Miracle-dz/UniFAR-FTUs huggingface dataset:
Mathematical Analysis and Clinical Implications of an HIV Model with Adaptive Immunity

In this paper, a mathematical model describing the human immunodeficiency virus (HIV) pathogenesis with adaptive immune response is presented and studied. The mathematical model includes six nonlinear differential equations describing the interaction between the uninfected cells, the exposed cells, the actively infected cells, the free viruses, and the adaptive immune response. The considered adaptive immunity will be represented by cytotoxic T-lymphocytes cells (CTLs) and antibodies. First, the global stability of the disease-free steady state and the endemic steady states is established depending on the basic reproduction number R0, the CTL immune response reproduction number R1z, the antibody immune response reproduction number R1w, the antibody immune competition reproduction number R2w, and the CTL immune response competition reproduction number R3z. On the other hand, different numerical simulations are performed in order to confirm numerically the stability for each steady state. Moreover, a comparison with some clinical data is conducted and analyzed. Finally, a sensitivity analysis for R0 is performed in order to check the impact of different input parameters.
Using Eye Tracking to Examine Working Memory and Verbal Feature Processing in Spanish

Using Eye Tracking to Examine Working Memory and Verbal Feature Processing in Spanish Erik William Arnold Department of Linguistics, BYU Master of Arts Second language acquisition (SLA) has been a dominant field in linguistics research over the past several decades. In this field, researchers have investigated what makes learning the grammar of a second language difficult and have identified many factors that may contribute to this difficulty, including agreement processing. In linguistic terms, agreement refers to the necessary covariation of grammatical features between two or more syntactic constituents. In early years, researchers examined how native speakers process varying grammatical features (e.g. number and gender) in agreement relations. In recent years, however, they have turned towards L2 learners and have investigated whether learners can attain native-like levels of processing agreement in a second language. While some studies have investigated differences between learners and native speakers, other studies have examined the effect of individual differences on agreement processing. Of particular interest to this thesis is working memory capacity (WMC) and its effect during the different processing stages of agreement. Lastly, features expressed through agreement may affect individuals’ processing behavior. Different features (e.g. person, number, gender) are regularly expressed in agreement relations by different manifestations of exponence. Many authors have investigated the effect different features have on processing agreement when those features are expressed by separative exponence. Fewer have examined the effect of cumulative exponence on agreement processing. Eye tracking is a useful psycholinguistic tool to investigate these questions. Using eye tracking, I examine English learners of Spanish and their eye behavior as they processed Spanish verbal agreement and investigate whether they demonstrate sensitivity similar to native Spanish speakers while processing verbal agreement errors. I investigate if individuals demonstrate similar sensitivity when processing three different types of verbal agreement errors—number, person, and tense. Additionally, I examine whether individuals’ sensitivity to agreement errors is affected by working memory capacity. Using a linear mixed effects model, I analyze the eye tracking data and share the results of the analyses and their implications for L2 research in agreement processing.
The EduPARK game-like app with Augmented Reality for mobile learning in an urban park

Mobile devices are pervasive and can be used to promote learning in a contextual way. When combined with innovative technologies, such as augmented reality (AR), and game-based approaches, mobile devices can boost engagement with learning. Giving continuity to previous work, this paper presents the EduPARK game-like application (app) that is an interactive, interdisciplinary quiz with AR, based in Geocaching principles, intended to be played in a specific urban park. Furthermore, this app was developed through a design-based research methodology with four cycles of development involving user testing and evaluation. Several field tests were conducted, involving different convenience samples of potential users, from basic to higher education, including pre and in-service teacher training and lifelong learning (park visitors). The app was progressively refined according to the users’ feedback in each cycle, concerning usability and educational value. This contribution comprises an example of how to develop successfully learning opportunities based on mobile games with AR technology that may be relevant to educational game designers and educators. Future work involves the organization of regular activities for students, teacher trainees and tourists to collect data in order to analyse the processes of mobile learning in outdoor settings, namely in articulation with curricular contents.
Tools for Live 2D Animation

Live animation of 2D characters is an emerging form of storytelling that has begun to appear on streaming platforms and broadcast TV. Unlike traditional animation, human performers control characters in real time so that they can respond and improvise to live events. Current live animation systems provide a range of animation controls, such as camera input to drive head movements, audio for lip sync, and keyboard shortcuts to trigger discrete pose changes via artwork swaps. However, managing all of these controls during a live performance is challenging. This dissertation presents four tools to assist performers in creating compelling live 2D animation. To create more compelling characters, we propose three tools to control artwork layers’ swaps and deformations. First, we introduce an interactive system that addresses the problem of triggering artwork swaps in live settings. Next, another system transfers poses from an input video to a character, automatically driving new animation through pose data. We also describe a framework that augments the primary motion of a character by adding secondary motion subtle movement of parts like hair, foliage or cloth that complements and emphasizes the primary motion. To add more details to scene backgrounds, we show a mixed initiative interface which, from a few user-drawn scribbles, extracts repetitive texture elements in an image, and supports animating them.
Recuperação de Informação Multimídia em Big Data Utilizando OpenCV Python

O livro Minicursos do XXV Simposio Brasileiro de Sistemas Multimidia e Web oferece a sua comunidade textos relacionados a temas que norteiam os ultimos avancos na area de multimidia e Web, trazendo informacoes sobre novas tecnologias e topicos atuais de pesquisa em areas correlatas ao tema. Assim, este volume oferece uma excelente oportunidade para familiarizacao dos interessados com novos temas de pesquisa que podem vir a ser uteis em suas vidas profissionais.
Use of Intravenous Minocycline for the Treatment of Methicillin-Resistant Staphylococcus aureus (MRSA) and Resistant Gram-Negative Organisms: Experience in a Tertiary Care Hospital

BackgroundMinocycline (MINO), a tetracycline antibiotic, has been available since the 1960s. The intravenous (IV) formulation was withdrawn from the market in 2005 but was reintroduced in the US market in 2009. The drug has potent activity against methicillin-resistant Staphylococcus aureus (MRSA) and multidrug-resistant gram-negative organisms (MDRO), specifically Acinetobacter baumannii, extended-spectrum &bgr;-lactamase (ESBL), and Klebsiella pneumoniae carbapenemase-producing Klebsiella and Escherichia coli species. We used IV MINO in various clinical situations, where there were few or no other options for treatment. MethodsAll cases treated with IV MINO from 2009 to 2012 were reviewed. Data were collected for demographics, reasons for IV MINO use, and outcome. ResultsTwenty-one patients were treated with IV MINO; 12 patients had MRSA infections, 3 patients had mixed MRSA and gram-negative infections, and 8 patients had MDRO infections. There were 8 patients with bacteremia, 5 patients with pneumonia, 8 patients with skin and soft tissue infections, one patient each with endocarditis and osteomyelitis, and 2 patients had urinary tract infection. Twelve patients continued treatment with oral MINO. Twenty patients were discharged, and one patient died. ConclusionsIntravenous MINO is an excellent treatment option for certain MRSA and MDRO infections, where there is a paucity of treatment options. The drug has a favorable pharmacokinetic profile and allows for completion of treatment with an oral formulation.
Vision-based human activity analysis

Human activity recognition has been an active research topic for decades due to its potential applications in video surveillance, human-robot interaction, elderly care, and entertainment. Although significant progress has been made recently with the emergency of RGB-D sensors, it still remains a great challenge in applying it to practical scenarios. The main contribution of this thesis is a novel human activity framework including four algorithms, namely, Geometry property and Bag of Semantic moving Words (GBSW) for human action recognition, Spatial Relation and temporal Moving Similarity (SRMS) for human interaction recognition, Skeleton Motion Distribution model (SMD) for human action detection, and Multi-stage Soft Regression (MSR) framework for online human activity recognition. Firstly, targeting at traditional human action recognition problem where the action sequences are manually pre-segmented, a spatio-temporal feature descriptor GBSW which aggregates a bag of semantic moving words (BSW) with the geometric feature (G) is proposed to effectively represent human actions from skeleton sequences. Experimental results have shown that GBSW can obtain superior performance over the state-of-theart methods. Secondly, taking advantage of the BSW feature extracted from individuals, the moving similarity between body parts is further explored to describe the mutual relationship for effective human interaction recognition. A new large RGB-D based human-human interaction dataset, namely, Online Human Interaction (OHI) Dataset is collected for the evaluation of human interaction recognition algorithms. The effectiveness of the proposed method has been proven by the experimental results on both the public dataset and the newly collected dataset. Thirdly, to remove the manual segmentation requirement in the traditional action recognition and achieve automatic action detection for a given video sequence, a novel SMD model is developed. Specifically, an adaptive density estimation function is built to calculate the density distribution of skeleton movements. Experimental results have demonstrate that our method outperforms the state-of-the-art methods in terms of both detection accuracy and recognition precision. Fourthly, a MSR framework is developed for online activity recognition where the action needs to be recognized immediately for a continuously incoming video stream. The developed framework delicately assembles overlapped activity observations in all periods to improve its robustness against arbitrary activity segments. Extensive experimental results on several public available databases have demonstrated the outstanding performance of the MSR method over the state-of-the-art approaches.
Fluoride and oxidative stress in postmenopausal women

–Fluorosis is a serious public health problem in many parts of the world where drinking water contains more than 1 ppm of fluoride. Higher intake of fluoride will result in dental and skeletal fluorosis and affect collagen synthesis and bone mineralization. Approximately 99% of the body burden of fluoride is associated with calcified tissues. The fluoride concentration in bone is not uniform. Fluoride concentration in bone is not uniform and higher fluoride levels in the body are associated with calcified tissues. The study was conducted on 100 subjects with their consent which consisted of control group of 50 nonpregnant women in their reproductive age group. Study group consisted of 50 women who had attained menopause either naturally or surgically, all residing in the endemic fluorotic area of Nalgonda district, Andhra Pradesh, India. 5 ml of venous blood was collected from both the groups. Serum fluoride levels (0.68  0.39; p<0.005) was statically significant when compared to the controls (0.45  0.28). Serum malondialdehyde, a lipid peroxidation product, was measured as an indicator of oxidative stress induced by fluorosis, was statistically significant (287.86  49.79; p<0.001). Keywords––Fluorosis, post menopausal women, malondialdehyde, oxidative stress, osteoporosis
Detecting Anomalies over Message Streams in Railway Communication Systems

This work focuses on the detection of anomalies in a railway communication system. We present the detection method as well as the architecture of the system supporting it. The method is based on two preliminary stages to collect and aggregate the data. It then combines the use of a multidimensional indexation tree (i.e., iSax tree) to store time series, and the computation of an anomaly detection score (i.e., CFOF score). The information stored in the indexation tree enables a fast estimation of the anomaly score, that can therefore be computed on the fly over the stream of messages in the communication system. We show by mean of experiments that this estimation is close to the exact score. We describe the platform that has been implemented, and we show that it is effective to support abnormal behaviour detection in the real stream of messages within the communication system of the French Railway Company (SNCF).
A Time-Context-Dependent Resource Diffusion Algorithm Based on User Splitting

Personalized recommendation for smartphone applications is studied, and a time-context-dependent resource diffusion algorithm based on user splitting is proposed. The algorithm introduces time context information. According to this idea, the algorithm can make full use of user preference data information, cluster mobile applications to get more user preference categories and optimize recommendation results. In the personalized recommendation process of mobile applications, because users and projects are dynamic changes, this paper analyses the dynamic change process of the algorithm in detail, and enhances the applicability of the algorithm. Experimental results show that the proposed algorithm improves the accuracy of recommendation results compared with the previous resource diffusion algorithm.
Melanocortin-4 Receptor Gene Mutations and Obesity

Obesity is caused by the interaction of multiple genetic and environmental factors. Several genes involved with appetite have been identified: α-melanocyte-stimulating hormone is an anorexigenic peptide and a potent agonist of the melanocortin-4 receptor (MC4R). In 2 studies, researchers evaluated the link between MC4R gene mutations and obesity. In the first study, 5.8% of 500 subjects with severe early-onset obesity (before age 10) had mutations …
Towards Understanding Creative Language in Tweets

Extracting fine-grained information from social media is traditionally a challenging task, since the language used in social media messages is usually informal, with creative genre-specific terminology and expression. How to handle such a challenge so as to automatically understand the opinions that people are communicating has become a hot subject of research. In this paper, we aim to show that leveraging the pre-learned knowledge can help neural network models understand the creative language in Tweets. In order to address this idea, we present a transfer learning model based on BERT. We fine-turned the pre-trained BERT model and applied the customized model to two downstream tasks described in SemEval-2018: Irony Detection task and Emoji Prediction task of Tweets. Our model could achieve an F-score of 38.52 (ranked 1/49) in Emoji Prediction task and 67.52 (ranked 2/43) and 51.35 (ranked 1/31) in Irony Detection subtask A and subtask B. The experimental results validate the effectiveness of our idea.
Predicting sales by online searching data keywords based on text mining: Evidence from the Chinese automobile market

Online searching data reflects consumers’ real footprints in information collection and purchase decision-making processes, which is greatly valued in understanding their needs. This paper which is at the background of China’s automobile market, studies the relationship between online searching data and automobile sales through approaches that differ from existing research to extract keywords. First the online searching data keywords are determined, primarily by using text-mining technology to extract them, and specifically: i) Jieba was used to tokenize crawled automotive forum posts’ text into segmented words; ii) All word-segmented Chinese corpus were segmented into word vector space by Word2vec model; and iii) Similar keywords were discovered by calculating the word vector’s similarity indexes. A fixed effect model was then built based on 108 months of long panel data. Finally, combing with panel vector autoregressive model (PVAR), we used rolling window to predict Chinese automobile sales from January to December 2015.The empirical results demonstrate that: a long equilibrium exists between online searching data and automobile sales; our regression model can explain 76% of the variance. The holdout analysis suggests that online searching data can be of substantial use in forecasting Chinese automobile sales.
Classifying neural networks and methods of their illogical behaviour revealing

In this article a feedforward error backpropagation artificial neural network is investigated and the analysis of its illogical behaviour is presented. The problem of illogical behavior arises in various models of artificial neural networks. In the presented work a classifying artificial neural network (CANN) is considered and several learning algorithms were implemented and compared. CANN was designed for automatic differentiaition of cyanobacterial strains during environmental monitoring and some of trained networks demonstrated illogical behavior in further testing. Several original techniques were elaborated for estimation of the quality and accuracy of classification in addition to the traditional ones. Novel visualization methods were suggested for classification and generalization results representation.
Deep learning goes to school: toward a relational understanding of AI in education

ABSTRACT In Applied AI, or ‘machine learning’, methods such as neural networks are used to train computers to perform tasks without human intervention. In this article, we question the applicability of these methods to education. In particular, we consider a case of recent attempts from data scientists to add AI elements to a handful of online learning environments, such as Khan Academy and the ASSISTments intelligent tutoring system. Drawing on Science and Technology Studies (STS), we provide a detailed examination of the scholarly work carried out by several data scientists around the use of ‘deep learning’ to predict aspects of educational performance. This approach draws attention to relations between various (problematic) units of analysis: flawed data, partially incomprehensible computational methods, narrow forms of educational’ knowledge baked into the online environments, and a reductionist discourse of data science with evident economic ramifications. These relations can be framed ethnographically as a ‘controversy’ that casts doubts on AI as an objective scientific endeavour, whilst illuminating the confusions, the disagreements and the economic interests that surround its implementations.
Transmission Line Component Inspection Method Based on Deep Learning under Visual Navigation

The efficiency of the operation and maintenance for transmission lines can be greatly enhanced with the help of UAV. However, the images of the transmission line component collected by UAV could be spoiled due to the incorrect operations of the controllers, which causes the defects of the transmission line component couldn’t be diagnosed accurately. A visual inspection algorithm for the UAV based on the sensing of the tower components is proposed which uses a parallel network structure and can be applied on embedded devices, which based on Faster R-CNN target recognition framework and the ZFNet. Thus, the classification and posture of the component can be acquired. The UAV’s pose and position can be adjusted by the distance sensing model and components’ posture. Results show that the maximum mAP (mean Average Precision) of component recognition and posture perception are 0.95 and 0.91, respectively. Additionally, the posture of the UAV can be adjusted effectively and optimum images of the component can be obtained.
Is child death the crucible of human evolution

Child death has played an important role in the evolution of humans. Of all stages of development, and at all historical times beyond Modern history, childhood has been associated with the highest levels of mortality. Compared to other evolutionary pressures such as surviving as an adult or finding a mate and having children, the odds of genetic failure (i.e., failure to directly contribute to one's genetic line) are greatest in childhood. The enormous potential evolutionary pressure exerted by child death should have significantly influenced human psychological adaptations. Despite this potential influence, child death may be one of the least studied influences on human evolutionary psychology. This paper discusses the historical rates of child mortality, the relative odds associated with passing on one's genes, adult mental adaptations to child mortality, and child mental adaptations to child mortality. (PsycINFO Database Record (c) 2010 APA, all rights reserved) (journal abstract)
Lending Club Default Prediction using Naïve Bayes and Decision Tree

Currently P2P lending is one of the most emerging disruptors in the financial sector. Lending Club is a P2P platform based in America. Besides its flexibility to give instant lending this industry have high risk for their investors to lending money. In order to mitigate this risk, this study aims to predict the default risk using decision tree J48 and naive bayes. One of the results in this research show that J48 and Naïve Bayes are both good in predicting the default in P2P lending sector. Another contribution of the paper might be useful for similar companies to see which factors that influence the most to default loan status.