Miracle-dz/UniFAR-FTUs huggingface dataset:
Fluorocarbons as volatile surfactants

Surface active properties of volatile vapors of fluorocarbon substances such as C6F14, C8F18, and C10F18 have been discovered. In a new phenomenon of capillary instability some perpetual oscillatory and rotary movements of many liquids take place due to a change of their surface tensions by 10−30% at contact with C8F18 vapor at room temperature. Features and possible applications of volatile surfactants are discussed.
Communication of Scientists Through Scientific Publications: Math-Net.Ru as a Case Study

We present a study of two scientific collaboration graphs built using data drawn from Math-Net.Ru, an all-Russian mathematical portal. One of the graphs is a citation-based scientific collaboration graph. It is an oriented graph with no loops and multiple edges. Its vertices denote authors of papers, while the arcs connecting these vertices denote that the first author has, in at least one of his papers, cited the work of the second author. The second graph is a coauthorship-based graph. It is a non-oriented graph, where the vertices denote authors, while edges connecting two vertices indicates that the two authors have coauthored at least a paper. We conduct a traditional study of the main characteristics of both graphs, such as degrees of vertices, influence of vertices, diameter, mean distance, connected components and clustering. Both graphs are found to have a similar connectivity structure – both have a giant component and several small components. Using the two graphs, we split the set of Math-Net.Ru authors. In this set, it was revealed that more than 40% of authors who have co-authored a paper with someone have not ever cited their co-authors. This means there is no deliberate plan to cite each other’s work in the journals registered in Math-
A Nudge-based Recommender System Towards Responsible Online Socializing

In recent years, the popularity of social media has been on the rise. Driven by a multitude of motivations, users have grown accustomed to sharing online most aspects of their lives. This self-disclosure has not only proven to be dangerous for peoples’ privacy and security but also harmful to their personal, professional and intimate relationships. However, in the age of social media, it seems improbable for people to completely discontinue using social media platforms such as Facebook, Twitter and Instagram. Hence, there is an urgent need to find a balanced compromise between online sociability and privacy. In this work we propose a platform to mitigate the dangers of self-disclosure through the use of a personalized harm-aware recommender system. Specifically, the recommender system balances the requirements for privacy protection with the users’ need to share with their social circles. To achieve this, the platform starts by evaluating the risks of disclosing the personal information and then, if necessary, proceeds to recommend to the user how to reduce that risk. While the evaluation of the risk is done in an objective manner, personalization is of the essence since users have different preferences and sharing needs. As such, when performing the recommendation, the systems will provide personalized nudge-based recommendations, raising the users’ awareness of the privacy issues stemming from self-disclosure.
TexTrolls: Identifying Trolls on Twitter with Textual and Affective Features

The growing suspicious online users, that usually are called trolls, are one of the main sources of hate, fake, and deceptive online messages. Some agendas are utilizing these harmful accounts to spread incitement tweets, and as a consequence, the online users get deceived. The challenge in detecting such accounts is that they conceal their real identities, adding more difficulty to identify them using just their social network information. In this paper we propose affective and lexical information -based models to detect the online trolls such as those that were discovered during the US 2016 presidential elections. Our approach is mainly based on features that take into account topic information and profiling features to identify the accounts from their way of writing tweets. We inferred the topic information in an unsupervised way and we show that coupling them with the affective and lexical features enhanced the performance of the proposed models. We find that the proposed profiling features perform the best comparing to the other features. Our approach shows superior results in comparison to several strong baselines.
Decentralized Management of Patient Profiles and Trajectories through Semantic Web Agents

The usage of healthcare data for analytics and patient applications has increased in recent years opening a number of technical, ethical and scientiﬁc challenges. Among these, those related to the management of personal and sensitive health data have been addressed through decentralized solutions for patient data, often implemented and modelled using distributed agents and semantic technologies. In this paper 1 we present a technical summary of our previous works in this area, comprising eﬀorts to: (i) use ontology models to represent patient trajectories, (ii) employ agent-based architectures to model and employ decentralized patient data exchanges, (iii) deﬁne agent cooperation and negotiation strategies for healthcare data interactions, (iv) adopt semantic data models for privacy-aware agents, and (v) implement multi-agent systems for real-time healthcare data processing.
Drug Ontology for the Public Mexican Health System

With the emergence of the COVID-19 pandemic, a growing need has emerged for well-structured medical knowledge bases that are accessible to physicians, specialists, pharmacists, patients, and the general public This article describes the process of development and evaluation of an Mexican Drug Ontology4 with information from the “Basic Table and Catalog of Medicines” published by the Secretary of Public Health The resulting ontology is composed by 64 classes, 5 object properties, 18 data properties, and has a value ALCQ(D) of “DL Expressivity” measure The evaluation ontology was carried out in two ways: through the competence of the model and through the review of the quality criteria Copyright © 2020 for this paper by its authors Use permitted under Creative Commons License Attribution 4 0 International (CC BY 4 0)
Hypernet semantics of programming languages

Comparison is common practice in programming, even regarding a single programming language. One would ask if two programs behave the same, if one program runs faster than another, or if one run-time system produces the outcome of a program faster than another system. To answer these questions, it is essential to have a formal specification of program execution, with measures such as result and resource usage. This thesis proposes a semantical framework based on abstract machines that enables analysis of program execution cost and direct proof of program equivalence. These abstract machines are inspired by Girard’s Geometry of Interaction, and model program execution as dynamic rewriting of graph representation of a program, guided and controlled by a dedicated object (token) of the graph. The graph representation yields fine control over resource usage, and moreover, the concept of locality in analysing program execution. As a result, this framework enjoys novel flexibility, with which various evaluation strategies and language features, whether they are effects or not, can be modelled and analysed in a uniform way.
Measuring the Impact of Library Dependency on Maintenance

Reusing code from open-source libraries is a useful practice for developers to avoid implementing the same functionalities multiple times. However, when a library is used in another software product, it creates a dependency that may spread the security vulnerabilities of the library to the product. Most package managers have dependency managers which only perform a binary evaluation of the dependencies. Thus, developers have no information about how much products depend on a library or how much eﬀort would be needed to replace a dependency. In this research, we propose a way to measure the degree of library dependency, as well as how much eﬀort would be required to replace the usage of a library with another one. We leverage existing coupling metrics and revisit them in the context of library dependencies. We present two metrics to measure the coupling generated by dependencies: method invocation and aggregation coupling, and brieﬂy discuss the next steps.
Visually Exploring SPARQL Endpoints with Murmuration

We present Murmuration, which is a tool to discover various paths between resourced in a Linked Data dataset serviced by a SPARQL endpoint. Murmuration was developed to run entirely on a client. While this computation is heavy on the client, we provide a reasonably smooth experience by generating a series of queries that cover the different possible paths between two resources. This allows us to use, for each query, a callback to display the results as they come along. This approach also reduces the complexity of queries that the SPARQL endpoint has to process. Future work will look into scalable approaches for distributed computing, as we recognize the need for exploring multiple Linked Data datasets as a whole.
Evaluating a usability, user experience and accessibility checklist for assistive technologies for deaf people in a context of mobile applications

Assistive Technologies (ATs) for Deaf Community are technologies that support Deafs to carry out their daily activities, contributing to their independence and quality of life. However, not all ATs are presented with quality for their users, resulting in abandonment, frustration or incorrect use. To obtain an AT with higher quality, this work presents an AT evaluation technology for Deaf Community, which covers the perspectives of Usability, User eXperience (UX) and Accessibility for mobile devices, entitled Usability, User eXperience, and Accessibility Checklist for Deaf Assistive Technology (UUXAC-DAT). UUXAC-DAT aims to support professionals of Information Technology (IT) during the assessment of ATs in software development projects. To assess UUXAC-DAT, a case study was carried out, with nine IT professionals. Perceptions of professionals about the ease of use, perceived usefulness and intent for future use of UUXAC-DAT were obtained. All professionals were interviewed and the results were analyzed qualitatively. This paper also presents some improvements in UUXAC-DAT, regarding its structure, description of verification items and presentation.
Photochemical Mechanism of the Formation of Nanometer-Sized Copper by UV Irradiation of Ethanol Bis(2,4-pentandionato)copper(II) Solutions

The formation of colloidal metallic copper and of nanometer films on quartz and silicon substrates by irradiation of bis(2,4-pentanedionato)copper(II) ethanol solutions with UV light was investigated. The 254-nm-light absorption in the bulk of the solution causes formation of stable colloidal copper through two consecutive steps that could be distinguishable at low light intensity. In the first step, the starting complex releases one ligand and forms of copper(I) species that quickly release the second ligand and evolve to copper(I) alkoxides. In the second step, these latter undergo reduction to colloidal copper by the released acetylacetone with an electronic transfer process. Acetone sensitizes reduction to metallic copper with radical pathways. Features of the process depend on the acetone concentration so that either nanoparticles of colloidal copper or fine powder can be fabricated from the solution. In the presence of quartz or silicon substrate, placed in the suitable apparatus, the light absorpti.
Hierarchical assembly of zeolite nanoparticles into ordered macroporous monoliths using core-shell building blocks.

Latex beads are used as a combined template and porogen in the fabrication of monolithic silica containing a hierarchy of pores. Prefabricated core/shell particles, prepared by the layer-by-layer assembly of zeolite (silicalite) nanoparticles onto spherical latex templates, are assembled into macroscopic close-packed structures. Calcination removes all organic components and causes densification of the inorganic structure producing a macroporous zeolite in which both the pore size and wall thickness can be varied.
GamiCSM: relating education, culture and gamification - a link between worlds

The potential of gamification to improve users' motivation and engagement influenced many researchers and professionals to analyse its effects in educational settings. While some studies focus on adapting game elements according to demographic and behavioural information of the user profile, few of them explore (or even consider) cultural factors. These cultural factors play an essential role in our societies' development. Thus, this work proposes and evaluates a representative model to understand better the relationship between cultural factors and gamification within educational domains, namely the Gamification for Cultural Studies Model (GamiCSM). Through a qualitative approach, we map Hofstede's cultural dimensions (i.e., power distance, individualism/collectivism, uncertainty avoidance, masculinity/femininity, long/short-term orientation, and indulgence/restraint) with a Taxonomy of Gamification Elements for Education (TGEEE), a recent model for gamification elements for educational environments. Furthermore, we adapted a survey to evaluate the resultant model with eight domain experts in gamification and education. Based on this evaluation, we are able to propose a starting model, containing some additional refinements and improvements. Thus, the main contributions of this work are: (i) the first model to relate game elements and cultural dimensions within educational domains and (ii) a state-of-the-art empirical study intersecting culture, gamification and education.
Does this persona represent me?: investigating an approach for automatic generation of personas based on questionnaires and clustering

Personas are fictional representations of an end user based on data collected from a specific target audience. The creation of personas takes time, because part of the process is manual, which causes difficulties during the stages of data generation and analysis, if the data sample is large. The present work aims to investigate the results of the automatic generation of personas through a questionnaire and the Clustering method. Initially, a questionnaire was applied in order to outline the profile of students in the Computer Science and Software Engineering courses at the university Federal of Ceará. 130 responses were obtained from this applied questionnaire. The automatic generation of personas used the collected responses as a database, these data were applied to the Clustering method. From Clustering, using a specific tool for this purpose, it was possible to generate four personas automatically. In relation to the data generated from the personas, it was possible to identify some characteristics, such as: (a) course choice factor: the opportunity to work with new technologies, influence of family and friends and the possibility of living abroad; (b) factor of withdrawal from the course: lack of identification with the course, learning difficulties in the disciplines and finance; and, (c) area of interest: software engineering, human-computer interaction and computer graphics. A new questionnaire was applied in order to validate the personas generated in relation to credibility, empathy and similarity. Based on the results obtained, it was observed that two of the four personas generated achieved greater prominence in relation to the validation criteria, showing that the personas look like real people, students think like the personas and share similar interests.
Data visualization on focus: exploring communicability of dashboards generated from BI tools

Technological evolution has led to the development of an increasingly connected society, in which the volume and variety of data generated grow constantly. In this context, data visualization becomes a challenge, since it has the potential to facilitate users' understanding of the analyzed data. Nowadays, there are several tools to support the construction of visualizations, each one offering resources for the construction of dashboards from a set of data. Considering this scenario and based on the application of semiotic inspection and communicability assessment methods, this study presents an analysis of three visualization tools available on the market (Tableau, Power BI and Zoho Analytics), in order to understand the interaction of users with the developed visualizations and to identify possible existing communicability problems. We used the 2010 Brazilian Census data to build dashboards in these three tools as a method of validating our study. The results show a greater number of communication disruptions in the interaction with Tableau and Power BI in relation to Zoho Analytics, indicating that Zoho Analytics allows the user a greater understanding of the result of the actions taken.
Comparing users' perception of different chatbot interaction paradigms: a case study

In this work, we used the results of applying the Semiotic Inspection Method (SIM) to popular chatbots[12, 13], which yielded eleven strategies for conveying features to users, as well as six sign classes used for designing their interaction. We conducted user studies comparing two prototype chatbots with same features and different approaches to interaction: one using Natural Language Processing, and other using the sign classes and strategies to guide the user interaction. After that, we interviewed the participants, asking about their preferred aspects of each chatbot and their opinions regarding some of these aspects, and, later, analyzed the results. These point to the effectiveness of the strategies and sign classes. Then, we discuss users' perceptions of different ways of interacting with chatbots and their communicative strategies.
Chemistry of 1-iodopropane on copper(110): formation, bonding, and reactions of adsorbed propyl groups

The adsorption and thermal decomposition of 1-iodopropane on Cu(110) has been investigated under ultrahigh vacuum conditions using temperature-programmed desorption/reaction spectroscopy and reflection-Adsorption infrared spectroscopy. Adsorption is molecular at 100 K, but at 110 K the 1-iodopropane decomposes to form surface-bound propyl groups and coadsorbed iodine and the propyl groups assume an orientation such that the methyl group is upright. The subsequent chemistry of the propyl moieties is dominated by the evolution of propylene at 200-230 K and H 2 at 315 K
Engineering management of starter cultures in study of temperature of fermentation of sour-milk drink with apiproducts

The article considers the solution of problematic issues of engineering management of poly fermentation in the study of fermentation temperature of sour-milk drink with apiproducts. In the development of fermented dairy products, the components that are part of them, changes in their composition, and properties in the interconnection are considered as a technological system. The authors took into account that food technologies based on the use of the pure culture of one microorganism are limited by the capabilities of its fermentation system systems, the ultimate goal may not be achieved even by changing the conditions and parameters of cultivation. To successfully carry out fermentation processes in the technological system, a combination of cultures, associations of microorganisms with a wide range of fermentation products in contrast to one culture is promising to use. All experimental samples on a set of indicators prevailed control ones. The leader was a sample fermented with yeast with an equal ratio of cultures at a temperature of 38 – 40 °C. The authors found that the set of indicators of finished products for the production of sour-milk drinks with a complex of apiproducts, it is necessary to choose a three-strain poly fermentation product with a congruent ratio of cultures and set optimal fermentation regimes 39 ±1oC for 5.0 ±0.3 hours.
Modified Pooled Cohort Atherosclerotic Cardiovascular Disease Risk Prediction Equations in American Indians

Page 5 of 14 Modified Pooled Cohort Atherosclerotic Cardiovascular Disease Risk Prediction Equations in American Indians Nawar M. Shara, PhD1,2,3*, Sameer Desale, MS1, Barbara V. Howard, PhD1,2,3, Zeid Diab1,4, Wm. James Howard, MD5, Lyle G. Best, MD6, Wenyu Wang, PhD7, Elisa T. Lee, PhD7, Richard B. Devereux, MD8, Xiyao Ai, MS9, Jason G. Umans, MD, PhD1,2,3 1MedStar Health Research Institute, Hyattsville, MD, United States. 2Georgetown-Howard Universities Center for Clinical and Translational Science, Washington, DC, United States. 3Georgetown University, Washington, DC, United States. 4Virginia Commonwealth University, Richmond, VA, United States. 5MedStar Washington Hospital Center, Washington, DC, United States. 6Missouri Breaks Industries Research Inc., Eagle Butte, SD, United States. 7College of Public Health, University of Oklahoma, Oklahoma City, OK, United States. 8Weill Cornell Medicine, New York, NY, United States. 9American University, Washington, DC, United States.
Cryptocurrency Exchange Market Prediction and Analysis Using Data Mining and Artificial Intelligence

This thesis discusses the ability of using data mining and artificial intelligence techniques in order to predict crypto currency exchange market prices. The prices of crypto currency are virtual unpredictable and pose numerous liabilities and risks for its investors such as huge sums of money lost in unforeseen variations of crypto currency prices. Furthermore, there are some challenges when it comes to crypto currency prediction such as fake news. Cryptocurrency exchange market have a lot of fake news articles which can affect the market prices by fooling people either to buy or to sell. This research aims to tackle the unpredictability of fluctuating crypto currency prices by researching peer-reviewed articles and determine the applicability of AI algorithm and data mining and its implications for the cryptocurrency market. This thesis will also apply the algorithms in order to determine how applicable it is to predict crypto currency exchange market prices. Furthermore, this thesis discusses blockchain technology in general since this will be the building block for the student research. This thesis is trying to answer the following questions: 1. To which extend can we predict Cryptocurrency exchange market prices using well known prediction algorithms? 2. How accurate is the prediction of the prices? Crypto currency data has been downloaded from different sources and cleansing and normalization methods were used on them. The thesis has examined different algorithms that can be used for prediction such as Moving Average, Auto ARIMA, kNN, Linear Regression and Long Short Term Memory (LSTM). One of the best algorithms in terms of the result is the Long Short Term Memory (LSTM) since it is based on recurrent neural networks which uses loop as a method to learn from heuristics data. For the future improvements can be done to this work by integrating other related data such as social media news using sentimental analysis. Furthermore, more advanced algorithms can be used such as Support Vector machine (SVM) and XGBoost.