Miracle-dz/UniFAR-FTUs huggingface dataset:
Exercise and Prospective Memory

The purpose of this paper was to review the literature to evaluate the potential effects of exercise on prospective memory (PM). A narrative review was employed. In this review, we provide a brief description of PM; indicate the effects of PM on health; evaluate the effects of age and neurological impairment on PM; examine the neural correlates of PM; provide a description of specific components that subserve PM; identify non-behavioral strategies used to enhance PM; and evaluate the literature and plausibility through which exercise behavior may influence PM. Regarding the latter, this paper aims to burgeon the development of a new research paradigm that will play a critical role in patient health, given that memory function, and in particular, the prospective (vs. retrospective) component of memory, is highly sensitive to aging and is critically associated with health status. This is an emerging line of research that has critical implications for patient health.
A Smart System for Selection of Optimal Product Images in E-Commerce

In e-commerce, content quality of the product catalog plays a key role in delivering a satisfactory experience to the customers. In particular, visual content such as product images influences customers’ engagement and purchase decisions. With the rapid growth of e-commerce and the advent of artificial intelligence, traditional content management systems are giving way to automated scalable systems. In this paper, we present a machine learning driven visual content management system for extremely large e-commerce catalogs. For a given product, the system aggregates images from various suppliers, understands and analyzes them to produce a superior image set with optimal image count and quality, and arranges them in an order tailored to the demands of the customers. The system makes use of an array of technologies, ranging from deep learning to traditional computer vision, at different stages of analysis. In this paper, we outline how the system works and discuss the unique challenges related to applying machine learning techniques to real-world data from e-commerce domain. We emphasize how we tune state-of-the-art image classification techniques to develop solutions custom made for a massive, diverse, and constantly evolving product catalog. We also provide the details of how we measure the system’s impact on various customer engagement metrics.
Temperature Gradient Exploration Method for Determining the Appropriate Number of Cells in Mesh-Based Thermal Analysis

When multiprocessor system-on-chip designs are subjected to aggressive scaling of the processing technology, severe thermal effects will influence the performance, power, and reliability of integrated circuits (ICs). Hence, IC designers employ mesh-based transient thermal analysis (MTTA) to effectively predict the variations and distribution of temperatures in a chip during the early stages of IC design. The number of cells used in MTTA tools is decided by the user. However, users are generally unclear about whether the number of cells used for three dimensions is appropriate. An unsuitable number of cells may reduce the analytical performance or cause larger analysis errors in MTTA. Hence, MTTA using an appropriate number of cells can obtain higher analytical performance while maintaining acceptable accuracy. In this paper, we developed a temperature gradient exploration (TGE) method for exploring the appropriate number of cells to use in MTTA. The experimental results showed that the TGE method can find the appropriate number of cells and enhance the analytical performance of MTTA with acceptable accuracy.
Literature Review: Analyzing and Organizing User Search Queries

A broad topic may have wide interpretations for a particular query, Search goals differ from user to user when they submit a particular query to the search engine . The understanding of users search goals is very useful in improving the user experience . Primarily, user specific search goals are recognized by the clustering of the feedback sessions. User's click through logs help to build the feedback sessions which efficiently reflect the user's search goals. Secondly, an Incremental approach to create pseudo-documents for better representations of feedback session can be used for clustering. Experimental results will be shown by the user's click through logs from a commercial search engine to show the accurate search results entered by the user. Keywords— User search goals, feedback sessions,
The diagnosis conundrum: Comparison of crowdsourced and expert assessments of toddlers with high and low risk of autism spectrum disorder

As the diagnosis and treatment of Autism Spectrum Disorder (ASD) becomes a greater public health concern, access to professionals and expert assessment becomes increasingly more challenging. Novel techniques to improve efficiency of assessment of young children with social communication concerns are desperately needed to address significant barriers to accessing diagnostic evaluations. Utilizing crowdsourcing technology, we compared ratings by experts and crowdworkers of social communication behaviors in videos of 18‐month‐old children. Of note, 563 crowdworkers and 24 expert responses were collected in approximately 7 hr and 57 days, respectively. Summed scores of social communication behaviors observed by crowdworkers showed high correlation to those of experts. These data introduce a novel and perhaps efficient way in which to examine of social communication impairments in toddlers. Autism Research 2018, 11: 1629–1634. © 2018 International Society for Autism Research, Wiley Periodicals, Inc.
Loop Closure Detection with RGB-D Feature Pyramid Siamese Networks

In visual Simultaneous Localization And Mapping (SLAM), detecting loop closures has been an important but difficult task. Currently, most solutions are based on the bag-of-words approach. Yet the possibility of deep neural network application to this task has not been fully explored due to the lack of appropriate architecture design and of sufficient training data. In this paper we demonstrate the applicability of deep neural networks by addressing both issues. Specifically we show that a feature pyramid Siamese neural network can achieve state-of-the-art performance on pairwise loop closure detection. The network is trained and tested on large-scale RGB-D datasets with a novel automatic loop closure labeling algorithm. Each image pair is labelled by how much the images overlap, allowing loop closure to be computed directly rather than by labor intensive manual labeling. We present an algorithm to adopt any large-scale generic RGB-D dataset for use in training deep loop-closure networks. We show for the first time that deep neural networks are capable of detecting loop closures, and we provide a method for generating large-scale datasets for use in evaluating and training loop closure detectors.
On the relationship between augmented transition network and attributed grammar

Finite state attributed grammars are known to be rich in descriptive power because semantic information is included along with the structural information. They have applied to a number of practical problems in pattern recognition. The generative power of a finite-state attributed grammar is studied. The approach adopted is to establish a relationship between this grammar and the augmented transition network, which is known to be as powerful as a Turing machine. It is established that the grammar can generate at least some of the type phi language. The potential application of this grammar is in the areas of computer vision and artificial intelligence. <<ETX>>
Assessing the methodological and reporting quality of network meta-analyses in Chinese medicine

Background An increasing number of network meta-analyses (NMAs) in traditional Chinese medicine (TCM) have been published recently, but the quality of them was lack of assessment. This study aims to evaluate the methodological and reporting quality of NMAs in TCM. Methods Six electronic databases, including PubMed, the Cochrane Central Register of Controlled Trials (CENTRAL), Embase, China National Knowledge Infrastructure (CNKI), Wanfang and Chinese Biomedical Literature Database (CBM) from inception to January 2018, were searched. NMAs of TCM were included. A measurement tool to assess the methodological quality of systematic reviews (AMSTAR) and the PRISMA Extension Statement for Reporting of Systematic Reviews Incorporating Network Meta-analyses of Health Care Interventions (PRISMA-NMA) were used to assess the methodological and reporting quality of the included NMAs. Results A total of 40 NMAs, including 2535 randomized controlled trials (RCTs), were included. They were published between December 2012 and November 2017. The median score and interquartile range of methodological and reporting quality was 7 (6–8) and 22 (19.1–27.1). Serious methodological flaws existed in the following aspects: the status of publication (22.5%), a list of studies provided (0%), assessment of publication bias (37.5%), and conflicts of interest (12.5%). Several items need to be improved in reporting, especially for Protocol and registration (2.5%), Data items (22.5%), Risk of bias across studies (Methods section) (37.5%), Results of individual studies (27.5%), Risk of bias across studies (Results section) (40%), Results of additional analyses (35%), and Funding (15%). Conclusions The methodological and reporting quality of NMAs in TCM is moderate. Identified shortcomings of published NMAs should be taken into consideration in further trainings of authors and editors of NMAs in TCM. Future researchers should be encouraged to apply PRISMA-NMA, and a recognized tool for the assessment of NMA methodology was wanted.
Spatial-Temporal Visualization of Dengue Incidences Using Gaussian Kernel

The dengue disease has been reported as a major cause of morbidity and mortality for the last 40 years worldwide including in Malaysia. According to the Malaysian Statists Department, total reported number of dengue cases for the year 2008 to 2010 is 136,992 and it is increasing every year. According to Ministry of Health (MoH) of Malaysia, in urban area 77% of people are suffering from dengue virus compared to 23% people suffer in rural areas. Several studies on dengue are based on their serotype, epidemiology, weather forecasting, but this thesis look into the prediction modelling based on clustering algorithm. Since, the motivation to work on this projects comes from old existing studies which only represent the spatial map with undefined number of incidences and no clustering model exist. Since, in our study we have proposed the spatial- temporal mapping along with the Clustering techniques which comes with k-mean as the initial step to generate the clusters of incidences after that to optimize K-means, K-NN techniques is applied to find best fit K values. After that Gaussian Mixture Model is applied to find density of the dengue incidences, since where K-means is used to find the centroid of the incidences. To process the Gaussian Mixture Model, Estimation Maximization (EM) algorithm is used to relate the cluster with their respective clusters. To optimize the EM algorithm Bayesian Information Criteria (BIC) is performed which gives the best fitting model of BIC and optimizes the EM algorithm. In the end, Geographical Information System (GIS) technique is use to visualize vulnerability mapping to locate the accurate prediction location for dengue incidences in state Selangor of Malaysia (area of study). This research work discusses and implements the visualization and prediction modelling based on machine learning concepts, for the vector borne diseases (dengue). The results are tested for a region (Petaling district of Selangor state) in Malaysia and they showed good performance in predicting the dengue incidences. Thus, the proposed method is able to localize the nature of dengue incidence that can further be utilized for vector disease controlled process. The results confirms location of the predicted coordinates based on the previous data for the year 2014.
Sexual Assault and Cancer Screening Among Men and Women

Emerging research has shown that experiences of sexual violence are associated with cancer screening behaviors. However, to date, little attention has been given to the impact of sexual assault on cancer screening behaviors, particularly among men. To address this, we used data from the 2014 Kansas Behavioral Risk Factor Surveillance System. This survey is conducted annually via telephone. Logistic regressions were used to calculate odds of ever engaging (i.e., lifetime) in specific screening behaviors (clinical breast exam [CBE], mammogram, Pap test, colonoscopy/sigmoidoscopy, fecal occult blood test and prostate-specific antigen [PSA] test) and current compliance with cancer screening recommendations (CBE, mammogram, Pap test, colorectal cancer screening, and PSA test), with lifetime sexual assault as the independent variables. Colorectal cancer screening models were stratified by gender. All models adjusted for confounders. Sexual assault was associated with lower odds of ever receiving a PSA test and, for women, higher odds of ever receiving a colonoscopy or sigmoidoscopy. Sexual assault was associated with lower odds of current compliance with CBE, mammogram, Pap test, and PSA test screening recommendations. A history of sexual assault was associated with lower odds of current compliance with cancer screening procedures, with the notable exception of colorectal cancer screening. Less consistent patterns were seen for lifetime screenings. Results suggest that alternatives to currently recommended procedures (like self-collection of HPV [human papilloma virus] DNA) or modified screening procedures may be a promising route to increase current compliance with cancer screening among a population that may avoid these procedures due to pain or fear of retraumatization.
Surgical quality in organ procurement during day and night: an analysis of quality forms

Objectives To analyse a potential association between surgical quality and time of day. Design A retrospective analysis of complete sets of quality forms filled out by the procuring and accepting surgeon on organs from deceased donors. Setting Procurement procedures in the Netherlands are organised per region. All procedures are performed by an independent, dedicated procurement team that is associated with an academic medical centre in the region. Participants In 18 months’ time, 771 organs were accepted and procured in The Netherlands. Of these, 17 organs were declined before transport and therefore excluded. For the remaining 754 organs, 591 (78%) sets of forms were completed (procurement and transplantation). Baseline characteristics were comparable in both daytime and evening/night-time with the exception of height (p=0.003). Primary outcome measure All complete sets of quality forms were retrospectively analysed for the primary outcome, procurement-related surgical injury. Organs were categorised based on the starting time of the procurement in either daytime (8:00–17:00) or evening/night-time (17:00–8:00). Results Out of 591 procured organs, 129 organs (22%) were procured during daytime and 462 organs (78%) during evening/night-time. The incidence of surgical injury was significantly lower during daytime; 22 organs (17%) compared with 126 organs (27%) procured during evening/night-time (p=0.016). This association persists when adjusted for confounders. Conclusions This study shows an increased incidence of procurement-related surgical injury in evening/night-time procedures as compared with daytime. Time of day might (in)directly influence surgical performance and should be considered a potential risk factor for injury in organ procurement procedures.
Book Review: Antibiotic and Chemotherapy: Anti-Infective Agents and Their Use in Therapy. 8th ed.

Antibiotic and Chemotherapy: Anti-Infective Agents and Their Use in Therapy. 8th ed. By Roger G Finch MB BS FRCP FRCPath FRCPEd FFPM, David Greenwood BSc PhD DSc FRCPath, S Ragnar Norrby MD PhD FRCP(Edin), and Richard J Whitley MD. Published by Churchill Livingstone, an imprint of Elsevier Science Ltd., Philadelphia, PA, 2003. ISBN 0-443-07129-2. Clothbound, xii + 964 pp. (28.5 × 22.5 cm), $179. www.elsevierhealth. com
A Technique for Spurious Suppression in Substrate Integrated Waveguide Filters

This paper presents an efficient technique for the broadband suppression of spurious frequency bands in substrate integrated waveguide (SIW) filters. The technique is based on the combined use of a regular SIW filter and a pre-filter, with a broader pass band and a much larger spurious-free band. The effect is a limitation of the spurious frequency bands of the SIW filter, which are attenuated according to an envelope corresponding to the transmission of the pre-filter. The proposed technique is validated through an example, consisting of a three-pole SIW inline filter combined with a two-pole pre-filter with shorted microstrip stubs.
Two-point-based binary search trees for accelerating big data classification using KNN

Big data classification is very slow when using traditional machine learning classifiers, particularly when using a lazy and slow-by-nature classifier such as the k-nearest neighbors algorithm (KNN). This paper proposes a new approach which is based on sorting the feature vectors of training data in a binary search tree to accelerate big data classification using the KNN approach. This is done using two methods, both of which utilize two local points to sort the examples based on their similarity to these local points. The first method chooses the local points based on their similarity to the global extreme points, while the second method chooses the local points randomly. The results of various experiments conducted on different big datasets show reasonable accuracy rates compared to state-of-the-art methods and the KNN classifier itself. More importantly, they show the high classification speed of both methods. This strong trait can be used to further improve the accuracy of the proposed methods.
On Quaternary 1-of-4 ID Generator Circuits

A quaternary l-of-4 ID generator circuit is presented. It exploits the properties of quaternary metastability to provide stable n-bit IDs tolerant to the effects of nanoscale process scaling and temperature variations, which is achieved by increasing the margin between threshold voltage and metastability voltage via quaternary metastability. A 128-bit ID generator hardware implementation and electrical evaluation yields an average of 26.6% uniqueness of ID bits and 100% stability rate over a $10^{\mathrm {o}}C$ to $40^{\mathrm {o}}C$ temperature range.
Study of the Hand Anatomy Using Real Hand and Augmented Reality

Anatomy is considered one of the foundation studies for all of the health science students especially medical and nursing students. Anatomy of the hand is complicated. It composes of bones, nerves, blood veins, muscles, and tendon. Memorising all the details about all those parts is tedious work and need much imagination. With the advances in computer graphics and human-computer interaction techniques, understanding how those body parts move is easy to understand in a visual presentation. Augmented Reality (AR) is the technique that allowed the computer-generated objects to overlay on top of the real world. In this study, we concentrate on studying the bones only. We have selected the Leap Motion, which is the device that can detect the hands and fingers, like a tracking device, and marker-based AR technique for displaying the computer generated bones on top of the real hand. Since the Leap Motion detects the hands and shows the bone in real time, so when a user moves the hands such as waving, all the 3D virtual bones move to the new position just like the real hand. Besides using this tool as the educational tool to help the students have better learning about anatomy, it can also be used as an assessment tool for anatomy class as well. Results from testing this tool with volunteer students indicate that it helps them to understand the hand anatomy better and faster than traditional ways.
Comparative Efficacy and Safety of Long-acting Injectable and Oral Second-generation Antipsychotics for the Treatment of Schizophrenia: A Systematic Review and Meta-analysis

We aimed to compare the efficacy and safety of long-acting injectable (LAI) and oral second-generation antipsychotics (SGAs) in treating schizophrenia by performing a systematic review and meta-analysis. MEDLINE, EMBASE, PsycINFO, CINAHL, and the Cochrane Library, as well as five Korean databases, were systemically searched to identify studies published from 2000 to 16 April 2015, which compared the efficacy and safety of LAI and oral SGAs. Using data from randomized controlled trials (RCTs), meta-analyses were conducted. In addition, the GRADE (the Grading of Recommendations, Assessment, Development and Evaluation) approach was applied to explicitly assess the quality of the evidence. A total of 30 studies including 17 RCTs and 13 observational studies were selected. The group treated with LAI SGAs was characterized by significantly lower relapse rates, longer times to relapse and fewer hospital days, but also by a higher occurrence of extrapyramidal syndrome and prolactin-related symptoms than that in the group treated with oral SGAs. Our findings demonstrate that there is moderate to high level of evidence suggesting that in the treatment of schizophrenia, LAI SGAs have higher efficacy and are associated with higher rates of extrapyramidal syndrome and prolactin-related symptoms. Additionally, the use of LAI SGAs should be combined with appropriate measures to reduce dopamine D2 antagonism-related symptoms.
Impactos da Gestão Escolar Sobre o Desempenho Educacional: uma análise longitudinal

Este trabalho investiga a influencia da gestao escolar na proficiencia de portugues e de matematica dos alunos de escolas publicas e privadas. Para tal, utilizam-se os microdados longitudinais do projeto GERES, que acompanha os alunos de uma amostra de escolas durante os quatro primeiros anos do Ensino Fundamental, para os anos de 2005 a 2008, controlando a heterogeneidade nao-observada. Os resultados apontam que a variavel de gestao – medida como um componente de variaveis relativas ao diretor respondidas pelos professores – e importante para explicar a proficiencia dos alunos. O impacto da gestao escolar sobre o desempenho educacional em portugues e menor do que em matematica, mas e negativo em ambos. Tambem foi constatado que o controle das habilidades nao observadas e necessario para que as estimativas sejam consistentes. Palavras-chave: gestao escolar; proficiencia; efeitos fixos;GERES
Robustness against the channel effect in pathological voice detection

Many people are suffering from voice disorders, which can adversely affect the quality of their lives. In response, some researchers have proposed algorithms for automatic assessment of these disorders, based on voice signals. However, these signals can be sensitive to the recording devices. Indeed, the channel effect is a pervasive problem in machine learning for healthcare. In this study, we propose a detection system for pathological voice, which is robust against the channel effect. This system is based on a bidirectional LSTM network. To increase the performance robustness against channel mismatch, we integrate domain adversarial training (DAT) to eliminate the differences between the devices. When we train on data recorded on a high-quality microphone and evaluate on smartphone data without labels, our robust detection system increases the PR-AUC from 0.8448 to 0.9455 (and 0.9522 with target sample labels). To the best of our knowledge, this is the first study applying unsupervised domain adaptation to pathological voice detection. Notably, our system does not need target device sample labels, which allows for generalization to many new devices.