nils-herrmann/clean_scirepeval huggingface dataset:
The effect of an interactive delirium e learning tool on healthcare workers' delirium recognition, knowledge and strain in caring for delirious patients: a pilot pre test/post test study
Venue: BMC medical education

BackgroundStudies investigating the effectiveness of delirium e learning tools in clinical practice are scarce. The aim of this study is to determine the effect of a delirium e learning tool on healthcare workers' delirium recognition, delirium knowledge and care strain in delirium.MethodsA pilot pre posttest study in a convenience sample of 59 healthcare workers recruited from medical, surgical, geronto psychiatric and rehabilitation units of a university hospital. The intervention consisted of a live information session on how to use the e learning tool and, a 2 month self active learning program. The tool included 11 e modules integrating knowledge and skill development in prevention, detection and management of delirium. Case vignettes, the Delirium Knowledge Questionnaire, and the Strain of Care for Delirium Index were used to measure delirium recognition, delirium knowledge and experienced care strain in delirium respectively. Subgroup analyses were performed for healthcare workers completing 0 to 6 versus 7 to 11 modules.ResultsThe delirium recognition score improved significantly (mean 3.1 SD 0.9 versus 2.7 1.1; P 0.04) and more healthcare workers identified hypoactive (P 0.04) and hyperactive (P 0.007) delirium in the posttest compared to the pretest phase. A significant difference in the change of recognition levels over time between the 0 to 6 and 7 to 11 module groups was demonstrated (P 0.03) with an improved recognition level in the posttest phase within the 7 to 11 module group (P 0.007) After adjustment for potential confounders, this difference in the change over time was not significant (P 0.07) and no change in recognition levels within the 7 to 11 module group was noted (P 0.19) The knowledge score significantly improved in the posttest compared to the pretest phase (mean 31.7 SD2.6 versus 28.3 4.5; P 0.001) with a significant increased level within the 7 to 11 module group (unadjusted P 0.001/adjusted P 0.02) Overall, no difference between posttest and pretest phases was documented for care strain (P 0.46).ConclusionThe e learning tool improved healthcare workers' delirium recognition and knowledge. The effect of the tool is related to its level of completion, but was less explicit after controlling for potential confounders and warrants further investigation. The level of strain did not improve.
A Practical Framework for Executing Complex Queries over Encrypted Multimedia Data
Venue: DBSec

Over the last few years, data storage in cloud based services has been very popular due to easy management and monetary advantages of cloud computing. Recent developments showed that such data could be leaked due to various attacks. To address some of these attacks, encrypting sensitive data before sending to cloud emerged as an important protection mechanism. If the data is encrypted with traditional techniques, selective retrieval of encrypted data becomes challenging. To address this challenge, efficient searchable encryption schemes have been developed over the years. Almost all of the existing searchable encryption schemes are developed for keyword searches and require running some code on the cloud servers. However, many of the existing cloud storage services (e.g. Dropbox (https:/www.dropbox.com) Box (https:/www.box.com/ Google Drive (http:/drive.google.com/ etc. only allow simple data object retrieval and do not provide computational support needed to realize most of the searchable encryption schemes.
The Shadow Nemesis: Inference Attacks on Efficiently Deployable, Efficiently Searchable Encryption
Venue: CCS

Encrypting Internet communications has been the subject of renewed focus in recent years. In order to add end to end encryption to legacy applications without losing the convenience of full text search, ShadowCrypt and Mimesis Aegis use a new cryptographic technique called "efficiently deployable efficiently searchable encryption" (EDESE) that allows a standard full text search system to perform searches on encrypted data. Compared to other recent techniques for searching on encrypted data, EDESE schemes leak a great deal of statistical information about the encrypted messages and the keywords they contain. Until now, the practical impact of this leakage has been difficult to quantify. In this paper, we show that the adversary's task of matching plaintext keywords to the opaque cryptographic identifiers used in EDESE can be reduced to the well known combinatorial optimization problem of weighted graph matching (WGM) Using real email and chat data, we show how off the shelf WGM solvers can be used to accurately and efficiently recover hundreds of the most common plaintext keywords from a set of EDESE encrypted messages. We show how to recover the tags from Bloom filters so that the WGM solver can be used with the set of encrypted messages that utilizes a Bloom filter to encode its search tags. We also show that the attack can be mitigated by carefully configuring Bloom filter parameters.
A Searchable Hierarchical Conditional Proxy Re encryption Scheme for Cloud Storage Services
Venue: Inf. Technol. Control.

As cloud technologies thrive, researches in the field of cloud storage have switched their focus from encryption decryption techniques that help data owners protect their privacy and data confidentiality to the application of searching techniques on encrypted data while maintaining high level security and privacy of outsource data. To begin with, Song et al. offered some practical techniques for searches on encrypted data. After that, Weng et al. presented their conditional proxy re encryption scheme where the data owner can decide which ciphertext satisfies a certain keyword condition set and then can have the retrieved data re encrypted by the semi trusted proxy server. The basic concepts of the above schemes are indeed quite innovative and do lead the way towards the solutions to the major practical cloud storage application problems; however, of all the researches that follow, none has had both searching on encrypted data and conditional proxy re encryption combined. In this paper, we propose a new scheme for cloud storage services that integrates keyword search with conditional proxy re encryption. This say, with a newly added keyword or new proxy, the cloud service provider is able to generate a hierarchical key. As far as data security is concerned, our scheme provides proven data owner authentication, re delegation, and chosen ciphertext security. The superior performance of the proposed scheme has been established by comparing it with related works, and our security analysis as well as BAN logic correctness check also offered solid proof that the new scheme is both secure and practical.
A vitamin D pathway gene gene interaction affects low density lipoprotein cholesterol levels.
Venue: The Journal of nutritional biochemistry

Much evidence suggests an association between vitamin D deficiency and chronic diseases such as obesity and dyslipidemia. Although genetic factors play an important role in the etiology of these diseases, only a few studies have investigated the relationship between vitamin D related genes and anthropometric and lipid profiles. The aim of this study was to investigate the association of three vitamin D related genes with anthropometric and lipid parameters in 542 adult individuals. We analyzed the rs2228570 polymorphism in the vitamin D receptor gene (VDR) rs2134095 in the retinoid X receptor gamma gene (RXRG) and rs7041 in the vitamin D binding protein gene (GC) Polymorphisms were genotyped by TaqMan allelic discrimination. Gene gene interactions were evaluated by the general linear model. The functionality of the polymorphisms was investigated using the following predictors and databases: SIFT (Sorting Intolerant from Tolerant) PolyPhen 2 (Polymorphism Phenotyping v2) and Human Splicing Finder 3. We identified a significant effect of the interaction between RXRG (rs2134095) and GC (rs7041) on low density lipoprotein cholesterol (LDL c) levels (P=.005) Furthermore, our in silico analysis suggested a functional role for both variants in the regulation of the gene products. Our results suggest that the vitamin D related genes RXRG and GC affect LDL c levels. These findings are in agreement with other studies that consistently associate vitamin D and lipid profile. Together, our results corroborate the idea that analyzing gene gene interaction would be helpful to clarify the genetic component of lipid profile.
Comparing federal and unitary models of sport governance: a case study investigation

This study explores how the unitary model of sport governance acts as an alternative system to the traditional federal model synonymous within many sport governance networks. A case study investigation was undertaken to identify and examine key issues that contrast federal and unitary models of governance in a non profit sporting network. The case study involved one sporting network in Australia, touch football, where 21 participants from both national and state level organisations took part in semi structured interviews. Interviews were analysed using a pattern matching process involving open and axial coding to extract a thematic structure that detailed the issues inherent in federal and unitary sport governance models. Challenges and complexities of the federal model, benefits of the unitary model of governance, role ambiguity of advisory councils, and leadership in the unitary model emerged as the dominant themes in the study. The member based nature of non profit sporting networks was implicit in each of these themes and emerged as a significant challenge requiring attention for networks attempting to adopt a unitary governance model. A number of implications for sport governance research and practice based on these findings conclude the paper.
Layered shape grammars for procedural modelling of buildings
Venue: The Visual Computer

The effort of creating virtual city environments is reduced using procedural modelling techniques. However, these typically use split based approaches which can impose limitations on the final geometry, usually enforcing a grid like structure and require complex geometry to be imported. Layered shape grammars can increase the variability of procedural buildings, while the vectorial definition of shapes introduces the possibility of creating complex shapes that seamlessly blend into the model. We evaluate the contributions with a modelling example and a comparison with split based procedural modelling techniques. Results show that layers allow more flexibility than split based techniques in creating variations. Vectorially defined shapes are a step forward in shape grammar expressiveness.
Optimal time window for temporal segmentation of sensor streams in multi activity recognition
Venue: MobiQuitous

Multi activity recognition in the urban environment is a challenging task. This is largely attributed to the influence of urban dynamics, the variety of the label sets, and the heterogeneous nature of sensor data that arrive irregularly and at different rates. One of the first tasks in multi activity recognition is temporal segmentation. A common temporal segmentation method is the sliding window approach with a fixed window size, which is widely used for single activity recognition. In order to recognise multiple activities from heterogeneous sensor streams, we propose a new time windowing technique that can optimally extract segments with multiple activity labels. The mixture of activity labels causes the impurity in the corresponding temporal segment. Hence, larger window size imposes higher impurity in temporal segments while increasing class separability. In addition, the combination of labels from multiple activity label sets (i.e. number of unique multi activity) may decrease as impurity increases. Naturally, these factors will affect the performance of classification task. In our proposed technique, the optimal window size is found by gaining the balance between minimising impurity and maximising class separability in temporal segments. As a result, it accelerates the learning process for recognising multiple activities (such as higher level and atomic human activities under different environment contexts) in comparison to laborious tasks of sensitivity analysis. The evaluation was validated by experiments on a real world dataset for recognising multiple human activities in a smart environment.
Native Immigrant Gaps in Educational and School to Work Transitions in the 2nd Generation: The Role of Gender and Ethnicity

We study how native immigrant gaps in educational trajectories and school to work transitions vary by gender. Using longitudinal Belgian data and adjusting for family background and educational sorting, we find that second generation immigrants, especially Turks and Moroccans, lag behind natives. In particular, we observe that immigrant students are less likely to finish secondary education or begin tertiary education on time. They are also less likely to transition into work successfully. These performance gaps are substantially larger for female immigrants. In addition, we study demographic behaviors to test the hypothesis that attributes the gender differences in educational and economic ethnic gaps to cultural differences between immigrants and natives.
STEM education K 12: perspectives on integration

This commentary was stimulated by Yeping Li's first editorial (2014) citing one of the journal's goals as adding multidisciplinary perspectives to current studies of single disciplines comprising the focus of other journals. In this commentary, I argue for a greater focus on STEM integration, with a more equitable representation of the four disciplines in studies purporting to advance STEM learning.The STEM acronym is often used in reference to just one of the disciplines, commonly science. Although the integration of STEM disciplines is increasingly advocated in the literature, studies that address multiple disciplines appear scant with mixed findings and inadequate directions for STEM advancement. Perspectives on how discipline integration can be achieved are varied, with reference to multidisciplinary, interdisciplinary, and transdisciplinary approaches adding to the debates. Such approaches include core concepts and skills being taught separately in each discipline but housed within a common theme; the introduction of closely linked concepts and skills from two or more disciplines with the aim of deepening understanding and skills; and the adoption of a transdisciplinary approach, where knowledge and skills from two or more disciplines are applied to real world problems and projects with the aim of shaping the total learning experience.Research that targets STEM integration is an embryonic field with respect to advancing curriculum development and various student outcomes. For example, we still need more studies on how student learning outcomes arise not only from different forms of STEM integration but also from the particular disciplines that are being integrated. As noted in this commentary, it seems that mathematics learning benefits less than the other disciplines in programs claiming to focus on STEM integration. Factors contributing to this finding warrant more scrutiny. Likewise, learning outcomes for engineering within K 12 integrated STEM programs appear under researched. This commentary advocates a greater focus on these two disciplines within integrated STEM education research. Drawing on recommendations from the literature, suggestions are offered for addressing the challenges of integrating multiple disciplines faced by the STEM community.
Evaluating the Benefit of the Maker Movement in K 12 STEM Education

The Maker Movement is a community of enthusiasts who personify a do it yourself mentality and who embody the model of lifelong learning. The community is comprised of hobbyists, tinkerers, engineers and artists who think critically to design, and engage in hand on projects for learning and leisure. There is a growing interest to incorporate this model of learning into K 12 education as a way to increase engagement in Science, Technology, Engineering, and Mathematics, or STEM, education through the use of Makerspace modeled laboratories. This article will investigate three criteria in determining whether there is value in integrating the Maker Movement into classroom instruction: 1) perceptions of Makerspaces; 2) comparison of the Maker Movement to 21st Century Learning skills; and 3) the viability of school district implementation.
Prospects and requirements for an operational modelling unit in flood crisis situations

Dike failure events pose severe flood crisis situations on areas in the hinterland of dikes. In recent decades the importance of being prepared for dike breaches has been increasingly recognized. However, the pre assessment of inundation resulting from dike breaches is possible only based on scenarios, which might not reflect the situation of a real event. This paper presents a setup and workflow that allows to model dike breachinduced inundation operationally, i.e. when an event is imminent or occurring. A comprehensive system setup of an operational modelling unit has been developed and implemented in the frame of a federal project in Saxony Anhalt, Germany. The modelling unit setup comprises a powerful methodology of flood modelling and elaborated operational guidelines for crisis situations. Nevertheless, it is of fundamental importance that the modelling unit is instated prior to flood events as a permanent system. Moreover the unit needs to be fully integrated in flood crisis management. If these crucial requirements are met, a modelling unit is capable of fundamentally supporting flood management with operational prognoses of adequate quality even in the limited timeframe of crisis situations.
Recent warming evidence inferred from a tree ring based winter half year minimum temperature reconstruction in northwestern Yichang, South Central China, and its relation to the large scale circulation anomalies
Venue: International Journal of Biometeorology

High resolution winter temperature reconstructions in China are rare, yet vital for the comprehensive understanding of past climate change. In the present work, the first winter half year minimum mean temperature from previous November to current April in northwestern Yichang, South Central China, was reconstructed back to 1875 based on tree ring material. The reconstruction can explain 55 of the variance over the calibration period during 1955 2011. The temperature maintained at comparatively low level before 1958, and an abnormal warming was seen since 1959. However, the warming trend stagnated after 2000 AD. 2001 2010 was the warmest decade not only during the instrumental period but also during the whole reconstructed period. The reconstruction indicates good spatial resemblance to other temperatures series in adjacent areas and Northern Hemisphere, yet the recent warming in this study is earlier and more prominent than that of Southeast China. This work also manifests that the winter half year minimum temperature in study area has good agreement with summer (June September) maximum temperature variation in Southeast China at decadal scale, except that the winter half year warming in recent decades is more evident than summer. This reconstruction is not only useful in improving our knowledge of long term temperature variation but also useful in predicting the tree growth dynamics in the future in the study area.
Model Compression Applied to Small Footprint Keyword Spotting
Venue: INTERSPEECH

Several consumer speech devices feature voice interfaces that perform on device keyword spotting to initiate user interactions. Accurate on device keyword spotting within a tight CPU budget is crucial for such devices. Motivated by this, we investigated two ways to improve deep neural network (DNN) acoustic models for keyword spotting without increasing CPU usage. First, we used low rank weight matrices throughout the DNN. This allowed us to increase representational power by increasing the number of hidden nodes per layer without changing the total number of multiplications. Second, we used knowledge distilled from an ensemble of much larger DNNs used only during training. We systematically evaluated these two approaches on a massive corpus of far field utterances. Alone both techniques improve performance and together they combine to give significant reductions in false alarms and misses without increasing CPU or memory usage.
Online Bagging and Boosting for Imbalanced Data Streams
Venue: IEEE Transactions on Knowledge and Data Engineering

While both cost sensitive learning and online learning have been studied separately, these two issues have seldom been addressed simultaneously. Yet, there are many applications where both aspects are important. This paper investigates a class of algorithmic approaches suitable for online cost sensitive learning, designed for such problems. The basic idea is to leverage existing methods for online ensemble algorithms, and combine these with batch mode methods for cost sensitive bagging/boosting algorithms. Within this framework, we describe several theoretically sound online cost sensitive bagging and online cost sensitive boosting algorithms, and show that the convergence of the proposed algorithms is guaranteed under certain conditions. We then present extensive experimental results on benchmark datasets to compare the performance of the various proposed approaches.
Making Sense of Entities and Quantities in Web Tables
Venue: CIKM

HTML tables and spreadsheets on the Internet or in enterprise intranets often contain valuable information, but are created ad hoc. As a result, they usually lack systematic names for column headers and clear vocabulary for cell values. This limits the re use of such tables and creates a huge heterogeneity problem when comparing or aggregating multiple tables. This paper aims to overcome this problem by automatically canonicalizing header names and cell values onto concepts, classes, entities and uniquely represented quantities registered in a knowledge base. To this end, we devise a probabilistic graphical model that captures coherence dependencies between cells in tables and candidate items in the space of concepts, entities and quantities. We give specific consideration to quantities which are mapped into a "measure, value, unit" triple over a taxonomy of physical (e.g. power consumption) monetary (e.g. revenue) temporal (e.g. date) and dimensionless (e.g. counts) measures. Our experiments with Web tables from diverse domains demonstrate the viability of our method and its benefits over baselines.