nils-herrmann/clean_scirepeval huggingface dataset:
SANA: A Large Scale Multi-Genre, Multi-Dialect Lexicon for Arabic Subjectivity and Sentiment Analysis

The computational treatment of subjectivity and sentiment in natural language is usually significantly improved by applying features exploiting lexical resources where entries are tagged with semantic orientation (e.g., positive, negative values). In spite of the fair amount of work on Arabic sentiment analysis over the past few years, e.g., (Abbasi et al., 2008; Abdul-Mageed et al., 2014; Abdul-Mageed et al., 2012; Abdul-Mageed and Diab, 2012a; Abdul-Mageed and Diab, 2012b; Abdul-Mageed et al., 2011a; Abdul-Mageed and Diab, 2011), the language remains under-resourced as to these polarity repositories compared to the English language. In this paper, we report efforts to build and present SANA, a large-scale, multi-genre, multi-dialect multi-lingual lexicon for the subjectivity and sentiment analysis of the Arabic language and dialects.
Dialogue Session Segmentation by Embedding-Enhanced TextTiling

In human-computer conversation systems, the context of a userissued utterance is particularly important because it provides useful background information of the conversation. However, it is unwise to track all previous utterances in the current session as not all of them are equally important. In this paper, we address the problem of session segmentation. We propose an embedding-enhanced TextTiling approach, inspired by the observation that conversation utterances are highly noisy, and that word embeddings provide a robust way of capturing semantics. Experimental results show that our approach achieves better performance than the TextTiling, MMD approaches.
Implicit bias in healthcare professionals: a systematic review

Implicit biases involve associations outside conscious awareness that lead to a negative evaluation of a person on the basis of irrelevant characteristics such as race or gender. This review examines the evidence that healthcare professionals display implicit biases towards patients.



PubMed, PsychINFO, PsychARTICLE and CINAHL were searched for peer-reviewed articles published between 1st March 2003 and 31st March 2013. Two reviewers assessed the eligibility of the identified papers based on precise content and quality criteria. The references of eligible papers were examined to identify further eligible studies.



Forty two articles were identified as eligible. Seventeen used an implicit measure (Implicit Association Test in fifteen and subliminal priming in two), to test the biases of healthcare professionals. Twenty five articles employed a between-subjects design, using vignettes to examine the influence of patient characteristics on healthcare professionals' attitudes, diagnoses, and treatment decisions. The second method was included although it does not isolate implicit attitudes because it is recognised by psychologists who specialise in implicit cognition as a way of detecting the possible presence of implicit bias. Twenty seven studies examined racial/ethnic biases; ten other biases were investigated, including gender, age and weight. Thirty five articles found evidence of implicit bias in healthcare professionals; all the studies that investigated correlations found a significant positive relationship between level of implicit bias and lower quality of care.



The evidence indicates that healthcare professionals exhibit the same levels of implicit bias as the wider population. The interactions between multiple patient characteristics and between healthcare professional and patient characteristics reveal the complexity of the phenomenon of implicit bias and its influence on clinician-patient interaction. The most convincing studies from our review are those that combine the IAT and a method measuring the quality of treatment in the actual world. Correlational evidence indicates that biases are likely to influence diagnosis and treatment decisions and levels of care in some circumstances and need to be further investigated. Our review also indicates that there may sometimes be a gap between the norm of impartiality and the extent to which it is embraced by healthcare professionals for some of the tested characteristics.



Our findings highlight the need for the healthcare profession to address the role of implicit biases in disparities in healthcare. More research in actual care settings and a greater homogeneity in methods employed to test implicit biases in healthcare is needed.
An innovative augmented reality educational platform using Gamification to enhance lifelong learning and cultural education

This paper proves that Life-long learning and Cultural Education can be supported through the use of technological advances and techniques which so far were extensively used in Games and immersive applications. In order to promote learning, this research proposal focuses on Augmented Reality (AR) and on Gamification for the creation of an educational AR book. The suggested learning subject is Science aimed at children between the ages of 10-12 years old, although the platform can be easily applied also to Culture, Arts and History. In our project, users interact with a virtual laboratory and are able to perform experiments and complete challenges through gaming so as to expand and test their knowledge. Using AR and gamification techniques, the aim is to deliver a more comprehensive understanding of the subject matter while at the same time engage learners and increase their enjoyment during the learning process. By engaging learners in cultural subjects, the cultural heritage is delivered to next generations and remains alive.
Cyber Security Awareness Campaigns: Why do they fail to change behaviour?

The present paper focuses on Cyber Security Awareness Campaigns, and aims to identify key factors regarding security which may lead them to failing to appropriately change people’s behaviour. Past and current efforts to improve information-security practices and promote a sustainable society have not had the desired impact. It is important therefore to critically reflect on the challenges involved in improving information-security behaviours for citizens, consumers and employees. In particular, our work considers these challenges from a Psychology perspective, as we believe that understanding how people perceive risks is critical to creating effective awareness campaigns. Changing behaviour requires more than providing information about risks and reactive behaviours – firstly, people must be able to understand and apply the advice, and secondly, they must be motivated and willing to do so – and the latter requires changes to attitudes and intentions. These antecedents of behaviour change are identified in several psychological models of behaviour. We review the suitability of persuasion techniques, including the widely used ‘fear appeals’. From this range of literature, we extract essential components for an awareness campaign as well as factors which can lead to a campaign’s success or failure. Finally, we present examples of existing awareness campaigns in different cultures (the UK and Africa) and reflect on these.
The Impact of Tourism Marketing Mix Elements on the Satisfaction of Inbound Tourists to Jordan

The purpose of this paper is to study the impact of tourism marketing mix and how it affects tourism in Jordan, and to determine which element of the marketing mix has the strongest impact on Jordanian tourism and how it will be used to better satisfy tourists. The paper will focus on foreign tourists coming to Jordan; a field survey will be used by using questionnaires to collect data. Three hundred questionnaires will be collected from actual tourists who visited Jordan, the data will be collected from selected tourism sites like (Petra, Jarash,.... etc.) and classified from one to five stars hotels in Jordan. The questionnaire will be designed in different languages (English, French and Arabic) to meet all tourists from different countries. The study established that from all the marketing mix elements, the researcher studied, product & promotion had the strongest effect on foreign tourist's satisfaction, where price and distribution were also effective significant factors. The research recommends suitable marketing strategies for all elements especially product & promotion.
A Multi-scale Pyramid of 3D Fully Convolutional Networks for Abdominal Multi-organ Segmentation

Recent advances in deep learning, like 3D fully convolutional networks (FCNs), have improved the state-of-the-art in dense semantic segmentation of medical images. However, most network architectures require severely downsampling or cropping the images to meet the memory limitations of today’s GPU cards while still considering enough context in the images for accurate segmentation. In this work, we propose a novel approach that utilizes auto-context to perform semantic segmentation at higher resolutions in a multi-scale pyramid of stacked 3D FCNs. We train and validate our models on a dataset of manually annotated abdominal organs and vessels from 377 clinical CT images used in gastric surgery, and achieve promising results with close to 90% Dice score on average. For additional evaluation, we perform separate testing on datasets from different sources and achieve competitive results, illustrating the robustness of the model and approach.
The scientific basis for high-intensity interval training: optimising training programmes and maximising performance in highly trained endurance athletes.

While the physiological adaptations that occur following endurance training in previously sedentary and recreationally active individuals are relatively well understood, the adaptations to training in already highly trained endurance athletes remain unclear. While significant improvements in endurance performance and corresponding physiological markers are evident following submaximal endurance training in sedentary and recreationally active groups, an additional increase in submaximal training (i.e. volume) in highly trained individuals does not appear to further enhance either endurance performance or associated physiological variables [e.g. peak oxygen uptake (VO2peak), oxidative enzyme activity]. It seems that, for athletes who are already trained, improvements in endurance performance can be achieved only through high-intensity interval training (HIT). The limited research which has examined changes in muscle enzyme activity in highly trained athletes, following HIT, has revealed no change in oxidative or glycolytic enzyme activity, despite significant improvements in endurance performance (p < 0.05). Instead, an increase in skeletal muscle buffering capacity may be one mechanism responsible for an improvement in endurance performance. Changes in plasma volume, stroke volume, as well as muscle cation pumps, myoglobin, capillary density and fibre type characteristics have yet to be investigated in response to HIT with the highly trained athlete. Information relating to HIT programme optimisation in endurance athletes is also very sparse. Preliminary work using the velocity at which VO2max is achieved (V(max)) as the interval intensity, and fractions (50 to 75%) of the time to exhaustion at V(max) (T(max)) as the interval duration has been successful in eliciting improvements in performance in long-distance runners. However, V(max) and T(max) have not been used with cyclists. Instead, HIT programme optimisation research in cyclists has revealed that repeated supramaximal sprinting may be equally effective as more traditional HIT programmes for eliciting improvements in endurance performance. Further examination of the biochemical and physiological adaptations which accompany different HIT programmes, as well as investigation into the optimal HIT programme for eliciting performance enhancements in highly trained athletes is required.
BoardPUF: Physical Unclonable Functions for printed circuit board authentication

Physical Unclonable Functions (PUFs) are cryptographic primitives that can be used to generate volatile secret keys for cryptographic operations and enable low-cost authentication of integrated circuits. Existing PUF designs mainly exploit variation effects on silicon and hence are not readily applicable for the authentication of printed circuit boards (PCBs). To tackle the above problem, in this paper, we propose a novel PUF device that is able to generate unique and stable IDs for individual PCB, namely BoardPUF. To be specific, we embed a number of capacitors in the internal layer of PCBs and utilize their variations for key generation. Then, by integrating a cryptographic primitive (e.g. hash function) into BoardPUF, we can effectively perform PCB authentication in a challenge-response manner. Our experimental results on fabricated boards demonstrate the efficacy of BoardPUF.
A Bidirectional High-Power-Quality Grid Interface With a Novel Bidirectional Noninverted Buck–Boost Converter for PHEVs

Plug-in hybrid electric vehicles (PHEVs) will play a vital role in future sustainable transportation systems due to their potential in terms of energy security, decreased environmental impact, improved fuel economy, and better performance. Moreover, new regulations have been established to improve the collective gas mileage, cut greenhouse gas emissions, and reduce dependence on foreign oil. This paper primarily focuses on two major thrust areas of PHEVs. First, it introduces a grid-friendly bidirectional alternating current/direct current ac/dc-dc/ac rectifier/inverter for facilitating vehicle-to-grid (V2G) integration of PHEVs. Second, it presents an integrated bidirectional noninverted buck-boost converter that interfaces the energy storage device of the PHEV to the dc link in both grid-connected and driving modes. The proposed bidirectional converter has minimal grid-level disruptions in terms of power factor and total harmonic distortion, with less switching noise. The integrated bidirectional dc/dc converter assists the grid interface converter to track the charge/discharge power of the PHEV battery. In addition, while driving, the dc/dc converter provides a regulated dc link voltage to the motor drive and captures the braking energy during regenerative braking.
FINANCIAL MARKET IMPERFECTIONS AND BUSINESS CYCLES *

Because of financial market imperfections, such as those generated by asymmetric information in financial markets, which lead to breakdowns in markets, like that for equity, in which risks are shared, firms act in a risk-averse manner. The resulting macroeconomic model accounts for many widely observed aspects of actual business cycles: (a) cyclical movements in real product wages, (b) cyclical patterns of output and investment including inventories, (c) sensitivity of the economy to small perturbations, and (d) persistence. More downward flexibility in wages and prices may exacerbate the plight of an economy that is in a deep recession.
Deriving Neural Architectures from Sequence and Graph Kernels

The design of neural architectures for structured objects is typically guided by experimental insights rather than a formal process. In this work, we appeal to kernels over combinatorial structures, such as sequences and graphs, to derive appropriate neural operations. We introduce a class of deep recurrent neural operations and formally characterize their associated kernel spaces. Our recurrent modules compare the input to virtual reference objects (cf. filters in CNN) via the kernels. Similar to traditional neural operations, these reference objects are parameterized and directly optimized in end-to-end training. We empirically evaluate the proposed class of neural architectures on standard applications such as language modeling and molecular graph regression, achieving state-of-the-art or competitive results across these applications. We also draw connections to existing architectures such as LSTMs.
Automated Food Ordering System with Real-Time Customer Feedback

The Rampant growth of wireless technology and Mobile devices in this era is creating a great impact on our lives. Some early efforts have been made to combine and utilize both of these technologies in advancement of hospitality industry. This research work aims to automate the food ordering process in restaurant and also improve the dining experience of customers. In this paper we discuss about the design & implementation of automated food ordering system with real time customer feedback (AOS-RTF) for restaurants. This system, implements wireless data access to servers. The android application on user’s mobile will have all the menu details. The order details from customer’s mobile are wirelessly updated in central database and subsequently sent to kitchen and cashier respectively. The restaurant owner can manage the menu modifications easily. The wireless application on mobile devices provide a means of convenience, improving efficiency and accuracy for restaurants by saving time, reducing human errors and real-time customer feedback. This system successfully over comes the drawbacks in earlier PDA based food ordering system and is less expensive and more effective than the multi-touchable restaurant management systems.
The Promise of Heart Rate Variability Biofeedback : Evidence-Based Applications

Heart rate variability biofeedback (HRVB) has enjoyed a good deal of popularity in recent years. A number of commercial products have been introduced ranging in price from $80 (MyCalmBeat by Brain Resourcet) to over $200 (Alivee by Somatic Vision, Inc.). The advertising for these products claims efficacy for a large number of disorders and, in fact, we are quite optimistic about the potential for these techniques, especially when combined with mindfulness-based interventions (Khazan, 2013). In this article, I will review the state of the empirical literature for the use of HRVBwith and without other clinical components. Wheat and Larkin (2010) reviewed some of the HRVB literature and concluded that ‘‘Results revealed that HRV biofeedback consistently effectuates acute improvements during biofeedback practice, whereas the presence of short-term and long-term carry-over effects is less clear’’ (p. 229). A substantial number of studies have appeared since that review. As developed in the early 1980s, HRVBwas conceived as an intervention that would target the parasympathetic nervous system (PNS) in contrast to other peripheral techniques that focused the sympathetic nervous system (SNS) ormuscles (see Lehrer, this issue for a history).Wenowthinkof the training as affecting cardiovascular homeostatic reflexes so as to increase flexibility and recovery from fight/flight adaptive situations. In addition, we are now investigating the vagal afferent pathways that may explain some of the central effects described below (MacKinnon, Gevirtz, McCraty, & Brown, 2013). A third pathway has been described by Tracey (2002) and involves the cholinergic or parasympathetic systems regulating the inflammatory response. The following review is organized by these possible mechanisms of action. Much more research will be necessary to elucidate these and other mechanisms, but the trend to organize treatments by mechanistic pathways has become useful in many areas of science.
Methodology for the systematic reviews on occupational therapy for adults with Alzheimer's disease and related dementias.

Systematic reviews of literature relevant to adults with Alzheimer's disease and their families are important to the practice of occupational therapy. We describe the seven questions that served as the focus for systematic reviews of the effectiveness of occupational therapy interventions for adults with Alzheimer's disease and their families. We include the background for the reviews; the process followed for each question, including search terms and search strategy; the databases searched; and the methods used to summarize and critically appraise the literature. The final number of articles included in each systematic review; a summary of the results; the strengths and limitations of the findings; and implications for practice, education, and research are presented for the six questions addressing interventions in the areas of occupation, perception, environment, activity demands, fall prevention, and caregiver strategies.
Deep learning for skin lesion segmentation

Melanomas are the most aggressive form of skin cancer. Due to observer bias, computerized analysis of dermoscopy images has become an important research area. One of the most important steps in dermoscopy image analysis is the automated detection of lesion areas in the dermoscopy images. In this paper, we present a deep learning method for automatic skin lesion segmentation. We use a subset of the International Skin Imaging Collaboration (ISIC) Archive dataset, which contains dermoscopic images paired with their corresponding lesion binary masks, provided by IEEE International Symposium on Biomedical Imaging (ISBI) 2017 challenge for Skin Lesion Analysis Towards Melanoma Detection, and compare against the benchmark results submitted by other participants. The experimental results show that our proposed method can outperform the submissions in terms of segmentation accuracy.
Improved Control of DFIG Systems During Network Unbalance Using PI–R Current Regulators

This paper presents a new control strategy for a doubly fed induction generator (DFIG) under unbalanced network voltage conditions. Coordinated control of the grid- and rotor-side converters (GSC and RSC, respectively) during voltage unbalance is proposed. Under an unbalanced supply voltage, the RSC is controlled to eliminate the torque pulsation at double supply frequency. The oscillation of the stator output active power is then compensated by the active power output from the GSC, to ensure constant active power output from the overall DFIG generation system. In order to provide precise control of the positive- and negative-sequence currents of the GSC and RSC, a current control scheme consisting of a proportional integral (PI) controller and a resonant (R) compensator is presented. The PI plus R current regulator is implemented in the positive synchronous reference frame without the need to decompose the positive- and negative-sequence components. Simulations on a 1.5-MW DFIG system and experimental tests on a 1.5-kW prototype validate the proposed strategy. Precise control of both positive- and negative-sequence currents and simultaneous elimination of torque and total active power oscillations have been achieved.