Reviewerly/exHarmony huggingface dataset:
Experimental and Research Verification of the Methodology of Forming the High School Students Artistic Taste for the Vocal Heritage (Late XIX-Early XX Century)

The article highlights the results of research and experimental work on verifying the methodology of forming the artistic taste of high school students in the vocal heritage of Ukrainian composers of the late XIX â€“ early XX centuries. The study purpose was to evaluate the elaborated methodology for the formation of the artistic taste of high school students in the process of studying the vocal heritage of Ukrainian composers. The study was accomplished using the method of paired comparisons that resulted in the scale of the comparative assessments in the control and the experimental group of high school students during the extracurricular activities in Kyiv, Zaporizhia and Dnipropetrovsk regions. The effectiveness of the proposed method was supported by the results of the statistical purposes of processing the experimental data using the Ï‡2-criterion formula. The criteria for monitoring the formation of the high school studentsâ€™ artistic taste regarding the national heritage of the late XIX-early XX centuries were outlined; the three levels (high, medium, low) of high school studentsâ€™ artistic taste formation were identified. The effectiveness of the applied methodology was evaluated by the series of tests comparing the results at the ascertaining and forming stages of the study. The authors argue that the data of the forming experiment showed significant positive changes in the levels of formation of high school studentsâ€™ artistic taste according to all the determined criteria, which testifies to the effectiveness of the developed facilitation methodology. https://doi.org/10.26803/ijlter.19.4.18
Discriminative Active Learning for Domain Adaptation

Domain Adaptation aiming to learn a transferable feature between different but related domains has been well investigated and has shown excellent empirical performances. Previous works mainly focused on matching the marginal feature distributions using the adversarial training methods while assuming the conditional relations between the source and target domain remained unchanged, $i.e.$, ignoring the conditional shift problem. However, recent works have shown that such a conditional shift problem exists and can hinder the adaptation process. To address this issue, we have to leverage labelled data from the target domain, but collecting labelled data can be quite expensive and time-consuming. To this end, we introduce a discriminative active learning approach for domain adaptation to reduce the efforts of data annotation. Specifically, we propose three-stage active adversarial training of neural networks: invariant feature space learning (first stage), uncertainty and diversity criteria and their trade-off for query strategy (second stage) and re-training with queried target labels (third stage). Empirical comparisons with existing domain adaptation methods using four benchmark datasets demonstrate the effectiveness of the proposed approach.
Leveraging Text Data Using Hybrid Transformer-LSTM Based End-to-End ASR in Transfer Learning

In this work, we study leveraging extra text data to improve low-resource end-to-end ASR under cross-lingual transfer learning setting. To this end, we extend our prior work [1], and propose a hybrid Transformer-LSTM based architecture. This architecture not only takes advantage of the highly effective encoding capacity of the Transformer network but also benefits from extra text data due to the LSTM-based independent language model network. We conduct experiments on our in-house Malay corpus which contains limited labeled data and a large amount of extra text. Results show that the proposed architecture outperforms the previous LSTM-based architecture [1] by 24.2% relative word error rate (WER) when both are trained using limited labeled data. Starting from this, we obtain further 25.4% relative WER reduction by transfer learning from another resource-rich language. Moreover, we obtain additional 13.6% relative WER reduction by boosting the LSTM decoder of the transferred model with the extra text data. Overall, our best model outperforms the vanilla Transformer ASR by 11.9% relative WER. Last but not least, the proposed hybrid architecture offers much faster inference compared to both LSTM and Transformer architectures.
Virtual Companion Health Minder with Secured Wearable Networks [VCHM]

The Wearable Networks is the greatest boon of computer science tends to create wonders in health care application industries for the prominence of life. The purpose of this paper is to provide wide knowledge about the wearable networks and its efficient applications in various industries with its remarkable researches in medical and health care. This paper gives the importance of wearable systems for quality care of the patients in medical history. It also discusses about the wearable technology used in health care benefits like a headband used for brain injury detection, and the blood oxygen monitored by a wristband, smart kits for distant monitoring and home diagnosis. APlastic patch in the form of Tattoo that can observe the vital signs and a smart contact lens that can monitor the user’s blood sugar levels. One of the current challenges is performing rehabilitation exercises during   monitoring patients to provide them with an objective feedback. This paper describes about the importance of wearable multi-sensor system for monitoring human motions, that has been developed and used in rehabilitation[1]. In orderto transmit the information related to the body motion to an acquisition device, these wearables areself-possessedwitha few small modules that embed with high-precision accelerometers and wireless devices.The significant benefits about the field of wearable and implantable body sensor networks challenges and research problems and drawbacks of these systems are discussed in this paper.
Delving into the Imbalance of Positive Proposals in Two-stage Object Detection

Imbalance issue is a major yet unsolved bottleneck for the current object detection models. In this work, we observe two crucial yet never discussed imbalance issues. The first imbalance lies in the large number of low-quality RPN proposals, which makes the R-CNN module (i.e., post-classification layers) become highly biased towards the negative proposals in the early training stage. The second imbalance stems from the unbalanced ground-truth numbers across different testing images, resulting in the imbalance of the number of potentially existing positive proposals in testing phase. To tackle these two imbalance issues, we incorporates two innovations into Faster R-CNN: 1) an R-CNN Gradient Annealing (RGA) strategy to enhance the impact of positive proposals in the early training stage. 2) a set of Parallel R-CNN Modules (PRM) with different positive/negative sampling ratios during training on one same backbone. Our RGA and PRM can totally bring 2.0% improvements on AP on COCO minival. Experiments on CrowdHuman further validates the effectiveness of our innovations across various kinds of object detection tasks.
중국 대학 한국어학과 교과목 개설 현황 조사 -한국 문화․한국 문학․한국학을 중심으로-

The purpose of the study is to review the current state of establishment of subjects in the department of Korean language in Chinese universities, point out the deficiencies that exist in the curriculum of the department of Korean language and seek for the improvement in them. For this, this study categorized the subjects related to humanities and social science of the department of Korean language in 31 universities in China into three areas―Korean culture, Korean literature and Korean studies―and reviewed them. This study has a significance in that it reflects the currently-reformed curriculum targeting the universities that represent each area of China and understands the current state of the curriculum of the department of Korean language in Chinese universities and the improvement plan for it though it did not consider the current state of curriculum operation.
Space-Efficient Feature Maps for String Alignment Kernels

Abstract String kernels are attractive data analysis tools for analyzing string data. Among them, alignment kernels are known for their high prediction accuracies in string classifications when tested in combination with SVM in various applications. However, alignment kernels have a crucial drawback in that they scale poorly due to their quadratic computation complexity in the number of input strings, which limits large-scale applications in practice. We address this need by presenting the first approximation for string alignment kernels, which we call space-efficient feature maps for edit distance with moves (SFMEDM) , by leveraging a metric embedding named edit-sensitive parsing and feature maps (FMs) of random Fourier features (RFFs) for large-scale string analyses. The original FMs for RFFs consume a huge amount of memory proportional to the dimension d of input vectors and the dimension D of output vectors, which prohibits its large-scale applications. We present novel space-efficient feature maps (SFMs) of RFFs for a space reduction from O ( dD ) of the original FMs to O ( d ) of SFMs with a theoretical guarantee with respect to concentration bounds. We experimentally test SFMEDM on its ability to learn SVM for large-scale string classifications with various massive string data, and we demonstrate the superior performance of SFMEDM with respect to prediction accuracy, scalability and computation efficiency.
Research Guides: Streaming Resources for Courses: Home

Information about how to access and ensure the fair use of copyrighted material in face to face and online courses at Canisius College.
APPLICATION OF PICTURE AND PICTURE MODELS IN IMPROVING WRITING SKILLS DESCRIPTION CLASS IV STUDENT SUB THEMES THE WORK OF MY PARENTS MI MODERN MUTIARA IMAN KECAMATAN NGUNUT DISTRICT TULUNGAGUNG

Based on observations during the learning process in Class IV Madrasah Ibtidaiyah (MI) Modern Mutiara Iman students in writing activities, before writing a description of student activities listening to the teacher's explanation about writing a description through writing essays. Then, reading the text in the book, listing and interpreting difficult words with the group after that students with the group write a description essay then exchange the results of work on other groups. Individual students are given homework assigning descriptions with free themes. From the results of student writing there are some errors. From the results of interviews with students obtained data that students have difficulty in pouring ideas into writing. This can be seen in the unorganized writing ideas that will be developed, the suitability of cohesion and low coherence. Students immediately write what is in their minds without making a framework first. Students are less able to develop sentences into coherent paragraphs. There are many errors in the use of punctuation and spelling. This study aims to improve (1) the application of picture and picture models to improve description writing skills in Class IV students at MI Modern Mutiara Iman (2) increase learning outcomes of writing description skills in Class IV students at MI Modern Mutiara Iman. The subjects of this study were Class IV MI Modern Mutiara Iman students in the 2019/2020 school year with a total of 34 students. This type of research is a classroom action research (CAR) with three cycles, including four stages, namely: 1) planning, 2) implementing actions, 3) observations, and 4) reflection. Data collection techniques used in this study include: interviews, observations, tests, questionnaires, documentation, field notes. The results of the research during the process of writing descriptions in each cycle obtained data from 34 students in the first cycle as many as 14 students passed grade the specified scor 70. In the second cycle 26 students passed grade and 31 students passed grade in the third cycle. There are 2 students do not passing grade because they have difficulty writing essay writing so that they need more intensive study guidance compared to their peers.
“Digitizing Rural India” Through “Digital India” A Step to Achieve the Goal of Developing Modern Villages

India is aiming to become the Trillian dollar digital economy with expansion of digital infrastructure increasing accessibility to ICT - led development by use of technology that is affordable, inclusive and transformative(Ministry of Electronics & IT, Feb. 2019). Increasing number of mobile and smart phone users, doorstep internet connection, use of computers and electronic devices to connect with last mile are the examples of digital infrastructure while advance technologies enabling users to connect with social media which is improving the living standard of Indian citizens at each and every corner of the country.
Generative Adversarial Training Data Adaptation for Very Low-resource Automatic Speech Recognition

It is important to transcribe and archive speech data of endangered languages for preserving heritages of verbal culture and automatic speech recognition (ASR) is a powerful tool to facilitate this process. However, since endangered languages do not generally have large corpora with many speakers, the performance of ASR models trained on them are considerably poor in general. Nevertheless, we are often left with a lot of recordings of spontaneous speech data that have to be transcribed. In this work, for mitigating this speaker sparsity problem, we propose to convert the whole training speech data and make it sound like the test speaker in order to develop a highly accurate ASR system for this speaker. For this purpose, we utilize a CycleGAN-based non-parallel voice conversion technology to forge a labeled training data that is close to the test speaker's speech. We evaluated this speaker adaptation approach on two low-resource corpora, namely, Ainu and Mboshi. We obtained 35-60% relative improvement in phone error rate on the Ainu corpus, and 40% relative improvement was attained on the Mboshi corpus. This approach outperformed two conventional methods namely unsupervised adaptation and multilingual training with these two corpora.
LibGuides: Humanities: Home

The Associate of Applied Science Degree in Interdisciplinary Studies (AIS) allows customization of the program of study based on each student’s academic and professional goals.
On health and privacy: technology to combat the pandemic

Technology has been harnessed in the fight against the Covid-19 pandemic, eg to administer remote medical consultations, analyse aggregate movements and track paths of contact. Successful applications are predicated on broad public support. They must address concerns about data privacy, and the potential for misuse of data by governments and companies. Transparent public policies and clear governance frameworks can help to build trust. One possible approach is to differentiate data use during a pandemic and in normal times.
Property Preserving Embedding of First-order Logic

Logical reasoning as performed by human mathematicians involves an intuitive under- standing of terms and formulas. This includes properties of formulas themselves as well as relations between multiple formulas. Although vital, this intuition is missing when supplying atomically encoded formulae to (neural) down-stream models. In this paper we construct continuous dense vector representations of first-order logic which preserve syntactic and semantic logical properties. The resulting neural formula embeddings encode six characteristics of logical expressions present in the training-set and further generalise to properties they have not explicitly been trained on. To facilitate training, evaluation, and comparing of embedding models we extracted and generated data sets based on TPTP’s first-order logic library. Furthermore we examine the expressiveness of our encodings by conducting toy-task as well as more practical deployment tests.
THE DIVERSIFICATION OF HIGHER EDUCATION AS A GLOBAL TREND OF MODERNITY

Conference proceedings are devoted to modern global trends in the development of innovative scientific researches. General issues of the engineering, medical, psychological, philological, economic sciences, history of art, social communications and so on are considered. The publication is designed for scientists, lecturers, postgraduate students, students, as well as for the general readers. International Scientific Conference Modern Global Trends in the Development of Innovative Scientific Researches: Conference Proceedings, March 20th, 2020. Riga, Latvia: Baltija Publishing. 180 pages.
Perancangan Sistem Informasi Pencarian Dan Peminjaman Dokumen Penjualan Berbasis Web Pada PT. Astra International, Tbk â€“ Toyota Sales Operation (AI-TSO), Auto 2000 Cabang Setiabudi, Bandung

The archives are instrumental in the journey of the life of an office. Therefore, to keep the archives ' life cycle from the start of the stage of creation, use, maintenance and displacement as well as its destruction, a good system and the correct process is required. With increasing technological development, it demands us to obtain information quickly and accurately. So it takes a system that can speed up the process of data processing related to archival. E-Archive is an archive presented in electronic form, therefore need a new breakthrough to create an e-archive website, in order to facilitate the management of archival documents in particular the data archive in the institution. The topic of the study in this report is the design of information system of search and sales documents in PT. Astra International, TBK-Toyota Sales Operation AUTO2000 Branch of Setiabudi, Bandung. The purpose of this design is to design archival applications that facilitate, accelerate, and know data on loan and borrower sales documents through the sales document management information System. The result of this final task report is a web-based application that has been designed for managing sales documents at PT. Astra International, TBK-Toyota Sales Operation AUTO2000 Branch of Setiabudi, Bandung.
RIOT-POLICE: An implementation of spatial memory safety for the RIOT operating system

We present an integration of a safe C dialect, Checked C, for the Internet of Things operating system RIOT. We utilize this integration to convert parts of the RIOT network stack to Checked C, thereby achieving spatial memory safety in these code parts. Similar to prior research done on IoT operating systems and safe C dialects, our integration of Checked C remains entirely optional, i.e. compilation with a standard C compiler not supporting the Checked C language extension is still possible. We believe this to be the first proposed integration of a safe C dialect for the RIOT operating system. We present an incremental process for converting RIOT modules to Checked C, evaluate the overhead introduced by the conversions, and discuss our general experience with utilizing Checked C in the Internet of Things domain.
Teaching Speaking Through Techno Driven Task (Zig Zag Task)

This research is motivated by the importance of having good English speaking skills, especially for English teachers. The use of traditional learning methods has so far been less successful in improving students' speaking abilities. Therefore, one of the solutions offered is to use the Techno Driven Task (Zig Zag Task) in learning to speak students. This study aims to determine the speaking skills of English students at Pasir Pengaraian University by using computer-aided Techno Driven Task (Zig Zag Task). This research designs it as descriptive quantitative research. The participants of this research was the fourth semester students of English Study Program in University of Pasir Pengaraian. The number of samples of this research was 19 students. In collecting the data, the researcher used performance or speaking test to the students. The results showed that the level of students' speaking skills was at a reasonable to very good level and only a few students had poor skills.
Optimizing coronary angiography in Brazil: a legacy from the COVID-19 era

At the time this editorial is written, a new reality is under construction in medicine all over the world. The pandemic associated with the severe acute respiratory syndrome (SARS-CoV-2) coronavirus 2 continues to advance, imposing an unprecedented burden on several health systems around the world. In Brazil, the scarcity of resources and the risk of contamination are daily concerns for health managers and professionals, placing the rational use of diagnostic tests and therapeutic interventions as priority measures in all medical [...]
Featherweight Go

We describe a design for generics in Go inspired by previous work on Featherweight Java by Igarashi, Pierce, and Wadler. Whereas subtyping in Java is nominal, in Go it is structural, and whereas generics in Java are defined via erasure, in Go we use monomorphisation. Although monomorphisation is widely used, we are one of the first to formalise it. Our design also supports a solution to The Expression Problem.
Implementation of DSSS system of image using MATLAB

In direct sequence spread spectrum systems, pseudo sequence random (PN) are used to spreading data bits. There are various type of PN sequences are available with good correlation properties, on mostly are generating pn by linear feedback shift register (LFSR). Will examine in this paper we describe spreading binary random and spreading the image As well as studying probability bit error rate of DSSS system with change of the channel (Add white Gaussian noise channel, Rayleigh fading channel, Rician fading channel). Our study will be performed using MATLAB. Terms of completed my articles in building system developed to security transmitter and receiver the information of theft against communication digital system talk as well as the increase of data rate information. Was this design a way process of his used in applications military and have a major role also in the most applications requiring protect high for many information binary data and image. So that the system designed as fits practice and prevent the overlap the outside after it has become send signals in communication system exhibition penetration and Intentional attacks or other breaches.
Cyberinfrastructure for Emerging Science and Engineering Research (NSF)

Federal Grants & ContractsVolume 43, Issue 4 p. 1-2 Grants Alerts Cyberinfrastructure for Emerging Science and Engineering Research (NSF) First published: 15 January 2019 https://doi.org/10.1002/fgc.30212 Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume43, Issue417 January 2019Pages 1-2 RelatedInformation
Significance Testing in CFA

This chapter explains the five significance tests that are available in the von Eye program and/or R package confreq for use in the search for types and antitypes, including the binomial and chi-square tests and their normal approximations. Formulas for each test are provided, and the advantages and disadvantage of each are explained.
Cloud Computing

990. WHAT IS CLOUD COMPUTING? – Cloud computing has been defined in many ways. Often cited is the definition of the U.S. National Institute for Standards and Technology (NIST), which describes cloud computing as