Miracle-dz/UniFAR-FTUs huggingface dataset:
Determination of Personalized Asthma Triggers From Multimodal Sensing and a Mobile App: Observational Study

Background Asthma is a chronic pulmonary disease with multiple triggers. It can be managed by strict adherence to an asthma care plan and by avoiding these triggers. Clinicians cannot continuously monitor their patients’ environment and their adherence to an asthma care plan, which poses a significant challenge for asthma management. Objective In this study, pediatric patients were continuously monitored using low-cost sensors to collect asthma-relevant information. The objective of this study was to assess whether kHealth kit, which contains low-cost sensors, can identify personalized triggers and provide actionable insights to clinicians for the development of a tailored asthma care plan. Methods The kHealth asthma kit was developed to continuously track the symptoms of asthma in pediatric patients and monitor the patients’ environment and adherence to their care plan for either 1 or 3 months. The kit consists of an Android app–based questionnaire to collect information on asthma symptoms and medication intake, Fitbit to track sleep and activity, the Peak Flow meter to monitor lung functions, and Foobot to monitor indoor air quality. The data on the patient’s outdoor environment were collected using third-party Web services based on the patient’s zip code. To date, 107 patients consented to participate in the study and were recruited from the Dayton Children’s Hospital, of which 83 patients completed the study as instructed. Results Patient-generated health data from the 83 patients who completed the study were included in the cohort-level analysis. Of the 19% (16/83) of patients deployed in spring, the symptoms of 63% (10/16) and 19% (3/16) of patients suggested pollen and particulate matter (PM2.5), respectively, to be their major asthma triggers. Of the 17% (14/83) of patients deployed in fall, symptoms of 29% (4/17) and 21% (3/17) of patients suggested pollen and PM2.5, respectively, to be their major triggers. Among the 28% (23/83) of patients deployed in winter, PM2.5 was identified as the major trigger for 83% (19/23) of patients. Similar correlations were not observed between asthma symptoms and factors such as ozone level, temperature, and humidity. Furthermore, 1 patient from each season was chosen to explain, in detail, his or her personalized triggers by observing temporal associations between triggers and asthma symptoms gathered using the kHealth asthma kit. Conclusions The continuous monitoring of pediatric asthma patients using the kHealth asthma kit generates insights on the relationship between their asthma symptoms and triggers across different seasons. This can ultimately inform personalized asthma management and intervention plans.
Essential medicine stock-outs in rural primary health care – exploring the contribution of politics and resulting perceptions in Kasulu District, Tanzania

The Consortium for Advanced Research Training in Africa (CARTA) and the University of Dar es Salaam supported this research. CARTA is jointly led by the African Population and Health Research Center and the University of the Witwatersrand and funded by the Wellcome Trust (UK)(Grant No: 087547/Z/08/Z), the Department for International Development (DfID) under the Development Partnerships in Higher Education (DelPHE), the Carnegie Corporation of New York (Grant No: B 8606), the Ford Foundation (Grant No: 1100-0399),Google.Org (Grant No: 191994), Sida (Grant No: 54100029) and MacArthur Foundation Grant No: 10-95915-000-INP. The funding organizations did not contribute to the design of the study or analysis and interpretation of the data.
Infrared image recognition based on structure sparse and atomic sparse parallel

Use the redundancy of the super complete dictionary can capture the structural features of the image effectively, can achieving the effective representation of the image. However, the commonly used atomic sparse representation without regard the structure of the dictionary and the unrelated non-zero-term in the process of the computation, though structure sparse consider the structure feature of dictionary, the majority coefficients of the blocks maybe are non-zero, it may affect the identification efficiency. For the disadvantages of these two sparse expressions, a weighted parallel atomic sparse and sparse structure is proposed, and the recognition efficiency is improved by the adaptive computation of the optimal weights. The atomic sparse expression and structure sparse expression are respectively, and the optimal weights are calculated by the adaptive method. Methods are as follows: training by using the less part of the identification sample, the recognition rate is calculated by the increase of the certain step size and t the constraint between weight. The recognition rate as the Z axis, two weight values respectively as X, Y axis, the resulting points can be connected in a straight line in the 3 dimensional coordinate system, by solving the highest recognition rate, the optimal weights can be obtained. Through simulation experiments can be known, the optimal weights based on adaptive method are better in the recognition rate, weights obtained by adaptive computation of a few samples, suitable for parallel recognition calculation, can effectively improve the recognition rate of infrared images.
Feature Template-Based Parallel Computation Technique for Conditional Random Fields *

Conditional Random Fields (CRFs) is a popular probabilistic graphical model, which has been applied in a wide range of areas, including Natural Language Process (NLP), Bioinformatics, Computer Vision, etc. However, con- textual features-based methods usually lead to large-scale feature functions and result in high computational complexity and low model training efficiency. In this paper, a feature template-based parallel computation technique is proposed to parallelly process M matrix and reduce computational complexity through observing the main feature of contextual feature function created by the template. Experimental results show that our approach significantly outperforms tradi- tional feature function approach on computation speed.
CLASSIFICATION OF TREE SPECIES ON THE BASIS OF TREE BARK TEXTURE

Abstract. Forest inventory is an important topic in forestry and a digital solution which works on the basis of tree images is looked for. Implementing a system which automatically classifies tree species is the overall goal. In this paper the implementation of a convolutional neural net for solving this classification problem is executed and evaluated. The objective is creating a system which works well on unseen data and deriving guidelines and constraints to guarantee good accuracy results. Images including tree segmentation and the corresponding labels are provided as training data. The tree species classification takes the segmentation results of a stereo vision based image segmentation algorithm as input. The basic idea consists of cropping the tree images into quadratic boxes before feeding them into the neural net. First, each box is classified separately and then the results are evaluated to get a classification for the whole tree. Methods for result improvement include altering box size, using overlapping boxes, artificially enlarging the training set, pretraining and finetuning. Cropping a tree image into boxes of a specific size and accumulating the single results to get a classification of the whole tree leads to an accuracy of 96.7% provided that specific constraints like minimum box number and the projected size of the tree on image plane are considered. Finally, ways to further improve performance are pointed out.
The role of cognitive feelings and content information in the evaluations of sport brands

ABSTRACT Purpose: The purpose of this investigation was twofold. First, we examined the influence of cognitive feelings and content information on evaluations of sports brands coming from sports goods manufacturing. Second, we examined if the hypothesized mediation effect of cognitive feelings was moderated by experiential thinking style. Methodology: We randomly assigned sport consumers from Mexico to two experimental conditions: recalling two versus six brand attributes of their favorite brand and completed a set of questionnaires assessing brand quality, ease of recall, demographic questions, and thinking style (study 2). Findings: Results from study 1 and study 2, a conceptual replication, showed the hypothesized mediation effect of cognitive feelings on recalling fewer brand attributes and brand evaluations. This mediated effect was replicated in study 2; hence it was not conditioned by experiential thinking style. Practical implications: Our findings have implications for sport market research and brand managers. Research contribution: Our findings served to illustrate the importance of conducting experiments to understand some aspects of sport consumer behavior and to shade light on how brand evaluations were informed by cognitive feelings.
Zanzibar: Google's Consistent, Global Authorization System

Determining whether online users are authorized to access digital objects is central to preserving privacy. This paper presents the design, implementation, and deployment of Zanzibar, a global system for storing and evaluating access control lists. Zanzibar provides a uniform data model and configuration language for expressing a wide range of access control policies from hundreds of client services at Google, including Calendar, Cloud, Drive, Maps, Photos, and YouTube. Its authorization decisions respect causal ordering of user actions and thus provide external consistency amid changes to access control lists and object contents. Zanzibar scales to trillions of access control lists and millions of authorization requests per second to support services used by billions of people. It has maintained 95th-percentile latency of less than 10 milliseconds and availability of greater than 99.999% over 3 years of production use.
在Intel Knights Corner和NVIDIA Kepler架构上OpenACC的性能可移植性分析 (Performance Portability Evaluation for OpenACC on Intel Knights Corner and NVIDIA Kepler)

OpenACC is a programming standard designed to simplify heterogeneous parallel programming by using directives. Since OpenACC can generate OpenCL and CUDA code, meanwhile running OpenCL on Intel Knight Corner is supported by CAPS HMPP compiler, it is attractive to using OpenACC on hardwares with different underlying microarchitectures. This paper studies how realistic it is to use a single OpenACC source code for a set of hardwares with different underlying micro-architectures. Intel Knight Corner and Nvidia Kepler products are the targets in the experiment, since they are with the latest architectures and have similar peak performance. Meanwhile CAPS OpenACC compiler is used to compile EPCC OpenACC benchmark suite, Stream and MaxFlops of SHOC benchmarks to access the peformance. To study the performance portability, roofline model and relative performance model are built by the data of experiments. This paper shows that at most 82% performance compared with peak performance on Kepler and Knight Corner is achieved by specific benchmarks, but as the rise of arithmetic intensity the average performance is approximately 10%. And there is a big performance gap between Intel Knight Corner and Nvidia Kepler on several benchmarks. This study confirms that performance portability of OpenACC is related to the arithmetic intensity and a big performance gap still exsits in specific benchmarks between different hardware platforms.
Analysis of Slow Motion Based on Adaptive Detection Method in Tennis Video

: This paper select the tennis video which has logo transition both at the beginning and the end of slow motion as research material. An adaptive threshold based slow motion replay detection method is proposed. This method solves the problem of adaptively selects threshold in the process of slow motion replay detection. Unlike previous approaches, this method has great improvement in both precision rate and positioning accuracy of the boundaries, and can be used for other sports videos which also have logo transition.
SAR Image Registration Using an Improved SAR-SIFT Algorithm and Delaunay-Triangulation-Based Local Matching

Synthetic aperture radar (SAR) image registration is a challenging task in remote sensing because of the presence of significant intensity as well as geometric differences between the images. Moreover, the influence of multiplicative speckle noise in SAR images adversely affects the registration result. The synthetic aperture radar-scale invariant feature transform (SAR-SIFT) algorithm, specially developed for the SAR images, has been used popularly for the automatic registration of SAR images based on global feature matching. However, the algorithm suffers from lack of controllability of the number of extracted features and uneven distribution of the features. Moreover, global matching of the features reduces the number of correct matches. To address these problems, we propose an improved SAR-SIFT (I-SAR-SIFT) algorithm, which extracts features from the SAR images by considering three important factors of feature-based image registration: stability, distinctiveness, and distribution. Moreover, a novel approach is adapted to improve the distinctiveness of the feature–descriptor in the presence of speckle noise. In addition, a Delaunay-triangulation-based local matching algorithm is introduced to significantly improve the matching performance in SAR images. Experiments on different pairs of SAR images show that the proposed method gives better performance than the existing SAR image registration methods.
Detection and diagnosis of sensor and actuator failures using IMM estimator

An approach to detection and diagnosis of multiple failures in a dynamic system is proposed. It is based on the interacting multiple-model (IMM) estimation algorithm, which is one of the most cost-effective adaptive estimation techniques for systems involving structural as well as parametric changes. The proposed approach provides an integrated framework for fault detection, diagnosis, and state estimation. It is able to detect and isolate multiple faults substantially more quickly and more reliably than many existing approaches. Its superiority is illustrated in two aircraft examples for single and double faults of both sensors and actuators, in the forms of "total", "partial", and simultaneous failures. Both deterministic and random fault scenarios are designed and used for testing and comparing the performance fairly. Some new performance indices are presented. The robustness of the proposed approach to the design of model transition probabilities, fault modeling errors, and the uncertainties of noise statistics are also evaluated.
Variable Screening for Multinomial Logistic Regression on Very Large Data Sets as Applied to Direct Response Modeling

Beginning with version 8.2 SAS supports multinomial logistic regression as part of PROC LOGISTIC. As a direct response modeling provider, we have found that multinomial logistic regression models often provide us with our best solutions. However, using multinomial logistic regression presents some challenges. We are often faced with very large sample sizes and a large number of candidate predictor variables. The sheer sizes of our data sets make variable selection and scoring significant issues. The functionality that SAS has built into PROC LOGISTIC to handle variable selection and scoring is not feasible for the size of data sets that we use. I will present how we have addressed the issue of variable screening in our modeling environment. I will provide a comparison of gains charts that show that the multinomial logistic model is preferable to other solutions. I will discuss and share bits of code on how we utilize univariate screening, resampling methods, and clustering for variable reduction. Discussing the issues regarding creating efficient production scoring modules utilizing multinomial logistic regression is beyond the scope this presentation. Upon request I will supply code for a simple macro that creates a text file version of a coefficient file that can be incorporated into an efficient production scoring environment.
Online Marketing and Consumer Buying Behaviour of Electronic Products in the North Central Zone of Nigeria

This study examined effects of online marketing and consumer buying behaviour of electronic products in the north central zone of Nigeria. The population of this study comprised online marketers and online consumers in the seven states in north central zone of Nigeria. Because the population is infinite, a sample of 385 respondents were taken from marketers and the online consumers. Information was obtained from the respondents using a structured questionnaire. The data collected were analyzed using descriptive statistics such as frequency, simple percentage and the relationship between the variables of the model was tested using multiple linear regression analysis. The result of the regression analysis indicates that a negative relationship exist between perceived risk on consumer (PRK) and consumer buying behavior of electronic products in the north central zone of Nigeria (CBHV) and the relationship is not statistically significant (p>0.05). A positive relationship exist between perceived enjoyment (PEN) and consumer buying behavior of electronic products in the north central zone of Nigeria (CBHV) and the relationship is statistically significant (p<0.05). Perceived ease of use (PEU) was positively related to consumer buying behavior of electronic products in the north central zone of Nigeria (CBHV) and the relationship is statistically significant (p<0.05). Perceived usefulness (PUF) was negatively related to consumer buying behavior of electronic products in the north central zone of Nigeria (CBHV) and the relationship is not statistically significant (p>0.05). A positive relationship exist between perceived convenience (PCN) and consumer buying behavior of electronic products in the north central zone of Nigeria (CBHV) and the relationship is statistically significant (p<0.05). It was concluded that majority of the respondents whopatronize online shopping adopt that because of the convenience and time saving factor they obtain from it. It was recommended among others that owners of online marketing platform should ensure that they make their website dynamic and user friendly so as to attract customerswho will derive comfort and satisfaction transacting from the comfort of their homes.
Machine learning applications in operations management and digital marketing

In this dissertation, I study how machine learning can be used to solve prominent problems in operations management and digital marketing. The primary motivation is to show that the application of machine learning can solve problems in ways that existing approaches cannot. In its entirety, this dissertation is a study of four problems—two in operations management and two in digital marketing—and develops solutions to these problems via data-driven approaches by leveraging machine learning. These four problems are distinct, and are presented in the form of individual self-containing essays. Each essay is the result of collaborations with industry partners and is of academic and practical importance. In some cases, the solutions presented in this dissertation outperform existing state-of-the-art methods, and in other cases, it presents a solution when no reasonable alternatives are available. The problems are: consumer debt collection (Chapter 3), contact center staffing and scheduling (Chapter 4), digital marketing attribution (Chapter 5), and probabilistic device matching (Chapters 6 and 7). An introduction of the thesis is presented in Chapter 1 and some basic machine learning concepts are described in Chapter 2.
Complexity in organoleptic paths of motion in the genre of craft beer reviews: a comparative study of Spanish and English.

The study of how languages differ in their portrayal of motion events has received much attention since Talmy (1972) provided the first detailed account of the phenomenon. Interest has extended from real, or factive motion, to imagined or fictive motion, and from there to metaphorical motion, in which experience in one sensory domain is understood in terms of motion. Studies of metaphorical motion have, however, concentrated so far on a limited number of sensory domains, principally vision, and drawn data from a limited number of textual genres. The aim of this study is to identify the extent to which motion expressions are employed to express organoleptic sensory experiences, that is experiences of taste and smell. Drawing on analytical categories put forward by Talmy (2000) in his treatment of Satellite- and Verb-framed languages and on treatments based on Cognitive Metaphor Theory (Lakoff and Johnson 1980), and particularly the concept of ‘embodiment’, we investigate the variety and complexity of organoleptic Paths evidenced in two self-built corpora of craft beer reviews, one in Spanish, the other in English. We also compare the level of complexity evident in these sensory Path types with those of another, closely related, sensory domain: vision. The study finds, in line with related studies, that English has greater variety and complexity of sensory Paths than Spanish. A more unexpected result is the finding that organoleptic Paths are more varied and complex than visual Paths. This finding, then, may encourage us to reconsider Path complexity differences in sensory domains as a multifactorial issue and not simply as a cognitive-linguistic phenomenon. It is proposed here that external factors such as genre, or specifically, its communicative functions and linguistic constraints, coupled with the unique ‘perceptual landscapes’ created by each sensory modality, may transcend perceived physical boundaries of a Figure’s trajectory.
GENETIC ALGORITHM BASED SENTENCE EXTRACTION FOR TEXT SUMMARIZATION

The goal of text summarization is to generate summary of the original text that helps the user to quickly understand large volumes of information available in that text. This paper focuses on text summarization based on sentence extraction. One of the methods to obtain suitable sentences is to assign some numerical measure for sentences called sentence weighting and then select the best ones. The first step in summarization by extraction is the identification of important features. In this paper, we consider the effectiveness of the features selected using Genetic Algorithm (GA). GA is used for the training of 100 documents in DUC 2002 data set to learn the weight of each feature, which is evaluated using recall measurement generated by ROUGE for a fitness function. The weights obtained by GA were used to adjust the important features score. We compare our results with Microsoft Word 2007 summarizer and Copernic summarizer both for 100 documents and 62 unseen documents. Â The results show that the best average precision, recall, and f-measure for the summaries were obtained by GA. Â