Miracle-dz/UniFAR-FTUs huggingface dataset:
Interactive Natural Language Processing for Clinical Text

Free-text allows clinicians to capture rich information about patients in narratives and first-person stories. Care providers are likely to continue using free-text in Electronic Medical Records (EMRs) for the foreseeable future due to convenience and utility offered. However, this complicates information extraction tasks for big-data applications. Despite advances in Natural Language Processing (NLP) techniques, building models on clinical text is often expensive and time-consuming. Current approaches require a long collaboration between clinicians and data-scientists. Clinicians provide annotations and training data, while data-scientists build the models. With the current approaches, the domain experts - clinicians and clinical researchers - do not have provisions to inspect these models or give direct feedback. This forms a barrier to NLP adoption and limits its power and utility for real-world clinical applications. Interactive learning systems may allow clinicians without machine learning experience to build NLP models on their own. Interactive methods are particularly attractive for clinical text due to the diversity of tasks that need customized training data. Interactivity could enable end-users (clinicians) to review model outputs and provide feedback for model revisions within an closed feedback loop. This approach may make it feasible to extract understanding from unstructured text in patient records; classifying documents against clinical concepts, summarizing records and other sophisticated NLP tasks while reducing the need for prior annotations and training data upfront. In my dissertation, I demonstrate this approach by building and evaluating prototype systems for both clinical care and research applications. I built NLPReViz as an interactive tool for clinicians to train and build binary NLP models on their own for retrospective review of colonoscopy procedure notes. Next, I extended this effort to design an intelligent signout tool to identify incidental findings in a clinical care setting. I followed a two-step evaluation with clinicians as study participants: a usability evaluation to demonstrate feasibility and overall usefulness of the tool, followed by an empirical evaluation to evaluate model correctness and utility. Lessons learned from the development and evaluation of these prototypes will provide insight into the generalized design of interactive NLP systems for wider clinical applications.
Quantized Variational Bayesian Joint Channel Estimation and Data Detection for Uplink Massive MIMO Systems with Low resolution ADCS

In this paper, we consider the joint channel estimation and data detection in an uplink massive multiple input multiple output (MIMO) receiver with low resolution analog to digital converters (ADCs). The nonlinearities introduced by the ADCs make the existing linear multiuser detection (MUD) approaches suboptimal, and motivates a fresh look at the problem. Also, channel state information is necessary to obtain the channel quality metrics that are used for link adaptation by the base station (BS). We model the MIMO receiver system as a directed probabilistic graphical model, and propose a variational Bayesian procedure to estimate the channel and the posterior beliefs of the transmitted symbols. We evaluate the symbol error probability (SEP) and the normalized mean squared error (NMSE) of the channel estimates of the proposed algorithm using Monte Carlo simulations, and benchmark it against an unquantized variational Bayesian algorithm with perfect and imperfect channel state information at the receiver (CSIR).
How to Improve Customer Engagement: A Comparison of Playing Games on Personal Computers and on Mobile Phones

The popularity of multiplayer online battle arena games is closely linked to customer engagement. The purpose is to investigate the influences that individual motivators and environmental antecedents have upon customer engagement and continuous playing games intention, and compare the impacts between playing Multiplayer online battle arena games on personal computers and on mobile phones. A Triadic reciprocal determinism framework was developed to explain the relationship among individual motivator (flow experience), environmental antecedents (technical features and social interaction), individual behavior (continuous playing games intention), and customer engagement within multiplayer online battle arena gaming settings. Altogether 531 questionnaires from multiplayer online battle arena players were collected. Structural equation modeling with Smart-partial least squares was used to evaluate the research model. The findings suggest that the influence of flow experience on customer engagement in personal computers gaming is more significant than that in mobile gaming, while the effects of perceived ease of use and offline social interaction on customer engagement in mobile gaming are comparatively higher. This study indicates the importance of flow experience, perceived control, perceived ease of use, and social interaction in the Multiplayer online battle arena gaming development and the prosperity for better customers engaged, and to enhance customer experience.
Gait event detection algorithm based on smart insoles

Gait analysis is an effective clinical tool across a wide range of applications. Recently, inertial measurement units have been extensively utilized for gait analysis. Effective gait analyses require good estimates of heel‐strike and toe‐off events. Previous studies have focused on the effective device position and type of triaxis direction to detect gait events. This study proposes an effective heel‐strike and toe‐off detection algorithm using a smart insole with inertial measurement units. This method detects heel‐strike and toe‐off events through a time‐frequency analysis by limiting the range. To assess its performance, gait data for seven healthy male subjects during walking and running were acquired. The proposed heel‐strike and toe‐off detection algorithm yielded the largest error of 0.03 seconds for running toe‐off events, and an average of 0–0.01 seconds for other gait tests. Novel gait analyses could be conducted without suffering from space limitations because gait parameters such as the cadence, stance phase time, swing phase time, single‐support time, and double‐support time can all be estimated using the proposed heel‐strike and toe‐off detection algorithm.
The Method of Cutting Image of Vehicle Face based on Haar Feature and Improved Cascade Classifier

A method of car face image segmentation based on haar-like features and improved cascade classifier was proposed to solve the problems of insufficient classification recognition performance and too long training time of the current classifier. First, the haar-like features of the image are calculated based on integral graph. Then the extracted feature values are input into the cascade classifier for training. Finally, the trained classifier was used to detect the vehicle face and segment the image of the vehicle face. In this paper an adaptive threshold setting method is proposed. The experimental results show that the method has the better performance than the traditional methods, and has better application prospects.
Texture Traits with Uniform-Quantization in Handwriting Documents for Digital Forensics Investigation

In crime and falsification related to documents are often difficult to verify orauthenticated by the authorities. This is the basis of research to develop a system in assisting digital forensics to investigate and seek the truth of the evidence in the form of digital handwritten documents. The steps that being taken by researchers are collecting and digitizing the documents into image form, converting color from RGB to greyscale, separate the object through thresholding, color histogram, uniform quantization from 256 to 128 of greylevel, texture feature extraction ie variance, skew, relative smoothness, entropy and mean, normalize the feature value and similarity measures through Euclidean distance calculations. From the results of testing of 10 data that has been matched with 20 training data, 6 documents successfully recognized correctly the authenticity of the document owner. Thus, the system in this study produces an accuracy of 60% and can be used to assist digital forensics in analyzing the authenticity of handwritten documents.
An Improved Measure of Musical Noise Based on Spectral Kurtosis

Audio processing methods operating on a time-frequency representation of the signal can introduce unpleasant sounding artifacts known as musical noise. These artifacts are observed in the context of audio coding, speech enhancement, and source separation. The change in kurtosis of the power spectrum introduced during the processing was shown to correlate with the human perception of musical noise in the context of speech enhancement, leading to the proposal of measures based on it. These baseline measures are here shown to correlate with human perception only in a limited manner. As ground truth for the human perception, the results from two listening tests are considered: one involving audio coding and one involving source separation. Simple but effective perceptually motivated improvements are proposed and the resulting new measure is shown to clearly outperform the baselines in terms of correlation with the results of both listening tests. Moreover, with respect to the listening test on musical noise in audio coding, the exhibited correlation is nearly as good as the one exhibited by the Artifact-related Perceptual Score (APS), which was found to be the best objective measure for this task. The APS is however computationally very expensive. The proposed measure is easily computed, requiring only a fraction of the computational cost of the APS.
Uso de Mineração de Dados para o Auxílio de Produção de Material Didático em Disciplinas de Algoritmos

Algoritmo pode ser definido como uma sequência de ações executáveis para a obtenção de uma determinada solução buscando a obtenção da solução de um problema. A disciplina de Algoritmos, é o primeiro contato dos alunos com a área de Programação de Computadores e, além disso, a base para a Computação. Pórem, para muitos alunos a implementação de algoritmos é um desafio devido a dificuldade de ordem técnica, detalhes de linguagem de programação ou ainda configuração dos ambientes de desenvolvimendo. Diante dessas dificuldades, podem ocorrer casos nos quais o professor não consegue identificar a frequência dessas dificuldades, levando os alunos a uma maior frustração na aprendizagem dos conceitos subjacentes da disciplina. Este trabalho propõe a criação de um sistema de recomendação de conteúdo técnico programático. Este sistema tem como parte essencial um modelo gerado a partir da mineração da base de dados do StackOverflow. Alguns resultados preliminares deste modelo já foram obtidos com o nível de acurácia de treinamento maior que 90%.
A privacy‐preserving multifactor authentication system

In recent years, there has been a significant number of works on the development of multifactor authentication (MFA) systems. Traditionally, behavioral biometrics (eg, keystroke dynamics) have been known to have the best usability because they do not require one to know or possess anything—they simply communicate “how you type” to an authenticator. However, though highly usable, MFA approaches that are based on biometrics are highly intrusive, and users' sensitive information is exposed to untrusted servers. To address this privacy concern, in this paper, we present a privacy‐preserving MFA system for computer users, called PINTA. In PINTA, the second factor is a hybrid behavioral profile user, while the first authentication factor is a password. The hybrid profile of the user includes host‐based and network flow‐based features. Since the features include users' sensitive information, it needs to be protected from untrusted parties. To protect users' sensitive profiles and to handle the varying nature of the user profiles, we adopt two cryptographic methods: Fuzzy hashing and fully homomorphic encryption (FHE). Our results show that PINTA can successfully validate legitimate users and detect impostors. Although the results are promising, the trade‐off for privacy preservation is a slight reduction in performance compared with traditional identity‐based MFA techniques.
Hotspot-Aware Hybrid Memory Management for In-Memory Key-Value Stores

Emerging Non-Volatile Memory (NVM) technologies promise much higher memory density and energy efficiency than DRAM, at the expense of higher read/write latency and limited write endurance. Hybrid memory systems composed of DRAM and NVM have the potential to provide very large capacity of main memory for in-memory key-value (K-V) stores. However, there remains challenges to directly deploy DRAM-based K-V stores in hybrid memory systems. The performance and energy efficiency of K-V stores on hybrid memory systems have not been fully explored yet. In this paper, we propose HMCached, an in-memory K-V store built on a hybrid DRAM/NVM system. HMCached utilizes an application-level data access counting mechanism to identify frequently-accessed (hotspot) objects (i.e., K-V pairs) in NVM, and migrates them to fast DRAM to reduce the costly NVM accesses. We also propose an NVM-friendly index structure to store the frequently-updated portion of object metadata in DRAM, and thus further mitigate the NVM accesses. Moreover, we propose a benefit-aware memory reassignment policy to address the slab calcification problem in slab-based K-V store systems, and significantly improve the benefit gain from the DRAM. We implement the proposed schemes with Memcached and evaluate it with Zipfian-like workloads. Experiment results show that HMCached significantly reduces NVM accesses by 70 percent compared to the vanilla Memcached running on a DRAM/NVM hybrid memory system without any optimizations, and improves application performance by up to 50 percent. Moreover, compared to a DRAM-only system, HMCached achieves 90 percent of performance and 46 percent reduction of energy consumption for realistic (read-intensive) workloads while significantly reducing the DRAM usage by 75 percent.
Prevalence, prognosis and characteristics of subjects with short QT interval in an electrocardiogram

Short QT syndrome is an inherited arrhythmia disorder characterized by a short QT interval, typical T-wave and ST-segment morphology and an increased risk of sudden cardiac death. The purpose of this thesis was to study the epidemiology and prognosis of the subjects with short QT intervals. Special attention was paid to the ECG changes that could illustrate the prognosis of subjects with short QT interval. The first study comprised a group of patients with short QT syndrome. We report clinical presentation, ECG morphology, the prevalence of genetic mutations and the results of therapies in this group of patients. The second study population consisted of 10 822 randomly selected middle-aged subjects followed up for 29 ± 10 years. QT intervals were measured using three correction methods for the heart rate in order to assess the prevalence and prognosis of those subjects with short QT intervals. The third population consisted of three patients with short QT syndrome and nine controls. Holter recordings were analyzed to compare transmural dispersion of repolarization between patients and controls and also to study their capability to change repolarization indexes from baseline to maximal values. In the fourth study ECGs from 10 patients with short QT syndrome were compared with ECGs of 12 asymptomatic subjects with short QT intervals. The aim was to find ECG abnormalities that would predict the outcome of the patients. We found 62% of patients to be symptomatic, 34% had cardiac arrest. Atrial fibrillation was common. Most of the patients received an ICD or were placed on hydroquinidine. The prevalence of QTc < 320ms was 0.10% and QTc < 340ms was 0.4%, respectively. Mortality or other serious symptoms did not differ between subjects and controls. We also found that the TPE/QT ratio as an index for abnormal transmural dispersion of repolarization was high compared to controls. Short QT syndrome patients had also lesser capacity to change the QT interval, indicating blunted autonomic response in SQTS. Ten SQTS patients had significantly shorter Jpoint–Tpeak interval and higher TPE/QT ratio compared to controls. In conclusion, shorter than normal QT interval might represent a novel short QT syndrome. However, in the general community short QT interval can reflect only the extreme end of the normal Gaussian distribution of QT intervals and these subjects carry a good prognosis. TPE/QT ratio and Jpoint–Tend intervals can be used as risk stratifiers in subjects with short QT intervals.
EXPERIMENTING THE ENERGY ECONOMIC VARIABLES REGARDING THE LONG-HAUL CONSEQUENCES ON INDONESIA USING VECTOR ERROR CORRECTION MODEL

The Indonesian economy has recorded solid growth in the course of recent decades, and as of late, the firm pace of economic expansion has been joined by decreased yield instability and moderately stable inflation. As a sort of rare characteristic capital, energy makes increasingly more clear imperative consequences for economic growth (EG). Furthermore, energy consumption (EC) is the real wellspring of greenhouse gas emissions. This achieves the issues of the connections among energy economic variables, which is deserving of long-haul consideration between human capital (HC), EC, CO2 emissions, and EG in Indonesia. As a close neighbour, Australia has long had critical exchange ties with Indonesia. The effect of various types of EC and HC on per capita GDP growth. The information utilized world development indicator has acquired from the World Bank database amid 1985- 2017. The examination technique utilized vector error correction model. In this mainly three tests are conducted in order to know the relationship among variables which is unit root test, co-integration and temporal Granger causality the outcomes show that apportioning consumption energy and controlling carbon emissions, are probably going to have no antagonistic impact on the genuine GDP per capita. In the meantime, empirically, the development of HC has the effect on controlling CO2 emissions and EC in Indonesia.
Accelerating simulations of cardiac electrical dynamics through a multi‐GPU platform and an optimized data structure

Simulations of cardiac electrophysiological models in tissue, particularly in 3D require the solutions of billions of differential equations even for just a couple of milliseconds, thus highly demanding in computational resources. In fact, even studies in small domains with very complex models may take several hours to reproduce seconds of electrical cardiac behavior. Today's Graphics Processor Units (GPUs) are becoming a way to accelerate such simulations, and give the added possibilities to run them locally without the need for supercomputers. Nevertheless, when using GPUs, bottlenecks related to global memory access caused by the spatial discretization of the large tissue domains being simulated, become a big challenge. For simulations in a single GPU, we propose a strategy to accelerate the computation of the diffusion term through a data‐structure and memory access pattern designed to maximize coalescent memory transactions and minimize branch divergence, achieving results approximately 1.4 times faster than a standard GPU method. We also combine this data structure with a designed communication strategy to take advantage in the case of simulations in multi‐GPU platforms. We demonstrate that, in the multi‐GPU approach performs, simulations in 3D tissue can be just 4× slower than real time.
Evaluation of patient-level retrieval from electronic health record data for a cohort discovery task

Abstract Objective Growing numbers of academic medical centers offer patient cohort discovery tools to their researchers, yet the performance of systems for this use case is not well understood. The objective of this research was to assess patient-level information retrieval methods using electronic health records for different types of cohort definition retrieval. Materials and Methods We developed a test collection consisting of about 100 000 patient records and 56 test topics that characterized patient cohort requests for various clinical studies. Automated information retrieval tasks using word-based approaches were performed, varying 4 different parameters for a total of 48 permutations, with performance measured using B-Pref. We subsequently created structured Boolean queries for the 56 topics for performance comparisons. In addition, we performed a more detailed analysis of 10 topics. Results The best-performing word-based automated query parameter settings achieved a mean B-Pref of 0.167 across all 56 topics. The way a topic was structured (topic representation) had the largest impact on performance. Performance not only varied widely across topics, but there was also a large variance in sensitivity to parameter settings across the topics. Structured queries generally performed better than automated queries on measures of recall and precision but were still not able to recall all relevant patients found by the automated queries. Conclusion While word-based automated methods of cohort retrieval offer an attractive solution to the labor-intensive nature of this task currently used at many medical centers, we generally found suboptimal performance in those approaches, with better performance obtained from structured Boolean queries. Future work will focus on using the test collection to develop and evaluate new approaches to query structure, weighting algorithms, and application of semantic methods.
Private computation on genomic data

Capturing the vast amount of information encoded in the human genome is a fascinating research problem. The outcomes of this research have significant influences on a number of health-related fields, such as personalized medicine, paternity testing, and disease susceptibility testing. To facilitate these types of large-scale biomedical research projects, it oftentimes requires sharing genomic and clinical data collected by disparate organizations among themselves. In that case, it is of utmost importance to ensure that sharing, managing, and analyzing the data does not reveal the identity of the individuals who contribute their genomic samples. The task of storage and computation on the shared data can be delegated to third-party cloud infrastructures, equipped with large storage and high-performance computation resources. Outsourcing these sensitive genomic data to the third party cloud storage is associated with the challenges of the potential loss, theft, or misuse of the data as the server administrator cannot be completely trusted as well as there is no guarantee that the security of the server will not be breached. In this thesis, I propose methods for secure sharing and computation of three different functions on genomic data.