Miracle-dz/UniFAR-FTUs huggingface dataset:
Tracking-by-Detection of 3D Human Shapes: From Surfaces to Volumes

3D Human shape tracking consists in fitting a template model to temporal sequences of visual observations. It usually comprises an association step, that finds correspondences between the model and the input data, and a deformation step, that fits the model to the observations given correspondences. Most current approaches follow the Iterative-Closest-Point (ICP) paradigm, where the association step is carried out by searching for the nearest neighbors. It fails when large deformations occur and errors in the association tend to propagate over time. In this paper, we propose a discriminative alternative for the association, that leverages random forests to infer correspondences in one shot. Regardless the choice of shape parameterizations, being surface or volumetric meshes, we convert 3D shapes to volumetric distance fields and thereby design features to train the forest. We investigate two ways to draw volumetric samples: voxels of regular grids and cells from Centroidal Voronoi Tessellation (CVT). While the former consumes considerable memory and in turn limits us to learn only subject-specific correspondences, the latter yields much less memory footprint by compactly tessellating the interior space of a shape with optimal discretization. This facilitates the use of larger cross-subject training databases, generalizes to different human subjects and hence results in less overfitting and better detection. The discriminative correspondences are successfully integrated to both surface and volumetric deformation frameworks that recover human shape poses, which we refer to as ‘tracking-by-detection of 3D human shapes. ’ It allows for large deformations and prevents tracking errors from being accumulated. When combined with ICP for refinement, it proves to yield better accuracy in registration and more stability when tracking over time. Evaluations on existing datasets demonstrate the benefits with respect to the state-of-the-art.
Fast multiplication-free QWDCT for DV coding standard

This paper deals with fast computation of some operations included in the digital video (DV) coding standard. The proposed solution converts floating-point arithmetic operations into integer arithmetic operations, replacing highly computational demanding blocks such as discrete cosine transform (DCT), weighting (W) and quantization (Q) by fast integer calculations using only shifts and additions. The overall computational complexity was reduced by 73% in comparison to a floating-point implementation. Our solution is suitable to be programmed into any fixed-point arithmetic processor decreasing the consumer equipment cost. This solution is still compatible with the standard in terms of the DCT precision requirements.
Continuity of care: a multidisciplinary review

The concept—and reality—of continuity of care crosses disciplinary and organisational boundaries. The common definitions provided here should help healthcare providers evaluate continuity more rigorously and improve communication Patients are increasingly seen by an array of providers in a wide variety of organisations and places, raising concerns about fragmentation of care. Policy reports and charters worldwide urge a concerted effort to enhance continuity,1–3 but efforts to describe the problem or formulate solutions are complicated by the lack of consensus on the definition of continuity. To add to the confusion, other terms such as continuum of care, coordination of care, discharge planning, case management, integration of services, and seamless care are often used synonymously. This synthesis was commissioned by three Canadian health services policy and research bodies. The aim was to develop a common understanding of the concept of continuity as a basis for valid and reliable measurement of practice in different settings. We searched academic and policy literature for documents in which the principal focus was continuity of patient care or continuity. We searched electronic databases (Medline, HealthSTAR, Embase, CINAHL, Current Contents, PsychINFO, AIDSLINE, CancerLit, Cochrane Library, Dissertation abstracts, Papers1st (conferences and paper abstracts), Web of Science, WorldCat) as well as web library catalogues, peer reviewed internet sites, internet search engines, and several in-house databases. The search included documents dated from 1966 to November 2001 written in English, French, or Spanish. The reviewers (RJR, JLH, RMcK) used a data abstraction form to summarise relevant documents from every health discipline, and all reviewers read key documents. We presented the results of an initial review of 314 documents to participants of a workshop on continuity held in Vancouver in June 2001. We obtained structured feedback to a discussion paper, problem based scenarios, and expert presentations. Participants validated the common themes …
A preliminary study on overlapping and data fracture in imbalanced domains by means of Genetic Programming-based feature extraction

The classification of imbalanced data is a well-studied topic in data mining. However, there is still a lack of understanding of the factors that make the problem difficult. In this work, we study the two main reasons that make the classification of imbalanced datasets complex: overlapping and data fracture. We present a Genetic Programming-based feature extraction method driven by Rough Set Theory to help visualize the data in a bidimensional graph, to better understand how the presence of overlapping and data fractures affect classification performance.
Baseball: an automatic question-answerer

<u>Baseball</u> is a computer program that answers questions phrased in ordinary English about stored data. The program reads the question from punched cards. After the words and idioms are looked up in a dictionary, the phrase structure and other syntactic facts are determined for a content analysis, which lists attribute-value pairs specifying the information given and the information requested. The requested information is then extracted from the data matching the specifications, and any necessary processing is done. Finally, the answer is printed. The program's present context is baseball games; it answers such questions as "Where did each team play on July 7? "
Essay 1 Title: An exploration into a mediating role of adaptive capability on strategic orientation-firm performance linkage

ion of similar level (Chin, 1998). Explaining the rational behind the conceptualization of higher order construct, Law et al. (1998, p. 741) contend that “[. ] a construct as multidimensional when it consists of a number of interrelated attributes or dimensions and exists in multidimensional domains. In contrast to a set of interrelated unidimensional constructs, the dimensions of a multidimensional construct can be conceptualized under an overall abstraction, and it is theoretically meaningful and parsimonious to use this overall abstraction as a representation of the dimensions. ” Thus two constructs, used in this study viz strategic orientation, adaptive capability qualify for the conceptualization as higher order multidimensional constructs comprising six and four dimensions respectively. Law et al. (1998, p.741) further argue that “multidimensional construct differs when different interpretations are attributed to the relations between the overall construct and its dimensions”. The higher order construct is a formative, if various specific dimensions are aggregated to a general concept (Edwards, 2001; Wetzels et al., 2009). In respect of strategic orientation, Chan et al. (1997) contend that a particular business unit might be very aggressive, defensive, and proactive but at the same time low or moderate on other dimensions – riskiness, futurity and analysis. The characteristics of a business unit may differ from every other business unit of a firm under consideration. Thus, higher order construct approach is entailed to examine the overall characteristics of the firm. The strategic orientation construct comprises three dimensionsaggressiveness, proactiveness and risk taking of entrepreneurial orientation, as conceptualized by Lumpkin and Dess (1996). Lumpkin and Dess (1996) contend that dimensions of entrepreneurial orientation may vary independent of each other. Further, Becker et al. (2012) argue that
Random Deep Belief Networks for Recognizing Emotions from Speech Signals

Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN) can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ensemble of random deep belief networks (RDBN) method for speech emotion recognition. It firstly extracts the low level features of the input speech signal and then applies them to construct lots of random subspaces. Each random subspace is then provided for DBN to yield the higher level features as the input of the classifier to output an emotion label. All outputted emotion labels are then fused through the majority voting to decide the final emotion label for the input speech signal. The conducted experimental results on benchmark speech emotion databases show that RDBN has better accuracy than the compared methods for speech emotion recognition.
A controller testability analysis and enhancement technique

This paper presents a testability analysis and improvement technique for the controller of an RT level design. It detects hard-to-reach states by analyzing both the data path and the controller of a design. The controller is modified using register initialization, branch control, and loop termination methods to enhance its state reachability. This technique complements the data path scan method and can be used to avoid scanning registers involved in the critical paths. Experimental results show the improvement of fault coverage with a very low area overhead.
What Can Software Engineering Do for Sustainability: Case of Software Product Lines

Sustainable living, i.e., Living within the bounds of the available environmental, social, and economic resources, is the focus of many present-day social and scientific discussions. But what does sustainability mean within the context of Software Product Line Engineering (SPLE)? And what does SPLE do for sustainable living? In this paper we take the first step towards identification of the sustainability-related characteristics relevant to SPLE. The paper also discusses how the key areas of interest to the current SPL community (as reflected by what is measured and optimised in SPLs today) relate to these sustainability characteristics.
Technology-enhanced suicide prevention interventions: A systematic review

Objective Suicide prevention is a high priority. Scalable and sustainable interventions for suicide prevention are needed to set the stage for population-level impact. This systematic review explores how technology-enhanced interventions target suicide risk and protective factors, using the Centers for Disease Control and Prevention (CDC, 2015) Risk and Protective Factors Ecological Model. Methods Information databases (PsycINFO, PubMed and CINAHL) were systematically searched and records including technology-enhanced interventions for suicide prevention (n = 3764) were reviewed. Records with varying technologies and diverse methodologies were integrated into the search. Results Review of the records resulted in the inclusion of 16 studies that utilized technology-enhanced interventions to address determinants of suicidal behaviour. This includes the use of standalone or, in most cases, adjunct technology-enhanced interventions for suicide prevention delivered by mobile phone application, text message, telephone, computer, web, CD-ROM and video. Conclusion Intervention effectiveness was variable, but several technology-enhanced interventions have demonstrated effectiveness in reducing suicidal ideation and mental health co-morbidities. Large-scale research and evaluation initiatives are needed to evaluate the costs and long-term population-level impact of these interventions.
Boolean Equi-propagation for Concise and Efficient SAT Encodings of Combinatorial Problems

We present an approach to propagation-based SAT encoding of combinatorial problems, Boolean equi-propagation, where constraints are modeled as Boolean functions which propagate information about equalities between Boolean literals. This information is then applied to simplify the CNF encoding of the constraints. A key factor is that considering only a small fragment of a constraint model at one time enables us to apply stronger, and even complete, reasoning to detect equivalent literals in that fragment. Once detected, equivalences apply to simplify the entire constraint model and facilitate further reasoning on other fragments. Equi-propagation in combination with partial evaluation and constraint simplification provide the foundation for a powerful approach to SAT-based finite domain constraint solving. We introduce a tool called BEE (Ben-Gurion Equi-propagation Encoder) based on these ideas and demonstrate for a variety of benchmarks that our approach leads to a considerable reduction in the size of CNF encodings and subsequent speed-ups in SAT solving times.
Cardiac troponin I in Doberman Pinschers with cardiomyopathy.

BACKGROUND Cardiac troponin I (cTnI) is useful for detection of cardiac myocyte damage, but its efficacy in detecting various stages of dilated cardiomyopathy (DCM) in Doberman Pinschers is unclear. OBJECTIVES To evaluate the diagnostic value of cTnI in various stages of DCM in Dobermans. ANIMALS Six hundred and fifty-three cTnI measurements of 336 Doberman Pinschers. METHODS Using a longitudinal study design, staging of the disease was based upon 24-hour-ambulatory-ECG (Holter) and echocardiography. A total of 447 cTnI measurements were performed in 264 healthy Dobermans, and 206 cTnI measurements in 75 Dobermans with cardiomyopathy. Eighty-eight cTnI samples were from dogs with >100 ventricular premature contractions (VPCs)/24 hour, but without echocardiographic changes ("VPC group"). Additional 19 samples originated from dogs with only echocardiographic changes ("ECHO group"), and 56 samples from dogs with both VPCs and echocardiographic changes ("VPC plus ECHO group"). Twenty samples were from dogs with clinical signs ("clinical group"). The group "incipient" included 23 dogs, that were considered to be normal according to Holter and echocardiography at the time of the exam, but that developed DCM within 1.5 years. RESULTS cTnI values of dogs in all disease groups, including the "incipient" (0.30+/-0.20) and "VPC group" (0.36+/-0.34), were significantly (P=.04, P<.001) higher than the control group (0.07+/-0.16). A cut-off value of >0.22 ng/mL had a sensitivity of 79.5% and a specificity of 84.4% to detect all forms of cardiomyopathy. CONCLUSIONS AND CLINICAL IMPORTANCE cTnI measurement is a valuable diagnostic test that can detect cardiomyopathy in dogs that are otherwise clinically normal.
Implementation of non-zero initial conditions for multi-notch FIR filter using raspberry Pi

In this paper a concept of finite impulse response narrow multi-band-stop filter with non-zero initial conditions based on linear Kalman prototype filter is proposed. The FIR multi-notch filter is used for removing power-line interferences from biomedical sources such as ECG. In order to reduce the duration of the transient state of the proposed FIR multi-notch filter, optimal initial conditions for the filter have been determined. The algorithm for finding the length of the initial conditions vector is presented. The proposed filter is tested using sinusoidal and ECG signals. Computer simulations, hardware implementation, and performance indices demonstrate that the proposed FIR multi-notch filter with non-zero initial conditions based on the Kalman prototype filter possesses better properties than the traditional FIR multi-notch filter.
Spam Filter Based Approach for Finding Fault-Prone Software Modules

Because of the increase of needs for spam e-mail detection, the spam filtering technique has been improved as a convenient and effective technique for text mining. We propose a novel approach to detect fault-prone modules in a way that the source code modules are considered as text files and are applied to the spam filter directly. In order to show the applicability of our approach, we conducted experimental applications using source code repositories of Java based open source developments. The result of experiments shows that our approach can classify more than 75% of software modules correctly.
Acquiring Plausible Uni cation-Based Grammars using Model-Based and Data-Driven Learning

Undergeneration is a problem that undermines successful parsing of unrestricted texts. A popular solution to this problem is automatic grammar correction (or machine learning of grammar). Broadly speaking, grammar correction approaches can be classiied as being either data-driven, or model-based. Data-driven learners use data-intensive methods to acquire grammar. They typically use grammar formalisms unsuited to the needs of practical text processing. That is, data-driven learners acquire grammars that overgen-erate and fail to assign linguistically plausible parses. Model-based learners are knowledge-intensive and are reliant for success upon the completeness of a model of grammaticality. But, in practice, the model will be incomplete and so since we deal with undergeneration by learning, we hypothesise that the combined use of data-driven and model-based learning would allow data-driven learning to compensate for model-based learning's incompleteness, whilst model-based learning would compensate for data-driven learning's inadequacy. We describe a system that we have used to test this hypothesis empirically. The system combines data-driven and model-based learning to acquire uniication-based grammars that are more suitable for practical text parsing. The system is used to illustrate the strengths and weaknesses of both learning styles. Finally, using the Spoken English Corpus as data, and by quantitatively measuring undergeneration, overgeneration and parse plaus-ibility, we show that the hypothesis is promising.
Hybrid language models: is simpler better?

The use of several n-gram and hybrid language models with and without cache is examined in the context of producing court transcripts. Language models with cache (in which words which have recently been uttered are preferred) have seen considerable use. The suitability of cache models (with fixed size cache) in the production of court transcripts is not clear. A decrease in perplexity and an improvement in the word error rate is observed with some of the models when using a cache, however, performance deteriorates with increasing cache size.
Body Cues, Not Facial Expressions, Discriminate Between Intense Positive and Negative Emotions

Joy or Pain? Face recognition and processing are so completely central to human social interactions that these functions are supported by specialized regions in the brain. One of the fundamental aspects being processed is emotion, particularly whether the emotion being expressed is positive or negative. Nevertheless, neuroimaging studies have documented that perceiving opposite emotions often activates the same or overlapping regions. Aviezer et al. (p. 1225) report that the recognition of positive versus negative emotions actually relies on information communicated by the body—the extent to which perceivers identified joy versus grief in composite figures was driven by whether the body came from a joyous (versus grievous) image rather than the face. The body reveals what the face conceals. The distinction between positive and negative emotions is fundamental in emotion models. Intriguingly, neurobiological work suggests shared mechanisms across positive and negative emotions. We tested whether similar overlap occurs in real-life facial expressions. During peak intensities of emotion, positive and negative situations were successfully discriminated from isolated bodies but not faces. Nevertheless, viewers perceived illusory positivity or negativity in the nondiagnostic faces when seen with bodies. To reveal the underlying mechanisms, we created compounds of intense negative faces combined with positive bodies, and vice versa. Perceived affect and mimicry of the faces shifted systematically as a function of their contextual body emotion. These findings challenge standard models of emotion expression and highlight the role of the body in expressing and perceiving emotions.
A Unified Framework for Knowledge Assessment and Progression Analysis and Design

Designing engaging learning content is important but difficult, and typically involves a lot of manual specification. We present a unified framework that utilizes automatic problem decomposition and partial ordering graph construction to facilitate multiple workflows: knowledge assessment and progression analysis and design. We present results from a study with 847 participants in an online Japanese-language assessment tool demonstrating that our framework can efficiently measure student ability and predict student performance on specific problems. We also present results from analysis of curricula showing that the progressions of two different textbooks are surprisingly similar, and that our framework can lead to the discovery of general principles of expert progression design. Finally, we demonstrate automatic progression generation with desired sequencing and pacing, allowing for tailoring of progressions and mapping of parameters extracted from one curriculum onto another.
Viewpoint Paper: Towards Automatic Recognition of Scientifically Rigorous Clinical Research Evidence

The growing numbers of topically relevant biomedical publications readily available due to advances in document retrieval methods pose a challenge to clinicians practicing evidence-based medicine. It is increasingly time consuming to acquire and critically appraise the available evidence. This problem could be addressed in part if methods were available to automatically recognize rigorous studies immediately applicable in a specific clinical situation. We approach the problem of recognizing studies containing useable clinical advice from retrieved topically relevant articles as a binary classification problem. The gold standard used in the development of PubMed clinical query filters forms the basis of our approach. We identify scientifically rigorous studies using supervised machine learning techniques (Naïve Bayes, support vector machine (SVM), and boosting) trained on high-level semantic features. We combine these methods using an ensemble learning method (stacking). The performance of learning methods is evaluated using precision, recall and F(1) score, in addition to area under the receiver operating characteristic (ROC) curve (AUC). Using a training set of 10,000 manually annotated MEDLINE citations, and a test set of an additional 2,000 citations, we achieve 73.7% precision and 61.5% recall in identifying rigorous, clinically relevant studies, with stacking over five feature-classifier combinations and 82.5% precision and 84.3% recall in recognizing rigorous studies with treatment focus using stacking over word + metadata feature vector. Our results demonstrate that a high quality gold standard and advanced classification methods can help clinicians acquire best evidence from the medical literature.