nils-herrmann/clean_scirepeval huggingface dataset:
Energy-aware traffic engineering in hybrid SDN/IP backbone networks

Software defined network (SDN) can effectively improve the performance of traffic engineering and will be widely used in backbone networks. Therefore, new energy-saving schemes must take SDN into consideration; this action is extremely important owing to the rapidly increasing energy consumption in telecom and Internet service provider (ISP) networks. Meanwhile, the introduction of SDN in current networks must be incremental in most cases, for technical and economic reasons. During this period, operators must manage hybrid networks in which SDN and traditional protocols coexist. In this study, we investigate the energy-efficient traffic engineering problem in hybrid SDN/Internet protocol (IP) networks. First, we formulate the mathematical optimization model considering the SDN/IP hybrid routing mode. The problem is NP-hard; therefore, we propose a fast heuristic algorithm named hybrid energy-aware traffic engineering (HEATE) as a solution. In our proposed HEATE algorithm, the IP routers perform shortest-path routing by using distributed open shortest path first (OSPF) link weight optimization. The SDNs perform multi-path routing with traffic-flow splitting managed by the global SDN controller. The HEATE algorithm determines the optimal setting for the OSPF link weight and the splitting ratio of SDNs. Thus, the traffic flow is aggregated onto partial links, and the underutilized links can be turned off to save energy. Based on computer simulation results, we demonstrate that our algorithm achieves a significant improvement in energy efficiency in hybrid SDN/IP networks.
Challenges for Toxic Comment Classification: An In-Depth Error Analysis

Toxic comment classification has become an active research field with many recently proposed approaches. However, while these approaches address some of the task’s challenges others still remain unsolved and directions for further research are needed. To this end, we compare different deep learning and shallow approaches on a new, large comment dataset and propose an ensemble that outperforms all individual models. Further, we validate our findings on a second dataset. The results of the ensemble enable us to perform an extensive error analysis, which reveals open challenges for state-of-the-art methods and directions towards pending future research. These challenges include missing paradigmatic context and inconsistent dataset labels.
Reducing cyberbullying: A theory of reasoned action-based video prevention program for college students.

Few studies have evaluated the effectiveness of cyberbullying prevention/intervention programs. The goals of the present study were to develop a Theory of Reasoned Action (TRA)-based video program to increase cyberbullying knowledge (1) and empathy toward cyberbullying victims (2), reduce favorable attitudes toward cyberbullying (3), decrease positive injunctive (4) and descriptive norms about cyberbullying (5), and reduce cyberbullying intentions (6) and cyberbullying behavior (7). One hundred sixty-seven college students were randomly assigned to an online video cyberbullying prevention program or an assessment-only control group. Immediately following the program, attitudes and injunctive norms for all four types of cyberbullying behavior (i.e., unwanted contact, malice, deception, and public humiliation), descriptive norms for malice and public humiliation, empathy toward victims of malice and deception, and cyberbullying knowledge significantly improved in the experimental group. At one-month follow-up, malice and public humiliation behavior, favorable attitudes toward unwanted contact, deception, and public humiliation, and injunctive norms for public humiliation were significantly lower in the experimental than the control group. Cyberbullying knowledge was significantly higher in the experimental than the control group. These findings demonstrate a brief cyberbullying video is capable of improving, at one-month follow-up, cyberbullying knowledge, cyberbullying perpetration behavior, and TRA constructs known to predict cyberbullying perpetration. Considering the low cost and ease with which a video-based prevention/intervention program can be delivered, this type of approach should be considered to reduce cyberbullying.
Empowerment: the holy grail of health promotion?

Potentially, empowerment has much to offer health promotion. However, some caution needs to be exercised before the notion is wholeheartedly embraced as the major goal of health promotion. The lack of a clear theoretical underpinning, distortion of the concept by different users, measurement ambiguities, and structural barriers make 'empowerment' difficult to attain. To further discussion, th is paper proposes several assertions about the definition, components, process and outcome of 'empowerment', including the need for a distinction between psychological and community empowerment. These assertions and a model of community empowerment are offered in an attempt to clarify an important issue for health promotion.
Initializing neural networks for hierarchical multi-label text classification

Many tasks in the biomedical domain require the assignment of one or more predefined labels to input text, where the labels are a part of a hierarchical structure (such as a taxonomy). The conventional approach is to use a one-vs.-rest (OVR) classification setup, where a binary classifier is trained for each label in the taxonomy or ontology where all instances not belonging to the class are considered negative examples. The main drawbacks to this approach are that dependencies between classes are not leveraged in the training and classification process, and the additional computational cost of training parallel classifiers. In this paper, we apply a new method for hierarchical multi-label text classification that initializes a neural network model final hidden layer such that it leverages label co-occurrence relations such as hypernymy. This approach elegantly lends itself to hierarchical classification. We evaluated this approach using two hierarchical multi-label text classification tasks in the biomedical domain using both sentenceand document-level classification. Our evaluation shows promising results for this approach.
Neuromyths in Education: Prevalence and Predictors of Misconceptions among Teachers

The OECD's Brain and Learning project (2002) emphasized that many misconceptions about the brain exist among professionals in the field of education. Though these so-called "neuromyths" are loosely based on scientific facts, they may have adverse effects on educational practice. The present study investigated the prevalence and predictors of neuromyths among teachers in selected regions in the United Kingdom and the Netherlands. A large observational survey design was used to assess general knowledge of the brain and neuromyths. The sample comprised 242 primary and secondary school teachers who were interested in the neuroscience of learning. It would be of concern if neuromyths were found in this sample, as these teachers may want to use these incorrect interpretations of neuroscience findings in their teaching practice. Participants completed an online survey containing 32 statements about the brain and its influence on learning, of which 15 were neuromyths. Additional data was collected regarding background variables (e.g., age, sex, school type). Results showed that on average, teachers believed 49% of the neuromyths, particularly myths related to commercialized educational programs. Around 70% of the general knowledge statements were answered correctly. Teachers who read popular science magazines achieved higher scores on general knowledge questions. More general knowledge also predicted an increased belief in neuromyths. These findings suggest that teachers who are enthusiastic about the possible application of neuroscience findings in the classroom find it difficult to distinguish pseudoscience from scientific facts. Possessing greater general knowledge about the brain does not appear to protect teachers from believing in neuromyths. This demonstrates the need for enhanced interdisciplinary communication to reduce such misunderstandings in the future and establish a successful collaboration between neuroscience and education.
Online hotel booking : The effects of brand image , price , trust and value on purchase intentions

Leisure travelers increasingly prefer to book hotel online when considering the convenience and cost/ time saving. This research examines the direct and mediating effects of brand image, perceived price, trust, perceived value on consumers' booking intentions and compares the gender differences in online hotel booking. The outcomes confirm most of the direct and indirect path effects and are consistent with findings from previous studies. Consumers in Taiwan tend to believe the hotel price is affordable, the hotel brand is attractive, the hotel is trustworthy, the hotel will offer good value for the price and the likelihood of their booking intentions is high. Brand image, perceived price, and perceived value are the three critical determinants directly influencing purchase intentions. However, the impact of trust on purchase intentions is not significant. The differences between males and females on purchase intentions are not significant as well. Managerial implications of these results are discussed.
The Organizational Context of Nursing Practice

In this chapter, the intuitive link between balanced, healthy, and supportive psychosocial work environments and a variety of vitally important patient, nurse, and organizational outcomes is discussed with reference to a number of clearly defined and well-researched concepts. Among the essential concepts that ground the rest of the book is the notion of a bundle of factors that provide a context for nurses’ work and are known collectively as the practice environment. Landmark studies that focused specifically on nurses’ experiences of their work environments in exemplary hospitals examined so-called Magnet hospitals, leading to a framework that describes the practice environment and its linkage with professional wellbeing, occupational stress, and quality of practice and productivity. Many ideas and models have obvious connections to the notion of practice environment such as Job Demand– Control–Support model, worklife dimensions and burnout, concepts related to burnout such as compassion fatigue, and work engagement as a mirror image concept of burnout, as well as notions of empowerment and authentic leadership. These concepts have been chosen for discussion here based on critical masses of evidence pointing to their usefulness in healthcare management and specifically in the management of nursing services. Together all of these concepts and supporting research and scholarship speak to a common point: intentional leadership approaches, grounded in a comprehensive understanding of nurses’ psychosocial experiences of their work, are essential to nurses’ abilities to respond to complex patients’ needs in rapidly changing healthcare contexts and socioeconomic conditions.
Scalable Nearest Neighbor Algorithms for High Dimensional Data

For many computer vision and machine learning problems, large training sets are key for good performance. However, the most computationally expensive part of many computer vision and machine learning algorithms consists of finding nearest neighbor matches to high dimensional vectors that represent the training data. We propose new algorithms for approximate nearest neighbor matching and evaluate and compare them with previous algorithms. For matching high dimensional features, we find two algorithms to be the most efficient: the randomized k-d forest and a new algorithm proposed in this paper, the priority search k-means tree. We also propose a new algorithm for matching binary features by searching multiple hierarchical clustering trees and show it outperforms methods typically used in the literature. We show that the optimal nearest neighbor algorithm and its parameters depend on the data set characteristics and describe an automated configuration procedure for finding the best algorithm to search a particular data set. In order to scale to very large data sets that would otherwise not fit in the memory of a single machine, we propose a distributed nearest neighbor matching framework that can be used with any of the algorithms described in the paper. All this research has been released as an open source library called fast library for approximate nearest neighbors (FLANN), which has been incorporated into OpenCV and is now one of the most popular libraries for nearest neighbor matching.
A review of wearable sensors and systems with application in rehabilitation

The aim of this review paper is to summarize recent developments in the field of wearable sensors and systems that are relevant to the field of rehabilitation. The growing body of work focused on the application of wearable technology to monitor older adults and subjects with chronic conditions in the home and community settings justifies the emphasis of this review paper on summarizing clinical applications of wearable technology currently undergoing assessment rather than describing the development of new wearable sensors and systems. A short description of key enabling technologies (i.e. sensor technology, communication technology, and data analysis techniques) that have allowed researchers to implement wearable systems is followed by a detailed description of major areas of application of wearable technology. Applications described in this review paper include those that focus on health and wellness, safety, home rehabilitation, assessment of treatment efficacy, and early detection of disorders. The integration of wearable and ambient sensors is discussed in the context of achieving home monitoring of older adults and subjects with chronic conditions. Future work required to advance the field toward clinical deployment of wearable sensors and systems is discussed.
Identifying Customer Needs from User-Generated Content

Firms traditionally rely on interviews and focus groups to identify customer needs for marketing strategy and product development. User-generated content (UGC) is a promising alternative source for identifying customer needs. However, established methods are neither efficient nor effective for large UGC corpora because much content is non-informative or repetitive. We propose a machine-learning approach to facilitate qualitative analysis by selecting content for efficient review. We use a convolutional neural network to filter out non-informative content and cluster dense sentence embeddings to avoid sampling repetitive content. We further address two key questions: Are UGCbased customer needs comparable to interview-based customer needs? Do the machine-learning methods improve customer-need identification? These comparisons are enabled by a custom dataset of customer needs for oral care products identified by professional analysts using industry-standard experiential interviews. The analysts also coded 12,000 UGC sentences to identify which previously identified customer needs and/or new customer needs were articulated in each sentence. We show that (1) UGC is at least as valuable as a source of customer needs for product development, likely morevaluable, than conventional methods, and (2) machine-learning methods improve efficiency of identifying customer needs from UGC (unique customer needs per unit of professional services cost).
A triple band arc-shaped slot patch antenna for UAV GPS/Wi-Fi applications

In this paper, a triple band capacitive-fed circular patch antenna with arc-shaped slots is proposed for 1.575 GHz GPS and Wi-Fi 2.4/5.2 GHz communications on unmanned aerial vehicle (UAV) applications. In order to enhance the impedance bandwidth of the antenna, a double-layered geometry is applied in this design with a circular feeding disk placed between two layers. The antenna covers 2380 - 2508 MHz and 5100 - 6030 MHz for full support of the Wi-Fi communication between UAV and ground base station. The foam-Duroid stacked geometry can further enhance the bandwidths for both GPS and Wi-Fi bands when compared to purely Duroid form. The simulation and measurement results are reported in this paper.
Personality Traits as Predictors of Leadership Style Preferences : Investigating the Relationship Between Social Dominance Orientation and Attitudes Towards Authentic Leaders

Cite this article Romager JA, Hughes K, Trimble JE. Personality traits as predictors of leadership style preferences: Investigating the relationship between social dominance orientation and attitudes towards authentic leaders.  Investigating the Relationship Between Social Dominance Orientation and Attitudes Towards Authentic Leaders Original Research
A Randomized Controlled Trial Determining Variances in Ostomy Skin Conditions and the Economic Impact (ADVOCATE Trial)

To compare ostomy-related costs and incidence of peristomal skin complications (PSCs) for ceramide-infused ostomy skin barriers and control skin barriers.



The ADVOCATE trial is a multi-centered randomized controlled trial, and double-blinded international study with an adaptive design.



The sample comprised 153 adults from 25 sites from the United States, Canada, and Europe. Participants were seen in hospital and outpatient care settings.



Data were collected by investigators at each site during face-to-face visits and during telephone check-in calls between visits. Cost of care data were collected using a questionnaire developed specifically for the study. The peristomal skin was assessed using the Ostomy Skin Tool. Health-related quality of life was measured using the SF-12v2. Patient-reported outcomes were collected using a patient-centered study-specific questionnaire. Cost of care was analyzed via analysis of covariance comparing total cost of care for 12 weeks between the 2 groups. The incidence of PSC was analyzed via Barnard's exact test comparing the incidence of PSCs between the control and treatment groups. Tertiary outcomes were exploratory in nature and not statistically powered.



Use of the ceramide-infused barrier significantly reduced stoma-related cost of care over a 12-week period, resulting in a $36.46 decrease in cost (14% relative decrease). The adjusted average costs were $223.73 in the treatment group and $260.19 in the control group (P = .017). The overall incidence of PSCs in the study was 47.7%; PSC incidence was 40.5% for the treatment group versus 55.4% for controls (P = .069, 95% confidence interval of the difference: -1.2 to 30.4). Significantly more participants using the ceramide-infused skin barrier were "very satisfied" with barrier performance (75% vs 55%; P = .033), prevention of leakage (63% vs 38%; P < .01), and prevention of itching (53% vs 31%; P = .016). General postoperative improvement in health-related quality of life was noted in both groups.



The use of a ceramide-infused barrier significantly decreased cost and increased satisfaction with patient-reported outcomes.
A Gauss-Newton Method for Markov Decision Processes

Approximate Newton methods are a standard optimization tool which aim to maintain the benefits of Newton’s method, such as a fast rate of convergence, whilst alleviating its drawbacks, such as computationally expensive calculation or estimation of the inverse Hessian. In this work we investigate approximate Newton methods for policy optimization in Markov decision processes (MDPs). We first analyse the structure of the Hessian of the objective function for MDPs. We show that, like the gradient, the Hessian exhibits useful structure in the context of MDPs and we use this analysis to motivate two Gauss-Newton Methods for MDPs. Like the Gauss-Newton method for non-linear least squares, these methods involve approximating the Hessian by ignoring certain terms in the Hessian which are difficult to estimate. The approximate Hessians possess desirable properties, such as negative definiteness, and we demonstrate several important performance guarantees including guaranteed ascent directions, invariance to affine transformation of the parameter space, and convergence guarantees. We finally provide a unifying perspective of key policy search algorithms, demonstrating that our second Gauss-Newton algorithm is closely related to both the EM-algorithm and natural gradient ascent applied to MDPs, but performs significantly better in practice on a range of challenging domains.
Powerful and consistent analysis of likert-type ratingscales

Likert-type scales are used extensively during usability evaluations, and more generally evaluations of interactive experiences, to obtain quantified data regarding attitudes, behaviors, and judgments of participants. Very often this data is analyzed using parametric statistics like the Student t-test or ANOVAs. These methods are chosen to ensure higher statistical power of the test (which is necessary in this field of research and practice where sample sizes are often small), or because of the lack of software to handle multi-factorial designs nonparametrically. With this paper we present to the HCI audience new developments from the field of medical statistics that enable analyzing multiple factor designs nonparametrically. We demonstrate the necessity of this approach by showing the errors in the parametric treatment of nonparametric data in experiments of the size typically reported in HCI research. We also provide a practical resource for researchers and practitioners who wish to use these new methods.
MinIE: Minimizing Facts in Open Information Extraction

The goal of Open Information Extraction (OIE) is to extract surface relations and their arguments from naturallanguage text in an unsupervised, domainindependent manner. In this paper, we propose MinIE, an OIE system that aims to provide useful, compact extractions with high precision and recall. MinIE approaches these goals by (1) representing information about polarity, modality, attribution, and quantities with semantic annotations instead of in the actual extraction, and (2) identifying and removing parts that are considered overly specific. We conducted an experimental study with several real-world datasets and found that MinIE achieves competitive or higher precision and recall than most prior systems, while at the same time producing shorter, semantically enriched extractions.
Learning object repositories as knowledge management systems

Over the past years, a number of international initiatives that recognize the importance of sharing and reusing digital educational resources among educational communities through the use of Learning Object Repositories (LORs) have emerged. Typically, these initiatives focus on collecting digital educational resources that are offered by their creators for open access and potential reuse. Nevertheless, most of the existing LORs are designed more as digital repositories, rather than as Knowledge Management Systems (KMS). By exploiting KMSs functionalities in LORs would bare the potential to support the organization and sharing of educational communities’ explicit knowledge (depicted in digital educational resources constructed by teachers and/or instructional designers) and tacit knowledge (depicted in teachers’ and students’ experiences and interactions of using digital educational resources available in LORs). Within this context, in this paper we study the design and the implementation of fourteen operating LORs from the KMSs’ perspective, so as to identify additional functionalities that can support the management of educational communities’ explicit and tacit knowledge. Thus, we propose a list of essential LORs’ functionalities, which aim to facilitate the organization and sharing of educational communities’ knowledge. Finally, we present the added value of these functionalities by identifying their importance towards addressing the current demands of web-facilitated educational communities, as well as the knowledge management activities that they execute.