is necessary to use this at the full extent in the electronic subject retrieval. The authors of the recent studies on subject retrieval consider being the most effective the functioning of classification systems as part of the complex of linguistic tools of electronic catalogue. In the electronic environment, it becomes possible to use classification indexes as a set of characters that maximally reflect the content of the document, what involves the use of all fragments of the index as independent retrieval elements. However, the lexical composition of the classification information retrieval languages needs to be modified for effective use in the electronic catalogue. The main directions here can be as follows: unification of the structure and vocabulary of classification schedules, optimization of the structure of classification systems (using facetization principles), as well as formalization of the use of grammar tools, (it is necessary to ensure the unambiguity of their interpretation in the electronic catalogue). The review presents and analyses the areas where the subject retrieval has been developed in the electronic environment for over the past 16 years. For example, there has been developed and is being implemented the superstructure over Universal Decimal Classification (Library for Natural Sciences of the Russian Academy of Sciences), which allows users of the library to remotely conduct full-fledged subject retrieval. The paper also presents the new developments by the Russian State Library on subject retrieval, in particular, the project “Presentation of classification metadata of electronic libraries using linked data technology”. The author concludes that it is necessary to form a common space of related open data of various libraries.
Bento: Bringing Network Function Virtualization to Tor

Tor is a powerful and important tool for providing anonymity and censorship resistance to users around the world. Yet it is surprisingly difficult to deploy new services in Tor---it is largely relegated to proxies and hidden services---or to nimbly react to new forms of attack. Conversely, "non-anonymous" Internet services are thriving like never before because of recent advances in programmable networks, such as Network Function Virtualization (NFV) which provides programmable in-network middleboxes.
On Fair Virtual Conference Scheduling: Achieving Equitable Participant and Speaker Satisfaction

The (COVID-19) pandemic-induced restrictions on travel and social gatherings have prompted most conference organizers to move their events online. However, in contrast to physical conferences, virtual conferences face a challenge in efficiently scheduling talks, accounting for the availability of participants from different time-zones as well as their interests in attending different talks. In such settings, a natural objective for the conference organizers would be to maximize some global welfare measure, such as the total expected audience participation across all talks. However, we show that optimizing for global welfare could result in a schedule that is unfair to the stakeholders, i.e., the individual utilities for participants and speakers can be highly unequal. To address the fairness concerns, we formally define fairness notions for participants and speakers, and subsequently derive suitable fairness objectives for them. We show that the welfare and fairness objectives can be in conflict with each other, and there is a need to maintain a balance between these objective while caring for them simultaneously. Thus, we propose a joint optimization framework that allows conference organizers to design talk schedules that balance (i.e., allow trade-offs) between global welfare, participant fairness and the speaker fairness objectives. We show that the optimization problem can be solved using integer linear programming, and empirically evaluate the necessity and benefits of such joint optimization approach in virtual conference scheduling.
Image datasets generation system for computer vision applications based on photorealistic rendering

In this paper we present an approach to image datasets generation based on photorealistic rendering with controlled parameter randomization of 3d scenes. These datasets can be used, for example, for training artificial intelligence models in computer vision. We describe the architecture of system implementing proposed approach and show examples of applying it to specific problems.
Blockchain-based Supply Chain System

In today's world, blockchain-based systems are in demand across various industries, especially in transportation and supply chain, because of its secure, trusted, and decentralized network as well as more efficient than the traditional methods. However, the traditional ways these days are facing a lot of issues and challenges because of the complex and less secure network. Blockchain network integration overcomes the problems faced by traditional methods across industries. The Blockchain integrated network provides benefits such as a reduction in cost, scalability, improve efficiency by connecting the links between the flows, increase Transparency, the trace of the product. Although the blockchain-based systems provide various benefits, due to lack of knowledge about this technology, the implementation rate is low. In this work, it highlights the distinction between the traditional transportation and supply chain as well as Blockchain network-based transportation and supply chain and the benefits of implementing Blockchain in different sectors such as health care, insurance, food industry, retail. This work highlights the issues and challenges faced by the industries, by using the traditional methods as well as the solutions to the problems provided by the blockchain network-based systems to those industries. This work helps the people to understand the benefits of blockchain network-based systems in their respective industries as well as execute it to improve the Transparency, efficiency, and security of altogether system.
A Novel Solution for Distributed Database Problems

Distributed Databases Systems (DDBS) are a set of logically networked computer databases, managed by different sites, locations and accessible to the user as a single database.DDBS is an emerging technology that is useful in data storage and retrieval purposes.Still, there are some problems and issues that degrade the performance of distributed databases.The Aim of this paper is to provide a novel solution to distributed database problems that is based on distributed database challenges collected in one diagram and on the relationship among DDB challenges in another diagram.This solution presents two methodologies for Distributed Databases management Systems: deep learning-based fragmentation and allocation, and blockchain technology-based security provisioning.The contribution of this paper is twofold.First, it summarizes major issues and challenges in the distributed database.Additionally, it reviews the research efforts presented to resolve the issues.Secondly, this paper presents a distributed database solution that resolves the major issue of distributed database technology.This paper also highlights the future research directions that are appropriate for distributed database technology after the implementation in a large-scale environment and recommended the technologies that can be used to ensure the best implementation of the proposed solution.
Use Of Information Technologies As An Important Factor Of Motivation To Learning

The article explores various factors determined by new information technologies that motivate students to acquire new knowledge.The authors focus not only on the positive effect obtained from the use of information technology in the educational process.Particular attention is paid to the ambiguous impact of computer technology on the younger generation.It is shown that the possession of information technology is not only a criterion for the level of general preparedness of a student, but also serve as a motivation for further studies.Information technology can act as a stimulus for young people in the context of the development of global communication systems.The factors that play a fundamental role in the formation of motivation for more effective knowledge acquisition are analyzed.In the concepts of the development of national education, modern information technologies have a different place.High-tech education, based on spiritual and moral values, encourages students to further a cognitive act that can last a lifetime.
e-Learning 품질이 사용자 만족 및 학습 근전이에 미치는 영향에 관한 연구 : 항공서비스전공 학생을 대상으로

The purpose of this study was intended to identify the effect of the quality of e-Learning on user satisfaction and training near transfer in airline service major students. In order to achieve the results, 210 survey questionnaires were distributed from 6th May, 2020 to 19th May, 2020. Due to the reliability of data, 7 samples were discarded and 203 samples were put to actual analysis. Further results were produced by the researcher through conducting and analyzing via SPSS Ver. 21.0 statistics package. The results of the study are as follows. First, the quality of e-Learning which consists of system quality and service quality had significant effects on user satisfaction. Secondly, user satisfaction had significant effects on training near transfer. Thirdly, the quality of e-Learning which consists of system quality and service quality had significant effects on training near transfer. This study shows that In a new environment where everyone must perform e-learning, we must make every effort to improve e-learning quality to create continuous learning outcomes.
FUSIONET: A Scalable Framework for Image Classification

Convolutional Neural Networks have become state-of-the-art methods for image classification in recent times. CNNs have proven to be very productive in identifying objects, human faces, powering machine vision in robots as well as self-driving cars. At this point, they perform better than human subjects on a large number of image datasets. A large portion of these datasets depends on the idea of solid classes. Hence, Image classification has become an exciting and appealing domain in Artificial Intelligence (AI) research. In this paper, we have proposed a unique framework, FUSIONET, to aid in image classification. Our proposition utilizes the combination of 2 novel models in parallel (MainNET, a 3 x 3, architecture and AuxNET, a 1 x 1 architecture) Successively; these relatively feature maps, extracted from the above combination are fed as input features to a downstream classifier for classification tasks about the images in question. Herein FUSIONET, has been trained, tested, and evaluated on real-world datasets, achieving state-of-the-art on the popular CINIC-10 dataset.
Guides @ UF: UFDC Metadata: Home

To introduce to data contributors the frequently used fields by University of Florida Digital Collections (UFDC)
Issues of studying physics by technical universities students and ways to settle them

The study of Physics at a university often presents a certain kind of difficulty for students, which is associated with the specifics of the discipline and the need to form a physical mindset. This process is stretched and is not always synchronized in time with entering a university and following the educational route. The article considers and analyzes these aspects. Some solutions are proposed.
Neural Machine Translation Doesn't Translate Gender Coreference Right Unless You Make It

Neural Machine Translation (NMT) has been shown to struggle with grammatical gender that is dependent on the gender of human referents, which can cause gender bias effects. Many existing approaches to this problem seek to control gender inflection in the target language by explicitly or implicitly adding a gender feature to the source sentence, usually at the sentence level. In this paper we propose schemes for incorporating explicit word-level gender inflection tags into NMT. We explore the potential of this gender-inflection controlled translation when the gender feature can be determined from a human reference, or when a test sentence can be automatically gender-tagged, assessing on English-to-Spanish and English-to-German translation. We find that simple existing approaches can over-generalize a gender-feature to multiple entities in a sentence, and suggest effective alternatives in the form of tagged coreference adaptation data. We also propose an extension to assess translations of gender-neutral entities from English given a corresponding linguistic convention, such as a non-binary inflection, in the target language.
Online education and research: Context Covid-19

Education activities in schools, colleges and universities are closed in the country. The students are under house arrest. Some countries have started opening schools and colleges depending on the situation, it is very limited. The Ministry of Education has launched education activities in the field of secondary education through Sangsad TV. However, there are various discussions and criticisms within the country about the quality of the class and its effectiveness. This study based on secondary data but primary data also used for purposive objectives. The present study post corona world change in education system. Farther more, the paper showed some problems faced in e-education system and gives some suggestion and recommendation to overcome those problems.
Research on Teaching Reform of PUTONGHUA SHUIPING CESHI in Colleges Based on Computer Assist

Abstract Computer testing is the mainstay of the current Putonghua proficiency test in colleges and universities. Using computer technology can not only improve the fairness of the Putonghua proficiency test, but also actively promote the reform of Putonghua teaching in colleges and universities. In the current teaching process of Putonghua in colleges and universities, we need to study and analyze the existing problems, at the same time study the impact of the computer-aided evaluation model on Putonghua teaching, and put forward strategies for the reform of Putonghua teaching in colleges and universities under the computer test mode. This can not only improve the efficiency of Mandarin teaching in colleges and universities, but also ensure the quality of teaching, and ultimately improve the Mandarin level of college students.
Predicting breast cancer risk, recurrence and survivability

This thesis focuses on predicting breast cancer at early stages by using machine learning algorithms based on biological datasets. The accuracy of those algorithms has been improved to enable the physicians to enhance the success of treatment, thus saving lives and avoiding several further medical tests.
Quantifying Learnability and Describability of Visual Concepts Emerging in Representation Learning

The increasing impact of black box models, and particularly of unsupervised ones, comes with an increasing interest in tools to understand and interpret them. In this paper, we consider in particular how to characterise visual groupings discovered automatically by deep neural networks, starting with state-of-the-art clustering methods. In some cases, clusters readily correspond to an existing labelled dataset. However, often they do not, yet they still maintain an "intuitive interpretability". We introduce two concepts, visual learnability and describability, that can be used to quantify the interpretability of arbitrary image groupings, including unsupervised ones. The idea is to measure (1) how well humans can learn to reproduce a grouping by measuring their ability to generalise from a small set of visual examples (learnability) and (2) whether the set of visual examples can be replaced by a succinct, textual description (describability). By assessing human annotators as classifiers, we remove the subjective quality of existing evaluation metrics. For better scalability, we finally propose a class-level captioning system to generate descriptions for visual groupings automatically and compare it to human annotators using the describability metric.
Identifying and Isolating Zombie Attack in Cloud Computing

The cloud computing architecture is a berth in which third party, virtual machine and cloud service providers are involved in data uploading and downloading. A major challenge in this architecture, however, is the security of the data as there exist various forms of attacks from malicious peopleand devices. Among these security attacks, the zombie attack is the most advance type of attack. The zombie attack reduces network performance in terms of delay and bandwidth consumption. With zombie attack, some malicious users may join the network which, in turn takes off the data of legitimate users and at the same time enable zombie nodes to communicate with a virtual machine on behalf of the legitimate user. In this paper, a technique based on strong authentication which, is able to detect malicious users from a network and isolates them from the cloud architecture is proposed.
Library in the Unadapted Premises: Development of Architectural Transformation of Space

Siting, reconstruction, modernization of libraries in the unadapted premises has always been and remains till now a challenging practical problem in the global librarianship, which in each case obtains unique, unconventional solution. Soviet architects involved in the design of libraries — F.N. Pashchenko, L.Z. Amlinsky, V.G. Gelfreikh, A.R. Zimonenko and others — contributed to the development of this problem. They laid the modern and progressive theoretical and methodological foundations of the interdisciplinary section of library science — architecture and construction of library buildings which is actively developing in the 21st century, making the transition from the long-standing concept of the library as a repository for books to understanding it as a constantly changing, flexible and technological information space for all its subjects — visitors, readers and employees. Actualization of the complex task of creating a comfortable environment for the reader and librarian in the adopted for this purpose architectural space has come to the fore. The purpose of the article is to present an original approach in the pre-project study of the University library located in the Palace building in the form of an individual matrix of ideas and solutions. For the key conceptual ideas in the matrix of possible redesigning of the University scientific library the authors chose human centrism, openness, mobility and uniqueness. The paper presents architectural plans of the library, performed by master’s students of the St. Petersburg State Art and Industry Academy named after A.L. Stieglitz under the guidance of the architect-artist, Professor V.P. Timonin, as well as the matrix of ideas and possible solutions for the transformation of the material space of the library of the St. Petersburg State Institute of Culture, prepared by Professor M.N. Kolesnikova. The results of this work are of practical interest from the point of view of architectural design of libraries in the premises of historical buildings, that is an urgent global problem, as well as of theoretical interest for the development of modern library science in its sections related to library design, material and technical base of libraries and library imageology.
Topology Recognition of Courts Based on the Improved PCA

With the further development of smart grid, the accurate recognition of each user in each transformer has become the basis of many high-end applications. In order to solve the problem of courts’ topology recognition, this paper proposes a new improved method based on the principle of Principal Component Analysis. First, the eigenvectors with larger eigenvalues from divergence matrix are selected, and these eigenvectors contain most of the information of the original time series. After analysis, this paper indicates that the essence of PCA for dimensionality reduction of time series is to compare the correlation between the original sequence and the characteristic waveform sequence. So, the similarity curves can be obtained from characteristic sequence waveforms. By comparing the Cosine similarity of the similarity curves, the non-local users can be excluded accurately. With the k–means, the relationship between local courts users can be accurately obtained. This method avoids the error caused by noise and abnormal data after directly using PCA to reduce dimension, and improves the accuracy of resolution. Subsequently, the normalized distance is used as the standard to match the user and the transformer voltage, and the identification of the courts’ topology is realized. In the end, the algorithm is validated with simulation model data. At the same time, the data are used for other algorithms, and get the conclusion that the topological recognition algorithm based on improved PCA is more effective.
Audio Dequantization Using (Co)Sparse (Non)Convex Methods

The paper deals with the hitherto neglected topic of audio dequantization. It reviews the state-of-the-art sparsity-based approaches and proposes several new methods. Convex as well as non-convex approaches are included, and all the presented formulations come in both the synthesis and analysis variants. In the experiments the methods are evaluated using the signal-to-distortion ratio (SDR) and PEMO-Q, a perceptually motivated metric.