Miracle-dz/UniFAR-FTUs huggingface dataset:
Recursos de informática na terapia fonoaudiológica de crianças do espectro autístico

Background: the use of computerized technology in language therapy with children of the autistic spectrum. Aim: to assess the interference of using computers and specific programs during language therapy in the functional communicative profile and socio-cognitive performance of children of the autistic spectrum. Method: 23 children with ages ranging between 3 and 12 years were individually video recorded prior to and after a set of 10 regular language therapy sessions (i.e. a total of two video samples per subject) using computerized games according to the child's choice. Results: the following expressions were used by the therapists to describe the children's performance during the use of computers: more attentive, more communicative initiatives, more eye contact, more interactive, more verbalizations, more attention and more action requests. Qualitative and quantitative progresses were identified, although without statistical significance. Those progresses were observed after a time period that is smaller than the usually applied to this kind of comparison and it seems to be a promising result. Conclusion: more controlled associations and comparisons were not possible due to the groups' heterogeneity and therefore more consistent conclusions are not possible. It was clear that the subjects presented different reactions to the use of computerized resources during language therapy.
Predictive Models of Hard Drive Failures Based on Operational Data

Hard drives are an essential component of modern data storage. In order to reduce the risk of data loss, hard drive failure prediction methods using the Self-Monitoring, Analysis and Reporting Technology attributes have been proposed. However, these methods were developed from datasets not necessarily representative of operational systems. In this paper, we consider the Backblaze public dataset, a recent operational dataset from over 47,000 drives, exhibiting hard drive heterogeneity with 81 models from 5 manufacturers, an extremely unbalanced ratio of 5000:1 between healthy and failure samples and a realworld loosely controlled environment. We observe that existing predictive models no longer perform sufficiently well on this dataset. We therefore selected machine learning classification methods able to deal with a very unbalanced training set, namely SVM, RF and GBT, and adapted them to the specific constraints of hard drive failure prediction. Our results reach over 95% precision and 67% recall on a one year real-world public dataset of over 12 million records with only 2586 failures.
Expecting the Unexpected

Like most major life decisions, the decision whether to have children is fraught with uncertainty. How would your child turn out? What would your relationship with her be like? How much would you enjoy parenting? As such, it would seem a paradigm of a decision amenable to philosophers’ favorite tool for making decisions under uncertainty—decision theory. Very roughly, according to decision theory, you should gauge how good or bad the possible outcomes of each option are, and weight these “utilities” by how likely you think these outcomes are, in order to calculate how much “expected utility” would result from each option. The decision-theoretic recommendation is that you choose the option with the highest expected utility. Since the purpose of this formal tool is to provide a lamp to guide us through the fogs of the future, we might have hoped that it would help with the decision about whether to have kids. These would be false hopes, according to L. A. Paul, in an article that has perhaps had more impact outside of academia than any other philosophy essay in recent years (Paul 2015). Paul argues that parenting decisions are 2
PRED: Periodic Region Detection for Mobility Modeling of Social Media Users

The availability of massive geo-annotated social media data sheds light on studying human mobility patterns. Among them, periodic pattern, \ie an individual visiting a geographical region with some specific time interval, has been recognized as one of the most important. Mining periodic patterns has a variety of applications, such as location prediction, anomaly detection, and location- and time-aware recommendation. However, it is a challenging task: the regions of a person and the periods of each region are both unknown. The interdependency between them makes the task even harder. Hence, existing methods are far from satisfactory for detecting periodic patterns from the low-sampling and noisy social media data. We propose a Bayesian non-parametric model, named \textbf{P}eriodic \textbf{RE}gion \textbf{D}etection (PRED), to discover periodic mobility patterns by jointly modeling the geographical and temporal information. Our method differs from previous studies in that it is non-parametric and thus does not require priori knowledge about an individual's mobility (\eg number of regions, period length, region size). Meanwhile, it models the time gap between two consecutive records rather than the exact visit time, making it less sensitive to data noise. Extensive experimental results on both synthetic and real-world datasets show that PRED outperforms the state-of-the-art methods significantly in four tasks: periodic region discovery, outlier movement finding, period detection, and location prediction.
Network-based clustering with mixtures of L1-penalized Gaussian graphical models: an empirical investigation

In many applications, multivariate samples may harbor previously unrecognized heterogeneity at the level of conditional independence or network structure. For example, in cancer biology, disease subtypes may differ with respect to subtype-specific interplay between molecular components. Then, both subtype discovery and estimation of subtype-specific networks present important and related challenges. To enable such analyses, we put forward a mixture model whose components are sparse Gaussian graphical models. This brings together model-based clustering and graphical modeling to permit simultaneous estimation of cluster assignments and cluster-specific networks. We carry out estimation within an L1-penalized framework, and investigate several specific penalization regimes. We present empirical results on simulated data and provide general recommendations for the formulation and use of mixtures of L1-penalized Gaussian graphical models.
Langerhans Cell Homeostasis and Turnover After Nonmyeloablative and Myeloablative Allogeneic Hematopoietic Cell Transplantation

Background Langerhans cells (LCs) are self-renewing epidermal myeloid cells that can migrate and mature into dendritic cells. Recipient LCs that survive cytotoxic therapy given in preparation for allogeneic hematopoietic cell transplantation may prime donor T cells to mediate cutaneous graft-versus-host disease (GVHD). This possible association, however, has not been investigated in the setting of nonmyeloablative allografting. Methods We prospectively studied the kinetics of LC-chimerism after sex-mismatched allogeneic hematopoietic cell transplantation with nonmyeloablative (n=23) or myeloablative (n=25) conditioning. Combined XY-FISH and Langerin-staining was used to assess donor LC-chimerism in skin biopsies obtained on days 28, 56, and 84 after transplant. The degree of donor LC-chimerism was correlated with the development of skin GVHD. Results We observed significantly delayed donor LC-engraftment after nonmyeloablative transplantation compared with other hematopoietic compartments and compared with LC-engraftment after myeloablative conditioning. In most recipients of nonmyeloablative transplants, recipient LCs proliferated in situ, recruitment of donor-LCs was delayed by two months, and full donor LC-chimerism was only reached by day 84 after transplant. Although persistence of host LCs on day-28 after transplant was not predictive for acute or chronic skin GVHD, the recruitment of donor-derived LCs was associated with nonspecific inflammatory infiltrates (P=0.009). Conclusions These results show that LCs can self-renew locally but are replaced by circulating precursors even after minimally toxic nonmyeloablative transplant conditioning. Cutaneous inflammation accompanies donor LC-engraftment, but differences in LC conversion-kinetics do not predict clinical or histopathological GVHD.
An objective method to measure cell survival by computer-assisted image processing of numeric images of Petri dishes

This work establishes an objective method to measure cell clonogenic survival by computer-assisted image processing using images of cell cultures fixed and stained in Petri dishes. The procedure, developed by Samba Technologies, consists of acquiring Petri dish pictures with a desktop scanner and analysing them by computer, using algorithms based on the ‘top hat’ filter. The results from the automated count for the cell line SQ20B are compared with those found by two observers, before and after normalization of the counting. After normalization, the shape of the survival curves of the ‘manual’ counting of the Petri dishes shows a good correlation between both observers. The software enables the small visible differences in count between observers to be eliminated. The comparison between the absolute number of colonies shows an increased difference between the two manual scorings that can be as great as 67 colonies, whereas the difference between the two automated counts is never greater than 8 colonies. These results demonstrate that the ‘manual’ count is inter- and intra-observer variable, whereas the automatic count performs reproducible cell colony counts, thereby minimizing user-generated bias. The large amount of data produced also gives information about cell and colony characteristics. Thus, this computer-assisted method has considerably improved the reliability of our statistical results.
The New Science of Learning and Why Students Forget Their Economics So Quickly

The human brain has automatic, built-in abilities to filter and discard information so that the vast majority of the information that enters our senses is deleted. Consequently, the result is that teachers need to understand, model, and program student learning to work in harmony with these natural abilities. New discoveries in the "science of learning" that employ multi-disciplinary work in psychology, neuroscience, machine learning, and education have shown us how the human brain works. Furthermore, studies of child development, plasticity of the human brain, and computational approaches to learning have contributed to new understandings of how we learn and how long-term memory is formed. The most important of these findings are: 1. Learning is computational and probabilistic, using Bayesian Logic. 2. Learning is fundamentally social in nature. 3. Designated brain circuits link perception and action. 4. The human hippocampus and REM sleep collaborate to form and store long-term memories. In addition, we present the implications of this research for the teaching of economics and also present practical techniques that can be incorporated into classroom teaching to help students study more efficiently and improve student learning.
MultiCol-SLAM - A Modular Real-Time Multi-Camera SLAM System

The basis for most vision based applications like robotics, self-driving cars and potentially augmented and virtual reality is a robust, continuous estimation of the position and orientation of a camera system w.r. t the observed environment (scene). In recent years many vision based systems that perform simultaneous localization and mapping (SLAM) have been presented and released as open source. In this paper, we extend and improve upon a state-of-the-art SLAM to make it applicable to arbitrary, rigidly coupled multi-camera systems (MCS) using the MultiCol model. In addition, we include a performance evaluation on accurate ground truth and compare the robustness of the proposed method to a single camera version of the SLAM system. An open source implementation of the proposed multi-fisheye camera SLAM system can be found on-line this https URL
PAID I UNDERSTANDING FACTORS IN EMOTION PERCEPTION

Emotion in speech is an important and challenging research area. Addition or understanding of emotions from speech is challenging. But an equally difficult task is to identify the intended emotion from an audio or speech. Understanding emotions is important not only in itself as a research area, but also, for adding emotions to synthesized speech. Evaluating synthesized speech with emotions can be simplified if the correct factors in emotion perception can be first identified. To this end, this work explores various factors that could influence the perception of emotions. These factors include semantic information of the text, contextual information, language understanding and knowledge. This work also investigates the right framework for a subjective perceptual evaluation by providing different options to the listeners and checking which ones among these are the most effective response to evaluate the perception of the emotion.
Widening access and student non‐completion: an inevitable link? Evaluating the effects of the Top‐Up Programme on student completion

This paper explores the links between socio‐economic disadvantage, non‐completion in higher education and preparation initiatives. It does this through investigating the effectiveness of the Top‐Up Programme, a preparation course for 17 and 18 year olds from schools that have low participation rates in higher education. The University of Glasgow, on behalf of the West of Scotland Wider Access Forum, has responsibility for running the Top‐Up Programme. It is part of the Scottish Higher Education Funding Council and local education authority funded GOALS (Greater Opportunity of Access and Learning with Schools) Project. The paper examines the hypothesis that students who undertake programmes such as the Top‐Up Programme can counteract the effects of disadvantage through being prepared academically. The investigation is undertaken within the contexts of widening access research and theoretical models of student non‐completion. This study reviewed the available data for the 44 schools involved in the Top‐Up Programme, their levels of disadvantage as defined by their participation rates in higher education, and levels of non‐completion at the University of Glasgow. This revealed that groupings of schools with extremely low participation levels have higher non‐completion rates than less disadvantaged schools. At the same time, the authors attempted to investigate this finding further through examining a group of 44 similar comparative schools. The students' perceptions of the effectiveness, in terms of preparation for higher education, of undertaking the Top‐Up Programme were also assessed by conducting interviews and administering questionnaires to the Top‐Up students and other groups of students. The Top‐Up students perceived that it had provided highly relevant preparation and had eased the transition from school to higher education. The effectiveness of the Top‐Up Programme was also quantified by reviewing the pass/fail rates of the first‐year students who had undertaken Top‐Up in comparison to those who had come from the comparative groups. The results indicated that the Top‐Up students were progressing at better rates than the other groups despite many more having come from the extremely low participation schools, known to be ‘at risk’. The main outcome determined was that there is a link between attending an extremely low participation rate school and non‐completion, despite similar pre‐entry qualifications to other students. This indicates that disadvantage continues to impact on students' achievements throughout their educational careers. We argue that the result has implications for both wider access policies and non‐completion research.
Survival, Deoxyribonucleic Acid Breakdown, and Synthesis in Salmonella typhimurium as Compared with Escherichia coli B Strains

Salmonella typhimurium LT-2 was compared with radioresistant (B/r) and radiosensitive (Bs−2) strains of Escherichia coli in respect to the survival, deoxyribonucleic acid (DNA) breakdown, and DNA synthesis after X irradiation. It is shown that S. typhimurium LT-2 is about four times more sensitive than E. coli B/r but less sensitive than Bs−2. The DNA breakdown is in S. typhimurium LT-2 lower than the postirradiation breakdown of DNA in both E. coli strains and DNA synthesis proceeds in this bacterium in spite of a much lower survival, as in the radioresistant E. coli B/r.
Environmental Worldview, Place Attachment, and Awareness of Environmental Impacts in a Marine Environment

Place attachment provides insight on why and to what extent individuals value a particular setting. Most investigations involving place attachment and environmental attitudes have been conducted in terrestrial settings; little work has addressed proenvironmental behavior in marine settings. The purpose of the current investigation was to extend Stern et al.’s work, which indicates that individuals’ environmental worldviews (EWVs) influence their attitudes toward anthropogenic impacts on the environment. We hypothesized a model wherein place attachment partially mediates the relationship between recreational visitors’ EWV and their awareness of consequences of negative impacts on Australia’s Great Barrier Reef. We then compared this model with competing models. Our results suggest that place attachment is a useful addition to studies that use Stern et al.’s value-belief-norm model.
FoxA1 is a Key Mediator of Hormonal Response in Breast and Prostate Cancer

Hormonally regulated breast and prostate cancers are the most common cause of cancer in females and males respectively. FoxA1 acts as a pioneer factor for both androgen receptor (AR) and estrogen receptor-α (ER), dictating the binding location, and therefore function of these transcription factors. It is an essential protein for the transcriptional activity of both ER and AR, yet it has distinct roles with the two different nuclear receptors. In both malignancies, FoxA1 plays a pivotal role from early stage cancer through to drug resistant and metastatic disease. Due to this key role in mediating ER and AR function, FoxA1 is not only an attractive therapeutic target but could potentially function as a novel biomarker.
2011 ASA/ACCF/AHA/AANN/AANS/ACR/ASNR/CNS/SAIP/SCAI/SIR/SNIS/SVM/SVS Guideline on the Management of Patients With Extracranial Carotid and Vertebral Artery Disease: Executive summary

These practice consensus after a thorough review of available current scientific evidence and are intended to improve patient care. The guidelines attempt to define practices that meet the needs of most patients in most circumstances. The ultimate judgment regarding care of a particular patient must be made by the healthcare provider and patient in light of all the circumstances presented by that patient. Thus, there are situations in which deviations from these guidelines may be appropriate. Clinical decision making should consider the quality and availability of expertise in the area where care is provided. When these guidelines are used as the basis for regulatory or payer decisions, the goal should be improvement in quality of care. The Task Force recognizes that situations arise for which are patient these areas will be identified within each respective when with Protection in Patients at High Risk for Endarterectomy; and SPACE, Stent-Protected Angioplasty versus Carotid Endarterectomy.
An Improved Dataset and Extraction Process for Starcraft AI

In order to experiment with machine learning and data mining techniques in the domain of Real-Time Strategy games such as StarCraft, a dataset is required that captures the complex detail of the interactions taking place between the players and the game. This paper describes a new extraction process by which game data is extracted both directly from game log (replay) files, and indirectly through simulating the replays within the StarCraft game engine. Data is then stored in a compact, hierarchical, and easily accessible format. This process is applied to a collection of expert replays, creating a new standardised dataset. The data recorded is enough for almost the complete game state to be reconstructed, from either player's viewpoint, at any point in time (to the nearest second). This process has revealed issues in some of the source replay files, as well as discrepancies in prior datasets. Where practical, these errors have been removed in order to produce a higher-quality reusable dataset.
Comparative Validation of the S-STS, the ISST-Plus, and the C-SSRS for Assessing the Suicidal Thinking and Behavior FDA 2012 Suicidality Categories.

OBJECTIVE This exploratory study examines the concurrent validity for mapping symptoms of suicidal ideation, self-harm, and suicidal behavior as recorded on the InterSePT Scale for Suicidal Thinking-Plus, the Sheehan-Suicidality Tracking Scale (clinician- and patient-rated and reconciled patient/clinician versions), and the Columbia-Suicide Severity Rating Scale to the 11 United States Food and Drug Administration-Classification Algorithm of Suicide Assessment (September 2012) categories. METHOD Forty subjects with varying degrees of suicidal ideation and behavior severity (from not present to extremely severe) were recruited from inpatient, outpatient, and emergency room settings. Each patient was interviewed using all three scales (InterSePT Scale for Suicidal Thinking-Plus, the Sheehan-Suicidality Tracking Scale, and the Columbia-Suicide Severity Rating Scale) on the same day. The scales were administered in a random sequence by three independent raters who were blind to the ratings on the other scales. RESULTS The Sheehan-Suicidality Tracking Scale and the InterSePT Scale for Suicidal Thinking-Plus show acceptable agreement with the Columbia-Suicide Severity Rating Scale in detecting the presence or absence of the 2012 Food and Drug Administration-Classification Algorithm of Suicide Assessment categories 1, 5, 6, 10, and 11 (passive ideation; active ideation with method, intent, and plan; completed suicide; preparatory actions; and self-injurious behavior) but not of categories 2, 3, and 4 (3 other active suicidal ideation combination categories) or to 8 and 9 (aborted and interrupted attempt). Despite the significant disagreement between the Columbia-Suicide Severity Rating Scale on the one side and the InterSePT Scale for Suicidal Thinking-Plus and the Sheehan-Suicidality Tracking Scale on the other in the ability to accurately map to the 2012 Food and Drug Administration-Classification Algorithm of Suicide Assessment categories on some items, there was close agreement between the InterSePT Scale for Suicidal Thinking-Plus and the Sheehan-Suicidality Tracking Scale on these categories. CONCLUSION The results of this exploratory study invite discussion and debate about the validity of the Columbia-Suicide Severity Rating Scale and its ability to accurately assess key active suicidal ideation categories, since it disagrees so much with the other two standardized scales that agree so closely with each other.
An Applicable Approach to Ontology Mapping Based on Knowledge Base

In semantic web, ontology mapping is the basis of the interoperation of heterogeneous ontologies. Ontologies are usually distributed and heterogeneous and thus it is necessary to find the mapping between them before processing across them. The current ontology mapping methods merely use massive web pages in the internet and they are unpractical to subsequent interoperation and integration of heterogeneous data sources. This paper proposes a method based on internet search engine and realizes it to a component of the framework for knowledge management system. Ontologies to be mapped and mapping results are all stored in the database. Finally we evaluate our approach with experiments which demonstrate that our solution yields a better performance.