Miracle-dz/UniFAR-FTUs huggingface dataset:
Platform of Opportunity for Data Collection

Most data used to assess the distribution and abundance of whales are derived from scientific surveys that are designed carefully to minimize sources of variation and reduce the potential for bias. Gathering data in this fashion tends to be both time consuming and costly. Similar data, however, can be collected from platforms of opportunity, including commercial vessels, ecotourism operations and whale watch vessels. The goal of the current project was to make recommendations for data collection procedures aboard whale watch vessels so that such observations are as comparable as possible to data collected from scientific surveys. To accomplish this goal, a sightings data set was obtained from three whale watch companies on Stellwagen Bank, Massachusetts during summer 2007. Vessel and sightings data were obtained via GPS for the duration of whale watches out of Gloucester and Boston, Massachusetts. Whale sightings data were combined with vessel trackline data to calculate species-specific sightings-per-unit-effort (SPUE). These SPUE values were then compared to similar data derived from scientific surveys conducted in the same area. Additionally, sightings data from whale watches from 1994-2006 were analyzed for long-term trends in abundance and distribution. Whale watch vessels offer a large, valuable and largely untapped source of data on the distribution and abundance of whales. Whale watch data collected in a standardized fashion could contribute significantly to scientific and conservation efforts.
DISCOVERY OF COMPLEX MODEL IMPLEMENTATION PATTERNS IN SOURCE CODE

We present a method for discovering complex model implementation patterns in a hand written program code. A model implementation pattern is understood as a repeatedly used humanly meaningful transformation from a set of model elements to a piece of corresponding program code. Complex model implementation patterns are compo- sed from atomic model implementation patterns treated as black boxes. We use the same definition of model imple- mentation pattern for recognizing implementation patterns and generating program code. The method is applied for automated configuring a program code generator.
Red flags in minimally invasive thymoma resections

Despite all the efforts for the standardization of the minimally invasive procedures for mediastinal mass lesions, there still exists traps for surgeons to fall into. In this manuscript, possible red flags are discussed. This could prevent a minimally invasive surgeon from meeting possible problems related to the minimally invasive intervention. Operating on a non-thymomatous, non-myasthenia gravis (MG) thymus (useless surgery) is one of the most common problems that a minimally invasive thoracic surgeon could meet. Performing thymomectomy instead of thymothymectomy, in order to perform the operation minimally invasively, is another potential problem. Technically difficult or oncological risky operation is another red flag. These operations could be performed by some surgeons who had the chance to develop their minimally invasive surgery (MIS) capabilities. However, not so many surgeons could demonstrate similar performance. Insisting on not converting to open surgery although it is necessary. This may cause major morbidity or even mortality which is the reddest flag.
Renal Changes in Cocaine Abuse and Addiction

Abstract Cocaine is a natural alkaloid extracted from the leaves of the South American plant Erythroxylum coca or synthesized chemically. After cannabis, it is the second most frequently abused recreational substance worldwide. Cocaine can affect every tissue and organ within the human body, including the kidneys, causing tissue ischemia due to vasoconstriction, endothelial dysfunction and damage, procoagulant activity and oxidative stress with subsequent ischemic infarctions and fibrosis. The renal changes in cocaine abuse and addiction are due to rhabdomyolysis, ischemic, hypertensive, and inflammatory changes with the development of cell proliferation and fibrosis. The authors present three patients with cocaine-associated renal damage and discuss the underlying mechanisms of cocaine-induces tissue changes.
Ensemble Learning, Comparative Analysis and Further Improvements with Dynamic Ensemble Selection. (Apprentissage Ensembliste, Étude comparative et Améliorations via Sélection Dynamique)

Ensemble methods has been a very popular research topic during the last decade. Their success arises largely from the fact that they offer an appealing solution to several interesting learning problems, such as improving prediction accuracy, feature selection, metric learning, scaling inductive algorithms to large databases, learning from multiple physically distributed data sets, learning from concept-drifting data streams etc. In this thesis, we first present an extensive empirical comparison between nineteen prototypical supervised ensemble learning algorithms, that have been proposed in the literature, on various benchmark data sets. We not only compare their performance in terms of standard performance metrics (Accuracy, AUC, RMS) but we also analyze their kappa-error diagrams, calibration and bias-variance properties. We then address the problem of improving the performances of ensemble learning approaches with dynamic ensemble selection (DES). Dynamic pruning is the problem of finding given an input x, a subset of models among the ensemble that achieves the best possible prediction accuracy. The idea behind DES approaches is that different models have different areas of expertise in the instance space. Most methods proposed for this purpose estimate the individual relevance of the base classifiers within a local region of competence usually given by the nearest neighbours in the euclidean space. We propose and discuss two novel DES approaches. The first, called ST-DES, is designed for decision tree based ensemble models. This method prunes the trees using an internal supervised tree-based metric; it is motivated by the fact that in high dimensional data sets, usual metrics like euclidean distance suffer from the curse of dimensionality. The second approach, called PCC-DES, formulates the DES problem as a multi-label learning task with a specific loss function. Labels correspond to the base classifiers and multi-label training examples are formed based on the ability of each classifier to correctly classify each original training example. This allows us to take advantage of recent advances in the area of multi-label learning. PCC-DES works on homogeneous and heterogeneous ensembles as well. Its advantage is to explicitly capture the dependencies between the classifiers predictions. These algorithms are tested on a variety of benchmark data sets and the results demonstrate their effectiveness against competitive state-of-the-art alternatives
Destroy Your Classroom! Re-conceptualizing the Instructor/Student Model in Academic Libraries

ASBTRACT Pedagogies and technologies evolve, yet academic library learning spaces are unable to keep up with the change. Budgetary and space limitations, traditional teaching practices, and staid attitudes all contribute to tethered spaces hindering active learning. This is a documentation of the authors’ urgent need to break free from tradition while providing students and faculty interactive equipment meeting pedagogical needs in an un-tethered, flexible learning space in three library and non-library spaces at the University of Florida's George A. Smathers Libraries Education and Architecture and Fine Arts Libraries.
Learning Tensor-structured Dictionaries with Application to Hyperspectral Image Denoising

Dictionary learning, paired with sparse coding, aims at providing sparse data representations, that can be used for multiple tasks such as denoising or inpainting, as well as dimensionality reduction. However, when working with large data sets, the dictionary obtained by applying unstructured dictionary learning methods may be of considerable size, which poses both memory and computational complexity issues. In this article, we show how a previously proposed structured dictionary learning model, HO-SuKro, can be used to obtain more compact and readily-applicable dictionaries when the targeted data is a collection of multiway arrays. We introduce an efficient alternating optimization learning algorithm, describe important implementation details that have a considerable impact on both algorithmic complexity and actual speed, and showcase the proposed algorithm on a hyperspectral image denoising task.
Protective effect of crocetin from Crocus sativus L. on myocardial ischemia-reperfusion injury in rats

With the increase of elderly population proportion and the improvement of people’s living standard, the incidence of ischemic heart disease is increasing year by year. On recent years, although the vascular recanalization and reconstruction techniques such as thrombolysis, interventional therapy and coronary artery bypass grafting can quickly restore the coronary perfusion, the incidence of myocardial ischemia-reperfusion injury (MORO) is still high, and it has become an important cause of death in ischemic heart disease (Venardos et al., 2007). MORO can lead to the myocardial irreversible damage, and cause the myocardial morphological changes. Ot is characterized by myocardial fiber rupture, arrangement disorder, interstitial edema and inflammatory cell infiltration (Zhai et al., 2000; Wang et al., 2018). The mechanism of MORO may involve oxidative stress and inflammatory reaction (Cheng et al., 2015; Hu et al., 2015). How to effectively delay and control the occurrence and development of MORO is an urgent problem in the prevention and treatment of ischemic heart disease. Crocetin is the active substance extracted from the stigma of Crocus sativus L. , a popular traditional Chinese medicine. Previous study has proved that, crocin has obvious antiatherogenic and anti-hypertension effect (Rameshrad et al., 2018). On addition, crocetin has protective effects on myocardium, and its mechanism may be related to its anti-lipid peroxidation, inhibition of free radical production and calcium overload, and inhibition of cardiomyocyte apoptosis (Shen et al., 2006; Zhang et al., 2009). However, the action mechanism of crocetin on myocardial ischemia is not very clear. Ot is reported that, the inflammatory response and oxidative stress are involved in the occurrence and development of MORO (Vinten-Johansen et al., 2007; Yu et al., 2016). This study investigated the protective effect of crocetin on MORO in rats and the related mechanisms. The objective was to provide a experimental basis for the development of crocetin related medicines and the clinical prevention and treatment of MORO.
Efficiently and Effectively Answering Why-Not Questions based on Provenance Polynomials

The problem of answering Why-Not questions consists in explaining why the result of a query does not contain some expected data, i.e., missing answers. To solve this problem, we resort to identifying where in the query, data relevant to the missing answer were lost. Existing algorithms producing such query-based explanations rely on a query tree representation, potentially leading to different or partial explanations. This significantly impairs on the effectiveness of computed explanations. Here we present an effective, query-tree independent representation of query-based explanations, for a wide class of Why-Not questions, based on provenance polynomials. We further describe an algorithm that efficiently computes the complete set of these explanations. An experimental evaluation validates our statements
An efficient semantic recommender method forArabic text

PurposeThis paper aims to propose a new efficient semantic recommender method for Arabic content.Design/methodology/approachThree semantic similarities were proposed to be integrated with the recommender system to improve its ability to recommend based on the semantic aspect. The proposed similarities are CHI-based semantic similarity, singular value decomposition (SVD)-based semantic similarity and Arabic WordNet-based semantic similarity. These similarities were compared with the existing similarities used by recommender systems from the literature.FindingsExperiments show that the proposed semantic method using CHI-based similarity and using SVD-based similarity are more efficient than the existing methods on Arabic text in term of accuracy and execution time.Originality/valueAlthough many previous works proposed recommender system methods for English text, very few works concentrated on Arabic Text. The field of Arabic Recommender Systems is largely understudied in the literature. Aside from this, there is a vital need to consider the semantic relationships behind user preferences to improve the accuracy of the recommendations. The contributions of this work are the following. First, as many recommender methods were proposed for English text and have never been tested on Arabic text, this work compares the performance of these widely used methods on Arabic text. Second, it proposes a novel semantic recommender method for Arabic text. As this method uses semantic similarity, three novel base semantic similarities were proposed and evaluated. Third, this work would direct the attention to more studies in this understudied topic in the literature.
From Dollars to Sense: Placing a Monetary Value on Non-Cash Compensation Encourages Employees to Value Time over Money

When deciding where to work, employees may focus too much on salary and not enough on noncash benefits such as paid time-off, potentially undermining their long-term happiness. We propose a simple solution to encourage employees to recognize the value of non-cash benefits: list the financial value of non-cash compensation. Results from one archival data set (n = 42,271) and eight studies (n = 3,190) provide evidence for these ideas. First, as expected, employees who receive non-cash compensation are happier than employees who do not. Yet, prospective employees underestimate the happiness benefit of non-cash benefits. Second, and most critically, prospective employees are more likely to choose jobs with greater non-cash benefits and lower salaries when the cash value of these non-cash benefits are listed. Providing a mechanism for our results: organizations that list the cash value of non-cash benefits are perceived as caring more about their employees and about work-life balance. We document a boundary condition: listing the cash value of non-cash rewards effectively shifts employment preferences only when the starting salary of the job is sufficiently high. These findings provide the first evidence that listing the cash value of non-cash rewards increases the attractiveness of non-cash compensation.
Defining infertility: what infertility means for clinicians and clients.

Infertility is complex. It has multiple causes and consequences depending on the gender sexual history lifestyle society and cultural background of the people it affects. Partly due to its complexity and to difficulty preventing diagnosing and treating it infertility is a global public health concern. More than 80 million people -- about 8 percent to 12 percent of all couples worldwide -- are or have been infertile. Although infertility is considered by some to be primarily a womans problem men often contribute to and are also affected by it. "Infertility is not really an issue of either partner" says Dr. Timothy Farley previously a member of the World Health Organization (WHO) Task Force on Diagnosis and Treatment of Infertility and currently coordinator of the Department of Reproductive Health and Research at WHO. "Infertility is an issue of the couple. " (excerpt)
One-to-One Learning - Success Factors For Meaningful Technology Integration

iii PREFACE . iv ACKNOWLEDGEMENTS . v LIST OF TABLES . x SECTION ONE: INTRODUCTION . 1 Purpose . 1 Rationale . 5 Goals . 6 Research Questions . 7 SECTION TWO: REVIEW OF LITERATURE . 9 Background . 9 Components of a Successful One-to-One Program . 11 Planning and Technical Elements . 12 Professional Development . 13 Teacher Preparations toward Change . 16 Administrative Leadership . 18 SECTION THREE: METHODOLOGY. 22 Research Design Overview . 22 Participants . 23 Data Gathering Techniques . 24 Survey . 25 Focus Group . 25 ix Classroom Observations . 26 Data Analysis Techniques . 27 SECTION FOUR: FINDINGS AND INTERPRETATION. 29 Findings. 29 Integration Matrix and Research Questions of the Study . 30 The Relationship between Teacher Attitudes towards Technology and Technology Integration . 31 Attitudes toward Technology Integration . 32 Types of Technology Usage in the Classroom . 38 The Role of Professional Development in Technology Integration . 44 How Does the Curriculum Impact Technology Integration? 51 How Does the Administrative Leadership Impact Technology Integration? 53 Interpretations . 56 Possible Areas for Future Research . 62 SECTION FIVE: JUDGMENT AND RECOMMENDATIONS . 64 Judgment(s) . 64 Recommendation(s) . 68 REFERENCES . 74 APPENDIX A: RECRUITMENT EMAIL FOR FOCUS GROUPS . 79 APPENDIX B: INFORMED CONSENT . 80 APPENDIX C: FOCUS GROUP PROTOCOL . 82 APPENDIX D: FOCUS GROUP QUESTIONS . 83 APPENDIX E: TEACHER QUESTIONNAIRE. 84 x APPENDIX F: LEI AND ZHOU’S OBSERVATION MATRIX . 88 APPENDIX G: THE TECHNOLOGY INTEGRATION MATRIX (TIM) 2018, FLORIDA CENTER FOR INSTRUCTIONAL TECHNOLOGY. 89
On the Efficient Design and Testing of Dependable Systems Software

Modern computing systems that enable increasingly smart and complex applications permeate our daily lives. We strive for a fully connected and automated world to simplify our lives and increase comfort by offloading tasks to smart devices and systems. We have become dependent on the complex and ever growing ecosystem of software that drives the innovations of our smart technologies. With this dependence on complex software systems arises the question whether these systems are dependable, i.e., whether we can actually trust them to perform their intended functions. As software is developed by human beings, it must be expected to contain faults, and we need strategies and techniques to minimize both their number and the severity of their impact that scale with the increase in software complexity. Common approaches to achieve dependable operation include fault acceptance and fault avoidance strategies. The former gracefully handle faults when they occur during operation, e.g., by isolating and restarting faulty components, whereas the latter try to remove faults before system deployment, e.g., by applying correctness testing and software fault injection (SFI) techniques. On this background, this thesis aims at improving the efficiency of fault isolation for operating system kernel components, which are especially critical for dependable operation, as well as at improving the efficiency of dynamic testing activities to cope with the increasing complexity of software. Using the widely used Linux kernel, we demonstrate that partial fault isolation techniques for kernel software components can be enhanced with dynamic runtime profiles to strike a balance between the expected overheads imposed by the isolation mechanism and the achieved degree of isolation according to user requirements. With the increase in software complexity, comprehensive correctness and robustness assessments using testing and SFI require a substantially increasing number of individual tests whose execution requires a considerable amount of time. We study, considering different levels of the software stack, if modern parallel hardware can be employed to mitigate this increase. In particular, we demonstrate that SFI tests can benefit from parallel execution if such tests are carefully designed and conducted. We furthermore introduce a novel SFI framework to efficiently conduct such experiments. Moreover, we investigate if existing test suites for correctness testing can already benefit from parallel execution and provide an approach that offers a migration path for test suites that have not originally been designed for parallel execution.
Literation Hearing Impairment (I-Chat Bot): Natural Language Processing (NLP) and Naïve Bayes Method

A part from advances in Artificial Intelligence and Natural Language Processing, designed CHATBOT will try to understand user requests accurately, so that they do not give the wrong answer or no response. The difficulty of getting specific information about hearing impairments that can be asked properly is asking someone who understands that it still cannot be found on search engines on the internet which is a favorite tool for most people in obtaining information. By using Machine Learning and with the help of Artificial Intelligence, a question and answer (conversation) interaction mechanism is created to gain literacy knowledge that supports the educational process. Therefore, the CHATBOT application was developed in this study as a media for retrieving information about hearing loss. Using the NLP method and the Naïve Bayes Algorithm for classifications used to get input classes to I-Chat Bot, as well as to test hypotheses using the Technology Acceptance Model developed (extended). The result is an I-Chat Bot with artificial intelligence that understands user input and provides an appropriate response and produces a preferred and easy system model to be used in the search for information about required hearing impairments. This result paper also gets the value of the test accuracy with Precision 98.6%, Recall 88.75% and Accuracy 88.75%.
Griotte: Improving Code Review with Fine-Grained IDE Events

Code review is a difficult process because: (1) developers often create tangled commits, (2) a change may be scattered across many different parts of a project, (3) many changes are shadowed, (4) commit messages can be inaccurate or wrong. This work aims to propose a solution to these problems by exploiting the information provided by fine-grained IDE events. To put this solution in practice, we will develop a code review tool named Griotte in the Pharo IDE.
Multifunctional Cosmetic Containing Blueberry and Tinosorb M®-Loaded Microparticles Improves Sunscreen Performance

Purpose: We aimed to evaluate the effect of blueberry extract and microparticles (MP) on sunscreen performance of multifunctional cosmetics. Octocrylene (OCT), benzophenone-3 (BENZ-3) and Tinosorb® M (MBBT) were employed as UV filters. Methods: An in-silico modeling was used to determine the UV filters concentrations to obtain high values of sunscreen protection factor (SPF) and UVA protection factor (UVA-PF). MBBT and blueberry-loaded microparticles (MPMB+B) and MBBT-loaded microparticles (MPMBBT) were prepared by spray-drying. OCT and BENZ-3 were added in the oil phase of cosmetics. Cosmetics A and B contained MPMB+B and MPMBBT, respectively, and cosmetic C was prepared without MP. Characterization, physicochemical stability and in vitro SPF was performed. UV filters distribution in human stratum corneum (SC) for each cosmetic was performed. Anti-oxidant activity of blueberry extract was evaluated. Results: Sunscreen combination with the highest SPF was selected for formulations. Formulations A and B maintained their rheological behavior over time, unlike formulation C. In-vitro SPFs for formulations A, B and C were 51.0, 33.7 and 49.6, respectively. We also developed and validated a method for analysis of the UV filters by HPLC/ PDA suitable for the in-vivo assay. In Tape stripping test, MBBT showed SC distribution similar for all cosmetic formulations. OCT and BENZ-3 distribution to formulation A and C was also similar. Blueberry extract showed antioxidant capacity of 16.71 μg/mL equivalent to vitamin C. Conclusion: Cosmetics containing MPs presented better physical stability. Blueberry increased the photoprotective capacity of the formulations and added extra benefits due to its anti-oxidant and anti-aging properties.