Miracle-dz/UniFAR-FTUs huggingface dataset:
Obesidade Abdominal, Resistência à Insulina e Hipertensão: Impacto sobre a Massa e a Função do Ventrículo Esquerdo em Mulheres Abdominal Obesity, Insulin Resistance and Hypertension: Impact on Left Ventricular Mass and Function in Women

Summary Objective: To evaluate the relationship between central obesity, hyperinsulinemia and arterial hypertension with left ventricular mass and geometry in women. Methods: This study included 70 women (35-68 years), divided into four groups according to the presence of central obesity and hypertension. Visceral fat area was determined. Blood glucose and plasma insulin were determined before and two hours after an oral 75g glucose load and the patients were submitted to cardilogical evaluation. Results: Compared to NT-OB, HT-OB presented higher levels of plasma insulin at 2h-OGTT (127.5 ± 73.0 vs 86.8 ± 42.7 µU/ml; p = 0.05), and reduced E wave/A wave ratio (E/A) (0.8 ± 0.1 vs 1.2 ± 0.3; p < 0.05). Compared to NT-NO, HT-NO showed higher insulin levels before glucose load (7.46 ± 3.1 vs 4.32 ± 2.1 µU/ml; p < 0.05), higher HOMAr (1.59 ± 0.72 vs 0.93 ± 0.48 mmol.mU/l²; p = 0.006), higher leptin level (19.1 ± 9.6 vs 7.4 ± 3.5 ng/ml; p = 0.028), greater VF area (84.40 ± 55.7 vs 37.50 ± 23.0 cm²; p = 0.036), increased IVSTd (9.6 ± 1.2 vs 8.2 ± 1.7 mm; p <0.05) and (LVM/height) (95.8 ± 22.3 vs 78.4 ± 15.5 g/m; p < 0.05). Multiple linear regression analysis showed age, BMI and fasting glucose as determinants on LVM/height (R² = 0.59; p < 0.05). Conclusion: Our results indicate an association among hypertension, central obesity and left ventricular hypertrophy through increases in sympathetic activity and insulin resistance. (Arq Bras Cardiol 2007;89(2):77-82)
Password typo correction using discrete logarithms

As passwords remain the main online authentication method, focus has shifted from naive entropy to how usability improvements can increase security. Chatterjee et al. recently introduced the ﬁrst two typo-tolerant password checkers, which improve usability at no security cost but are technically complex. We look at the more general problem of computing an edit distance between two strings without having direct access to those strings — by storing the equivalent of a hash. We propose a simpler algorithm for this problem that is asymptotically quasi-optimal in both bits stored and exchanged, at the cost of more computation on the server.
Does your attention allocation affect how motion sick you can get?

Virtual reality (VR) applications desire maximum vection which is often accompanied by unwanted symptoms of Visually Induced Motion Sickness (VIMS). We report an experiment examining visual attention allocation in the central and peripheral visual field among VIMS susceptible and resistant participants, when exposed to large coherently rotating scene. Results supported our hypothesis that individual VIMS susceptibility is negatively associated with visual attention re-allocation during vection. Findings may enrich our understanding of VIMS and provide potential solutions to optimize vection without causing VIMS.
Evaluating Participatory Modeling Methods for Co‐creating Pathways to Sustainability

The achievement of global sustainability agendas, such as the Sustainable Development Goals, relies on transformational change across society, economy, and environment that are co‐created in a transdisciplinary exercise by all stakeholders. Within this context, environmental and societal change is increasingly understood and represented via participatory modeling for genuine engagement with multiple collaborators in the modeling process. Despite the diversity of participatory modeling methods to promote engagement and co‐creation, it remains uncertain what the extent and modes of participation are in different contexts, and how to select the suitable methods to use in a given situation. Based on a review of available methods and specification of potential contextual requirements, we propose a unifying framework to guide how collaborators of different backgrounds can work together and evaluate the suitability of participatory modeling methods for co‐creating sustainability pathways. The evaluation of method suitability promises the integration of concepts and approaches necessary to address the complexities of problems at hand while ensuring robust methodologies based on well‐tested evidence and negotiated among participants. Using two illustrative case studies, we demonstrate how to explore and evaluate the choice of methods for participatory modeling in varying contexts. The insights gained can inform creative participatory approaches to pathway development through tailored combinations of methods that best serve the specific sustainability context of particular case studies.
Detection of bacterial contamination in filled and dried biscuit products of young children

The grain and Bakery products widely as a potential natural stores to various kinds of microorganism transmitted food due to consist of essential ingredient like carbohydrate, fats ,proteins and minerals that have a role of microorganism propagation in products like biscuit made from cereal grains and kept under incorrect storage condition. This study was carried out on 100 local and imported samples of filled and dried biscuit collected from Baghdad governorates supermarkets randomly during ganuary to july (2019) to determine the microbial load of biscuits. Have been described bacterial isolates using a specific media and methods of diagnostic. These samples were analyzed for total Staphylococcus aureus (TSA), total coliforms (TC) and total Enterobacteriaceae (TE). The results showed that (TSA) and (TC) counts were significantly differences (p< 0.05) in local market samples compared to those of super markets samples in both filled and dried biscuit (Table 5) while (TE) counts were significantly higher (p< 0.05) in local market filled samples compared to other types of biscuit (Table 5). Was higher than the acceptable limits. This study demonstrates that biscuit in local markets are more contaminated than in biscuits from super markets in both filled and dried samples of biscuit.
Towards an Evaluation Process around Active Learning based Methods

This Research to Practice Full Paper proposes a novel incremental approach in teaching a Software Engineering related course, underpinned by active-learning methods. Teaching a Software Engineering related course for undergraduate students is a challenging task due to extremely frequent changes that appeared in programming paradigms and in software process development methodologies. In this everchanging domain, which presents us with opportunities and challenges every single day, it might be difficult and almost impossible for teachers to predict what are those knowledge that will be of use to students, on the long term, maybe even for five years from now. This rapid growth in the software development evolution has led to a gap between how teachers do knowledge transfer to students and how students acquire these knowledge. To bridge this gap there is a need to change how we teach these subjects. Thus, the new theory of education suggests that learner should be in the center of the learning process and the instructors playing an advising and facilitating role. A shift in education theory to a more student-centered approach using active learning is recommended because this approach has its own role to make the students creative and competent in their study. In this respect, active learning's main drive is to put the responsibility of learning at the hands of the learners themselves and to delegate the role of facilitator to the teacher. In this paper we provide novel approaches in teaching an undergraduate Software Engineering related course at the Babeş-Bolyai University. Its contribution is threefold: firstly, we introduce a new learning process design which selects active learning methods intertwining in teaching this course; secondly, we aim to study the impact of applying active learning methods on students' grades over the three academic years. Thirdly, we investigate students perceptions, their feedback and learning experiences on the use of applying active learning methods. Also, the paper reviews challenges and constraints that we faced while trying to teach this course. The analysis results show the effectiveness of our approach. Also, our students have expressed a high level of satisfaction, and in a survey, they indicated that the skills they learned in the course are highly applicable to their careers, facilitating their interviews for the engagement within IT companies.
Transfert de matiere entre un fritte metallique et un liquide application aux electrodes poreuses percolees

Cette these se rapporte aux electrodes poreuses percolees (e. P. P. ) constituees de metal fritte et travaillant dans des conditions de limitation diffusionnelle. Les electrodes etudiees repondent a l'une ou l'autre des configurations: axiale, radiale, combinant ecoulement longitudinal et aspiration ou soufflage. Le coefficient volumetrique de transfert de matiere entre l'electrolyte et le metal (nickel) fritte est determine a l'aide de la methode electrochimique mettant en jeu la reduction des ions ferricyanure en milieu alcalin. Le transfert de matiere en l'absence d'ecoulement est lui-aussi etudie, ainsi que la question de la chute globale de potentiel dans le cas des e. P. P. Axiales. Le dernier chapitre fait le point de la litterature sur le transfert de matiere aux materiaux pour electrodes poreuses percolees, en integrant les resultats obtenus pour les frittes de nickel
Deep Entity Classification: Abusive Account Detection for Online Social Networks

Online social networks (OSNs) attract attackers that use abusive accounts to conduct malicious activities for economic, political, and personal gain. In response, OSNs often deploy abusive account classiﬁers using machine learning (ML) approaches. However, a practical, effective ML-based defense requires carefully engineering features that are robust to adversarial manipulation, obtaining enough ground truth labeled data for model training, and designing a system that can scale to all active accounts on an OSN (potentially in the billions). To address these challenges we present Deep Entity Classiﬁ-cation (DEC) , an ML framework that detects abusive accounts in OSNs that have evaded other, traditional abuse detection systems. We leverage the insight that while accounts in isolation may be difﬁcult to classify, their embeddings in the social graph—the network structure, properties, and behaviors of themselves and those around them—are fundamentally dif-ﬁcult for attackers to replicate or manipulate at scale . Our system:
Mixing of Hamiltonian Monte Carlo on strongly log-concave distributions: Continuous dynamics

We obtain several quantitative bounds on the mixing properties of an “ideal” Hamiltonian Monte Carlo (HMC) Markov chain for a strongly log-concave target distribution π on R. Our main result says that the HMC Markov chain generates a sample with Wasserstein error in roughly O(κ log(1/ )) steps, where the condition number κ = M2 m2 is the ratio of the maximum M2 and minimum m2 eigenvalues of the Hessian of − log(π). In particular, this mixing bound does not depend explicitly on the dimension d. These results significantly extend and improve previous quantitative bounds on the mixing of ideal HMC, and can be used to analyze more realistic HMC algorithms. The main ingredient of our argument is a proof that initially “parallel” Hamiltonian trajectories contract over much longer steps than would be predicted by previous heuristics based on the Jacobi manifold.
Towards Designing an Experience-based Course around Innovation Bootcamps — A Cohort Study

Context: This full research paper presents an experience-based course designed around a semester-long external Innovation Bootcamp activity. Objective: We sought to evaluate the impact of Innovation Bootcamp on students’ learning and startup formation. To this end, we measured how the Innovation Bootcamp affected students' perceived challenges related to technical, soft project management skills and the startup formation mindset. Method: We conducted a cohort study comprising questionnaires, interviews, and focus groups with both students and stakeholders participating in the Innovation Bootcamp. In total, 44 students participated in the questionnaires run before and after the Innovation Bootcamps during both academic years. Moreover, we conducted four individual interviews (student cohort 1), four focus group interviews (student cohort 2), and six individual interviews with different stakeholders participating in the Innovation Bootcamp during both years. Results: We find that perceptions of challenges regarding soft and project management skills drop, while perceptions regarding technical skills challenges do not vary during the course. Students exhibit increased motivation to engage in startup formation following close collaboration with external stakeholders only after developing their first minimum viable product. Contribution: The overall outcomes of the study contribute to validating a new course design model, which will help develop a course framework to help future educators, researchers, and practitioners adopt Innovation Bootcamp activities within a software engineering-focused experience-based course.
Plagiarism detection based on blinded logical test automation results and detection of textual similarity between source codes

This Research to Practice Full Paper presents in this paper. Finding logical errors is the most difficult skill for students from all sorts of disciplines that involve computer programming. Unfortunately, because of this difficulty, some students resort to plagiarism. Plagiarism corrupts the evaluation process. Several tools are used in different researches for this purpose. Recent research has defined a taxonomy of the most relevant types of plagiarism that can be found in source codes. The Hybrid Framework was made to map the student plagiarisms using NLP tecnhics and software test automation techniques for automatic exercise correction with tools available for this purpose. This framework has been tested in the laboratory with promising results.
The Effects of Physical Exertion on Eyewitness Memory among RCMP Cadets

Previous research concerning the accuracy of eyewitness lineup identifications suggests various factors impact cognitive performance. Most relevant to the present study was the effect of physical exertion on facial recognition memory, as well as the factors that contribute to incorrect misidentifications of an innocent person from a lineup. The present study considered both of these factors, and executed a replication and extension of Hope et al.’s (2012) work, which examined the effect of physical exertion on recall and recognition memory among Canadian police officers. Unlike Hope et al.’s research, the present study included a target-absent condition to specifically consider the effects of physical exertion on misremembering. In order to do so, RCMP cadets from Regina Depot Division were assigned to either a Physical Exertion or Control group. Participants in the Physical Exertion group completed a physically demanding task to exert them to a similar degree as they would be exerted in the field. All participants viewed a video that depicted a theft, and then completed a recognition memory task. Non-exerted participants were found to be more accurate in identifying the culprit in a target present lineup than exerted participants, therefore highlighting the negative impact of exertion on cognitive performance. However, exerted participants showed greater accuracy in correctly rejecting target absent lineups than non-exerted participants. Possible implications of metamemory on response accuracy were explored.
FIDEO: Food Interactions with Drugs Evidence Ontology

In this paper we propose the Food Interactions with Drugs Evidence Ontology (FIDEO), an ontology used for annotation and retrieval of scientific articles about food-drug interactions. Currently available ontologies address mainly drugdrug interactions, but much less attention has been given to clinically significant food-drug interactions. This work proposes an extension of a drug interaction ontology following the METHONTOLOGY methodology with the goal of representing potential drug interactions with foods, food components and food categories. To evaluate the proposed formal ontological model, we discuss the results of populating the ontology with information from manually annotated abstracts and from a compendium.
A Comparison between Performances of Turbulence Models on Simulation of Solitary Wave Breaking by WCSPH Method

The modeling of Solitary wave breaking is an important subject in coastal and marine engineering, because the damage associated with tsunamis is related to their wave breaking and run-up on the shoreline. In this paper a space-averaged Navier–Stokes approach has been deployed to simulate the Solitary wave breaking on a plane slope. The developed model is based on the smoothed particle hydrodynamic (SPH) method which is a pure Lagrangian approach and can handle large deformations of the free surface with high accuracy. Since breaking waves are characterized by high distortion, the turbulence modeling has been of major concern in such simulations. For this study, the large eddy simulation (LES) model was chosen as the turbulence model to couple with the weakly compressible version of the smoothed particle hydrodynamics (WCSPH) method to simulate the Solitary wave breaking on a plane slope. In order to investigate the Effects of turbulence models on numerical simulations of wave breaking, the results of present study were compared with experimental results and numerical data found in the literatures. The results shown that turbulence modeling have a strong influence on the quality of the results. Furthermore, capability of the WCSPH method along with a LES approach to simulate the Solitary wave breaking on a plane slope was compared with result of ISPH method in the literatures. Finally, it is shown that the WCSPH coupled with LES model performs better than ISPH method.
A Systematic Identification of Pedagogical Conversational Agents

Conversational agents have been established to support educational practices, solving students’ questions, recommending teaching materials, increasing the motivation of students who have little interest in certain content, among others. Although many studies are discussing the establishing of conversational agents, there is no global understanding of what has been investigated in this area. There are some previous secondary studies that have attempted to map the conversational agents used for educational purposes (i.e., pedagogical conversational agents). However, these studies are limited to include primary studies published in specific sets of conferences and journals. Therefore, an overview of the current state of the art associated with pedagogical conversational agents has not been produced yet. This research paper aims to contribute to the theme, through the presentation of planning, conduction, and results analysis of a systematic mapping on pedagogical conversational agents. We address the following questions: (i) in which areas have conversation agents been investigated? ; (ii) at which education levels are conversational agents used? ; (iii) which mechanisms do conversational agents use to interact with students? We also present some discussions about research opportunities that can be explored in future works. This study aims to contribute to the topic of pedagogical conversational agents, helping researchers to have a deeper understanding of what has already been done and to guide their research to subjects not explored yet.
Developing Computational Thinking and Reading and Writing Skills through an Approach for Creating Games

Research and full paper. Digital games are increasingly a part of our daily lives and are already considered teaching and learning tools. However, its production and documentation is a very complicated task that requires programming skills and knowledge from different areas. This has hampered the development of games in the classroom environment. On the other hand, some teachers have proposed this activity to enable their students to learn school content in a more meaningful way and, consequently, improve learning rates. An alternative for the adoption of game-based learning is an unplugged approach for creating games based on natural language, in which students learn the fundamentals of computing in a playful way and without the use of computers. In this context, this article presents an approach that proposes the creation of games in an unplugged way by using texts produced by students, thus favoring the development of computational thinking and reading and writing skills in the classroom. Also, it may support the students' interest in the computing area by motivating them to enter a higher education course or pursue a career in that area. The results of the exploratory study show that the proposed approach can favor the development of computational thinking, as well as motivate the production of texts, developing students' reading and writing skills, therefore contributing to the improvement of learning assessments.
Literature Survey: Machine Translation

Machine Translation lies at the heart of Natural Language Processing, which in turn is one of the of the core components of the Ar-tiﬁcial Intelligence discipline. In this report, we have a brief look at the legacy approaches to machine translation, while also examining the path that Machine Translation is headed on, with the newest approach to this classic problem - namely neural machine translation This report is a brief introduction to the major works in the ﬁeld of neural machine translation.
Work-in-Progress: Emergent Themes from "High Impact" Role Model and Mentor Narratives

This work-in-progress study continues prior work aimed at increasing the access of underrepresented minorities (URMs) in STEM fields to role models and mentors through values-focused, narrative profiles. We developed narrative profiles in which URM role models and majority mentors share their personal and professional experiences. Profile content follows from our work in identifying what URMs in STEM value in role models and mentors, allowing the profiles to serve as a vehicle to encourage URM students working toward STEM degrees. In this paper, we address the following research question: what themes emerge from the comparative analysis of "high-impact" role-model and mentor profiles? Specifically, we use emergent thematic analysis to explore similarities and differences across role model and mentor profiles that are considered "high-impact. " We see that high-impact profiles tend to contain longer, more complete narratives that mention challenges in adjusting to their university, self-transcendent reasons for giving back to others and viewing failure as a process. These results will inform future profile development processes. By further refining these windows into the lives of role models and mentors, we can expand the definition of success in STEM and create emergent pathways of resilience for URM students. Through integrating profiles into the recruitment process, orientation, courses, and other campus resources, we can help students connect with role models and mentors as they transition to college life and continue into their future careers. We believe the flexibility and adaptability of this intervention has great potential for impact across STEM contexts.