nils-herrmann/clean_scirepeval huggingface dataset:
Evaluating the influence of cultural competence education on students' transcultural self-efficacy perceptions.

Guided by the cultural competence and confidence (CCC) model, the Transcultural Self-Efficacy Tool (TSET) was used to evaluate the influence of cultural competence education on the transcultural self-efficacy (TSE) perceptions of undergraduate nursing students following an integrated approach to cultural competence education. Results continue to support that TSE is influenced by formalized education and other learning experiences. As hypothesized, compared with novice students, advanced students' scores were higher for all subscales in both cross-sectional (n = 147) and longitudinal (n = 36) study designs. Results from analysis of variance and covariance demonstrated that none of the demographic variables predicted change; semester was the sole predictor, lending additional support that (a) the educational interventions throughout the four semesters influenced TSE changes and (b) all students regardless of background benefit (and require) formalized cultural competence education. Implications for nurse educators conclude the article.
Learning narrative structure from annotated folktales

Narrative structure is an ubiquitous and intriguing phenomenon. By virtue of structure we recognize the presence of Villainy or Revenge in a story, even if that word is not actually present in the text. Narrative structure is an anvil for forging new artificial intelligence and machine learning techniques, and is a window into abstraction and conceptual learning as well as into culture and its influence on cognition. I advance our understanding of narrative structure by describing Analogical Story Merging (ASM), a new machine learning algorithm that can extract culturally-relevant plot patterns from sets of folktales. I demonstrate that ASM can learn a substantive portion of Vladimir Propp’s influential theory of the structure of folktale plots. The challenge was to take descriptions at one semantic level, namely, an event timeline as described in folktales, and abstract to the next higher level: structures such as Villainy, StuggleVictory, and Reward. ASM is based on Bayesian Model Merging, a technique for learning regular grammars. I demonstrate that, despite ASM’s large search space, a carefully-tuned prior allows the algorithm to converge, and furthermore it reproduces Propp’s categories with a chance-adjusted Rand index of 0.511 to 0.714. Three important categories are identified with F-measures above 0.8. The data are 15 Russian folktales, comprising 18,862 words, a subset of Propp’s original tales. This subset was annotated for 18 aspects of meaning by 12 annotators using the Story Workbench, a general text-annotation tool I developed for this work. Each aspect was doubly-annotated and adjudicated at inter-annotator F-measures that cluster around 0.7 to 0.8. It is the largest, most deeply-annotated narrative corpus assembled to date. The work has significance far beyond folktales. First, it points the way toward important applications in many domains, including information retrieval, persuasion and negotiation, natural language understanding and generation, and computational creativity. Second, abstraction from natural language semantics is a skill that underlies many cognitive tasks, and so this work provides insight into those processes. Finally, the work opens the door to a computational understanding of cultural influences on cognition and understanding cultural differences as captured in stories. Dissertation Supervisor: Patrick H. Winston Professor, Electrical Engineering and Computer Science Dissertation Committee: Whitman A. Richards Professor, Brain & Cognitive Sciences Peter Szolovits Professor, Electrical Engineering and Computer Science & Harvard-MIT Division of Health Sciences and Technology Joshua B. Tenenbaum Professor, Brain & Cognitive Sciences
Transductive Adversarial Networks (TAN)

Transductive Adversarial Networks (TAN) is a novel domain-adaptation machine learning framework that is designed for learning a conditional probability distribution on unlabelled input data in a target domain, while also only having access to: (1) easily obtained labelled data from a related source domain, which may have a different conditional probability distribution than the target domain, and (2) a marginalised prior distribution on the labels for the target domain. TAN leverages a fully adversarial training procedure and a unique generator/encoder architecture which approximates the transductive combination of the available sourceand target-domain data. A benefit of TAN is that it allows the distance between the sourceand target-domain label-vector marginal probability distributions to be greater than 0 (i.e. different tasks across the source and target domains) whereas other domain-adaptation algorithms require this distance to equal 0 (i.e. a single task across the source and target domains). TAN can, however, still handle the latter case and is a more generalised approach to this case. Another benefit of TAN is that due to being a fully adversarial algorithm, it has the potential to accurately approximate highly complex distributions. Theoretical analysis demonstrates the viability of the TAN framework.
Management of a Large Frontal Encephalocoele With Supraorbital Bar Remodeling and Advancement.

Of all the craniofacial abnormalities, facial clefts are the most disfiguring. Facial clefts are classified according to the affected anatomical area as described by Tessier. Through this classification, the location and extent of the cleft can be designated numerically.A 2-month-old male infant was referred to authors' craniofacial unit, from a hospital in a rural province of South Africa, with a problem of a supranasal encephalocoele. Bilateral raised eyebrows were noted as was a right-sided upper lid central third coloboma. Computed tomography and magnetic resonance imaging scans confirmed the presence of a supranasal encephalocoele with a large frontal bone defect and splayed nasal bones. Bilateral enlarged orbits were noted with tented orbital roofs that we classified as Tessier number 10 facial clefts. The child was booked for an encephalocoele excision and calvarial reconstruction at 4 months of age.As a result of the encephalocoele, the supraorbital bar with its adjacent nasal bones was cleaved in 2, resulting in a significant frontal bone defect. Osteotomies were performed to remove the supraorbital bar and nasal bones from the calvarium. The supraorbital bar segment was remodeled and plated with absorbable poly-L-lactic acid plates. Osteotomies of the nasal bones allowed them to be united centrally, also with absorbable plates. This entire construct was transferred and secured to the calvarium, but in a more caudal position thereby obliterating the frontal bone and Tessier number 10 facial cleft defects with a naturally contoured construct.
Chapter 6 Reconciling Self-Protection with Self-Improvement Self-Affirmation Theory

A persistent puzzle emerges from a review of the psychological research literature on how people defend the self from potentially threatening information and events: People can be adamantly protective of their sense of self-worth and defensively resistant to threatening information, on the one hand, and yet capable of accepting threatening, critical information and changing their behavior, on the other. The general tendency for self-enhancement that is the focus of so many of the chapters in this book (see also Sedikides & Gregg, 2008; Taylor & Brown, 1988) is a testament to people's ability to maintain a view of the self as capable, adaptive, and culturally appropriate despite the many threats that they may encounter. However, only a very maladaptive organism would be so engaged in self-protection that it was incapable of change. And so the question is raised as to how individuals can put aside their ego-protective needs when faced with criticism and threats and come to recognize that potentially painful and self-threatening pursuits could also help one improve. For example, at times people will resist information from their doctors that they need to change their health behavior, from their teachers that they need to improve their study skills, and from their family members that they need to adjust their interpersonal style. Yet, despite the self-threat that comes with acknowledging imperfections, flaws, and mistakes, individuals can come to change such important behaviors. In this chapter, we propose that one way that people can resolve the tension between self-protection and self-improvement is by affirming the self in an important domain of self-worth in response to threat, thereby reaffirming an overall image of self-integrity. Since the inception of self-affirmation theory
Learning Procedures from Text: Codifying How-to Procedures in Deep Neural Networks

A lot of knowledge about procedures and how-tos are described in text. Recently, extracting semantic relations from the procedural text has been actively explored. Prior work mostly has focused on finding relationships among verb-noun pairs or clustering of extracted pairs. In this paper, we investigate the problem of learning individual procedure-specific relationships (e.g. is method of, is alternative of, or is subtask of ) among sentences. To identify the relationships, we propose an end-to-end neural network architecture, which can selectively learn important procedure-specific relationships. Using this approach, we could construct a how-to knowledge base from the largest procedure sharing-community, wikihow.com. The evaluation of our approach shows that it outperforms the existing entity relationship extraction algorithms.
Relevant Acoustic Group Features for Automatic Sleepiness Recognition

This paper compares the discriminating powers of various acoustic group features for the task of automatic sleepiness recognition using three different classifiers: Voted Perceptron, Simple Logistic, and Random Forest. Interspeech 2011 Sleepiness Sub-Challenge’s “Sleepy Language Corpus” (SLC) is used to generate the 4368 acoustic features of the official baseline feature set. The feature space is divided into Low-Level Descriptor (LLD) partitions. We consider the resulting feature space in groups rather than individually. A group feature corresponds to a set of one or more LLD partitions. The relevance of various group features to sleepiness state is then evaluated using the mentioned classifiers. Employing larger feature sets has been shown to increase the classification accuracy in sleepiness classification. Our results, however, demonstrate that a much smaller subset of the baseline feature set outperforms the official Sub-Challenge baseline on the SLC test data.
A Review of Image Processing and Deep Learning Based Methods for Automated Analysis of Digital Retinal Fundus Images

Retinal fundus imaging is a medical procedure used by medical professionals in the discovery and tracking of various retinal abnormalities. Sometimes the analysis of retinal fundus images can be slow and difficult when performed by medical staff, and in response to this many automated, image-processing based methods for the analysis of these images exist. In recent years, deep learning methods have become increasingly popular in machine learning applications, so it is no surprise that they are also being used in the image processing based analysis of retinal fundus images. In this paper we discuss recently proposed methods that use deep learning techniques in the image processing based analysis of digital retinal fundus images. Special attention is given to the analysis of retinal fundus image datasets and various techniques employed to the images from these datasets in order to make them suitable for deep learning based applications.
Generating Affective Captions using Concept And Syntax Transition Networks

The area of image captioning i.e. the automatic generation of short textual descriptions of images has experienced much progress recently. However, image captioning approaches often only focus on describing the content of the image without any emotional or sentimental dimension which is common in human captions. This paper presents an approach for image captioning designed specifically to incorporate emotions and feelings into the caption generation process. The presented approach consists of a Deep Convolutional Neural Network (CNN) for detecting Adjective Noun Pairs in the image and a graphical network architecture called "Concept And Syntax Transition (CAST)" network for generating sentences from these detected concepts.
Teaching Kodu with physical manipulatives

In Microsoft's Kodu Game Lab, a visual programming environment for children, rules are constructed from sequences of tiles that act as virtual manipulatives. To further scaffold very young students' introduction to programming, I developed a set of physical tiles, now publicly available, to be used in pair programming activities and teacher demonstrations. While Kodu's virtual tiles are simple rectangles, the physical tiles use complex shapes and color to reinforce Kodu's 2D syntactic structure. The tiles were tested successfully in a pilot study with second grade students who had no prior programming experience and were seeing Kodu for the first time.
Wristband-Type Driver Vigilance Monitoring System Using Smartwatch

Studies have presented that the driver vigilance level has serious implication in the causation of road accidents. This paper focuses on integrating both the vehicle-based control behavior and physiological state to predict the driver vigilance index which is evaluated by using a smartwatch. The vehicle control behavior can be observed from the steering wheel movement. Our study utilized the smartwatch motion sensors to study the steering wheel behavior. Meanwhile, physiological state of driver reflects the driver capability of safety alert driving which is estimated by photoplethysmogram (PPG) and respiration signals in this paper. The PPG sensor is integrated in a sport wristband with a Bluetooth low energy module, transmitted the PPG signals to smartwatch in real time. The steering angle is derived by the reading from smartwatch built-in accelerometer and gyroscope sensors. On the other hand, the respiration is derived using the PPG peak baseline method. In order to utterly investigate the sleepiness-induced factors, the time, spectral, and phase space domain features are calculated. Considering the smartwatch processing capability, mutual-information technique is applied to designate the ten most descriptive features. Then, the extracted descriptive features are serve as parameters to a classifier to determine the driver aptitude status. The features are analyzed for their correlation with the subjective Koralinska sleepiness scale and through recorded video observations. The experimental results reveal that our system is capable of estimating driver hypervigilance at average of 96.5% accuracy rate by evaluating on both driving behavior and driver physiological state, provided a novel and low-cost implementation.
Single-Stage Single-Switch Four-Output Resonant LED Driver With High Power Factor and Passive Current Balancing

A resonant single-stage single-switch four-output LED driver with high power factor and passive current balancing is proposed. By controlling one output current, the other output currents of four-output LED driver can be controlled via passive current balancing, which makes its control simple. When magnetizing inductor current operates in critical conduction mode, unity power factor is achieved. The proposed LED driver uses only one active switch and one magnetic component, thus it benefits from low cost, small volume, and light weight. Moreover, high-efficiency performance is achieved due to single-stage power conversion and soft-switching characteristics. The characteristics of the proposed LED driver are studied in this paper and experimental results of two 110-W four-output isolated LED drivers are provided to verify the studied results.
Design and Implementation of a Voice-Coil Motor Servo Control IC for Auto-Focus Mobile Camera Applications

This paper presents the design methodology to implement a fully digital control IC for the position control of the auto-focus (AF) lens module in applications to mobile phone camera using the field programmable gate array (FPGA). As compared with the conventional stepping motor control with a spring return fixture, the AF lens module is driven by a voice-coil motor (VCM) with a digital servo drive IC. The proposed digital servo drive IC includes a digital servo controller, a digital current controller, and a full-bridge dc-dc converter. The designed digital servo drive IC provides a total digital solution to the position control of the AF lens module in applications to high- performance light-weight slim-type mobile phone camera and video. The designed AF lens module can reach a control range of 0.6 mm within 30 ms with a control resolution less than 5 mum, a peak driving force of 30 mN, and a peak output current of 120 mA.
Using information technology: engagement modes, flow experience, and personality orientations

The engagement mode (EM) model describes how an IT user (subject) engages in an activity with an object in a certain mode. The model specifies five engagement modes (Enjoying/Acceptance, Ambition/Curiosity, Avoidance/Hesitation, Frustration/Anxiety, and Efficiency/Productivity), which are characterized on three dimensions (evaluation of object, locus of control between subject and object, and intrinsic or extrinsic focus of motivation). Using questionnaire data from 290 participants, we extended previous empirical support for the model as well as described the model’s relationship to flow experience. In addition, it was found that autonomy, controlled and impersonal orientation in conjunction with socio-demographic variables differentiated among specific engagement modes and flow experience. We conclude that the EM-model, flow experience, and causality orientation theories provide a uniform framework for understanding how people adapt to information technology.
Modular Petri Net Modeling of Healthcare Systems-draft -

This paper presents a modular approach for modeling healthcare systems using Petri Nets. It is shown that a healthcare system can be constructed by different modules whose inputs and outputs are connected according to their geographical location. Each module can be modeled in two phases: (1) obtain the sequences of treatments and cares received by a patient in the case of a particular disease/condition, and (2) add the resources necessary to perform the previous sequences. The global model is obtained by fusion the inputs and outputs of the modules and by adding information on the patients. The constructed modules together with the resources are Petri nets belonging to a new subclass called Healthcare Petri nets that is proved to have equivalent behavior with SPR nets, a wellknown class of Resource Allocation Systems. This allows us to apply the structural results already existing in the literature for SPR to the context of healthcare systems. In order to illustrate the results, a case study of a Public Healthcare Area in Zaragoza is considered as a use case.  Modeling of Healthcare Systems,” Flexible Services and Manufacturing Journal, Special Issue on Analysis, Design and Management of Health Care Systems, 2017.
DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment

Worldwide, in 2014, more than 1.9 billion adults, 18 years and older, were overweight. Of these, over 600 million were obese. Accurately documenting dietary caloric intake is crucial to manage weight loss, but also presents challenges because most of the current methods for dietary assessment must rely on memory to recall foods eaten. The ultimate goal of our research is to develop computer-aided technical solutions to enhance and improve the accuracy of current measurements of dietary intake. Our proposed system in this paper aims to improve the accuracy of dietary assessment by analyzing the food images captured by mobile devices (e.g., smartphone). The key technique innovation in this paper is the deep learning-based food image recognition algorithms. Substantial research has demonstrated that digital imaging accurately estimates dietary intake in many environments and it has many advantages over other methods. However, how to derive the food information (e.g., food type and portion size) from food image effectively and efficiently remains a challenging and open research problem. We propose a new Convolutional Neural Network (CNN)-based food image recognition algorithm to address this problem. We applied our proposed approach to two real-world food image data sets (UEC-256 and Food-101) and achieved impressive results. To the best of our knowledge, these results outperformed all other reported work using these two data sets. Our experiments have demonstrated that the proposed approach is a promising solution for addressing the food image recognition problem. Our future work includes further improving the performance of the algorithms and integrating our system into a real-world mobile and cloud computing-based system to enhance the accuracy of current measurements of dietary intake.
Node Detection and Internode Length Estimation of Tomato Seedlings Based on Image Analysis and Machine Learning

Seedling vigor in tomatoes determines the quality and growth of fruits and total plant productivity. It is well known that the salient effects of environmental stresses appear on the internode length; the length between adjoining main stem node (henceforth called node). In this study, we develop a method for internode length estimation using image processing technology. The proposed method consists of three steps: node detection, node order estimation, and internode length estimation. This method has two main advantages: (i) as it uses machine learning approaches for node detection, it does not require adjustment of threshold values even though seedlings are imaged under varying timings and lighting conditions with complex backgrounds; and (ii) as it uses affinity propagation for node order estimation, it can be applied to seedlings with different numbers of nodes without prior provision of the node number as a parameter. Our node detection results show that the proposed method can detect 72% of the 358 nodes in time-series imaging of three seedlings (recall = 0.72, precision = 0.78). In particular, the application of a general object recognition approach, Bag of Visual Words (BoVWs), enabled the elimination of many false positives on leaves occurring in the image segmentation based on pixel color, significantly improving the precision. The internode length estimation results had a relative error of below 15.4%. These results demonstrate that our method has the ability to evaluate the vigor of tomato seedlings quickly and accurately.
An algorithm image Enhancement for Segmentation Palm-Leaf Manuscript

This paper presents Enhancement of Palm-Leaf Manuscript as noise occurred in images. It's suitable for Segmentation. The proposed method consists of image adjustment, contrast stretching and histogram equalization technique. All these techniques improve the quality of images for human viewing. The method we proposed, it made more effective images of palm-leaf manuscripts. The color image of palm-leaf manuscripts became pale and the visible images are not beautiful and hardly colorless because of image enhancement in image processing. In the other hand, the proposed method enhanced clarity of black alphabets. Thus, the color images were transformed into grayscale images affects the clearest alphabets on palm-leaf manuscripts. It also reduces noise. Next, the output images are suitable for carrying out recognition.