Reviewerly/exHarmony huggingface dataset:
Stand-alone artificial intelligence in breast cancer screening with digital breast tomosynthesis - Potential implementations in a screen-reading workflow

Thank you very much for joining EUSOBI 2021 ONLINE and celebrating the Breast Cancer Awareness Month with us! This year we had a terrific programme and we are indebted to Dr. Ritse Mann and his Programme Committee for all their hard work in putting this together. We had a wonderful selection of international speakers who gave the latest updates in all your favourite topics and ensured that you are kept up to date with the latest developments in breast imaging. A special focus was given to the interactivity of this year’s programme, by adding expert discussions and concluding with the first virtual edition of our famous Breast Interpretation Quiz. Thanks to all of you who submitted abstracts and therefore having been an important part of this scientific event. We are also very grateful for the support of our industry partners who are listed here: https://www.eusobi.org/congress-sponsors-2/ Most of them have contributed to the extension of our scientific programme by offering interesting sessions and workshops. Have a look by scrolling through all sessions. All sessions have been recorded and are available on-demand.
AAD-KWS: a sub-$\mu\mathrm{W}$ keyword spotting chip with a zero-cost, acoustic activity detector from a 170nW MFCC feature extractor in 28nm CMOS

As a widely used speech-triggered interface, deep-learning based keyword spotting (KWS) chips require both ultra-low power and high detection accuracy. We propose an always-on keyword spotting chip with an acoustic activity detection (AAD) to achieve the above two requirements. Extracted from feature extractor, this AAD has zero overhead and zero miss rate. It is used to clock gate the neural network and post processing unit to achieve ultra-low power at silent scenarios. We also propose a tunable detection window to fit keywords with different widths to get better accuracy. Besides, a non-overlapping-frame Mel frequency cepstrum coefficient (MFCC) is used in the KWS system to reduce memory footprint and processing cycles. Implemented in a 28nm CMOS technology, its power consumption is only <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.36\mu\mathrm{W}$</tex> for AAD at quiet scenarios and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.8\mu\mathrm{W}$</tex> for KWS, operating at 0.4V supply voltage with 8kHz for MFCC and 200kHz for other parts. And MFCC circuit has only 170nW power consumption. The accuracy can reach 97.8% for two keywords in the Google speech command data set (GSCD).
Historical Changes in History between Goryeo and the Early Joseon Dynasty, Viewed as Related index Terms Related to Drought(Hanjae) and Ritual for Rain(Giwooje)

The purpose of this study is to examine the historical and ideological trends and context from the Goryeo Dynasty to the early Joseon Dynasty through the change of index words related to a ritual for rain(Giwooje). Therefore, this study aims to examine the use of DB in historical and ideological research and the possibility of new research. The change from the Goryeo Dynasty to the Joseon Dynasty can be regarded as a change from Buddhism to Neo-Confucianism in a historical and ideological way, and from the pluralistic ideological landscape to the unity-oriented ideological landscape, going beyond a simple dynasty change. The related index term is an index word that is expressed together in the search results when the entire historical records are searched with the index word. In general, the index terms related to a ritual for rain are composed of words derived from not only a combination of three letters：‘Gi (praying, 祈)’, ‘Do(wishing, 禱)’, and ‘Woo (ritual for rain, 雩)’, but also a combination with other letters. Each of these three letters represents an act related to faith and the form of performing a ritual for rain. In the Goryeo Dynasty, ‘Gi’ and ‘Do’ tended to be expressed together with related index words about folk beliefs or Buddhism. From the Goryeo Dynasty to the early Joseon Dynasty, ‘Gi ’ and ‘Do’ tended to be used much more often than ‘Woo’. However, ‘Woo’ is strongly connected with Confucianism. In China, ‘Woo’ was used in commination with Confucian political philosophy since shamanism was accepted by the state, and a similar case was found in the Goryeo Dynasty. However, in Goryeo, folk beliefs, or Buddhist rituals for rain were held together with Woosa (雩祀). Although ‘Woo’ has a strong connection with Confucian political philosophy, it can be a result of reflecting the pluralistic ideological landscape of the Goryeo Dynasty. Nonthenless, ‘Woo’ was found to be increasingly distanced from folk beliefs and Buddhism after the founding of the Joseon Dynasty. This suggests that it is related to a phenomenon where Neo-Confucianism was gradually deepening and accordingly ideologies other than Neo-Confucianism were excluded from national rituals.
HYDROGEL

Chemistry & IndustryVolume 85, Issue 12 p. 28-31 MATERIALS HYDROGEL First published: 16 December 2021 https://doi.org/10.1002/cind.8512_8.xAboutPDF 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 onFacebookTwitterLinked InRedditWechat Volume85, Issue12December 2021Pages 28-31 RelatedInformation
Segmenting lecture video into partitions by analyzing the contents of video

Teaching strategy using slides through video conference and recorded lectures are uploaded for imparting the knowledge to the learners via various online modes. There are few hurdles to understand a specific topic for learner from the video. The learners needs to put efforts and more time in listening and to reach the exact topic from the recorded lecture twice or thrice or sometimes moving to & fro the clips to understand the exact topic explained in the video for gaining knowledge. To overcome such issues, an attempt is made where the annotated indexing of lecture video to be embedded within the video for non-linear navigation to better understand the concerned topics point-wise. In this paper an algorithm to sequentially partition the lecture video into clusters and annotates the cluster by analyzing the contents of video by organizing the video in structured format which aids for efficient non-linear navigation. To access the performance of video partitioning process, we have chosen seven (7) online lecture videos of tutorials from different courses. Initially, we manually annotated required slides from the lecture videos and indexed it. Then, we used the proposed algorithm for these videos. We compared the results of extracted slides with the proposed approach using Recall and precision metrics. Our method achieved the good results when compared to other methods.
Azure Arc As an Extension of the Azure Control Plane

Welcome to the first chapter in this Azure Arc-Enabled Kubernetes and Servers book. This book overall is going to take you on a journey into the world of both Azure Arc-enabled Kubernetes and Azure Arc-enabled servers. Azure Arc has many offerings, and we wanted to focus in this book on Azure Arc’s Kubernetes and Server offerings to give you the most value we can on two focused topics.
Toward a Cloud based Disease Diagnosis System Using Sequential Quadratic Programming Approach

The Internet of Things (IoT) relates to the process of utilizing computer networks to plan and model Internet-connected things. The Internet of Things (IoT)-based m-healthcare technologies have provided multi-dimensional functionality and real-time resources over the last few years. These apps provide millions of individuals with a forum to get wellness alerts for a healthy lifestyle constantly. Several aspects of these systems have been revitalized with the introduction of IoT devices in the healthcare sector. This work proposed a data-driven disease signal analytics by inventing a novel combination learning approach. The proposed Combination learning integrates different machine learning models to price disease signal for different options by leveraging the availability of a large amount of data through solving a sequential quadratic programming problem. The proposed approach demonstrates its superiority in prediction accuracy and strong model independence by overcoming traditional model-driven approaches' generalization issue. The findings illustrate the efficacy of the task for an effective disease signal diagnosis. It could be a modern and useful health approach to adopt the proposed procedure with potential changes and incorporate it into a low-cost unit.
Education 4.0 in cartography: an integrated e-learning materials portal for cartography and digital mapping course

Abstract. World’s technology development is defined by the level of advancement in the current industrial sector that is called industrial revolution (IR). The evolution of the technologies may affect the learning system for educators and learners. Throughout the years, student discovering troubles to have an effective learning method for their subject. To keep up the mushroom growth of technologies, e-learning has been identified as the best learning method that suits with the current generation’s way of learning. This paper aimed at designing and developing an integrated e-learning material portal for assisting university students of Centre of Study for Surveying Science and Geomatics (PPSUG) University of MARA (UiTM) Malaysia to have a helpful learning process in Cartography subject. The study conducted in three main objectives: i. To study the user requirement, ii. To create and design the portal, iii. To test the proposed portal using WordPress platform. The finding has revealed that most of the respondents significantly agreed and willingly to have an integrated e-learning portal to assist in the subject. The proposed portal offers special functions for learning materials process in particular integrating all learning materials in one platform such online notes, assignments, lab work sheets and other benefits that are easy – free to access and user friendly knowledge transfer for the public. The portal it still needs using the university hosting for comprehensive practice among the SUG students.
“Full-Text” Access Evaluation: Are We Getting the Real Thing?

The workshop examined the comparative value of full-text access and journal subscriptions by sampling journal issues available online through IAC and in print at James Madison University. Differences between the two formats were discussed. A model for assessing the value of full-text online subscriptions was presented, which can be used to evaluate journal subscription, back file retention, document delivery options, index selection, and online access. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-342-9678. E-mail address: getinfo@haworth.com]
Japanese

Japanese is spoken by virtually the entire population of Japan — some 115 million people as of 1 March 1980. The most persuasive is the Altaic theory, but even evidence is hardly as firm as that which relates the languages of the Indo-European family, as can be seen in ongoing speculations among both scholars and linguistic amateurs. Hiragana, on the other hand, were mainly used by women and were not mixed with characters. The contemporary practice is to use Cninese characters, called kanji, for content words, and hiragana for grammatical function words such as particles and inflectional endings. Katakana is used to write foreign loanwords, telegrams and in certain onomatopoeic expressions. The fact that Japan has never been invaded by a foreign force or colonised by a foreign interest causes surprise when one examines the Japanese lexicon, for it shows a characteristic of those languages whose lands have been under foreign control at one time or another.
A REAL – TIME SYSTEM FOR RECOGNITION OF AMERICAN SIGN LANGUAGE BY USING DEEP LEARINING

Every day we see many people, who are facing illness like deaf, dumb etc. There are not as many technologies which help them to interact with each other. They face difficulty in interacting with others. Sign language is used by deaf and hard hearing people to exchange information between their own community and with other people. Computer recognition of sign language deals from sign gesture acquisition and continues till text/speech generation. Sign gestures can be classified as static and dynamic. However static gesture recognition is simpler than dynamic gesture recognition but both recognition systems are important to the human community. The ASL American sign language recognition steps are described in this survey. There are not as many technologies which help them to interact with each other. They face difficulty in interacting with others. Image classification and machine learning can be used to help computers recognize sign language, which could then be interpreted by other people. Earlier we have Glove-based method in which the person has to wear a hardware glove, while the hand movements are getting captured. It seems a bit uncomfortable for practical use. Here we use visual based method. Convolutional neural networks and mobile ssd model have been employed in this paper to recognize sign language gestures. Preprocessing was performed on the images, which then served as the cleaned input. Tensor flow is used for training of images. A system will be developed which serves as a tool for sign language detection. Tensor flow is used for training of images. Keywords: ASL recognition system, convolutional neural network (CNNs), classification, real time, tensor flow
A Case Study on Educational Program for Multicultural Learning Support Group: From the Perspective of Social Contribution and Korean Language Education of the Middle-aged Generation

The purpose of this study was to examine a case study of educational program for social contribution activist program from the perspective of social contribution and Korean language education of the middle-aged generation. The paper principally studied the educational program for multicultural learning support group as a research subject and comprehensively discussed the process of previous, main, and later stages of course management. Furthermore, the paper revealed practical needs, area of interest of the middle-aged preliminary activists and points to be taken into consideration for the management of future courses. The paper also presented contents and critical direction of the feedback for the management of educational program for multicultural studies support group. The study has significance because it highlights the correlation and importance of ‘social contribution for the middle-aged generation and Korean language education’, collects and analyse basic data for developing future related programs and follow-up discussions. (Anyang University)
A Study on the Evaluation and Revitalization of Temiorae Consignment Operation : Focused on the Operating Program of Temiorae

본 연구는 테미오래의 수탁 운영평가와 활성화 방안에 관한 연구이다. 테미오래의 근대문화유산으로서 문화적 가치와 장소적 가치를 고찰하였다. 그리고 테미오래의 운영 실태와 성과, 수탁 운영에서의 과제를 파악하였다. 이것을 토대로 2기 테미오래 운영의 활성화 방안을 모색하였다. 이와 함께 향후 운영체계, 정책 및 민간 거버넌스 등의 구축 방향을 제시하고자 하였다. 테미오래 운영의 목적은 시민과 관광객에게 문화적 공공서비스로 제공하는데 있다. 지방자치단체는 시민에게 효율적이고 효과적인 민간위탁 서비스(문화·관광)를 해야 한다. 민간수탁운영은 대리인 측면에서 테미오래 운영 통제·자율의 적절한 균형이 요청된다. 테미오래 수탁운영 성과를 바탕으로 공공의 문화향유 서비스 개선과 보다 구체적인 비용절감 및 업무개선 방안과 같은 효율적인 운영을 모색해야 한다.This is a study on the consignment operation evaluation and activation plan of Temiorae. Cultural and place values were considered as Temiorae s modern cultural heritage. In addition, the operational status and performance of Temiorae, and tasks in the consignment operation were identified. Based on this, the activation plan sought ways to revitalize the operation of the 2nd Temiorae. Along with this, it was intended to present future directions for establishing operating systems, policies, and private governance. The purpose of the Temiorae operation is to provide cultural and public services to citizens and tourists. Local governments should provide efficient and effective private consignment services (such as cultural and touristic) to citizens. Private consignment operation requires an appropriate balance of control and autonomy for Temiorae operation from the perspective of deputies. It is necessary to seek ways to improve public cultural enjoyment services and operation efficiency, more specifically cost reduction and work improvement, based on the performance of the entrusted operation of Temiorae.
Decompression of Bluetooth-transmitted Audio using Super Resolution for Low-Latency Applications

Bluetooth devices experience a common trade-off between quality and latency. This affects applications that require fast and accurate transmission of audio signals, especially in the medical and musical industry. In this paper, the use of super resolution techniques such as the Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN) were utilized to improve the quality of Bluetooth-transmitted audio signals. We have shown that these two models were able to improve a number of speech and non speech audio signals based on our performance metrics: (1) Signal-to-Noise Ratio (SNR); (2) Log Spectral Distance (LSD); (3) Perceptual Evaluation of Audio Quality (PEAQ); and (4) Multi Stimulus test with Hidden Reference and Anchor (MUSHRA). Both models were able to improve the Bluetooth-transmitted audio signals, although the GAN model produced better results on both the objective and subjective evaluation tests compared to the CNN model. For the SNR, LSD, PEAQ, and MUSHRA, the GAN model averaged 20.4170 dB, 2.1408 dB, −2.7190, and 42.6500 respectively, while the CNN model averaged −1.7190 dB, 1.9410 dB, −2.7190, and 17.1600 respectively. For this project, the subjective test MUSHRA bear more weight in the results as the objective tests shows inconsistencies and cannot be heavily relied on.
Distinguishing the concept of mentoring in the National police of Ukraine from related concepts.

The purpose of writing this scientific article is to determine the features of mentoring in the National Police of Ukraine on the basis of distinguishing this concept from related concepts. In order to achieve this goal, the author defines a range of concepts related to mentoring in the National Police, which will analyze in the work: training, coaching, consulting, mentoring, supervision. The paper emphasizes the need to adapt mentoring in the National Police of Ukraine to the modern realities of Ukrainian society and to modernize it to European standards. An example of an attempt to implement this thesis is the introduction of the institute of mentors in the patrol police of Ukraine in 2019. Mentoring in the patrol police is the first step towards overcoming urgent problems with the young potential of the police. In the future, it is proposed to determine the legal basis for the implementation of this and similar institutions in the police. According to the author, the method of supervision can be introduced into the internal activities of the National Police of Ukraine after careful preparation and definition of the main methodological goals, objectives and methods of application. However, the institution of supervision is not identical to mentoring, as it has a number of differences: their subjects and objects, as well as the relationship between them. Emphasis is placed on the mandatory use of training in the process of professional training of police officers: initial training; training in higher educational institutions with specific learning conditions; postgraduate education; job training. But the key difference between training and mentoring is the acquisition of knowledge in artificially created conditions or in real life situations during the performance of police duties. The author considers coaching a necessary element for mentoring in the National Police of Ukraine. The mentor should not indicate the algorithm of actions for the police officer, not solve work tasks by his own example, but give the police officer the opportunity to make decisions independently, perform actions under the strict supervision of the mentor with prior and subsequent analysis of the work situation. The paper focuses on the inappropriateness of the use of consulting in the activities of the National Police of Ukraine, as the police officer has no opportunity not to follow the recommendations, instructions, orders, etc.
ENHANCED SCADA IDS SECURITY BY USING MSOM HYBRID UNSUPERVISED ALGORITHM

In Self-Organizing Maps (SOM) are unsupervised neural networks that cluster high dimensional data and transform complex inputs into easily understandable inputs. To find the closest distance and weight factor, it maps high dimensional input space to low dimensional input space. The Closest node to data point is denoted as a neuron. It classifies the input data based on these neurons. The reduction of dimensionality and grid clustering using neurons makes to observe similarities between the data. In our proposed Mutated Self Organizing Maps (MSOM) approach, we have two intentions. One is to eliminate the learning rate and to decrease the neighborhood size and the next one is to find out the outliers in the network. The first one is by calculating the median distance (MD) between each node with its neighbor nodes. Then those median values are compared with one another. In case, if any of the MD values significantly varies from the rest then it is declared as anomaly nodes. In the second phase, we find out the quantization error (QE) in each instance from the cluster center.
Adaptive Deep Incremental Learning — Assisted Missing Data Imputation for Streaming Data

With the rapid explosion of the data streams from the applications, ensuring accurate data analysis is essential for effective real-time decision making. Nowadays, data stream applications often confront the missing values that affect the performance of the classification models. Several imputation models have adopted the deep learning algorithms for estimating the missing values; however, the lack of parameter and structure tuning in classification, degrade the performance for data imputation. This work presents the missing data imputation model using the adaptive deep incremental learning algorithm for streaming applications. The proposed approach incorporates two main processes: enhancing the deep incremental learning algorithm and enhancing deep incremental learning-based imputation. Initially, the proposed approach focuses on tuning the learning rate with both the Adaptive Moment Estimation (Adam) along with Stochastic Gradient Descent (SGD) optimizers and tuning the hidden neurons. Secondly, the proposed approach applies the enhanced deep incremental learning algorithm to estimate the imputed values in two steps: (i) imputation process to predict the missing values based on the temporal-proximity and (ii) generation of complete IoT dataset by imputing the missing values from both the predicted values. The experimental outcomes illustrate that the proposed imputation model effectively transforms the incomplete dataset into a complete dataset with minimal error.