Reviewerly/exHarmony huggingface dataset:
Covid-19 Detection Using Machine Learning and Deep Learning

The current COVID-19 pandemic threatens human life, health, and productivity. AI plays an essential role in COVID-19 case classification as we can apply machine learning models on COVID-19 case data to predict infectious cases and recovery rates using chest x-ray. Accessing patient’s private data violates patient privacy and traditional machine learning model requires accessing or transferring whole data to train the model. In recent years, there has been increasing interest in federated machine learning, as it provides an effective solution for data privacy, centralized computation, and high computation power. In this paper, we studied the efficacy of federated learning versus traditional learning by developing two machine learning models (a federated learning model and a traditional machine learning model) using Keras and TensorFlow federated, we used a descriptive dataset and chest x-ray (CXR) images from COVID19 patients. During the model training stage, we will try to identify which factors affect model prediction accuracy and loss like activation function, model optimizer, learning rate, number of rounds, and data Size, we kept recording and plotting the model loss and prediction accuracy per each training round, to identify which factors affect the model performance, and we found that softmax activation function and SGD optimizer give better prediction accuracy and loss, changing the number of rounds and learning rate has slightly effect on model prediction accuracy and prediction loss but increasing the data size did not have any effect on model prediction accuracy and prediction loss. finally, we build a comparison between the proposed models’ loss, accuracy, and performance speed, the results demonstrate that the federated machine learning model has a better prediction accuracy and loss but higher performance time than the traditional machine learning model.
STEM Librarians’ Presence on Academic Profile Websites

This study explores STEM librarians’ presence on academic profile websites (APWs) at American and Canadian research universities. It was found that Google Scholar Citations was the most used APW, followed by ResearchGate, ORCiD, and academia.edu. The rate of profile without a publication list in ORCiD of these librarians was significantly lower than other users across this platform, suggesting that it may be necessary for ORCiD to improve the publication addition function in order to help other users to increase their presence. The social networking functions were not well adopted, as such, ResearchGate’s composite metric, RG Score, adds little new information to bibliometric indicators.
Text Sentiment Analysis for Intelligent Transportation Systems by Using Web-Based Traffic Data: A Descriptive Study

Transportation systems serve the people in essence, but the modern intelligent transportation systems (ITSs) failed to be concerned about public opinions. For the completeness of ITS space, it is necessary to collect and analyze the public wisdom and opinions. However, only a few studies focused on the field of transportation, which failed to meet the stringent requirements of safety, efficiency, and information exchange of intelligent transportation systems (ITSs). We propose traffic sentiment analysis (TSA) as a new tool to tackle this problem, which provides a new perspective for modern ITSs. Methods and models in TSA are proposed in this work, and the advantages and disadvantages of rule- and learning-based approaches are analyzed based on web data. Practically, we applied the rule-based approach to deal with real problems, presented an architectural design, constructed related bases, demonstrated the process, and discussed the online data collection. Two cases were studied to demonstrate the efficiency of our method: the “yellow light rule” and “fuel price” in China. Our work will help the development of TSA and its applications. This research work proposing a novel classification system for our future enhancement. To overcome this issue, we use a fuzzy extension known as the Fuzzy Naive Bayes classifier for the opinion mining process that generalizes the meaning of an attribute so it does not have exactly one value, but a set of values to a certain degree of truth. This fuzzy naive Bayesian classification is consisting of a hybrid classifier bringing together Fuzzy Set Theory and a Naive Bayes classifier.
Evaluating the Impact of Features for Twitter Spammers Detection

Social networking sites connect users around the globe. Twitter is one of the widely used social networking platforms for users to discuss social concerns, share opinions, and read the news. The popularity of the Twitter platform has also attracted spammers to disperse insignificant spam information that diverts legitimate users’ opinions. The detection of spammers is essential to disrupt their further activities. There is the primary role of feature components to detect spamming users effectively. The present work evaluates the impact of features on the performance of the classifiers to detect Twitter spammers. The impact of features is evaluated by incorporating the major feature categories of textual features, user profile-based features, URL-based features, and miscellaneous features. The different feature categories are combined to train the four machine learning classifiers of random forest (RF), decision tree (DT), support vector machine (SVM), and naïve Bayes (NB). The performance of the classifiers with different feature categories is determined with evaluation metrics of precision, recall, f-measure, and accuracy. The experiments with Social Honeypot dataset indicate the random forest as the most efficient classifier for all the tested feature combinations.
Unsub in Real Life: Using Unsub as Part of Serials Decisions and Negotiations

This presentation introduced attendees to the benefits and limitations of Unsub, a data analysis tool designed by OurResearch. In this presentation, OurResearch co-founder, Heather Piwowar, demonstrated the use of Unsub for analyzing usage and cost data on a library’s “Big Deal.” The other two presenters, Jessica Harris of the University of Chicago, and Eric Schares of Iowa State University, discussed how they used the tool at their libraries to make collection development decisions for their libraries’ journal subscriptions.
Introduction

During the first two decades of the twenty-first century, the concept of human development has become firmly established as the ultimate goal of national and global economic and social policies. The idea that a person’s skills tend to determine their economic status and standard of living was first discussed by the social philosophers of the eighteenth and nineteenth centuries. Individuals need to decide whether to invest time, effort, and money into a university degree, and then to choose between various specializations which differ in their monetary and non-pecuniary returns. Policymakers are interested in allocating funding to primary, secondary, and tertiary education sectors to maximize the social gains from education, laying foundations for the national economic performance in the future and reducing socioeconomic inequality. Modern literature in the field places much emphasis on using causal inference and policy evaluation. The chapter also presents an overview of the key concepts discussed in this book.
EXPERIENCE REPORT ON THE INTEGRATION OF INSTRUCTIONAL DESIGN AND ACCESSIBILITY IN THE CONSTRUCTION OF DIDACTIC MATERIAL

C. Cesario Spigel , C. Amato , M.A. Eliseo, I.F. Silveira, V. Farinazzo Martins Universidade Presbiteriana Mackenzie (BRAZIL)
On the recoverability of geomechanical model complexities from surface deformation data above the Groningen gas field

&amp;lt;p&amp;gt;Since the start of production in 1968 in the Groningen gas field (Netherlands) considerable land subsidence (&amp;gt;30&amp;amp;#160;cm) has occurred above the field. The PS-InSAR technique provides surface deformation data with a high spatial and temporal resolution. However, using this data to obtain a detailed solution for the reservoir compaction distribution is not trivial because of: (1) uncertainties in the data (e.g., atmospheric noise), (2) soil deformation (e.g., clay shrinkage and peat oxidation), and (3) non-uniqueness of different reservoir compaction distributions leading to identical surface signals. This study is part of the DeepNL/Subsidence project, where we aim to identify the drivers of subsidence in the Groningen field by assimilating geodetic time series in geomechanical models of the subsurface, to disentangle the signals from deep (reservoir and overburden) and shallow (soils) drivers. In this study we perform a sensitivity analysis to investigate the level of complexity that can be resolved for the reservoir geometry, for the reservoir compaction, and for the mechanical properties of the reservoir and overburden.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We employ a semi-analytical mechanical model for the Groningen subsurface using the PSGRN/PSCMP code by Wang et al. (2006). The model geometry is based on the geological model constructed by the producer (NAM, 2020). We simplify the geology by using lateral uniform material properties and we construct multiple versions of the mechanical model with different levels of complexity (of reservoir geometry, reservoir compaction and mechanical properties). The synthetic surface deformation results produced by the models are used in an inversion to investigate whether the applied complexities can be recovered from inverting the surface signal. Information theory analysis is used to determine how much of the originally applied information is recovered by the inversion and to define a threshold complexity. Model versions that are more complex than the threshold complexity lead to non-uniqueness inhibiting a robust solution. We finally analyse the spatial characteristics of the surface deformation results, to determine whether the InSAR data uncertainty warrants an additional lowering of the complexity threshold.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;NAM (2020). Petrel geological model of the Groningen gas field, the Netherlands. Open access through EPOS-NL. Yoda data publication platform Utrecht University.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Wang, R., Lorenzo Mart&amp;amp;#237;n, F.,&amp;amp;#160;Roth, F.&amp;amp;#160;(2006): PSGRN/PSCMP - a new code for calculating co- and post-seismic deformation, geoid and gravity changes based on the viscoelastic-gravitational dislocation theory. Computers and Geosciences, 32, 4, 527-541. https://doi.org/10.1016/j.cageo.2005.08.006&amp;lt;/p&amp;gt;
A workflow to standardize collection and management of large-scale data and metadata from environmental observatories

&amp;lt;p&amp;gt;The Watershed Function Scientific Focus Area (WFSFA) is a U.S. Department of Energy research project that seeks to determine how mountainous watersheds retain and release water, carbon, nutrients, and metals. The WFSFA maintains a community field observatory at its primary field site in the East River, Colorado. The WFSFA collects diverse environmental data and has developed a &amp;amp;#8220;Field-Data&amp;amp;#8221; workflow that standardizes data management across the project, from field collection to laboratory analysis to publication. This workflow enables the WFSFA to address data quality and management challenges that environmental observatories face.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Through this workflow, the WFSFA has increased the use of the data curated from the project by (1) providing detailed metadata with unique identifiers for samples, locations, and sensors, (2) streamlining the data sharing and publication process through early sharing of data internally within the team and publication of data on the ESS-DIVE repository following curation, and (3) adopting machine-readable and FAIR community data standards (Findability, Accessibility, Interoperability, Reusability).&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We describe an example application of this workflow for geochemical data, which utilizes a community geochemical data standard for water-soil-sediment chemistry (https://github.com/ess-dive-community/essdive-water-soil-sed-chem) developed by Environmental Systems Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE). This data standard is designed to standardize geochemical data, metadata, and file-level metadata, and was applied to WFSFA geochemical data, including ICP-MS, Isotope, Ammonia-N, Anion, DIC/NPOC/TDN datasets. This ensures important metadata is contained within the data file, such as precision of data analysis, storage and sample processing information, detailed sample names, material information, and unique identifiers associated with the samples (IGSNs). This metadata is essential to understand and reuse data products, as well as enable machine-readability for future model applications. Detailed examples of the standardized geochemical data types were created and are now being used as templates by WFSFA researchers to standardize their geochemical data. The adoption of this community geochemical data standard and more broadly the Field-Data workflow will improve the findability and reusability of WFSFA datasets.&amp;amp;#160;&amp;lt;/p&amp;gt;
Improving Evolutionary Algorithms and Optimization Techniques using Hybridization

Evolutionary computation is a widely used optimization technique. These algorithms have been applied to numerous problems such as mathematical complex problems and real world problems. The good point about these algorithms is that they give benefits to people who are doing research on very challenging problems that need optimization. Some of the benefits that researchers get are that they are simple to implement and provide a good response to any type of input when the environment is changing at a rapid pace. In real world there are large combinatorial problems that are to be optimized and cannot be solved with exact answers. When these evolutionary algorithms are combined with local search, they provide improved approximation using local search technique. The bad thing about local search used along is that it gets stuck in local optimum, and in this situation the objective function of the problem can be enhanced when the changes are made out of the current state neighbors during the search. This problem gives a way to hybridize the evolutionary algorithms with some other search and optimization methods and also the local search algorithms. Current research paper identifies different ways that can be used for efficiently hybridizing the evolutionary algorithms with other search and optimization techniques to provide optimized solutions to engineering problems especially, the Wireless Sensor Networks (WSN).
Direct gamma estimation method in Fringe projection profilometry

Fringe projection profilometry has been widely applied to three-dimensional measurement. However, the nonlinear effect of the projector leads to errors in the unwrapped phase in the phase-shift method. In this paper, we propose a direct gamma estimation method. Theoretical derivation shows that the gamma factor is related to the three-step phase-shifted fringe patterns and the ideal unwrapped phase. The unwrapped phase after Gaussian low-pass filtering is taken as the initial estimate of the ideal unwrapped phase. We correct those abnormal values after calculating the gamma factor. The corrected gamma factor is used to inverse gamma correct the captured fringe patterns, and then the gamma-corrected unwrapped phase is obtained by phase demodulation and phase untangling from the inverse gamma corrected fringe patterns. Then we perform iterative operations on the gamma factor and ideal unwrapped phase. We consider the gamma-corrected unwrapped phase as the new ideal unwrapped phase, recalculate and update the new gamma factor until the gamma factor converges to a stable, desired state. Our method only needs to project and collect three frames of fringe pattern, which meets the high-speed measurements requirement. The experimental result of the face mask demonstrates that our method can effectively reduce the nonlinear phase errors.
Deep Learning Framework Mindspore and Pytorch Comparison

Deep learning has been well used in many fields. However, there is a large amount of data when training neural networks, which makes many deep learning frameworks appear to serve deep learning practitioners, providing services that are more convenient to use and perform better. MindSpore and PyTorch are both deep learning frameworks. MindSpore is owned by HUAWEI, while PyTorch is owned by Facebook. Some people think that HUAWEI's MindSpore has better performance than FaceBook's PyTorch, which makes deep learning practitioners confused about the choice between the two. In this paper, we perform analytical and experimental analysis to reveal the comparison of training speed of MIndSpore and PyTorch on a single GPU. To ensure that our survey is as comprehensive as possible, we carefully selected neural networks in 2 main domains, which cover computer vision and natural language processing (NLP). The contribution of this work is twofold. First, we conduct detailed benchmarking experiments on MindSpore and PyTorch to analyze the reasons for their performance differences. This work provides guidance for end users to choose between these two frameworks.
Fuzzy Sets and Fuzzy Logic

In the sixties, Zaheh introduced the concept of fuzzy sets. Since its inception more than 30 years ago, the theory and methods of fuzzy sets have developed considerably. The concept of the fuzzy set is a generalization of the concept of the ordinary set. It introduces vagueness by eliminating the clear boundary, defined by the ordinary set theory, between full nonmembers and full members. Problems in engineering could be very complex and involve various concepts of uncertainty. The use of fuzzy sets in engineering has been quite extensive during this decade. The area of fuzzy control is one of the most developed applications of fuzzy set theory in engineering. Fuzzy controllers have been created for the control of robots, aircraft autopilots, and industrial processes, among others.
The Text Analysis Framework for Interactive Statistical Classification Service

Statistical classification serves as the basis of national statistics such as the population census and economic census. Not only is it being used as the main basics for national policy establishment and execution (e.g SME support and employment stability), it is also used as a major classification criterion from the perspective of corporate analysis services such as economic, company, investment. Therefore, we innovate the statistical classification system by AI approaches, and develop accurate and timely interactive statistical services. In this paper, we introduce the methods for standardizing unstructured data processing, building a statistical classification system based on machine learning, and establishing an interactive statistical service. With this system, it is possible to implement reliable classification models in a minimum amount of time and without a complex knowledge building, only using machine learning-based methodologies and accumulated public and internal examination data. In addition, it can be implemented a web service that provides functions such as classification auto classification coding and auto-completion, and also provides a knowledge search that serves responses to the classification knowledge.
Fusing atomic W states with Rydberg superatom

Abstract Quantum entanglement is the basic resource of quantum information processing and quantum computation, especially multi-partite entanglement. The simple and efficient generation of multi-partite entanglement has always been one of the hot topics of scholars. In this paper, we propose a qubit-loss-free fusion scheme for generation of large-scale entangled W states with Rydberg superatom. By sending two superatoms belonging to a k -atom W states and a l -atom W states, respectively, into a cavity, we may obtain a ( k + l )-atom W states via detecting the cavity state after interaction. The numerical simulation analysis shows that the fidelity is robust against both spontaneous emission of superatoms and decay of the cavity. In addition, the feasibility analysis indicates this scheme can also be realized in experiment.
Data Circulation Platform Based on Privacy Computing

In order to protect the privacy and security of data circulation platform, this paper will focus on the research of privacy computing, mainly discuss the basic principles and significance of privacy computing, then design data circulation platform based on privacy computing, and finally introduce different application scenarios of privacy computing based on the platform. Through the study of this paper, privacy computing can improve the privacy and security of data circulation platform, improve the credibility of the platform, and also facilitate the platform to play a role in different scenarios, can improve work efficiency.
Logistics Robot Path Planning Based on Adaptive Ant Colony Algorithm

As for traditional ant colony algorithm using in mobile robot path planning, there are problems such as the convergence speed and searching ability existing unbalance, falling into local optimum easily and so on. An adaptive ant colony algorithm is proposed. To improve the global search capability of the ants, pseudo-random state transfer rules are used, while angle-inspired information is introduced into the state transfer probabilities, improving the distance heuristic information function to enhance the directionality of algorithm search; The global pheromone update mechanism and pheromone volatility coefficients are redesigned. The adaptive pheromone volatility coefficient update mechanism is proposed, which dynamically adjusts the pheromone volatility coefficient adaptation algorithm according to the number of iterations, so that the algorithm also maintains a strong global search capability at a later stage. By comparing this paper's algorithm with other algorithms through simulation experiments, the global search ability and convergence speed of this paper's algorithm are significantly improved.
FORMATION OF A MODEL OF PROFESSIONAL COMPETENCES OF A MODERN SPECIALIST

The article discusses the effective integration of ICT in education based on UNESCO recommendations. The substantial component of the methodological system is digital transformation. The matrix structure of ICT-competencies is considered. A matrix of professional development of a modern specialist in the field of ICT-competencies is proposed, in accordance with the recommendations of UNESCO, which is aimed at improving work, allowing to advance to international positions within the EAEU and the CIS.
Publishing fast and slow: A path toward generalizability in psychology and AI

Artificial intelligence (AI) shares many generalizability challenges with psychology. But the fields publish differently. AI publishes fast, through rapid preprint sharing and conference publications. Psychology publishes more slowly, but creates integrative reviews and meta-analyses. We discuss the complementary advantages of each strategy, and suggest that incorporating both types of strategies could lead to more generalizable research in both fields.
Health Monitoring in a Smart Home Environment

In the past few years, within the Home Environment context, the TV set has regained its place together with the Smart Home (SH) concept. In this very dynamic framework, the use of health monitoring devices combined with typical SH appliances and having the TV set as the main interaction point, could allow the entire home environment to monitor the consumer&#x0027;s health status. This paper presents an initial architecture integrating health monitoring devices within the SH environment through a HbbTV - enabled TV set.