Miracle-dz/UniFAR-FTUs huggingface dataset:
Play-making with migrant farm workers in Ontario, Canada: a kinesthetic and embodied approach to qualitative research

This article is a reflection on the use of theatre creation in qualitative research with migrant farm workers in Ontario, Canada. In this article I examine how the fundamentally embodied and kinesthetic dimensions of seasonal agricultural workers’ lives in Canada highlight the need to seek out and develop corresponding embodied approaches that are able to access and accurately represent the fraught and dynamic nature of workers’ experiences. I bring together ideas from both arts-informed research and participatory action research, and I examine how engaging research participants directly in collective theatre creation can effectively disrupt accepted ways of being and offer an important intervention on worker habitus. I reflect on how through incorporating an element of play-creation in the qualitative research process, I was able to a) access forms of knowledge that may otherwise have remained tacit and b) offer a disruption of the norms of isolation and antagonism endemic to daily life in Canada’s Seasonal Agricultural Worker Program. This article contributes to debates concerning the role of the arts in qualitative and action research, as well as to those researchers who are seeking innovative ways of designing and implementing qualitative research in the areas of precarious work and citizenship.
Reconstruction of surface of revolution from multiple uncalibrated views: a bundle-adjustment approach

This paper addresses the problem of recovering the 3D shape of a surface of revolution from multiple uncalibrated perspective views. In previous work, we have exploited the invariant properties of the surface of revolution and its silhouette to recover the contour generator and hence the meridian of the surface of revolution from a single uncalibrated view. However, there exists one degree of freedom in the reconstruction which corresponds to the unknown orientation of the revolution axis of the surface of revolution. In this paper, such an ambiguity is removed by estimating the horizon, again, using the image invariants associated with the surface of revolution. A bundle-adjustment approach is then proposed to provide an optimal estimate of the meridian when multiple uncalibrated views of the same surface of revolution are available. Experimental results on real images are presented, which demonstrate the feasibility of the approach.
Context-sensitive auto-sanitization in web templating languages using type qualifiers

Scripting vulnerabilities, such as cross-site scripting (XSS), plague web applications today. Most research on defense techniques has focused on securing existing legacy applications written in general-purpose languages, such as Java and PHP. However, recent and emerging applications have widely adopted web templating frameworks that have received little attention in research. Web templating frameworks offer an ideal opportunity to ensure safety against scripting attacks by secure construction, but most of today's frameworks fall short of achieving this goal. We propose a novel and principled type-qualifier based mechanism that can be bolted onto existing web templating frameworks. Our solution permits rich expressiveness in the templating language while achieving backwards compatibility, performance and formal security through a context-sensitive auto-sanitization (CSAS) engine. To demonstrate its practicality, we implement our mechanism in Google Closure Templates, a commercially used open-source templating framework that is used in GMail, Google Docs and other applications. Our approach is fast, precise and retrofits to existing commercially deployed template code without requiring any changes or annotations.
Supplemental Material : Discovering Groups of People in Images

In Fig. 1 and 2, we show additional qualitative examples obtained using our model with poselet [1] and ground truth (GT) detections, respectively. We show the image configuration of groups on the left and corresponding 3D configuration on the right. Different colors and different line types (solid or dashed) represent different groups, the type of each structured group is overlayed on the bottom-left of one participant. In 3D visualization, squares represent standing people, circles represent people sitting on an object, and triangles represent people sitting on the ground. The view point of each individual is shown with a line. The gray triangle is the camera position. The poses are obtained by using the individual pose classification output for visualization purposes. The figures show that our algorithm is capable of correctly associating individuals into multiple different groups while estimating the type of each group. Notice that our algorithm can successfully segment different instances of the same group type that appear in proximity. A distance-based clustering method would not be able to differentiate them. The last figure shows a typical failure case due to only reasoning about people while ignoring objects (such as the tables). Also, we notice that our algorithm can associate individuals into correct groups even in highly complicated scene when GT detections are available.
3T High-Spatial-Resolution Contrast-Enhanced MR Angiography of the Intracranial Venous System with Parallel Imaging

SUMMARY: The diagnostic image quality of contrast-enhanced (CE) 3D MR venography (MRV) was prospectively compared with that of 2D time-of-flight (TOF) MRV and contrast-enhanced 3D magnetization-prepared rapid acquisition of gradient echo (MPRAGE) sequences for the visualization of the intracranial venous system at 3T in 22 patients. CE MRV provides high-quality images and was shown to be superior to TOF MRV and MPRAGE sequences in visualizing the normal intracranial venous system.
A comparative study of two interventions to support reading comprehension in primary-aged students

Purpose The purpose of this paper is to explore the explicit teaching of information text schema with vocabulary instruction to primary-aged students in Hong Kong international education. Design/methodology/approach Data were collected through three quasi-experimental studies with different age groups and participants. Each study divided participants into two randomly assigned groups, either informational texts (IT) or vocabulary building (VB). Impact was evaluated with gain scores on a standardized reading comprehension test and researcher-designed cloze tests of fiction and nonfiction passages. Findings The explicit teaching of IT can benefit student reading comprehension from an early age, particularly to first language (L1) English students and possibly second language (L2) English learners. School reading programmes should include opportunities for students to experience IT (nonfiction) and fiction materials, and build their vocabulary through incidental learning and explicit teaching. For IT, they should be exposed to: layout – e.g., headings, sub-headings, glossary, and index; and content – photographs and specific/technical vocabulary. For fiction-based texts and VB, the following themes should be covered by younger aged students: antonyms, synonyms, and affixes. Research limitations/implications Several limitations apply to this study which will need to be addressed in future studies. These include: the random sampling of students from the overall student population was not an option, given the necessity of voluntary participation and avoiding disruption to school routines. This study used meta-analysis to aggregate results across multiple comparisons largely because of the extremely small samples available. The data show large standard errors as a consequence of small numbers of participants. Hence, the current results, notwithstanding the power of meta-analysis, need to be validated with much larger samples in future studies. Originality/value This paper suggests that greater comprehension and cloze performance among L1 students was found due to the teaching of IT compared to vocabulary training, with the reverse result for L2 English learners.
Piecing together app behavior from multiple artifacts: A case study

Recent research in mobile software analysis has begun to combine information extracted from an app's source code and marketplace webpage to identify correlated variables and validate an app's quality properties such as its intended behavior, trust or suspiciousness. Such work typically involves analysis of one or two artifacts such as the GUI text, user ratings, app description keywords, permission requests, and sensitive API calls. However, these studies make assumptions about how the various artifacts are populated and used by developers, which may lead to a gap in the resulting analysis. In this paper, we take a step back and perform an in-depth study of 14 popular apps from the Google Play Store. We have studied a set of 16 different artifacts for each app, and conclude that the output of these must be pieced together to form a complete understanding of the app's true behavior. We show that (1) developers are inconsistent in where and how they provide descriptions; (2) each artifact alone has incomplete information; (3) different artifacts may contain contradictory pieces of information; (4) there is a need for new analyses, such as those that use image processing; and (5) without including analyses of advertisement libraries, the complete behavior of an app is not defined. In addition, we show that the number of downloads and ratings of an app does not appear to be a strong predictor of overall app quality, as these are propagated through versions and are not necessarily indicative of the current app version's behavior.
Learning lane change intentions through lane contentedness estimation from demonstrated driving

To assure a safe, comfortable and especially a cooperative driving experience while driving semi, highly or even fully automated, anticipation of the driving behavior of other traffic participants is needed. Because of the amount of different traffic situations and influence factors on the task of driving, due to uncertainties in environmental sensor measurements, and as a consequence of variable and individual driving styles, probabilistic models in combination with machine learning techniques are often applied to learn driving behavior from data. In this paper, with the help of a simulator study, the driving behavior of a subject group is examined regarding their intention to change lane on highways. The simulator is set up as a partially automated driving system that takes discrete maneuver wishes as input (lane change left or lane change right). If the traffic situation permits it, the requested maneuvers is executed automatically by the system. This generates ground truth labels that are being used to train a lane change intention classifier. The results show that the approach is able to predict upcoming lane changes at an average of more than 3.5 seconds in advance.
Kaposi's Sarcoma Herpesvirus microRNAs Target Caspase 3 and Regulate Apoptosis

Kaposi's sarcoma herpesvirus (KSHV) encodes a cluster of twelve micro (mi)RNAs, which are abundantly expressed during both latent and lytic infection. Previous studies reported that KSHV is able to inhibit apoptosis during latent infection; we thus tested the involvement of viral miRNAs in this process. We found that both HEK293 epithelial cells and DG75 cells stably expressing KSHV miRNAs were protected from apoptosis. Potential cellular targets that were significantly down-regulated upon KSHV miRNAs expression were identified by microarray profiling. Among them, we validated by luciferase reporter assays, quantitative PCR and western blotting caspase 3 (Casp3), a critical factor for the control of apoptosis. Using site-directed mutagenesis, we found that three KSHV miRNAs, miR-K12-1, 3 and 4-3p, were responsible for the targeting of Casp3. Specific inhibition of these miRNAs in KSHV-infected cells resulted in increased expression levels of endogenous Casp3 and enhanced apoptosis. Altogether, our results suggest that KSHV miRNAs directly participate in the previously reported inhibition of apoptosis by the virus, and are thus likely to play a role in KSHV-induced oncogenesis.
When Grammars do not Suffice: Data and Content Integrity Constraints Verification in XML through a Conceptual Model

Complex applications can benefit greatly from using conceptual models and Model Driven Architecture during development, deployment and runtime. XML applications are not different. In this paper, we examine the possibility of using Object Constraint Language (OCL) for expressing constraints over a conceptual model for XML data. We go through the different classes of OCL expression and show how each class can be translated into XPath constructs. Subsequently we show how the constraints can be checked using Schematron. We introduce a function library OclX, which provides constructs necessary to translate those OCL constructs that have no counterpart in XPath. With our tool, it is possible to check validity of OCL constraints in XML data.
A 9T subthreshold SRAM bitcell with data-independent bitline leakage for improved bitline swing and variation tolerance

This paper describes a 9T SRAM bitcell with data-independent bitline leakage for improving bitline voltage swing and variation tolerance. The data-independent bitline leakage eliminates the bitline voltage swing dependency on column data and demonstrates better variation tolerance. Simulations show that the proposed SRAM bitcell enables 1024 cells to be attached in a bitline with the swing of 141mV at a 0.25V and 80°C condition. The proposed cell lowers the minimum operating voltage from 0.3V to 0.15V with 256cells/bitline. It has an area overhead of 5.5%.
Image Representation Using Fuzzy Morphological Wavelet

show that recognition descriptor in the aircraft shapes. A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition.
Mosaicing on Adaptive Manifolds

Image mosaicing is commonly used to increase the visual field of view by pasting together many images or video frames. Existing mosaicing methods are based on projecting all images onto a predetermined single manifold: A plane is commonly used for a camera translating sideways, a cylinder is used for a panning camera, and a sphere is used for a camera which is both panning and tilting. While different mosaicing methods should therefore be used for different types of camera motion, more general types of camera motion, such as forward motion, are practically impossible for traditional mosaicing. A new methodology to allow image mosaicing in more general cases of camera motion is presented. Mosaicing is performed by projecting thin strips from the images onto manifolds which are adapted to the camera motion. While the limitations of existing mosaicing techniques are a result of using predetermined manifolds, the use of more general manifolds overcomes these limitations.
Nonparametric bayesian models for machine learning

This thesis presents general techiques for inference in various nonparametric Bayesian models, furthers our understanding of the stochastic processes at the core of these models, and develops new models of data based on these findings. In particular, we develop new Monte Carlo algorithms for Dirichlet process mixtures based on a general framework. We extend the vocabulary of processes used for nonparametric Bayesian models by proving many properties of beta and gamma processes. In particular, we show how to perform probabilistic inference in hierarchies of beta and gamma processes, and how this naturally leads to improvements to the well known naive Bayes algorithm. We demonstrate the robustness and speed of the resulting methods by applying it to a classification task with 1 million training samples and 40,000 classes.
HIV-1 Nef and KSHV oncogene K1 synergistically promote angiogenesis by inducing cellular miR-718 to regulate the PTEN/AKT/mTOR signaling pathway

Kaposi's sarcoma (KS) is an AIDS-defining cancer with aberrant neovascularization caused by KS-associated herpesvirus (KSHV). Although the interaction between HIV-1 and KSHV plays a pivotal role in promoting the aggressive manifestations of KS, the pathogenesis underlying AIDS-KS remains largely unknown. Here we examined HIV-1 Nef protein promotion of KSHV oncoprotein K1-induced angiogenesis. We showed that both internalized and ectopic expression of Nef in endothelial cells synergized with K1 to facilitate vascular tube formation and cell proliferation, and enhance angiogenesis in a chicken CAM model. In vivo experiments further indicated that Nef accelerated K1-induced angiogenesis and tumorigenesis in athymic nu/nu mice. Mechanistic studies revealed that Nef and K1 synergistically activated PI3K/AKT/mTOR signaling by downregulating PTEN. Furthermore, Nef and K1 induced cellular miR-718, which inhibited PTEN expression by directly targeting a seed sequence in the 3′ UTR of its mRNA. Inhibition of miR-718 expression increased PTEN synthesis and suppressed the synergistic effect of Nef- and K1-induced angiogenesis and tumorigenesis. These results indicate that, by targeting PTEN, miR-718 mediates Nef- and K1-induced angiogenesis via activation of AKT/mTOR signaling. Our results demonstrate an essential role of miR-718/AKT/mTOR axis in AIDS-KS and thus may represent an attractive therapeutic target.
Multi-objective vehicle scheduling problem based on customer satisfaction and hybrid genetic algorithm

The mathematic model of multi-objective vehicle scheduling problem based on customer satisfaction is proposed firstly, whose two trade-off objective functions are maximizing the customer satisfaction degree and minimizing the transportation cost. Then the hybrid genetic algorithm is proposed and the test result indicates that our algorithm is effective to solve such multi-objective vehicle scheduling problem.
Best Agglomerative Ranked Subset for Feature Selection

The enormous increase of the size in databases makes finding an optimal subset of features extremely difficult. In this paper, a new feature selection method is proposed that will allow any subset evaluator -including the wrapper evaluation method- to be used to find a group of features that will allow a distinction to be made between the different possible classes. The method, BARS (Best Agglomerative Ranked Subset), is based on the idea of relevance and redundancy, in the sense that a ranked feature (or set) is more relevant if it adds information when it is included in the final subset of selected features. This heuristic method reduces dimensionality drastically and leads to improvements in the accuracy, in comparison to a complete set and as opposed to other feature selection algorithms.
Determining stability of pulses for partial differential equations with time delays

Partial differential equations with time delays serve as models for systems where both spatial structure and memory effects are important. The asymptotic stability of travelling waves in such systems is still determined entirely by the spectrum of the linearization about the wave. We compare the spectra of localized waves on the unbounded real line with spectra computed on large intervals with appropriate boundary conditions applied at their end points. We show that the spectrum on large intervals approximates the spectrum on the real line when periodic boundary conditions are used. If separated boundary conditions are applied, it is the so-called absolute spectrum together with the extended point spectrum that is approximated; their union typically differs from the spectrum on the real line.
Pelatihan Kepemimpinan Transformasional Pada Atasan Untuk Peningkatan Komitmen Organisasi Karyawan Di Hotel “X”

Penelitian ini bertujuan untuk mengetahui pengaruh pelatihan kepemimpinan transformasional pada atasan untuk peningkatan komitmen organisasi karyawan di hotel X Yogyakarta. Subjek penelitian adalah 34 karyawan Hotel X Yogyakarta yang merupakan subordinat atau bawahan sedangkan yang diberikan intervensi berupa pelatihan kepemimpinan transformasional adalah para Head of Departement (HOD) yang berjumlah 8 orang. Metode pengumpulan data yang digunakan adalah skala komitmen organisasi yang mengacu pada organizational commitment questionnaire (OCQ) yang dikembangkan oleh Allen & Meyer (1990), yang terdiri dari tiga komponen yaitu: komponen afektif, komponen kontinuans dan komponen normatif. Desain eksperimen yang digunakan dalam penelitian ini adalah one group pretest-posttest design dengan analisis data uji statistik parametrik, yaitu Paired Sample T-Test. Hasil penelitian menunjukkan bahwa t = 7,325, (p<0,001) sehingga hipotesis penelitian diterima. Artinya, ada perbedaan yang signifikan antara komitmen organisasi karyawan sebelum diberikan pelatihan kepemimpinan transformasional pada atasan dan setelah diberikan pelatihan kepemimpinan transformasional pada atasan.
A Conceptual Review of Lab-Based Aggression Paradigms

Aggression is often defined as a behavior that is done with the intent to harm an individual who is believed to want avoid being harmed (e.g., Baron & Richardson, 1994). Accordingly, social scientists have developed several tasks to study aggression in laboratory settings; tasks that we refer to as “lab-based aggression paradigms. ” However, because of the legal, ethical, and practical issues inherent in provoking aggression within the confines of a laboratory setting, it is feasible to study only very mildly harmful aggression. The current conceptual review examines the criteria that are necessary to study aggression in a laboratory setting, discusses the strengths and weaknesses of several new and/or commonly-used lab-based aggression paradigms, and offers recommendations for the future of lab-based aggression research. Collectively, we hope the current discussion helps researchers to describe the contributions and limitations of lab-based aggression research and, ultimately, helps to improve the informativeness of lab-based aggression research.
Use of Attribute Selection Criteria in Decision Trees in Uncertain Domains

There are many attribute selection criteria involved in the induction of decision trees. We present criteria derived from an impurity measure in a unified framework and we use the same background to describe ORT criterion [12]. We compare the theoretical backgrounds of C.M. criteria with those of the ORT criterion and we illustrate their differences. We set lazy decision trees with regards to these families of criteria, and we debate their use with uncertain data sets. Finally, we clarify the choice of these approaches and we separate their domains of application.