Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthetic ECG generation and Bayesian filtering using a Gaussian wave-based dynamical model

In this paper, we describe a Gaussian wave-based state space to model the temporal dynamics of electrocardiogram (ECG) signals. It is shown that this model may be effectively used for generating synthetic ECGs as well as separate characteristic waves (CWs) such as the atrial and ventricular complexes. The model uses separate state variables for each CW, i.e. P, QRS and T, and hence is capable of generating individual synthetic CWs as well as realistic ECG signals. The model is therefore useful for generating arrhythmias. Simulations of sinus bradycardia, sinus tachycardia, ventricular flutter, atrial fibrillation and ventricular tachycardia are presented. In addition, discrete versions of the equations are presented for a model-based Bayesian framework for denoising. This framework, together with an extended Kalman filter and extended Kalman smoother, was used for denoising the ECG for both normal rhythms and arrhythmias. For evaluating the denoising performance, the signal-to-noise ratio (SNR) improvement of the filter outputs and clinical parameter stability were studied. The results demonstrate superiority over a wide range of input SNRs, achieving a maximum 12.7 dB improvement. Results indicate that preventing clinically relevant distortion of the ECG is sensitive to the number of model parameters. Models are presented which do not exhibit such distortions. The approach presented in this paper may therefore serve as an effective framework for synthetic ECG generation and model-based filtering of noisy ECG recordings.
La innovación social en el contexto de la responsabilidad social empresarial

Faced with a reality characterized by unsolved social and environmental problems, it is common to observe the behavior of firms in terms of its contribution in the resolution or treatment of these problems. Many of these initiatives are examples of social innovations offering new products, processes and relationships in terms of benefiting the most disadvantaged groups in areas such as safety, health, education, environment, among others. In this sense, this documentary research examines the role of social innovation in the context of corporate social responsibility, through a review of theoretical topic of innovation, social innovation and corporate social responsibility. Also, through the filter of what can be considered social innovation, raises some examples of Venezuelan companies with socially responsible approaches moving toward maturity in a socially ethical enterprise.
The management of external marketing communication instruments in health care services

In order to become known and attract consumers, a health care organization has to develop suitable external communication campaigns. Consequently, management instruments are employed to effectively evaluate the success of a campaign. The BCG Matrix, SWOT analysis and the Gantt Diagram were used in this paper to ensure the consistency and accuracy of the external communication process at an empirical level.
Efficacy of Cognitive Behavioral Therapy on Opiate Use and Retention in Methadone Maintenance Treatment in China: A Randomised Trial

Aims Methadone maintenance treatment (MMT) is widely available in China; but, high rates of illicit opiate use and dropout are problematic. The aim of this study was to test whether cognitive behavioral therapy (CBT) in conjunction with MMT can improve treatment retention and reduce opiate use. Method A total of 240 opiate-dependent patients in community-based MMT clinics were randomly assigned to either weekly CBT plus standard MMT (CBT group, n=120) or standard MMT (control group, n=120) for 26 weeks. The primary outcomes were treatment retention and opiate-negative urine test results at 12 weeks and 26 weeks. The secondary outcomes were composite scores on the Addiction Severity Index (ASI) and total scores on the Perceived Stress Scale (PSS) at 12 weeks and 26 weeks. Results Compared to the control group in standard MMT, the CBT group had higher proportion of opiate-negative urine tests at both 12 weeks (59% vs. 69%, p<0.05) and 26 weeks (63% vs. 73%, p<0.05); however, the retention rates at 12 weeks (73.3% vs. 74.2%, p=0.88) and 26 weeks were not different (55.8% vs. 64.2%, p=0.19) between the two groups. At both 12 and 26 weeks, all of the ASI component scores and PSS total scores in the CBT group and control group decreased from baseline; but the CBT group exhibited more decreases in ASI employment scores at week 26 and more decrease in the PSS total score at week 12 and week 26. Conclusions CBT counselling is effective in reducing opiate use and improving employment function and in decreasing stress level for opiate-dependent patients in MMT in China. Trial Registration ClinicalTrials.gov NCT01144390
Fingerprint-free tracking with dynamic enhanced field division

Wireless sensor networks are often deployed for tracking moving objects. Many tracking algorithms have been proposed with two general assumptions: the preset fingerprints(prior landmark or context information) and an interference-free environment. These algorithms, however, cannot be used for on-demand deployment where finger-prints are unavailable and would perform poorly in interference-rich environments. In this paper, we present a fingerprint-free localizing and tracking algorithm, called Enhanced Field Division (EFD). The EFD algorithm is used to dynamically divide the field into areas with unique signatures and tracks the target, without any finger-prints. We also implemented a proof-of-concept localization platform to demonstrate the tracking accuracy and the algorithm performance in practical, interference rich environment.
Chicken Wing Training Model for Endoscopic Microsurgery

Abstract Objectives To present and validate a chicken wing model for endoscopic endonasal microsurgical skill development. Setting A surgical environment was constructed using a Styrofoam box and measurements from radiological studies. Endoscopic visualization and instrumentation were utilized in a manner to mimic operative setting. Design Five participants were instructed to complete four sequential tasks: (1) opening the skin, (2) exposing the main artery in its neurovascular sheath, (3) opening the neurovascular sheath, and (4) separating the nerve from the artery. Time to completion of each task was recorded. Participants Three junior attendings, one senior resident, and one medical student were recruited internally. Main Outcome Measures Time to perform the surgical tasks measured in seconds. Results The average time of the first training session was 48.8 minutes; by the 10th training session, the average time was 22.4 minutes. The range of improvement was 25.7 minutes to 72.4 minutes. All five participants exhibited statistically significant decrease in time after 10 trials. Kaplan-Meier analysis revealed that an improvement of 50% was achieved by an average of five attempts at the 95% confidence interval. Conclusions The ex vivo chicken wing model is an inexpensive and relatively realistic model to train endoscopic dissection using microsurgical techniques.
Finding axes of symmetry from potential fields

This paper addresses the problem of detecting axes of bilateral symmetry in images. In order to achieve robustness to variation in illumination, only edge-gradient information is used. To overcome the problem of edge breaks, a potential field is developed from the edge map which spreads the information in the image plane. Pairs of points in the image plane are made to vote for their axes of symmetry with some confidence values. To make the method robust to overlapping objects, only local features in the form of Taylor coefficients are used for quantifying symmetry. We define an axis of symmetry histogram, which is used to accumulate the weighted votes for all possible axes of symmetry. To reduce the computational complexity of voting, a hashing scheme is proposed, wherein pairs of points, whose potential fields are too asymmetric, are pruned by not being counted for the vote. Experimental results indicate that the proposed method is fairly robust to edge breaks and is able to detect symmetries even when only 0.05% of the possible pairs are used for voting.
Cognitive aspects of language use

On Pragmatic Change: The Borrowing of Discourse Functions (E.F. Prince). Thematic Subjecthood and Cognitive Constraints on Discourse Structure (S. Garrod, T. Sanford). On the Rationality of "Relevance" and the Relevance of "Rationality" (Y. Ziv). On the Informativeness Requirement (R. Giora). Retrieving Propositions from Context: Why and How (M. Ariel). A Formal Model of the Structure of Discourse (L. Polanyi). Schema Theory and the Processing of Narrative Texts: The X-Bar Story Grammar and the Notion of Discourse Topic (Y. Shen). Data-Driven Action Descriptions in Simultaneous Reports (A. van Berkel). Metaphor, Meaning and Interpretation (J. Berg). "Oceanic" Dedifferentiation and Poetic Metaphor (R. Tsur). Meanings, Culture and Communication (S. Kreitler, H. Kreitler). Review Article: Computation and the soul: A propos Dan Sperber and Deirdre Wilson's Relevance (J.L. Mey, M. Talbot). Indexes.
Parametric versus non parametric approaches to individual differences scaling

Latent trait models (LTMs) are one type of individual differences scaling models. Most commonly, these models use parametric functions to model the option response functions (ORFs) and latent trait distributions, although recently several nonparametric LTMs have also been proposed. In this paper, the strengths of each of these two approaches are discussed by comparing two models: Muthen's parametric LISCOMP model, and Levine's nonparametric MFS model. It was found that the MFS model is particularly suited for unidimensional scaling since it allows density estimation, it is more flexible at modeling the shape of the ORFs, and therefore may be more robust to mispecifications of the dimensionality of the data. The LISCOMP model, on the other hand, is particularly suited for multidimensional scaling, and for modeling the relationships between the scaling dimensions and external variables. Nonparametric models such as MFS are not easily generalized to multidimensional situations since they usually rely on smoothing constraints to reduce the estimation parameter space. These constraints are based on assumptions about the functional form of the ORFs and the latent trait densities, and it may be difficult to arrive at a set of constraints that will prove appropriate for different sampling schemes and dimensionality hypotheses. Key words: IRT; item response theory.
A Novel Approach to DRM Systems

Modern Digital Rights Management (DRM) systems are often developed as web software platforms designed as service oriented architectures composed of a federation of coordinated web entities that play distinct roles and dynamically interact within a trusted environment. Such a design approach requires that trustu relationships have to be dynamically established across multiple and heterogeneous organizational boundaries so as to facilitate an ``on-the-fly'' resource sharing. This introduces non-trivial security architectural requirements concerning with the mechanisms that allow different security realms of web entities to be federated. This paper presents a DRM system developed as a web software platform designed to give a specific support both to the interaction of the web entities involved in the platform and to the development of the trusted relationships among them, in a much more flexible way than before, dynamically and with minimal overheads and shared infrastructure.
Validity of DSM-IVADHD subtypes in a nationally representative sample of Australian children and adolescents.

OBJECTIVE To examine the discriminant validity of DSM-IV attention-deficit/hyperactivity disorder (ADHD) subtypes in a nationally representative sample of Australian youths. METHOD The Diagnostic Interview Schedule for Children, including the symptom-specific impairment questions, was administered to 3,597 parents of children aged 6 to 17 years (response rate = 70%). Parents also completed questionnaires assessing children's emotional and behavioral problems and quality of life. RESULTS Current DSM-IVADHD prevalence was 7.5% (6.8% with impairment) with inattentive types being more common than hyperactive-impulsive and combined types. ADHD was more prevalent among young males and was linked to social adversity, particularly for combined types. Compared with non-ADHD controls, all three ADHD subtypes were rated as having more emotional and behavioral problems and lower psychosocial quality of life, with combined types consistently rated the most impaired. Combined types received higher ratings than hyperactive-impulsive and inattentive types on externalizing behavior problems, disruption to family activities, and symptom-specific impairments with schoolwork and peer-related activities. Inattentive types were rated as having lower self-esteem, more social and school-related problems, but fewer externalizing problems than hyperactive-impulsive types. CONCLUSION These findings support the view of DSM-IVADHD subtypes as distinct clinical entities with impairments in multiple domains.
A Missense Mutation in the Arabidopsis COPII Coat Protein Sec24A Induces the Formation of Clusters of the Endoplasmic Reticulum and Golgi Apparatus[W]

How the endoplasmic reticulum (ER) and the Golgi apparatus maintain their morphological and functional identity while working in concert to ensure the production of biomolecules necessary for the cell's survival is a fundamental question in plant biology. Here, we isolated and characterized an Arabidopsis thaliana mutant that partially accumulates Golgi membrane markers and a soluble secretory marker in globular structures composed of a mass of convoluted ER tubules that maintain a connection with the bulk ER. We established that the aberrant phenotype was due to a missense recessive mutation in sec24A, one of the three Arabidopsis isoforms encoding the coat protomer complex II (COPII) protein Sec24, and that the mutation affects the distribution of this critical component at ER export sites. By contrast, total loss of sec24A function was lethal, suggesting that Arabidopsis sec24A is an essential gene. These results produce important insights into the functional diversification of plant COPII coat components and the role of these proteins in maintaining the dynamic identity of organelles of the early plant secretory pathway.
Speech Signal Enhancement Based on MAP Algorithm in the ICA Space

This paper presents a novel maximum a posteriori (MAP) denoising algorithm based on the independent component analysis (ICA). We demonstrate that the employment of individual ICA transformations for signal and noise can provide the best estimate within the linear framework. The signal enhancement problem is categorized based on the distribution of signal and noise being Gaussian or non-Gaussian and the estimation rule is derived for each of the categories. Our theoretical analysis shows that under the assumption of a Gaussian noise the proposed algorithm leads to some well-known enhancement techniques, i.e., Wiener filter and sparse code shrinkage. The analysis of the denoising capability shows that the proposed algorithm is most efficient for non-Gaussian signals corrupted by a non-Gaussian noise. We employed the generalized Gaussian model (GGM) to model the distributions of speech and noise. Experimental evaluation is performed in terms of signal-to-noise ratio (SNR) and spectral distortion measure. Experimental results show that the proposed algorithms achieve significant improvement on the enhancement performance in both Gaussian and non-Gaussian noise.
A tale of many explanations: towards an explanation generation system for robots

A fundamental challenge in robotics is to reason with incomplete domain knowledge to explain unexpected observations, and partial descriptions of domain objects and events extracted from sensor observations. Existing explanation generation systems are based on ideas drawn from two broad classes of systems, and do not support all the desired explanation generation capabilities for robots. The objective of this paper is to first compare the explanation generation capabilities of a state of the art system from each of these two classes, using execution scenarios of a robot waiter assisting in a restaurant. Specifically, we investigate KRASP, a system based on the declarative language Answer Set Prolog, which uses an elaborate system description and observations of system behavior to explain unexpected observations and partial descriptions. We also explore UMBRA, an architecture that provides explanations using a weaker system description, a heuristic representation of past experience, and other heuristics for selectively and incrementally searching through relevant ground literals. Based on this study, this paper identifies some key criteria, and provides some recommendations, for developing an explanation generation system for robots that exploits the complementary strengths of the two classes of explanation generation systems.
Depression, Anxiety, and the MCMI–III: Construct Validity and Diagnostic Efficiency

This study aimed to enhance knowledge of the construct validity and diagnostic efficiency of the depression- and anxiety-related scales of the MCMI–III (Millon, 1994). The MCMI–III, various concurrent depression and anxiety measures, and an Axis I structured diagnostic interview were administered in a total sample of 696 outpatients with depressive disorders, anxiety disorders, or both. Sound construct validity was found for the Dysthymia and Major Depression clinical syndrome scales and the Avoidant and Depressive personality disorder scales. The validity of the Anxiety scale was poor, showing moderate convergence with panic and worry-related anxiety measures, but problems discriminating from depression. Operating characteristics for discriminating depressed patients from anxious patients were fair for the Major Depression scale, but poor for the Anxiety and Dysthymia scales.
Self-organized intersection control management for motorized and non-motorized traffic

The rapid increase in urban population due to the urbanization has led to one of the major problems in urban areas, namely, traffic congestion. With the constraint on road surface in urban areas and limited city budget, it is almost impossible to build additional streets and/or increase the road surface to support such the increase in urban traffic. As a result, a new technology known as Virtual Traffic Lights (VTLs) was recently proposed that revolutionizes traffic management in urban areas. The VTL technology is an innovative approach that can efficiently and effectively manage traffic at intersections and it is shown to be able to significantly reduce commute time of urban workers. Despite the promising results of VTL, the VTL technology considers only vehicular traffic (i.e, motorized traffic) and has yet to provide support for non-motorized traffic (i.e, pedestrians and bicyclists on the road). This paper attempts to fill this gap by investigating the issue of pedestrian safety at crossings and proposing an integrated solution that enables the existing VTL technology to consider the presences of both motorized and non-motorized traffic in its implementation.
A New Approximate Maximal Margin Classification Algorithm

A new incremental learning algorithm is described which approximates the maximal margin hyperplane w.r. t. norm p ≥ 2 for a set of linearly separable data. Our algorithm, called ALMA_p (Approximate Large Margin algorithm w.r.t. norm p), takes O( (p-1) / (α2 γ2 ) ) corrections to separate the data with p-norm margin larger than (1-α)γ, where g is the (normalized) p-norm margin of the data. ALMA_p avoids quadratic (or higher-order) programming methods. It is very easy to implement and is as fast as on-line algorithms, such as Rosenblatt's Perceptron algorithm. We performed extensive experiments on both real-world and artificial datasets. We compared ALMA_2 (i.e., ALMA_p with p = 2) to standard Support vector Machines (SVM) and to two incremental algorithms: the Perceptron algorithm and Li and Long's ROMMA. The accuracy levels achieved by ALMA_2 are superior to those achieved by the Perceptron algorithm and ROMMA, but slightly inferior to SVM's. On the other hand, ALMA_2 is quite faster and easier to implement than standard SVM training algorithms. When learning sparse target vectors, ALMA_p with p > 2 largely outperforms Perceptron-like algorithms, such as ALMA_2.
Active Learning: Creating Excitement in the Classroom. ERIC Digest.

Active learning is generally defined as any instructional method that engages students in the learning process. In short, active learning requires students to do meaningful learning activities and think about what they are doing. While this definition could include traditional activities such as homework, in practice, active learning refers to activities that are introduced into the classroom. The core elements of active learning are student activity and engagement in the learning process. Active learning is often contrasted to the traditional lecture where students passively receive information from the instructor.
User-Interest based Community Extraction in Social Networks

The rapid evolution of modern social networks motivates the design of networks based on users’ interests. Using popular social media such as Facebook and Twitter, we show that this new perspective can generate more meaningful information about the networks. In this paper, we model userinterest based networks by deducing intent from social media activities such as comments and tweets of millions of users in Facebook and Twitter, respectively. This interactive content derives networks that are dynamic in nature as the user interests can evolve due to temporal and spatial activities occurring around the user. To understand and analyze these networks, we develop a new approach for mining communities to overcome the limitations of the widely used Clauset, Newman, and Moore (CNM) community detection algorithm. The key feature of the proposed approach is that the communities are extracted incrementally by removing the inuence of the communities identied in the previous steps. Experimental results show that our approach can nd many focused communities of similar interests compared to the large communities found by the CNM algorithm. Our user-interest based model and community extraction methodology together can be used to identify target communities in the context of business requirements.
Applying Emotional Intelligence : A Practitioner's Guide

Ciarrochi, Mayer, Introduction. Brackett, Katulak, Emotional Intelligence in the Classroom: Skill-Based Training for Teachers and Students. Boyatzis, Developing Emotional Intelligence Competencies. Kornacki, Caruso, A Theory-Based, Practical Approach to Emotional Intelligence Training: Ten Ways to Increase Emotional Skills. Ciarrochi, Blackledge, Bilich, Bayliss, Improving Emotional Intelligence: A Guide to Mindfulness-Based Emotional Intelligence Training. Mayer, Personality Function and Personality Change. Ciarrochi, Mayer, The Key Ingredients of Emotional Intelligence Interventions: Similarities and Differences.