Miracle-dz/UniFAR-FTUs huggingface dataset:
Structural Link Analysis from User Profiles and Friends Networks: A Feature Construction Approach

the problems of predicting, classifying, and annotating friends relations in friends networks, based upon network structure and user profile data. First, we document a data model for the blog service LiveJournal, and define a set of machine learning problems such as predicting existing links and estimating inter-pair distance. Next, we explain how the problem of classifying a user pair in a social network, as directly connected or not, poses the problem of selecting and constructing relevant features. We document feature analyzers for attributes that depend only on graph attributes, those that depend on individual user demographics and set-valued attributes (e.g., interests, communities, and educational institutions), and those that depend on candidate user pairs. We then extend our data model using whole-network attributes and report machine learning experiments on learning the concept of a connected pair of friends from LiveJournal data. Finally, we develop a theory of dependent types for deriving causal explanations and discuss how this can be used to scale statistical relational learning up to our full corpus, a recent crawl of over a million records from LiveJournal.
Optimal EEG feature extraction based on R-square coefficients for motor imagery BCI system

In cue based motor imagery brain computer interface (BCI) paradigm, subject was stimulated by different cue to distinguish different imagination task and proceed by imagination tasks. These evoked the specific related frequency rhythms. But the problem appears as theses rhythms change due subject and imagination task. While some subject is fast induce the electrical signal rhythms, other has long latency in induce them. This related time temporal problem of imagination. To boost the accuracy of EEG signal translation for reliable system, in this paper, we proposed a method for extracting optimal feature. First, EEG signals were extracted and apply to Laplace filter and band pass filter with pass band frequency of 7Hz to 40Hz. Channels C3, Cz, and C4 were applied to Short-Time Fourier Transform with frequency band of 1Hz. R-square correlation coefficient of three channels were found and selected the best frequency and time parameter. Finally, with the selected parameters, optimal STFT feature extracted. We simulated the classification accuracy with linear discriminant analysis with BCI competition IV, III and our laboratory dataset.
Biomarkers of necrotising soft tissue infections: aspects of the innate immune response and effects of hyperbaric oxygenation—the protocol of the prospective cohort BIONEC study

Introduction The mortality and amputation rates are still high in patients with necrotising soft tissue infections (NSTIs). It would be ideal to have a set of biomarkers that enables the clinician to identify high-risk patients with NSTI on admission. The objectives of this study are to evaluate inflammatory and vasoactive biomarkers as prognostic markers of severity and mortality in patients with NSTI and to investigate whether hyperbaric oxygen treatment (HBOT) is able to modulate these biomarkers. The overall hypothesis is that plasma biomarkers can be used as prognostic markers of severity and mortality in patients with NSTI and that HBOT reduces the inflammatory response. Methods and analysis This is a prospective, observational study being conducted in a tertiary referral centre. Biomarkers will be measured in 114 patients who have been operatively diagnosed with NSTI. On admission, baseline blood values will be obtained. Following surgery and HBOT, daily blood samples for measuring regular inflammatory and vasoactive biomarkers (pentraxin-3, interleukin-6 and nitrite) will be acquired. Samples will be analysed using validated ELISA assays, chemiluminescence and Griess reaction. Clinical data will be obtained during admission in the intensive care unit for a maximum of 7 days. The primary analysis will focus on pentraxin-3, interleukin-6 and nitrite as early markers of disease severity in patients with NSTI. Ethics and dissemination The study has been approved by the Regional Scientific Ethical Committee of Copenhagen (H-2–2014–071) and the Danish Data Protection Agency (J. no. 30–0900 and J. no. 30–1282). Results will be presented at national and international conferences and published in peer-reviewed scientific journals. Trial registration NCT02180906.
Interactive Gigapixel Prints : Large Paper Interfaces for Visual Context , Mobility , and Collaboration

Creative professionals in many domains use large paper artifacts for their size and resolution, mobility, and flexible support for collocated collaboration. In this paper, we suggest that large-format printers can be thought of as displays with high spatial resolution but low temporal resolution. Conversely, digital displays have a lower spatial resolution but higher temporal resolution. This work introduces Interactive Gigapixel Prints (GIGAprints), a set of techniques and tools for imbuing large paper media with interactive input (through the location-specifying Anoto pattern) and using the complementary affordances of paper and digital together as an ensemble. We introduce a design space and interaction techniques for GIGAprints. We also describe the design tools for creating these prints, introducing a software architecture that provides an event-based programming model for ensembles that integrate paper and digital artifacts to provide a unified user experience. Finally, we present the results of a first-use study of three applications (12 participants), demonstrating that GIGAprints supports our goals of context, mobility, and collaboration. ACM Classification
Multiple Trajectory Search for Uncapacitated Facility Location Problems

In this study, a novel metaheuristic called the Multiple Trajectory Search (MTS) is proposed to solve the uncapacitated facility location problem (UFLP). The Multiple Trajectory Search hybridizes a global search method (the Trajectory_Search) and a local search method (the Variable_Neighborhood_Search). The application of the Multiple Trajectory Search to the benchmarks ORLIB and GHOSH had been conducted. The performance comparison with other state-of-the-art methods reveals that the proposed method is very competitive.
Specification, Design and Implementation of a Reuse Repository

A repository is a necessary prerequisite to support software engineers and other users in the process of developing software for and with reuse. In the literature, there are several works that explore reuse repositories, however their focus is mostly on reusable component search and retrieval issues, while important aspects of reuse repositories have not been properly explored. On the other hand, some questions raised by companies that desire to adopt or build a reuse repository remain unanswered. Motivated by these questions, this paper presents the specification, design and implementation of a reuse repository that was successfully constructed and deployed in real Brazilian software factories.
Evaluating the control of the adaptive display rate for video capsule endoscopy diagnosis

The excessively long reviewing times for diagnosis of capsule endoscopy present a clinical problem. A new method, “adaptive speed”, which automatically controls display rates of the video capsule endoscopy images, was proposed to address the problem. In this paper, we investigate the effectiveness of this method versus a standard-view using the existing system. The main activities of examining doctors during a series of evaluations using both systems are recorded. For comparisons, logged actions are analyzed to show three criteria: 1. Diagnostic time, 2. Ability to capture abnormal regions, and 3. Operability for the examining doctors. We conclude that adaptive speed reduces examination time by ten minutes from that of the existing system, while the number of abnormalities found are similar. As well, examining doctors need less effort because of the systems efficient operability.
The Real Exchange Rate and the Balassa-Samuelson Effect: The Role of the Distribution Sector

This paper investigates the long-run impact of the distribution sector on the real exchange rate. The main result is that an increase in the productivity and product market competition of the distribution sector with respect to foreign countries leads to an appreciation of the real exchange rate, similar to what a relative increase in the domestic productivity of tradables does. This contrasts with the result that one would expect by considering the distribution sector as belonging to the non-tradable sector. One explanation may lie in the use of the services from the distribution sector in the tradable sector.
Ultrasound for the Pulmonary Consultant

Bedside ultrasonographic assessment of the lung and pleura provides rapid, noninvasive, and essential information in diagnosis and management of various pulmonary conditions. Ultrasonography helps in diagnosing common conditions, including consolidation, interstitial syndrome, pleural effusions and masses, pneumothorax, and diaphragmatic dysfunction. It provides procedural guidance for various pulmonary procedures, including thoracentesis, chest tube insertion, transthoracic aspiration, and biopsies. This article describes major applications of ultrasonography for the pulmonary consultant along with illustrative figures and videos.
Filling the Gap between Semantic Web and Model Driven Engineering: The TwoUse Toolkit

The interest in integrating ontology technologies and software engineering has gained more attention with commercial and scientific initiatives. The Semantic Web Best Practice and Deployment Working Group (SWBPD) in W3C included a Software Engineering Task Force (SETF) to explore how Semantic Web and Software Engineering can cooperate. The Object Management Group (OMG) has an Ontology Platform Special Interest Group (PSIG) aiming at formalizing the semantics in software by knowledge representation and related technologies. The concrete results of such initiatives are the specification of the OMGOntology Definition Metamodel, the Ontology Web Language (OWL) Metamodel, the introduction to Ontology Driven Architectures and a Semantic Web Primer for Object-Oriented Software Developers. However, practitioners interested in integrating ontology technologies and software engineering lack on tool support. Although guidelines for model transformations and implementations of these transformations exist, a comprehensive framework dedicated to fill the gap between model-driven engineering and ontology technologies is lacking so far. For example, the IBM Integrated Ontology Development Toolkit 1 does not provide support to instance editing or multiple ontologies. Moreover, ontology editors like the NeOn Toolkit 2 support exclusively ontology development and do not provide tools for software developers. Providing a framework for integrating MDE and ontology technologies requires dealing with the following challenges:
The impact of planned care and a diabetes electronic management system on community-based diabetes care: the Mayo Health System Diabetes Translation Project.

OBJECTIVE The Mayo Health System Diabetes Translation Project sought to assess models of community-based diabetes care and use of a diabetes electronic management system (DEMS). Planned care is a redesigned model of chronic disease care that involves guideline implementation, support of self-management, and use of clinical information systems. RESEARCH DESIGN AND METHODS We studied adult diabetic patients attending three primary care practice sites in Wisconsin and Minnesota. We implemented planned care at all sites and DEMS in the practice of 16 primary care providers. We assessed quality of diabetes care using standard indicators for 200 patients randomly selected from each site at baseline and at 24 months of implementation. We used multivariable analyses to estimate the association between planned care and DEMS and each quality indicator. RESULTS Planned care was associated with improvements in measurement of HbA(1c) (odds ratio 7.0 [95% CI 4.2-11.6]), HDL cholesterol (5.6 [4.1-7.5]), and microalbuminuria (5.3 [3.5-8.0]), as well as the provision of tobacco advice (6.9 [4.7-10.1]), among other performance measures. DEMS use was associated with improvements in all indicators, including microalbuminuria (3.2 [1.9-5.2]), retinal examination (2.4 [1.5-3.9]), foot examinations (2.3 [1.2-4.4]), and self-management support (2.6 [1.7-3.8]). Although planned care was associated with improvements in metabolic control, we observed no additional metabolic benefit when providers used DEMS. CONCLUSIONS Planned care was associated with improved performance and metabolic outcomes in primary care. DEMS use augmented the impact of planned care on performance outcomes but not on metabolic outcomes. Optimal identification of the best translation of evidence to diabetes practice will require longer follow-up or new care-delivery models.
A Regime Switching Jump-Diffusion Model and its Application to Credit Risk and Option Pricing

A vital issue in financial mathematics is the choice of an appropriate model for the financial market. It is well-known that the famous Black-Scholes model does not perform well in reality. The aim of this thesis is to extend the Black-Scholes model in order to improve its empirical performance. Our motivation originates from credit risk and option pricing. We start with credit risk and in particular with the valuation of risky debt. The approach initiated by Merton [57] will be adapted to the extended model. We will see that the obtained credit spreads are consistent with historical data. Further, several calibration methodologies for estimating the model parameters will be discussed. Due to the fact that calibration is feasible, we are able to use the model for practical purposes such as the calculation of probabilities of default. As a test example, the model will be applied to a real firm that has been in financial distress recently. In the second part of this thesis we will focus on option pricing. The volatility smiles produced by the model for short as well as for long maturities are adequate. Moreover, we will propose two different ways of option pricing: an analytical way and by means of the FFT algorithm. The last technique has the advantage that it can be extended to price more complex options, e.g., exotic options.
Comparative study of distinctive image classification techniques

Image classification is one of the most multifaceted disciplines in image processing. There are quite a few approaches to categorize images and they offer good classification outcome but they not be up to snuff to provide acceptable classification upshots when the image comprises blurry content. The two chief techniques for image classification are supervised and unsupervised classification. Mutually each possess its own pros and cons. The foremost intent of literature survey is to present a concise outline about some of most widespread image classification schemes and comparison between them. At this point in a survey on diverse classification practices for images and moreover its application for diagnosis of scores of diseases is provided. A few of the unsurpassed processes for classification comprise Artificial Neural Network, Support Vector Machine, and Decision Tree. Their characteristics, upshots and certain vital issues have been judged against each other in order to ascertain the effectual algorithm. To conclude it has been shown that the proposed system Hybrid RGSA and Support Vector Machine Framework is the paramount one to classify images competently.
Strategies for Efficient Experimental Design in Studies Probing 2-1-1 Mediation

Abstract When well-implemented, mediation analyses play a critical role in probing theories of action because their results help lay the ground work for the critical development of a treatment and the iterative advancement of theories that are foundational to a discipline. Despite strong interest in designs that incorporate mediation, few studies have developed effective and efficient strategies to plan experiments examining multilevel mediation. We probe several design strategies for cluster-randomized designs and derive sampling plans that maximize power under cost constraints. The results suggest that among the more durable design strategies for mediation is covariance adjustment on variables predictive of the outcome and optimal sample allocation. The statistical power and optimal sample allocation results are implemented in the R package PowerUpR.
Parent/Caregiver Involvement in 2018: Past Challenges and Future Possibilities in a Technology-Rich Society.

The purpose of the study was to explore how the use of technology can support parent/caregiver-teacher communications, thus allowing for parent/caregiver-teacher involvement through contemporary approaches. The study looked at how parent/caregivers and teachers define parent/caregiver involvement, what they see as barriers to parent/caregiver involvement, and how they believe technology can support the involvement experience. The review of literature examines varying factors that influence parent/caregivers’ level of involvement in their children’s schooling, as well as three major influences that have the greatest impact on student achievement: family, school, and community. It also identifies barriers to parent/caregiver involvement. Although challenges exist at the family, school, and community levels, the review of literature shows that the child receives maximum benefit when all parties collaborate. Interviews and surveys reveal how parent/caregivers and teachers of third-graders feel about parent/caregiver involvement and how technology affects communications between parent/caregivers and teachers. Findings indicate that parent/caregivers and teachers believe the use of technology supports parent/caregiver-teacher communications, therefore fostering parent/caregiver involvement. They believe it eases communication processes and allows parent/caregivers to be involved despite busy schedules. Parent/caregivers and teachers communicated that they still desire personal conversations with one another to address topics of concern, but, overall, they feel technology cultivates and supports parent/caregiver involvement.
Visual Tracking Based on Mixture Motion Model and Incorporate Observation Distribution

Accurate visual object tracking through long sequences is a challenging task since object's appearance changes and complex motion happens. We present mixture motion model and incorporate observation model within the Monte Carlo framework to achieve robust visual tracking. The mixture motion model which employs important history motion information of the target is built according to a motion measurement matrix to model the target's transition state. Meanwhile, the incorporate observation model is established by introducing SVM classification scores into normal tracking observation model. A particles filter's implementation with these mixture models is demonstrated, which leads to robust tracking results, especially in occlusion and complex scene.
An Analysis of the Perceived Competencies of Recreational Sport Administrators

This study sought to determine the core competencies necessary for the administration of sport programs in campus, public, and military recreation settings using the Competencies of Sport Managers instrument. Factor analysis of 457 responses on 96 items revealed that the competencies of sport administrators were represented by four factors, identified as Management Techniques, Sport Programming, Business Administration, and Theoretical Perspectives. Discriminant analysis revealed that sport administrators in campus recreation settings placed more emphasis on Theoretical Perspectives and less emphasis on Sport Programming. When compared to sport management faculty, administrators in all agency settings placed more emphasis on Sport Programming and Theoretical Perspectives, with faculty placing more emphasis on Management Techniques and Business Administration. This study reinforces the need for recreational sport administrators to possess a strong management and business base, along with competence in sport programming and recreational sport theory/foundations. Achieving greater understanding of the nature and importance of recreational sport competencies can help the field become more accountable for its programs in academic preparation, professional training, and continuing education.
Improved second generation electromagnetic MEMS energy scavenger

This paper presents an optimized second generation electromagnetic (EM) MEMS power generator which can harvest energy from low frequency external vibrations. In the second generation scavenger, power is generated through planar electromagnetic induction using a magnet on the low frequency actuation plane, and coils on the high frequency resonance plane. It is demonstrated in this work through modeling and simulation that the generated power level is maximized by geometric optimization of the high frequency resonance plane. The geometric improvements in the diaphragm arms and magnetic actuation area within the MEMS device yield 318 nW maximum power and 23.8 mV maximum voltage. This corresponds to 2.6× improvement in output power, and 1.5× improvement in output voltage compared to previously reported design.
A Multi-Approaches-Guided Preprocess Algorithm with Application to Chance Discovery

Unrepresentative data samples are likely to reduce the utility of data classifiers in practical application. This study presents a multi-approaches-guided preprocess algorithm in the design of an effective chance discovery model, which bases on data crystallization, clustering and neural network techniques. We used data crystallization to discover unobservable events of the input samples with the objective of indicating unrepresentative samples, used clustering techniques to process the samples into isolated and inconsistent clusters, and neural networks to construct the chance discovery data set model. The aim of this paper is to develop a combined method for data preprocess by using different methods to preprocess different data features in order for exerting their unique characteristics. The results show its effect to industrial decision making.
Using Electronic Health Record Systems in Medical Education – A Needs Assessment

The aim of this study was to determine the features needed in electronic health record systems by supervising physicians for teaching medical students and residents. We conducted in-depth semi-structured interviews with seven faculty members at a public-funded medical college. Emergent concepts were extracted in a qualitative thematic approach based on the realist paradigm. Thematic analysis revealed the need for clinical guidelines, medical calculators and research evidence. Documenting differential diagnoses for clinical reasoning and the use of medical images for pedagogical needs were requested as well. Clinical supervisors in this study suggested several features within electronic health records for teaching needs. The study findings provide a reference for developing teaching resources within electronic health records systems.
The Parallelization of an Adaptive Multigrid Eigenvalue Solver with LPARX

We have developed a parallel adaptive eigenvalue solver and applied it to a model problem in theoretical materials science. Our method combines adaptive mesh reenement techniques with a novel multigrid eigenvalue algorithm. By exploiting adaptivity, we have reduced computation time and memory consumption by more than two orders of magnitude. We have implemented our solver using the LPARX parallel programming system, which considerably simpliied the programming and enabled us to run the same code on a diversity of high performance parallel architectures.