Reviewerly/exHarmony huggingface dataset:
ERC-20: Fungible Tokens

Fungible tokens are tokens where each unit has the same value, in the same way as fiat currency. This means you can exchange one unit of this currency for another unit of this currency for the same amount. Thinking about replicating this behavior on the blockchain, Fabian Vogelsteller and Vitalik Buterin proposed the creation of ERC-20, “Ethereum Request for Comments 20,” in November 2015 to create a simple format for Ethereum-based tokens. These tokens work within the Ethereum blockchain and are able to interact with other cryptocurrencies on the network. In this chapter, you will create simple contracts in the ERC-20 standard and learn how to deploy them to test and production networks.
Sistem Informasi Keuangan Pada CV. Bali Indigo Computer

The process of making financial reports on the CV. Bali Indigo Computer is still done manually namely, accounting must recap transaction data taken from different Microsoft Excel sheets or files. In addition, the manual process in preparing financial reports is also time-consuming so that there can be delays in preparing financial reports and errors when preparing financial statements. Based on the above background, the formulation of the problem in this research is How to Design and Build a Financial Information System on the CV. Bali Indigo Computer, while the research objective to be achieved in the process of preparing the report is to be able to design and build a company's financial information system. The results of this study, namely to produce a financial information system that aims to assist accounting in making financial reports to determine the company's financial condition. After the system has been built and implemented, the system will then be tested with Blackbox Testing which can assist the company in processing customer data, supplier data, account data, general journal data, transaction data, user data, and financial reports.
DB "Ukrainika scientific": past, present and future

The history of creation of the national DB "Ukrainika scientific" which is one of the important and significant electronic resources of the Vernadsky National Library of Ukraine, is covered. Attention is paid to its role in the domestic and world information space, emphasis is placed on its uniqueness. It is noted that the main task of creation, replenishment and functioning of DB "Ukrainika scientific" is to achieve full reflection of the printed scientific works of domestic scientists from all branches of science. The formation, thematic and types of filling of DB "Ukrainika scientific" are considered. It is noted that the largest number of publications reflected in the DB "Ukrainika scientific" relates to the social sciences and humanities, the second place is occupied by abstracts of publications on technical and agricultural, the third — medical, the fourth — natural sciences. Peculiarities of abstracting scientific documents are revealed. Emphasis is placed on the responsibility and high professionalism of Vernadsky National Library of Ukraine specialists who participate in the creation and editing of abstract texts for DB "Ukrainika scientific". The advantages of using the information resource contained in the national abstract database are given. The current state and prospects of development of the system of cooperative abstracting in Ukraine are analyzed. It is noted that at the present stage the main participants in corporate cooperation in abstracting documents for DB "Ukrainika scientific" are the following institutions: Vernadsky National Library of Ukraine, Institute of Information Registration of the National Academy of Sciences of Ukraine, National Scientific Medical Library of Ukraine, V. O. Sukhomlynskyi State Scientific and Pedagogical Library of Ukraine. The Agreement on cooperation between these institutions is analyzed and the responsibilities of each of them are given. It is emphasized that cooperative cooperation helps to increase the amount of abstract resources in various fields of science. Prospects for the development of DB "Ukrainika scientific" are outlined. Emphasis is placed on the harmonious combination of the abstract database with full-text, which will provide a significant increase in the number of users.
Wavelets from a Statistical PerspectiveMaartenJansenChapman and Hall/CRC, 2022, xix + 325 pages, $96 (hardcover) ISBN: 978‐1‐032‐20067‐5 (hardcover)

International Statistical ReviewVolume 90, Issue 2 p. 414-415 Book Review Wavelets from a Statistical Perspective , Jansen, Maarten Chapman and Hall/CRC, 2022, xix + 325 pages, $96 (hardcover) ISBN: 978-1-032-20067-5 (hardcover) Krzysztof Podgórski, Corresponding Author Krzysztof Podgórski krzysztof.podgorski@stat.lu.se Department of Statistics, Lund University, Lund, SwedenSearch for more papers by this author Krzysztof Podgórski, Corresponding Author Krzysztof Podgórski krzysztof.podgorski@stat.lu.se Department of Statistics, Lund University, Lund, SwedenSearch for more papers by this author First published: 18 July 2022 https://doi.org/10.1111/insr.12515Read 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 onFacebookTwitterLinked InRedditWechat Volume90, Issue2August 2022Pages 414-415 RelatedInformation
Improving Multilingual Frame Identification by Estimating Frame Transferability

A recent research direction in computational linguistics involves efforts to make the field, which used to focus primarily on English, more multilingual and inclusive. However, resource creation often remains a bottleneck for many languages, in particular at the semantic level. In this article, we consider the case of frame-semantic annotation. We investigate how to perform frame selection for annotation in a target language by taking advantage of existing annotations in different, supplementary languages, with the goal of reducing the required annotation effort in the target language. We measure success by training and testing frame identification models for the target language. We base our selection methods on measuring frame transferability in the supplementary language, where we estimate which frames will transfer poorly, and therefore should receive more annotation, in the target language. We apply our approach to English, German, and French – three languages which have annotations that are similar in size as well as frames with overlapping lexicographic definitions. We find that transferability is indeed a useful indicator and supports a setup where a limited amount of target language data is sufficient to train frame identification systems.
Multi-Feature Matching GM-PHD Filter for Radar Multi-Target Tracking

Multi-target tracking (MTT) is one of the most important functions of radar systems. Traditional multi-target tracking methods based on data association convert multi-target tracking problems into single-target tracking problems. When the number of targets is large, the amount of computation increases exponentially. The Gaussian mixture probability hypothesis density (GM-PHD) filtering based on a random finite set (RFS) provides an effective method to solve multi-target tracking problems without the requirement of explicit data association. However, it is difficult to track targets accurately in real-time with dense clutter and low detection probability. To solve this problem, this paper proposes a multi-feature matching GM-PHD (MFGM-PHD) filter for radar multi-target tracking. Using Doppler and amplitude information contained in radar echo to modify the weights of Gaussian components, the weight of the clutter can be greatly reduced and the target can be distinguished from clutter. Simulations show that the proposed MFGM-PHD filter can improve the accuracy of multi-target tracking as well as the real-time performance with high clutter density and low detection probability.
INDIVIDUALIZED BOTTOM-UP LISTENING SKILLS TRAINING THROUGH SELF-ACCESS MATERIALS AND REAL-TIME INSTRUCTIONS

INDIVIDUALIZED BOTTOM-UP LISTENING SKILLS TRAINING THROUGH SELF-ACCESS MATERIALS AND REAL-TIME INSTRUCTIONS
EDUCATION OF NURSES IN THE REPUBLIC OF CROATIA IN THE FIELD OF COMPUTER AND INFORMATION LITERACY

Appears in: EDULEARN22 Proceedings Publication year: 2022Pages: 3490-3502ISBN: 978-84-09-42484-9ISSN: 2340-1117doi: 10.21125/edulearn.2022.0851Conference name: 14th International Conference on Education and New Learning TechnologiesDates: 4-6 July, 2022Location: Palma, Spain
Research on Data Transmission System of Aircraft Structural Strength Test based on Genetic Algorithm

With the development of aviation technology and the diversification of flight missions, the traditional structural strength verification methods can not fully meet the verification requirements under the new situation. Based on the problems existing in the aircraft structural strength test system, a genetic algorithm is proposed to optimize the data transmission system. Due to the complex structure, thin wall, easy deformation, high machining accuracy and many feature types and quantities of aircraft structural parts, it is difficult to process and test the structural strength. This problem can be solved by genetic algorithm. There will be a lot of installation work before static strength test of large aircraft structure, and the installation process involves the personnel going up and down in aerial work, the conversion of various special tools, and the adjustment of components during hoisting and so on. Under the condition that the installation components are determined, different installation sequences will significantly affect the installation efficiency. This paper introduces the basic working principle and main characteristics of genetic algorithm, and discusses the theory, technology and existing problems of genetic algorithm.
Music

Glass’s early works composed between 1967 and 1975 feature rhythm as the primary structural principle, while containing cyclical constructions adapted from Indian music. Glass recalled his early experiences with Indian music: “I [then] thought I was listening to music that was built in an additive way, but it turned out it really wasn’t. It was built in a cyclic way … In Indian music (and all the non-Western music with which I’m familiar), you stake small units, or ‘beats,’ and string them together to make up larger time values. For my whole generation, which was dominated by serialism, this music was a breath of fresh air. It allowed us to think of music in a different way.” Glass combined this approach with additive and subtractive elements commonly associated with minimalist technique. In the mid- to late 1970s, Glass began to combine rhythmic elements with drones and slow harmonic rhythm, the latter influenced not only by Indian music but also by the modal jazz of hard-bop saxophonist John Coltrane. Glass also included chromatic side-stepping in many of his works, a technique used by Debussy and other composers in the early twentieth century. In addition, Glass employed tonal–modal hybrid constructions that sounded harmonically and melodically enigmatic.
Deeply Learned Generalized Linear Models with Missing Data

Deep Learning (DL) methods have dramatically increased in popularity in recent years, with significant growth in their application to various supervised learning problems.However, the greater prevalence and complexity of missing data in such datasets present significant challenges for DL methods.Here, we provide a formal treatment of missing data in the context of deeply learned generalized linear models, a supervised DL architecture for regression and classification problems.We propose a new architecture, dlglm, that is one of the first to be able to flexibly account for both ignorable and non-ignorable patterns of missingness in input features and response at training time.We demonstrate through statistical simulation that our method outperforms existing approaches for supervised learning tasks in the presence of missing not at random (MNAR) missingness.We conclude with a case study of the Bank Marketing dataset from the UCI Machine Learning Repository, in which we predict whether clients subscribed to a product based on phone survey data.
Digital Mindset Revolution as a Basis for Future-Oriented Competence Development at Coca-Cola Europacific Partners

No AccessDigital Competence and Future SkillsJul 2022Digital Mindset Revolution as a Basis for Future-Oriented Competence Development at Coca-Cola Europacific PartnersCaroline Heide, Anna SimonCaroline HeideSearch for more papers by this author, Anna SimonSearch for more papers by this authorhttps://doi.org/10.3139/9783446474284.007SectionsAboutPDF ToolsAdd to FavoritesDownload CitationTrack CitationsCopy LTI LinkPDF key 'share (en)' returned an object instead of string.FacebookTwitterEmailLinkedIn previous chapternext chapter FiguresReferencesRelatedDetails 2022Pages: 109-128Print ISBN: 978-3-446-47312-6eISBN: 978-3-446-47428-4 Copyright & Permissions© 2022 Carl Hanser Verlag GmbH & Co. KGPDF downloadLoading ...
THE DESIGN OF TRAINING CENTRES USING VIRTUAL REALITY

Appears in: EDULEARN22 Proceedings Publication year: 2022Pages: 761-768ISBN: 978-84-09-42484-9ISSN: 2340-1117doi: 10.21125/edulearn.2022.0223Conference name: 14th International Conference on Education and New Learning TechnologiesDates: 4-6 July, 2022Location: Palma, Spain
Whole slide histologic grading of breast cancer using convolutional neural networks.

e13607 Background: The Nottingham histologic grade (NHG) is a strong prognostic factor in early-stage breast cancer. It consists of nuclear pleomorphism, tubular formation, and mitotic count. We recently developed an artificial intelligence (AI) based automatic grading system. Methods: In this study, we have retrospectively evaluated 179,651 hematoxylin and eosin-stained patches extracted from 402 digitized biopsies of 338 patients with confirmed invasive ductal carcinoma diagnosis. The data was collected at Acıbadem University Hospital between 2017 and 2021. The digitized slides were manually labeled by seven pathologists before being used to train the deep learning models (DL). The pre-trained (on ImageNet) DL architectures which are EfficientNet backbone U-Net, YOLOv5, DenseNet161, and a modified VGG-11 have been fine-tuned with this study’s dataset for the tubule segmentation, nuclei detection, mitosis classification, and the nuclear pleomorphism classification tasks, respectively. Data augmentation for boosting the accuracy was done. Semantic segmentation for tubular formation, object detection for detection of nuclei, and image classification for the mitotic count and nuclear pleomorphism was also performed. Results: The AI-based algorithms have obtained reproducible histologic scores with mean F1 scores, sensitivities, and specificities as presented in Table. Conclusions: The AI-based automatic grading system is accurate in the evaluation of the components of NHG. This system will speed up the pathology workflow in the clinic, provide decision support to pathologists and mitigate the sensitivity associated with the traditional grading process. [Table: see text]
A Simple Retrieval-based Method for Code Comment Generation

Code comments can effectively help developers comprehend programs. However, it is a challenging and time-consuming task to write good comments for source code. Therefore, automatic generation of code comments is a promising research direction. Recently, researchers have leveraged neural machine translation to generate comments from source code and achieved impressive results. Another line of work has tried to exploit information retrieval (IR) techniques and showed excellent performance improvement on this task. However, current retrieval-based methods usually involve complex retrieval and editing operations, which are difficult to implement. To tackle the problems, we propose kNN-Transformer, a simple end-to-end retrieval-based code comment generation method. Our method combines a simple nearest neighbor retrieval module and a powerful transformer-based model. When generating each token, the retrieval module estimates a probability distribution depending on the current translation context rather than obtaining the retrieved samples in advance. The experiment results on four widely used public datasets (two Java datasets and two Python datasets) demonstrate that our method outperforms all the baselines, and our <tex>$k$</tex> NN retrieval module brings significant improvement when similar code snippets are available.
Influence Evaluation and Confounding Treatment between Psychological and Physical Symptoms in UPI Based on Lift-value in Association Analysis

The UPI (University Personality Inventory) is a mental health check in which respondents answer yes or no to whether the symptoms indicated by each question match their own symptoms. The UPI consists of a questionnaire asking the presence of mental symptoms and a questionnaire asking the presence of physical symptoms. In this paper, we find strong causal relationships between mental symptoms and physical symptoms in UPI by calculating lift-value used in marketing. Then, by visualizing them as a network, we try to understand the overall causal relationships between mental and physical symptoms and obtain knowledge that can contribute to health of students. We also try to remove confounding from these lift-value. Confounding is the creation of spurious causal relationships by confounding factors. This paper has significance because 1) it shows effectiveness of using marketing methods to address health sector problem, and 2) it focuses confounding problem in association rules.
MR-LGC: A Mobile Application Recommendation Based on Light Graph Convolution Networks

With the rapid growth of the number and types of mobile applications, how to recommend mobile applications to users accurately has become a new challenge. Graph convolution neural networks is a typical technique to facilitate mobile application recommendation. However, non-linear activation, feature transformation, and other operations in the existing mobile application recommendation based on graph convolution neural networks, which are used to model and characterize high-order interaction relationships between users and mobile applications, increase the difficulty of model training and lead to over-smoothing effects, reducing recommendation performance. To solve this problem, this paper proposes a mobile application recommendation method based on light graph convolution networks. In this method, firstly, a bipartite graph is used to model the interaction between users and mobile applications. Then, light graph convolution networks is utilized to smooth the features on the graph and extract the high-order connection between users and mobile applications, and three convolution layers are exploited to generate the feature representations of users and mobile applications. Finally, the inner product is used to predict the user’s preference for different mobile applications and complete the recommendation task. “Shopify app store”, a real dataset of Kaggle, is used to perform many groups of comparative experiments, and the experimental results show that the proposed method is superior to other methods.
Differential Privacy for Statistical Data of Educational Institutions

Electronic methods of managing the educational process are gaining popularity. Recently, a large number of user programs have appeared for such accounting. Based on this, the issue of personal data protection requires increased attention. The coronavirus pandemic has led to a significant increase in the amount of data distributed remotely, which requires information security for a wider range of workers on a continuous basis. In this article, we will consider such a relatively new mechanism designed to help protect personal data as differential privacy. Differential privacy is a way of strictly mathematical definition of possible risks in public access to sensitive data. Based on estimating the probabilities of possible data losses, you can build the right policy to “noise” publicly available statistics. This approach will make it possible to find a compromise between the preservation of general patterns in the data and the security of the personal data of the participants in the educational process.
Evaluating the Impact of Data Anonymization in a Machine Learning Application

The data protection impact assessment is used to verify the necessity, proportionality and risks of data processing. Our work is based on the data processed by the technical support of a Wireless Service Provider. The team of WISP tech support uses a machine learning system to predict failures. The goal of our the experiments was to evaluate the DPIA with personal data and without personal data. In fact, in a first scenario, the experiments were conducted using a machine learning application powered by non-anonymous personal data. Instead in the second scenario, the data was anonymized before feeding the machine learning system. In this article we evaluate how much the Data Protection Impact Assessment changes when moving from a scenario with raw data to a scenario with anonymized data.
Development of an Arabic HQAS-based ASAG to consider an ignored knowledge in misspelled multiple words short answers

Many traditional question answering systems depend on an Automatic Short Answer Grading (ASAG) to evaluate misspelled multiple words short answers in an Arabic Language using common edit-based algorithms such as Hamming, Levenshtein, and Jaro_Winkler, but they ignore and hide a big amount of a significant knowledge of the student answer. In this paper, we have implemented a proposed edit-based Hierarchical question answering system (HQAS) using a traversing by Breadth-First Search (BFS) within an m-ary tree to consider the ignored significant knowledge due to the misspelling at the middle of the dual-ordered incomplete answer, the misspelling at middle and the end of the intra-ordered incomplete answer, and the misspelling due to switching in words of the intra-ordered complete answer. It can differentiate among the students based on their significant hidden knowledge and show a distribution of knowledge content on different depths of the topic to determine which of the topic depths the student has the most significant knowledge.
Modular Ontology Design: A State-of-Art of Diseases Ontology Modeling and Possible Issue

The evolution of ontological engineering leaded authors to use some techniques of software engineering to design ontologies. Are obtained from these techniques the monolithic or modularized Ontologies. When is difficult to reuse some concepts of monolithic ontologies, modularized Ontologies facilitate ontology management, understandability and reuse. This paper aims to survey on ontology modularization techniques and their contribution in biomedical ontologies design. Modularization reposed on appropriated techniques and some challenges related to ontology reused, scalable querying, collaborative authoring, and distributed reasoning. For most of disease ontologies, more especially ontologies which reused IDO, these challenges are not considered, and most of them are implemented with OWL language and the novel mode to construct ontology’s purpose is to facilitate reuse and interoperability of ontologies ensured by modularization.