Miracle-dz/UniFAR-FTUs huggingface dataset:
The SOBRAPAR Hospital Craniofacial Surgery Fellowship: Wide Craniofacial Surgery Training Based on Historical and Evolving Principles.

Accommodation Availability varies according to hospital policies in a Converseand Tessier-trained craniofacial and plastic surgeon. ‘‘It quickly became a mecca for children with severe craniofacial deformities throughout all of South America,’’ as emphasized by Dr. McCarthy. In the 1980s, Dr. Tessier and others traveled to operate with Raposo do Amaral on his seemingly endless supply of craniofacial malformations, thus resulting in many new and useful craniofacial surgery techniques. In addition, pioneers of craniofacial surgery have visited and significantly influenced all surgeons who graduated from the craniofacial and plastic surgery training program established by Raposo do Amaral. Although Raposo do Amaral died in 2005, his memory has lived on at several meetings and events, including a reflection by Mutaz Habal, the editor-in-chief of this journal. Raposo do Amaral’s legacy was continued by his wife (Dr. Vera Adami Raposo do Amaral), his craniofacial plastic surgery resident (Dr. Celso Luiz Buzzo), and, more recently, his 2 sons who are also craniofacial plastic surgeon (Dr. Cassio Eduardo Raposo-Amaral and Dr. Cesar Augusto Raposo-Amaral). The SOBRAPAR Hospital has been changing lives and giving hope to underprivileged patients with cleft and craniofacial deformities for the last 39 years. At the time of this writing, >1,200 surgeries are performed every year, and there are 20,000 clinical attendances per year in different multidisciplinary areas, including plastic surgery, otolaryngology, psychology, speech pathology, and orthodontics.
Dynamic Face and Upper-Body Emotion Recognition for Service Robots

Dynamic face and upper-body emotional expressions are two of the main non-verbal communication modalities use by humans when communicating with each other. Service robots should be able to understand the emotions through these two modalities in-order to interact with humans more efficiently when providing services. This paper presents an approach to recognize the face and upper-body emotions through RGB-D (Kinect) sensor for service robots because it enables robust detection of the upper-body. Face and upper-body emotions are recognized by Linear Discriminant Analysis (LDA) and Pattern Recognition Neural Network (PRNN) classifiers.
Atalaya at TASS 2018: Sentiment Analysis with Tweet Embeddings and Data Augmentation

TASS 2018 workshop proposes different challenges on semantic analysis in Spanish. This work presents our participation as team Atalaya in the task of polarity classification of tweets. We followed standard techniques in preprocessing, representation and classification, and also explored some novel ideas. In particular, to obtain tweet embeddings we trained subword-aware word embeddings and use a weighted scheme to average them. To deal with overfitting problems caused by training data scarcity, we tried a data augmentation strategy based on two-way machine translation. Experiments with linear classifiers and neural models show competitive results for the different subtasks proposed in the challenge.
A Digital Watermarking Approach using SMQT, OTSU, DWT and IDWT

In this paper, a new digital watermarking model is proposed for the medical images. An improved SMQT is used for image enhancement and the image is being segmented using OTSU thresholding. Discrete Wavelet Transform (DWT) and Inverse DWT are used to embed and extract the watermark on the host image. The goal of our scheme is to make the watermarking more robust against attacks and secure the image from privacy threats. In the experimental evaluation, we tested our proposed approach with various attacks on the watermarked image and then host image is extracted and matched with the original image. Experimental results demonstrate that our proposed model outperforms the existing state-of-art models by exhibiting higher PSNR, lower MSE and improved CC values.
Using the Kinetic Estimating Glomerular Filtration Rate Equation for Estimating Glomerular Filtration Rate and Detecting Acute Kidney Injury: A Pilot Study

Background: Estimating the glomerular filtration rate (eGFR) when the creatinine (Cr) is rapidly changing, as in acute kidney injury (AKI), has been a challenge. The Kinetic Estimated Glomerular Filtration Rate (KeGFR) formula by S. Chen estimates the GFR in the acute state by factoring the time interval between rising Cr values and the volume of distribution (VD). It provides the clinician with an eGFR value for each non-steady state Cr value. We applied the KeGFR formula to detect AKI in an adult non-ICU inpatient setting. We then compared KeGFR with the current standard Acute Kidney Injury Network (AKIN criteria) and Risk Injury Failure Loss End-Stage Kidney Disease (RIFLE criteria) and new criteria (Waikar-Bonventre, Delta check) for AKI detection. Methods: A total of 250 consecutive adult patients admitted to the Medical wards were screened. Patient episodes with a change in Cr of > 4.3% (Biological Variation) were included in the study (n = 80). The KeGFR equation was applied to this cohort after calculating the VD individually after estimating the initial GFR by using MDRD equation. A fall in KeGFR of 25% or more was considered as AKI. The AKIN, Waikar-Bonventre, RIFLE, and Delta Check criteria were also applied to this cohort and compared with the KeGFR criterion. Clinical adjudication was performed when there was discordance between AKI episodes detected by AKIN and KeGFR criteria. Results: There were 50 episodes of AKI by AKIN classification and 31 episodes by KeGFR criterion. All but 1 (30) episode detected by KeGFR criterion fulfilled the AKI definition by AKIN. AKIN diagnosed an additional 20 episodes. However, all of these had an elevated Cr level on admission; thus, requiring the incorporation of baseline Cr by AKIN which is not part of the KeGFR formula. Five of these 20 patients were deemed as not having AKI by clinical adjudication. All patients with in-hospital AKI and ongoing AKI were detected by both the criteria. The KeGFR criteria detected almost all (24/25) episodes of AKI identified by the Waikar-Bonventre method. The latter method detected 77% (24/31) of the AKI episodes identified by KeGFR. Conclusion: The KeGFR equation can be readily applied to estimate GFR in the non-steady state. A KeGFR-based criterion successfully detected ongoing and in-hospital AKI in this study. Community acquired AKI that did not progress after admission was not detected by the KeGFR criterion.
Cross-sectional analysis of sleep-promoting and wake-promoting drug use on health, fatigue-related error, and near-crashes in police officers

Objectives To examine sleep-promoting and wake-promoting drug use in police officers and associations between their use and health (excessive sleepiness, stress and burnout), performance (fatigue-related errors) and safety (near-crashes) outcomes, both alone and in combination with night-shift work. Design Cross-sectional survey. Setting Police officers from North America completed the survey either online or via paper/pencil at a police station. Participants 4957 police participated, 3693 online (91.9%, participation rate) and 1264 onsite (cooperation rate 63.1%). Main outcome measures Sleep-promoting and wake-promoting drug use, excessive sleepiness, near-crash motor vehicle crashes, dozing while driving, fatigue errors, stress and burnout. Results Over the past month, 20% of police officers reported using sleep-promoting drugs and drugs causing sleepiness, while wake-promoting agents were used by 28% of police (5% used wake-promoting drugs, 23% used high levels of caffeine and 4% smoked to stay awake). Use of sleep-promoting drugs was associated with increased near-crashes (OR=1.61; 95% CI 1.21 to 2.13), fatigue-related errors (OR=1.75; 95% CI 1.32 to 2.79), higher stress (OR=1.41; 95% CI 1.10 to 1.82), and higher burnout (OR=1.83; 95% CI 1.40 to 2.38). Wake-promoting drug use, high caffeine and smoking to stay awake were associated with increased odds of a fatigue-related error, stress and burnout (ORs ranging from 1.68 to 2.56). Caffeine consumption was common, and while smoking was not, of those participants who did smoke, one-in-three did so to remain awake. Night-shift work was associated with independent increases in excessive sleepiness, near-crashes and fatigue-related errors. Interactions between night-shift work and wake-promoting drug use were also found for excessive sleepiness. Conclusions Police who use sleep-promoting and wake-promoting drugs, especially when working night shifts, are most vulnerable to adverse health, performance and safety outcomes. Future research should examine temporal relationships between shift work, drug use and adverse outcomes, in order to develop optimal alertness management strategies.
RETUYT-InCo at TASS 2018: Sentiment Analysis in Spanish Variants using Neural Networks and SVM

Resumen: En este art´ıculo se presentan tres enfoques para el an´alisis de senimien-to de tweets en diferentes variantes del espa˜nol, en el marco del TASS 2018. Los clasiﬁcadores se basan en m´aquinas de vectores de soporte (SVM), redes neuronales convolucionales (CNN) y redes neuronales recurrentes de tipo LSTM. Si bien para las diferentes variantes se encontraron diferentes clasiﬁcadores que funcionaron mejor, en todos los casos el uso de word embeddings fue clave para mejorar el desempe˜no. Adem´as, utilizar una t´ecnica de entrenamiento de balance alternado para la LSTM permiti´o mejoras signiﬁcativas en la detecci´on de tweets neutros.
Operationalization and validation of a novel method to calculate adherence to polypharmacy with refill data from the Australian pharmaceutical benefits scheme (PBS) database

Background Electronic health care data contain rich information on medicine use from which adherence can be estimated. Various measures developed with medication claims data called for transparency of the equations used, predominantly because they may overestimate adherence, and even more when used with multiple medications. We aimed to operationalize a novel calculation of adherence with polypharmacy, the daily polypharmacy possession ratio (DPPR), and validate it against the common measure of adherence, the medication possession ratio (MPR) and a modified version (MPRm). Methods We used linked health data from the Australian Pharmaceutical Benefits Scheme and Western Australian hospital morbidity dataset and mortality register. We identified a strict study cohort from 16,185 patients aged ≥65 years hospitalized for myocardial infarction in 2003–2008 in Western Australia as an illustrative example. We applied iterative exclusion criteria to standardize the dispensing histories according to previous literature. A SAS program was developed to calculate the adherence measures accounting for various drug parameters. Results The study cohort was 348 incident patients (mean age 74.6±6.8 years; 69% male) with an admission for myocardial infarction who had cardiovascular medications over a median of 727 days (range 74 to 3,798 days) prior to readmission. There were statins (96.8%), angiotensin converting enzyme inhibitors (88.8%), beta-blockers (85.6%), and angiotensin receptor blockers (13.2%) dispensed. As expected, observed adherence values were higher with mean MPR (median 89.2%; Q1: 73.3%; Q3: 104.6%) than mean MPRm (median 82.8%; Q1: 68.5%; Q3: 95.9%). DPPR values were the most narrow (median 83.8%; Q1: 70.9%; Q3: 96.4%). Mean MPR and DPPR yielded very close possession values for 37.9% of the patients. Values were similar in patients with longer observation windows. When the traditional threshold of 80% was applied to mean MPR and DPPR values to signify the threshold for good adherence, 11.6% of patients were classified as good adherers with the mean MPR relative to the DPPR. Conclusion In the absence of transparent and standardized equations to calculate adherence to polypharmacy from refill databases, the novel DPPR algorithm represents a valid and robust method to estimate medication possession for multi-medication regimens.
Sequencing of high-efficacy disease-modifying therapies in multiple sclerosis: perspectives and approaches

Multiple sclerosis (MS) is characterized by chronic inflammation in conjunction with neurodegeneration within the central nervous system. Most individuals with MS begin with a relapsing remitting course that later transitions to secondary progressive MS. Currently available disease-modifying therapies (DMTs) for relapsing MS have been demonstrated to reduce disease activity, however most patients require a change in therapy over the course of their disease. Treatment goals include the prevention of relapses and disability accumulation and to achieve this objective requires careful planning. Sequencing of DMTs for individual patients should be designed in such a way to maximize disease control and minimize risk based on the mechanism of action, pharmacokinetic and pharmacodynamic properties of each therapy. This includes the DMT patients are being switched from to those they are being switched to. The reversibility of immune system effects should be a key consideration for DMT sequence selection. This feature varies across DMTs and should factor more prominently in decision making as newer treatments become available for the prevention of disability accumulation in patients with progressive MS. In this short review, we discuss the landscape of existing therapies with an eye to the future when planning for optimal DMT sequencing. While no cure exists for MS, efforts are being directed toward research in neuroregeneration with the hope for positive outcomes.
Design and Evaluation of a Reconfigurable Stacked Active Bridge DC–DC Converter for Efficient Wide Load Range Operation

This paper presents the design and implementation of a large-step-down soft-switched dc–dc converter based on the active bridge technique, which overcomes some of the limitations of the conventional dual active bridge converter. The topology comprises a double-stacked bridge inverter coupled to a reconfigurable rectifier through a special three-winding leakage transformer. This particular combination of stages enables the converter to run in an additional low-power mode that greatly increases light-load efficiency by reducing core loss and extending the zero-voltage switching range. The converter is implemented with a single compact magnetic component, providing power combining, isolation, voltage transformation, and energy transfer inductance. A 175 kHz, 300 W, 380–12 V GaN-based prototype converter achieves 95.9% efficiency at full load, a peak efficiency of 97.0%, an efficiency above 92.7% down to 10% load, and an efficiency above 79.8% down to 3.3% load.
Influence of study population definition on the effect of age on outcomes after blunt head trauma

ABSTRACT Objectives: The purpose of this study was to assess whether study population definition influences the effect of age on outcomes after blunt head trauma. We hypothesized that examining ‘all comers’ receiving head computerized tomography after blunt head trauma, fewer older individuals would meet Veterans Administration and Department of Defense (VA/DoD) criteria for traumatic brain injury (TBI), and would, therefore, display better outcomes than younger cohorts. However, restricting to participants meeting VA/DoD criteria for TBI, we hypothesized that older individuals would have worse outcomes. Methods: Data from a recently completed prospective cohort study were analysed with age dichotomized at 65 years. Logistic regression modelling, controlled for potential confounders including head trauma severity, was estimated to measure the effect of age on functional recovery, post-concussion symptoms (PCS), and depressive symptoms at 1-month post-TBI. Results: Fewer older than younger individuals met VA/DoD criteria for TBI. Older individuals had better functional, PCS, and depressive outcomes at 1 month. Restricting to those meeting VA/DoD criteria for TBI, older individuals continued to have better functional and PCS outcomes but had outcomes comparable to younger on depressive symptoms. Conclusions: Contrary to our hypothesis, there was a tendency for older adults to have better outcomes than younger, independent of the diagnostic criteria applied.
Chalktalk : A Visualization and Communication Language - As a Tool in the Domain of Computer Science Education

In the context of a classroom lesson, concepts must be visualized and organized in many ways depending on the needs of the teacher and students. Traditional presentation media such as the blackboard or electronic whiteboard allow for static hand-drawn images, and slideshow software may be used to generate linear sequences of text and pre-animated images. However, none of these media support the creation of dynamic visualizations that can be manipulated, combined, or re-animated in real-time, and so demonstrating new concepts or adapting to changes in the requirements of a presentation is a challenge. Thus, we propose Chalktalk as a solution. Chalktalk is an open-source presentation and visualization tool in which the user's drawings are recognized as animated and interactive "sketches," which the user controls via mouse gestures. Sketches help users demonstrate and experiment with complex ideas (e.g. computer graphics, procedural animation, logic) during a live presentation without needing to create and structure all content ahead of time. Because sketches can interoperate and be programmed to represent underlying data in multiple ways, Chalktalk presents the opportunity to visualize key concepts in computer science: especially data structures, whose data and form change over time due to the variety of interactions within a computer system. To show Chalktalk's capabilities, we have prototyped sketch implementations for binary search tree (BST) and stack (LIFO) data structures, which take advantage of sketches' ability to interact and change at run-time. We discuss these prototypes and conclude with considerations for future research using the Chalktalk platform.
MeaningCloud at TASS 2018: News Headlines Categorization for Brand Safety Assessment

This paper describes the participation of MeaningCloud in Task 4 at TASS 2018 (Martínez-Cámara et al., 2018), which is focused on Brand Safety assessment. The objective of systems is to predict whether ads should be hidden for specific news articles, depending on the topics covered and potential negative emotions that could be triggered. Based on the output of our APIs for lemmatization, topics extraction and sentiment analytics, different Natural Language Understanding techniques combined with Machine Learning were tested in our experiments. The experiment that achieved the best result consisted of a Deep Learning algorithm based on Word Embeddings and CNN, trained with features based on the headlines, plus entity extraction, and topic and sentiment analysis.
Efficient Implementation of Boolean and Full-Adder Functions With 1T1R RRAMs for Beyond Von Neumann In-Memory Computing

In-memory computing architecture is an emerging revolutionary computing paradigm that can break the von Neumann bottleneck. Computing methodology and circuit codesign using the CMOS compatible 1T1R resistive random access memory (RRAM) integration structure is presented in this paper. Functionally complete Boolean logic and arithmetic functions are experimentally demonstrated. With a single 40-nm CMOS process 1T1R unit, each of the 16 binary logics can be realized in two logic steps with an additional readout step for cascading, which shows functional reconfiguration and low computational complexity. Up to 107 cycles of NAND and XOR logic operations are performed to validate the correctness and reliability. Moreover, several fundamental adder circuits are designed and experimentally demonstrated in 1T1R devices as the proof of concept of the 1T1R computing architecture. The adders proposed in this paper include a ripple-carry adder and its optimized design and a carry-select adder, which all show promising advantages in nonvolatility, computation speed, and circuit area. This paper reports the most complex yet efficient RRAM-based 8-bit addition function experimentally so far and lays a solid foundation for constructing the future in-memory computing architecture.
Smart Grid Consumer Behavioral Model using Machine Learning

The data set of the power consumption embeds useful information about the consumer behavior. The behavioral models identify the consumer groups that have similar behavior. These behavioral models help the utility for numerous applications. Though the data set is same, different applications extract different behavioral models with the help of relevant features. The rationale for the choice of relevant features is described. Here, two applications are considered - demand response and rational tariff design. Clustering of voluminous data becomes a challenge due to its volatile nature. For dimensionality reduction, a representative curve is proposed which imbibes all the features that are considered for the analysis. Seasonal variations are also taken into account. Machine learning algorithms like k-means, expectation maximization and self-organizing maps are applied. The proposed method is tested on a practical system with 789 consumers. A performance index is used to evaluate various algorithms. The results obtained are discussed.
Common Program Similarity Metric Method for Anti-Obfuscation

Program similarity metrics, especially malware similarity metrics, have long been an area of active research. However, code obfuscation techniques bring many challenges to similarity analysis. Most prior techniques lack extensibility since they focus on problems of only one particular type, such as malicious programs on a specific platform. Moreover, some of these techniques cannot measure the similarity between the various components of two programs, and some cannot accommodate code obfuscation techniques, particularly for control flow obfuscation. To address these limitations, we present the new concept of the reductive instruction dependence graph (RIDG), which is platform-independent and stable throughout most code obfuscation processes. In addition, we propose a four-level similarity schema that is based on RIDG for measuring the similarity between two programs, which can be used to find the similarity between the components of programs. We use the proposed program similarity metric method to measure similarities among 100 different programs, and we evaluate the anti-obfuscation ability of this method by using four popular code obfuscation techniques. The experimental results show that the proposed method can overcome the limitations described above.