nils-herrmann/clean_scirepeval huggingface dataset:
Patient and nurse preferences for nurse handover-using preferences to inform policy: a discrete choice experiment protocol.

Nursing bedside handover in hospital has been identified as an opportunity to involve patients and promote patient-centred care. It is important to consider the preferences of both patients and nurses when implementing bedside handover to maximise the successful uptake of this policy. We outline a study which aims to (1) identify, compare and contrast the preferences for various aspects of handover common to nurses and patients while accounting for other factors, such as the time constraints of nurses that may influence these preferences.; (2) identify opportunities for nurses to better involve patients in bedside handover and (3) identify patient and nurse preferences that may challenge the full implementation of bedside handover in the acute medical setting.



We outline the protocol for a discrete choice experiment (DCE) which uses a survey design common to both patients and nurses. We describe the qualitative and pilot work undertaken to design the DCE. We use a D-efficient design which is informed by prior coefficients collected during the pilot phase. We also discuss the face-to-face administration of this survey in a population of acutely unwell, hospitalised patients and describe how data collection challenges have been informed by our pilot phase. Mixed multinomial logit regression analysis will be used to estimate the final results.


ETHICS AND DISSEMINATION
This study has been approved by a university ethics committee as well as two participating hospital ethics committees. Results will be used within a knowledge translation framework to inform any strategies that can be used by nursing staff to improve the uptake of bedside handover. Results will also be disseminated via peer-reviewed journal articles and will be presented at national and international conferences.
Adaptive Workflow Scheduling on Cloud Computing Platforms with IterativeOrdinal Optimization

The scheduling of multitask jobs on clouds is an NP-hard problem. The problem becomes even worse when complex workflows are executed on elastic clouds, such as Amazon EC2 or IBM RC2. The main difficulty lies in the large search space and high overhead of generating optimal schedules, especially for real-time applications with dynamic workloads. In this work, a new iterative ordinal optimization (IOO) method is proposed. The ordinal optimization method is applied in each iteration to achieve sub-optimal schedules. IOO aims at generating more efficient schedules from a global perspective over a long period. We prove through overhead analysis the advantages in time and space efficiency in using the IOO method. The IOO method is designed to adapt to system dynamism to yield suboptimal performance. In cloud experiments on IBM RC2 cloud, we execute 20,000 tasks in LIGO (Laser Interferometer Gravitational-wave Observatory) verification workflow on 128 virtual machines. The IOO schedule is generated in less than 1,000 seconds, while using the Monte Carlo simulation takes 27.6 hours, 100 times longer to yield an optimal schedule. The IOO-optimized schedule results in a throughput of 1,100 tasks/sec with 7 GB memory demand, compared with 60 percent decrease in throughput and 70 percent increase in memory demand in using the Monte Carlo method. Our LIGO experimental results clearly demonstrate the advantage of using the IOO-based workflow scheduling over the traditional blind-pick, ordinal optimization, or Monte Carlo methods. These numerical results are also validated by the theoretical complexity and overhead analysis provided.
Simple day-case surgery for pilonidal sinus disease.

Pilonidal disease is a common and usually minor disease. Although wide excisional surgery has been common practice, there are more simple alternatives. This review focused on the aetiology and management of pilonidal disease.



A comprehensive review of the literature on pilonidal disease was undertaken. MEDLINE searches for all articles listing pilonidal disease (1980-2010) were performed to determine the aetiology and results of surgical and non-surgical treatments. Single papers describing new techniques or minor modifications of established techniques were excluded. Further articles were traced through reference lists.



Patients with minimal symptoms and those having drainage of a single acute abscess can be treated expectantly. Non-surgical treatments may be of value but their long-term results are unknown. There is no rational basis or need for wide excision of the abscess and sinus. Simple removal of midline skin pits, the primary cause of pilonidal disease, with lateral drainage of the abscess and sinus is effective in most instances. Hirsute patients with extensive primary disease and deep natal clefts, or with recurrent disease and unhealed midline wounds, may also require flattening of the natal cleft with off-midline skin closure. These more conservative procedures are usually done as a day case, require minimal care in the community and are associated with a rapid return to work. They also avoid the occasional debilitating complications of surgical treatment.



Simple day-case surgery to eradicate midline skin pits without wide excision of the abscesses and sinus is rational, safe and effective for patients with pilonidal sinus disease.
Why web 2.0 is good for learning and for research: principles and prototypes

The term "Web 2.0" is used to describe applications that distinguish themselves from previous generations of software by a number of principles. Existing work shows that Web 2.0 applications can be successfully exploited for technology-enhance learning. However, in-depth analyses of the relationship between Web 2.0 technology on the one hand and teaching and learning on the other hand are still rare. In this article, we will analyze the technological principles of the Web 2.0 and describe their pedagogical implications on learning. We will furthermore show that Web 2.0 is not only well suited for learning but also for research on learning: the wealth of services that is available and their openness regarding API and data allow to assemble prototypes of technology-supported learning applications in amazingly small amount of time. These prototypes can be used to evaluate research hypotheses quickly. We will present two example prototypes and discuss the lessons we learned from building and using these prototypes.
Business Models - An Information Systems Research Agenda

The business model concept, although a relatively new topic for research, has garnered growing attention over the past decade. Whilst it has been robustly defined, the concept has so far attracted very little substantive research. In the context of the wide-spread digitization of businesses and society at large, the logic inherent in a business model has become critical for business success and, hence, a focus for academic inquiry. In our paper we identify the business model concept as the missing link between business strategy, processes, and Information Technology (IT). We argue that the BISE community offers distinct and unique competencies (e.g. translating business strategies into IT systems, managing business and IT processes, etc.) that can be harnessed for significant research contributions to this field. Within this research gap we delineate three distinct streams, namely, business models in IT industries; IT enabled or digital business models; and IT support for developing and managing business models, for which we outline the current state-of-the-art, suggest critical research questions and suitable research methodologies.
Hackerbot: Attacker Chatbots for Randomised and Interactive Security Labs, Using SecGen and oVirt

Capture the flag (CTF) has been applied with success in cybersecurity education, and works particularly well when learning offensive techniques. However, defensive security and incident response do not always naturally fit the existing approaches to CTF. We present Hackerbot, a unique approach for teaching computer security: students interact with a malicious attacker chatbot, who challenges them to complete a variety of security tasks, including defensive and investigatory challenges. Challenges are randomised using SecGen, and deployed onto an oVirt infrastructure. Evaluation data included system performance, mixed methods questionnaires (including the Instructional Materials Motivation Survey (IMMS) and the System Usability Scale (SUS)), and group interviews/focus groups. Results were encouraging, finding the approach convenient, engaging, fun, and interactive; while significantly decreasing the manual marking workload for staff. The cloud infrastructure deployment using SecGen/oVirt was a success, generating VMs with randomised challenges, and enabling students to work from home.
Efficient segmentation and surface classification of range images

Derivation of geometric structures from point clouds is an important step towards scene understanding for mobile robots. In this paper, we present a novel method for segmentation and surface classification of ordered point clouds. Data from RGB-D cameras are used as input. Normal based region growing segments the cloud and point feature descriptors classify each segment. Not only planar segments can be described but also curved surfaces. In an evaluation on indoor scenes we show the performance of our approach as well as give a comparison to state of the art methods.
Self-esteem and depression in patients presenting angle class III malocclusion submitted for orthognathic surgery.

To assess self-esteem and depression in patients submitted for orthognathic surgery.



Assessment was realized on 29 patients, male and female, aged 17 to 46, presenting Angle Class III malocclusion and referred for surgical treatment, during the preoperative (orthodontic preparation) and postoperative (six-month postoperative) periods. Either maxillomandibular or isolated procedures were performed. Two previously validated standardized measurement instruments for this area were used: the UNIFESP-EPM Rosenberg Self-Esteem Scale and the Self-Report Questionnaire-20 (SRQ-20). Descriptive and inferential statistical analysis (Repeated Measures Analysis of Variance and the Bonferroni test) verified possible interactions between the pre and postoperative periods, genders and types of surgery. Significance was determined at 5%.



Females showed improved self-esteem, presenting lower mean values after surgery (8.9 to 6.3). Regarding depression, a significant reduction in the number of depressive symptoms (p=0.002) occurred for female patients.



Female patients presented improved self-esteem and diminished depressive symptoms due to surgical intervention; whereas male patients showed no alteration in self-esteem and depression with surgical intervention.
You Are What You Tweet: Analyzing Twitter for Public Health

Analyzing user messages in social media can measure different population characteristics, including public health measures. For example, recent work has correlated Twitter messages with influenza rates in the United States; but this has largely been the extent of mining Twitter for public health. In this work, we consider a broader range of public health applications for Twitter. We apply the recently introduced Ailment Topic Aspect Model to over one and a half million health related tweets and discover mentions of over a dozen ailments, including allergies, obesity and insomnia. We introduce extensions to incorporate prior knowledge into this model and apply it to several tasks: tracking illnesses over times (syndromic surveillance), measuring behavioral risk factors, localizing illnesses by geographic region, and analyzing symptoms and medication usage. We show quantitative correlations with public health data and qualitative evaluations of model output. Our results suggest that Twitter has broad applicability for public health research.
Broadband Radial Waveguide Spatial Combiner

A broadband eight-way spatial combiner using coaxial probes and radial waveguides has been proposed and designed. The simple electromagnetic modeling for the radial waveguide power divider/combiner has been developed using equivalent-circuit method. The measured 10-dB return loss and 1-dB insertion loss bandwidth of this waveguide spatial combiner are all demonstrated to be about 8 GHz.
An 8.3 nW −72 dBm event driven IoE wake up receiver RF front end

This work presents an ultra-low power event driven wake-up receiver (WuRx) fabricated in a RF CMOS 130 nm process. The receiver consists of an off-chip lumped element matching network, an envelope detector, a decision circuit capable of detecting sub-mV baseband signal voltages and a clock source consuming 1.3 nW. This receiver has demonstrated a sensitivity of −72 dBm while consuming a total of 8.3 nW from 1 V and 0.65 V sources.
Computational approaches to motor control

This review will focus on four areas of motor control which have recently been enriched both by neural network and control system models: motor planning, motor prediction, state estimation and motor learning. We will review the computational foundations of each of these concepts and present specific models which have been tested by psychophysical experiments. We will cover the topics of optimal control for motor planning, forward models for motor prediction, observer models of state estimation arid modular decomposition in motor learning. The aim of this review is to demonstrate how computational approaches, as well as proposing specific models, provide a theoretical framework to formalize the issues in motor control.
Internet of Things (IoT) communication protocols: Review

Internet of Things (IoT) consists of smart devices that communicate with each other. It enables these devices to collect and exchange data. Besides, IoT has now a wide range of life applications such as industry, transportation, logistics, healthcare, smart environment, as well as personal, social gaming robot, and city information. Smart devices can have wired or wireless connection. As far as the wireless IoT is the main concern, many different wireless communication technologies and protocols can be used to connect the smart device such as Internet Protocol Version 6 (IPv6), over Low power Wireless Personal Area Networks (6LoWPAN), ZigBee, Bluetooth Low Energy (BLE), Z-Wave and Near Field Communication (NFC). They are short range standard network protocols, while SigFox and Cellular are Low Power Wide Area Network (LPWAN).standard protocols. This paper will be an attempt to review different communication protocols in IoT. In addition, it will compare between commonly IoT communication protocols, with an emphasis on the main features and behaviors of various metrics of power consumption security spreading data rate, and other features. This comparison aims at presenting guidelines for the researchers to be able to select the right protocol for different applications.
Statistical Tools for Digital Forensics

A digitally altered photograph, often leaving no visual clues of having been tampered with, can be indistinguishable from an authentic photograph. As a result, photographs no longer hold the unique stature as a definitive recording of events. We describe several statistical techniques for detecting traces of digital tampering in the absence of any digital watermark or signature. In particular, we quantify statistical correlations that result from specific forms of digital tampering, and devise detection schemes to reveal these correlations.
A multiview 3D modeling system based on stereo vision techniques

This paper introduces a stereo vision system to automatically generate 3D models of real objects. 3D model generation is based on the merging of multiview range images obtained from a digital stereo camera. Stereo images obtained from the camera are rectified, and a correlation-based stereo matching technique reconstructs range images from them. A turntable stage is also employed to obtain multiple range images of the objects. To register range images into a common coordinate system automatically, we introduce and calibrate a turntable coordinate system with respect to the camera coordinate system. After the registration of multiview range images, a 3D model is reconstructed using a volumetric integration technique. Error analysis on turntable calibration and 3D model reconstruction shows the accuracy of our 3D modeling system.
NLP Techniques for Term Extraction and Ontology Population

This chapter investigates NLP techniques for ontology population, using a combination of rule-based approaches and machine learning. We describe a method for term recognition using linguistic and statistical techniques, making use of contextual information to bootstrap learning. We then investigate how term recognition techniques can be useful for the wider task of information extraction, making use of similarity metrics and contextual information. We describe two tools we have developed which make use of contextual information to help the development of rules for named entity recognition. Finally, we evaluate our ontology-based information extraction results using a novel technique we have developed which makes use of similarity-based metrics first developed for term recognition.
A Prism-Free Method for Silhouette Rendering in Inverse Displacement Mapping

Silhouette is a key feature that distinguishes displacement mapping from normal mapping. However the silhouette rendering in the GPU implementation of displacement mapping (which is often called inversed displacement mapping) is tricky. Previous approaches rely mostly on construction of additional extruding prism-like geometry, which slows down the rendering significantly. In this paper, we proposed a method for solving the silhouette rendering problem in inverse displace mapping without using any extruding prism-like geometry. At each step of intersection finding, we continuously bends the viewing ray according to the current local tangent space associated with the surface. Thus, it allows mapping a displacement map onto an arbitrary curved surface with more accurate silhouette. While our method is simple, it offers surprisingly good results over Curved Relief Map (CRM) [OP05] in many difficult or degenerated cases.
Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis

This paper describes an application of fuzzy logic in designing controllers for industrial plants. A fuzzy logic is used to synthesize linguistic control protocol of a skilled operator. The method has been applied to pilot scale plants as well as in practical situations. The merits of this method and its usefulness to control engineering are discussed. An avenue for further work in this area is described where the need is to go beyond a purely descriptive approach, and means for implementing a prescriptive or a self-organizing system are explored.
Abstractive Summarization of Reddit Posts with Multi-level Memory Networks

ive Summarization of Reddit Posts with Multi-level Memory Networks Byeongchang Kim Hyunwoo Kim Gunhee Kim Department of Computer Science and Engineering & Center for Superintelligence Seoul National University, Seoul, Korea {byeongchang.kim,hyunwoo.kim}@vision.snu.ac.kr gunhee@snu.ac.kr http://vision.snu.ac.kr/projects/reddit-tifu
Distributional semantics for understanding spoken meal descriptions

This paper presents ongoing language understanding experiments conducted as part of a larger effort to create a nutrition dialogue system that automatically extracts food concepts from a user's spoken meal description. We first discuss the technical approaches to understanding, including three methods for incorporating word vector features into conditional random field (CRF) models for semantic tagging, as well as classifiers for directly associating foods with properties. We report experiments on both text and spoken data from an in-domain speech recognizer. On text data, we show that the addition of word vector features significantly improves performance, achieving an F1 test score of 90.8 for semantic tagging and 86.3 for food-property association. On speech, the best model achieves an F1 test score of 87.5 for semantic tagging and 86.0 for association. Finally, we conduct an end-to-end system evaluation through a user study with human ratings of 83% semantic tagging accuracy.
Implementing Remote Procedure Calls

Remote procedure calls (RPC) appear to be a useful paradig m for providing communication across a network between programs written in a high-level language. This paper describes a package providing a remote procedure call facility, the options that face the designer of such a package, and the decisions ~we made. We describe the overall structure of our RPC mechanism, our facilities for binding RPC clients, the transport level communication protocol, and some performance measurements. We include descriptioro~ of some optimizations used to achieve high performance and to minimize the load on server machines that have many clients.
Assortment Optimization under the Multinomial Logit Model with Random Choice Parameters

We consider assortment optimization problems under the multinomial logit model, where the parameters of the choice model are random. The randomness in the choice model parameters is motivated by the fact that there are multiple customer segments, each with different preferences for the products, and the segment of each customer is unknown to the firm when the customer makes a purchase. This choice model is also called the mixture-of-logits model. The goal of the firm is to choose an assortment of products to offer that maximizes the expected revenue per customer, across all customer segments. We establish that the problem is NP-complete even when there are just two customer segments. Motivated by this complexity result, we focus on assortments consisting of products with the highest revenues, which we refer to as revenue-ordered assortments. We identify specially structured cases of the problem where revenue-ordered assortments are optimal. When the randomness in the choice model parameters does not follow a special structure, we derive tight approximation guarantees for revenue-ordered assortments. We extend our model to the multi-period capacity allocation problem, and prove that, when restricted to the revenue-ordered assortments, the mixture-of-logits model possesses the nesting-by-fare-order property. This result implies that revenue-ordered assortments can be incorporated into existing revenue management systems through nested protection levels. Numerical experiments show that revenue-ordered assortments perform remarkably well, generally yielding profits that are within a fraction of a percent of the optimal.
The Adaptive Lasso and Its Oracle Properties

The lasso is a popular technique for simultaneous estimation and variable selection. Lasso variable selection has been shown to be consistent under certain conditions. In this work we derive a necessary condition for the lasso variable selection to be consistent. Consequently, there exist certain scenarios where the lasso is inconsistent for variable selection. We then propose a new version of the lasso, called the adaptive lasso, where adaptive weights are used for penalizing different coefficients in the 1 penalty. We show that the adaptive lasso enjoys the oracle properties; namely, it performs as well as if the true underlying model were given in advance. Similar to the lasso, the adaptive lasso is shown to be near-minimax optimal. Furthermore, the adaptive lasso can be solved by the same efficient algorithm for solving the lasso. We also discuss the extension of the adaptive lasso in generalized linear models and show that the oracle properties still hold under mild regularity conditions. As a byproduct of our theory, the nonnegative garotte is shown to be consistent for variable selection.