Reviewerly/exHarmony huggingface dataset:
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Artificial OrgansVolume 46, Issue 6 p. 1211-1212 UPCOMING MEETING Upcoming meetings First published: 18 May 2022 https://doi.org/10.1111/aor.14250Read 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 No abstract is available for this article. Volume46, Issue6June 2022Pages 1211-1212 RelatedInformation
COMBINATION OF KNN AND PARTICLE SWARM OPTIMIZATION (PSO) ON AIR QUALITY PREDICTION

The increase in the use of energy sources causes air pollution. The Air Pollutant Index (API) is information about the air quality of a place and at a certain time. API has several parameters, namely SO2, PM10, NO2, O3, and CO. In this study, the KNN method was used to assist categorize air quality. However, all training data were used during the classification process with KNN causes a long prediction process. Another problem with KNN is difficult to determine the optimal value of the K parameter in KNN. The Particle Swarm Optimization (PSO) method can be used for problems on KNN. Therefore, the aim of this study is to predict air quality based on the API by combining the KNN-PSO method. The dataset used is the API dataset for the DKI Jakarta area 2017-2019 totaling 1075 data. The results showed the accuracy for the KNN-PSO method was 98.42% with a precision value of 97.75% and a recall value of 98.13%. To further analyze the results on the combined method, the results of this study were compared with the KNN method only. The results obtained from the KNN method are lower than the KNN-PSO method. So it can be concluded that the KNN-PSO method is great and robust in air quality classification or prediction.
Many phish in the C$\mathcal{C}$: A coexisting‐choice‐criteria model of security behavior

Abstract Normative decision theory proves inadequate for modeling human responses to the social‐engineering campaigns of advanced persistent threat (APT) attacks. Behavioral decision theory fares better, but still falls short of capturing social‐engineering attack vectors which operate through emotions and peripheral‐route persuasion. We introduce a generalized decision theory, under which any decision will be made according to one of multiple coexisting choice criteria. We denote the set of possible choice criteria by . Thus, the proposed model reduces to conventional Expected Utility theory when , while Dual‐Process (thinking fast vs. thinking slow) decision making corresponds to a model with . We consider a more general case with , which necessitates careful consideration of how , for a particular choice‐task instance, one criterion comes to prevail over others. We operationalize this with a probability distribution that is conditional upon traits of the decisionmaker as well as upon the context and the framing of choice options. Whereas existing signal detection theory (SDT) models of phishing detection commingle the different peripheral‐route persuasion pathways, in the present descriptive generalization the different pathways are explicitly identified and represented. A number of implications follow immediately from this formulation, ranging from the conditional nature of security‐breach risk to delineation of the prerequisites for valid tests of security training. Moreover, the model explains the “stepping‐stone” penetration pattern of APT attacks, which has confounded modeling approaches based on normative rationality.
Internet of Things Devices, Citizen Science Research and the Right to Science

Although lay participation has long been a feature of scientific research, the past decades have seen an explosion in the number of citizen science projects. Simultaneously, the number of low-cost network connected devices collectively known as Internet of Things devices has proliferated. The increased use of Internet of Things devices in citizen science exists has coincided with a reconsideration of the right to science under international law. Specifically, the Universal Declaration of Human Rights and the International Covenant on Economic Social and Cultural Rights both recognise a right to benefit and participate in the scientific process. Whilst it is unclear whether this right protects participation by citizen scientists, it provides a useful framework to help chart the ethical issues raised by citizen science. In this chapter, we first describe the origins and boundaries of the right to science, as well as its relevance to citizen science. We then use the findings of a scoping review to examine three main ethical and legal issues for using Internet of Things devices in citizen science.
ITiCSE 2022 call for participation

The 27th annual ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) will take place in Dublin, Ireland, hosted by University College Dublin (UCD), with support from the Technological University of Dublin (TU Dublin).
Library Management System

Abstract: A Library Management System is a project that tries to create an automated and computerised version for a library so that the daily work of a library can be managed and monitored easily and efficiently. Earlier, the librarian used to manage the whole work in manual mode in the form of files and record books. Also, the process of adding new books, new students, issuing and returning books had to be managed in a manual manner which is very slow and inefficient. The library management system resolves this problem and provide a better solution to this. It provides a user-friendly interface application to the librarian where he can do all the operations of a library very easily. The application mainly consists of three modules which are admin module, librarian module and student module. The admin module will be managed by the system administrator. He manages the overall functioning of the application. The librarian module will be accessed by the librarian. He can perform various operations inside the application such as add new students, new books to the database, issuing and returning of books, updating student’s details, book’s details, generating weekly/monthly reports etc. The student module can be accessed by the registered students only. The operations that can be performed by the student includes: view all books available in the library, search the availability of a particular book, number of books he has issued, overall fine he has to pay etc. These three modules are interconnected with each other and also with the database. The application is built using Java technology and Sql database. Keywords: Library Management System, Module, Java, Sql, Database
Big Data Analytics

Essentials of machine learning algorithms are briefly discussed. High-dimensional nonparametric inference and inference for low-dimensional parameters in the presence of big data are introduced. Decision-based prediction vs. causal based predictions and aggregate vs. disaggregate data analysis as well as issues of combining data from different sources are also discussed.
Design and Implementation of Graduate Student Integrated Information Management System — Degree Management

Degree management is considered the kernel of the Degree Informationization Construction. With the continue population and development of information and technology, the government put great emphasis on college graduation student, effective and standard graduate student degree management system has been the mainstream direction of college graduation management system, as well as thenecessary means and effective ways to educate college graduation student. Firstly, this paper introduced the application status of graduate comprehensive information management system at home and abroad. Citing Anhui University of technology’s graduation student education, we have analysised and researched the reality problems and emergency situations in the process of degree management, proposing reasonable solutions. Next, basing on Anhui University of Technology graduation degree management rules and degree management requirements, we designed graduation comprehensive information management system—degeree management software. The system has been devided five subsystem: thesis proposal management subsystem, mid-term examination subsystem, degree management subsystem,scientific information subsystem and fostering plan subsystem. Lastly, this paper not only introduced the test cases of the system but also summarized the system and put forward the prospect. This system is based on B/S(Browse/Server) and used OOD(Object-Oriented Design) method. In addition, software reuse and MVC(Model + View + Hibernate) pattern has been used in the paper. The degeree management software has improved the development efficiency greatly and reduced the development cost.Users can learn and master the software by its simple interactive inference.
Pragmatic approach for Twitter analysis and perform prediction

Study of Pragmatics is a very important part of Natural Language Processing (NLP). Pragmatic analysis allows to analyze what the given text basically means. The aim is to draw inferences from the given text. This paper reports the work on pragmatics analysis performed for the Twitter data set. To understand the intended meaning behind a tweet, sentiment analysis, polarity and contextual information is used. Various machine learning algorithms including Logistic Regression, Decision Tree, Random Forests, and Support Vector Machines are used for implementation. A comparative analysis of evaluation using this algorithm is also reported.
Model-driven Machine Learning Approach for Mobility Classification in Intelligent 5G Network

Channel information is essential to unleash the benefits of 5G New Radio (NR) by enabling network intelligence that adapts transmissions to users&#x2019; channels. In this paper, we propose model-driven feature design and use support vector machine (SVM) to classify users&#x2019; speed range. Our model-driven features are designed based on stochastic channel modeling. Multiple features are derived from time-domain cross-correlation and time-domain auto-correlation function of the sounding reference signals. The classifier is trained and verified with extensive standard compliant simulation channels at different SNR levels and speeds, and attains greater than 90% accuracy.
Modeling Responsibly Toward a Fair, Interpretable, and Ethical Machine Learning for the Social Sciences

Researchers in the social sciences have become increasingly more cognizant of many of the machine learning models now available for analytical purposes.Of particular note is the advent of generative models-such as generative adversarial networks and variational autoencoders-that allow for the representation, reconstruction, and creation of novel data closely resembling a source dataset.Alongside this development is the consideration of "black box" methods, such as neural networks that allow for highly accurate predictions without concomitantly interpretable outputs.This paper examines one such example of the use of such complex models and the potential benefits they have to offer to the social sciences.Through this example and a discussion of historical ethical issues in statistical practice, the issue of ethical practice is considered with a solution proposed in the form of interpretable model characteristics (e.g., bias) through Shapley values and Local Interpretable Model-Agnostic Explanations.Several recommendations are offered for maintaining an ethical practice of data analysis and machine learning in the social sciences.
Design and research of college students' certification guide scheme based on WeChat applet

As the number of college students increases, the identity of college students gradually depreciates. The ensuing topic of "certification fever" also enables college students to enhance their value by obtaining various skill certificates. In recent years, the development environment for using WeChat mini-programs to develop various program requirements has become more and more mature. Tencent mini-programs have simple operations, take up almost no memory, and are born with the operation of WeChat. The scheme and design of the college student certification guide provides the basis for the subsequent developers. The applet is developed using a three-tier architecture, using HTML+CSS+JavaScript development language, and MySQL as the database. The WeChat applet transfers data to the backend interface in the form of json messages. The test guide applet for college students realizes the functions of various certificates classification, test information query, test forum and question bank, and realizes a friendly test guide user interface.
Improved Consistency Training for Semi-Supervised Sequence-to-Sequence ASR via Speech Chain Reconstruction and Self-Transcribing

Consistency regularization has recently been applied to semi-supervised sequence-to-sequence (S2S) automatic speech recognition (ASR). This principle encourages an ASR model to output similar predictions for the same input speech with different perturbations. The existing paradigm of semi-supervised S2S ASR utilizes SpecAugment as data augmentation and requires a static teacher model to produce pseudo transcripts for untranscribed speech. However, this paradigm fails to take full advantage of consistency regularization. First, the masking operations of SpecAugment may damage the linguistic contents of the speech, thus influencing the quality of pseudo labels. Second, S2S ASR requires both input speech and prefix tokens to make the next prediction. The static prefix tokens made by the offline teacher model cannot match dynamic pseudo labels during consistency training. In this work, we propose an improved consistency training paradigm of semi-supervised S2S ASR. We utilize speech chain reconstruction as the weak augmentation to generate high-quality pseudo labels. Moreover, we demonstrate that dynamic pseudo transcripts produced by the student ASR model benefit the consistency training. Experiments on LJSpeech and LibriSpeech corpora show that compared to supervised baselines, our improved paradigm achieves a 12.2% CER improvement in the single-speaker setting and 38.6% in the multi-speaker setting.
Exploring Supervised Techniques for Automated Recognition of Intention Classes from Portuguese Free Texts on Agriculture

Technical and scientific knowledge is vast and complex, particularly in interdisciplinary fields such as sustainable agriculture, which is available in several interrelated, geographically dispersed and interdisciplinary online textual information sources. In this context, it is essential to support people with computational mechanisms that allow them to retrieve and interpret information in an appropriate way, as communication in these software systems is typically asynchronous and textual. User’s intention recognition and analysis in textual documents results in benefits for better information retrieval. However, intentions are expressed implicitly in texts in natural language and the specificities of the domain and cultural aspects of language make it difficult to process and analyze the text by computer systems. This requires the study of methods for the automatic recognition of intention classes in text. In this article, we conduct extensive experimental analyses on techniques based on language models and machine learning to detect instances of intention classes in texts about sustainable agriculture written in Portuguese. In our methodology, we perform a morphological analysis of the sentences and evaluate four Word Embeddings techniques (Word2Vec, Wang2Vec, FastText and Glove) combined with four machine learning techniques (Support Vector Machine, Artificial Neural Network, Random Forest and Transfer Learning). The results obtained by applying the techniques proposed in a database with textual information on sustainable agriculture indicate promising possibilities in the recognition of intentions in free texts in Portuguese language on sustainable agriculture.
PAS: A Position-Aware Similarity Measurement for Sequential Recommendation

The common item-based collaborative filtering framework becomes a typical recommendation method when equipped with a certain item-to-item similarity measurement. On one hand, we realize that a well-designed similarity measurement is the key to providing satisfactory recommendation services. On the other hand, similarity measurements designed for sequential recommendation are rarely studied by the recommender systems community. Hence in this paper, we focus on devising a novel similarity measurement called position-aware similarity (PAS) for sequential recommendation. The proposed PAS is, to our knowledge, the first count-based similarity measurement that concurrently captures the sequential patterns from the historical user behavior data and from the item position information within the input sequences. We conduct extensive empirical studies on four public datasets, in which our proposed PAS-based method exhibits competitive performance even compared to the state-of-the-art sequential recommendation methods, including a very recent similarity-based method and two GNN-based methods.
Assessment of the digital activity of universities: Russian educational platform of the «Yuryat»

The problem of the modern system of higher education in Russia and the world is the search for an adequate and high-quality digital model for enhancing professional training in higher education using the remote access system. The pandemic has exacerbated and hastened finding an answer. Russian universities have found an answer in the use of digital educational platforms. One of the most widespread and adequate to the needs of higher education was the «Yuryat» platform. The goal is to assess digital educational activity in Russian universities from 10/26/2020 to 10/25/2021. The research is based on the primary data of «Yuryat.Statistics» and published empirical data. Statistical and quantitative methods were used to study the problem., in particular, the integral index of digital educational activity (IDEA) was used in general form, calculated as an additive convolution of its constituent sub-indices. In turn, each sub-index Ii allows quantitatively characterize different types of digital activity when using the resource, they were calculated using the formula for linear scaling of Pi values. The main result of the study was based on the indicators developed by the authors on the state of digitalization of higher education in Russia from 10/25/2020 to 10/26/2021. conclusion about the lack of digital educational activity of universities according to the data of «Yuryat.Statistics». The average IDEA value = 0.082 means that the average digital activity is 8.2% of the maximum level equal to 1. The country is characterized by low student activity in the use of tests and media resources (ISKK and IUEM sub-index), as well as in the creation of digital courses (IPDC sub-index) on average across Russian universities The study carried out in this work on 318 universities in Russia made it possible to conclude that there is high interuniversity differentiation in terms of the level of development of digital transformation of the educational process, both in terms of individual characteristics and on a territorial basis. The results of the study proved that the main centers of digital transformation of higher education are Moscow, St. Petersburg and Yekaterinburg. It is obvious that the system of professional training in the country in the university sphere is faced with the question of strengthening work on the formation of technologies and platforms for remote access.
FIRM CHARACTERISTICS, DEBT MATURITY STRUCTURE, AND INNOVATION PERFORMANCE OF SMALL FIRMS

Based on a large sample of 497 small Slovenian manufactory firms and applying structural equation modeling, we investigate the relationship between six different firm characteristics (firm legal status, firm age, assets tangibility, cash flow, long-term financing of long-term assets and inventories ratio, and the quick ratio) and debt maturity structure of small firms in terms of short-term debt and long-term debt.In addition, we uncover the relationship between debt maturity structure and innovation performance of small firms.The results of our study point to the importance of external financial sources for firm innovation performance and to the relevance of specific firm characteristics to explain debt maturity structure of small firms.
Knowledge graph semantics

Oh dear, there's that word again - "semantics!" Isn't that what doomed that Semantic Web thing and led to knowledge graphs instead? In fact, many of the same problems, and particularly problems with interoperability, arise again for KGs, and thus we must explore the old problem in this new area. This is even more important when we start to explore the "personal knowledge graph (PKG)," that is, the ability to have private and public information combined in KG technology. In this talk, I discuss how knowledge graphs, PKGs, linked data and, yes, semantics are all critically linked and why the latter is still relevant to the growth and scaling of knowledge graphs into the future - and specifically to the ability to extract better data from them.
A New Focus on Research Strategies in Determining the Structure of the Motivational Field of Scientific and Pedagogical Staff of Agricultural Universities

The article presents new research technologies that determine the features of motivational field structure of scientific and pedagogical staff (SPS) in the system of higher agricultural education in Russia based on mathematical statistics tools.Theoretical review of publications confirms the high relevance of the study of the motivational field by new tools: using mathematical apparatus based on principal components, estimates of structural similarity and amount of information.In the empirical part of the study, a comparative analysis of the motivational field structure of young scientific and pedagogical employees of agrarian universities (age group under 30) and the older generation (over 50) was conducted.For young workers the value of satisfaction from professional activity is defined due to qualitative improvement of labor processes and in general organization of labor on the basis of optimal ratio of labor costs and wages with preservation of good relations in labor collective.For the older age group -the value of constant increase in knowledge and interest in professional development with an active deterrent of the relationship with the immediate supervisor and the predominance of interest in work over wages.The new research strategy increases the objectivity of the conclusions and the high interpretability of the results of sociological research.