nils-herrmann/clean_scirepeval huggingface dataset:
Integration of Cloud computing and Internet of Things: A survey
Venue: Future Gener. Comput. Syst.

Cloud computing and Internet of Things (IoT) are two very different technologies that are both already part of our life. Their adoption and use are expected to be more and more pervasive, making them important components of the Future Internet. A novel paradigm where Cloud and IoT are merged together is foreseen as disruptive and as an enabler of a large number of application scenarios.In this paper, we focus our attention on the integration of Cloud and IoT, which is what we call the CloudIoT paradigm. Many works in literature have surveyed Cloud and IoT separately and, more precisely, their main properties, features, underlying technologies, and open issues. However, to the best of our knowledge, these works lack a detailed analysis of the new CloudIoT paradigm, which involves completely new applications, challenges, and research issues. To bridge this gap, in this paper we provide a literature survey on the integration of Cloud and IoT. Starting by analyzing the basics of both IoT and Cloud Computing, we discuss their complementarity, detailing what is currently driving to their integration. Thanks to the adoption of the CloudIoT paradigm a number of applications are gaining momentum: we provide an up to date picture of CloudIoT applications in literature, with a focus on their specific research challenges. These challenges are then analyzed in details to show where the main body of research is currently heading. We also discuss what is already available in terms of platforms both proprietary and open source and projects implementing the CloudIoT paradigm. Finally, we identify open issues and future directions in this field, which we expect to play a leading role in the landscape of the Future Internet. Vision and motivations for the integration of Cloud computing and Internet of Things (IoT).Applications stemming from the integration of Cloud computing and IoT.Hot research topics and challenges in the integrated scenario of Cloud computing and IoT.Open issues and future directions for research in this scenario.
Vehicles detection in complex urban traffic scenes using Gaussian mixture model with confidence measurement

Aiming to efficiently resolve the problem that the subtraction background model is easily contaminated by slow moving or temporarily stopped vehicles, the Gaussian mixture model with confidence measurement (GMMCM) is proposed for vehicle detection in complex urban traffic scenes. According to the current traffic state, each pixel of background model is set a CM. Whether to update the background model and the corresponding adaptive learning rate depends on if the current pixel point is in confidence period. Using the real world urban traffic videos, the first experiments are conducted by GMMCM, compared with three commonly used models including GMM, self adaptive GMM (SAGMM) and local parameter learning algorithm for the GMM (LPLGMM) The first experimental results show that GMMCM excels GMM, SAGMM and LPLGMM in keeping the background model being unpolluted from slow moving or temporarily stopped vehicles. The second experiments are conducted by GMMCM, compared with visual background extractor, sigma delta with CM, SAGMM, LPLGMM and GMM. The average recalls of six methods are 0.899, 0.753, 0.679, 0.420, 0.447 and 0.205, and the average F measures of six methods are 0.636, 0.612, 0.592, 0.373, 0.330 and 0.179, respectively. All experimental results show the effectiveness of the proposed GMMCM in vehicles detection of complex urban traffic scenes.
Applying Machine Learning for Human Seizure Prediction

We explore the use of both statistical and deep learning methods for the task of classifying iEEG signals as pre ictal or inter ical in order to perform human seizure prediction. Features were extracted that captured both the frequency and temporal properties of the raw data. The classifier performed adequately well by several performance metrics, especially given that the data set suffered from a significant class imbalance, with the majority of samples representing non seizure events. Thus, this paper demonstrates the feasibility of using a machine learning algorithm to accurately predict rare seizure events from a large set of EEG data. Such a model could have a significant impact on medicine, for its implementation on a medical device could predict and inform patients of seizure onset with a very high recall rate.
Comparison of pollution indices for the assessment of heavy metal in Brisbane River sediment.
Venue: Environmental pollution

Estuarine environment is complex and receives different contaminants from numerous sources that are persistent, bioaccumulative and toxic. The distribution, source, contamination and ecological risk status of heavy metals in sediment of Brisbane River, Australia were investigated. Sediment samples were analysed for major and minor elements using LA ICP MS. Principal component analysis and cluster analysis identified three main sources of metals in the samples: marine sand intrusion, mixed lithogenic and sand intrusion as well as transport related. To overcome inherent deficiencies in using a single index, a range of sediment quality indices, including contamination factor, enrichment factor, index of geo accumulation, modified degree of contamination, pollution index and modified pollution index were utilised to ascertain the sediment quality. Generally, the sediment is deemed to be "slightly" to "heavily" polluted. A further comparison with the Australian Sediment Quality Guidelines indicated that Ag, Cr, Cu, Ni, Pb and Zn had the potential to rarely cause biological effects while Hg could frequently cause biological effects. Application of potential ecological risk index (RI) revealed that the sediment poses moderate to considerable ecological risk. However, RI could not account for the complex sediment behaviour because it uses a simple contamination factor. Consequently, a modified ecological risk index (MRI) employing enrichment factor is proposed. This provides a more reliable understanding of whole sediment behaviour and classified the ecological risk of the sediment as moderate to very high. The results demonstrate the need for further investigation into heavy metal speciation and bioavailability in the sediment to ascertain the degree of toxicity.
Novel tools to inform animal breeding programs

Rapid development of novel tools for phenotyping, genetic and genomic analyses, reproduction, and modelling offers opportunities to speed up the genetic gain of complex traits to meet the needs of animal breeding programs. Successful application of novel tools depends on a complete understanding of the innovation circle of the particular technology and model, and its associated basic technical procedures and minimal management costs. Implementing new phenotyping and genotyping tools requires adequate infrastructure and training of farmers and national agricultural research system (NARS) scientists to enable early participation for enhanced impact in low income countries.
STITCH 5: augmenting protein chemical interaction networks with tissue and affinity data
Venue: Nucleic Acids Res.

Interactions between proteins and small molecules are an integral part of biological processes in living organisms. Information on these interactions is dispersed over many databases, texts and prediction methods, which makes it difficult to get a comprehensive overview of the available evidence. To address this, we have developed STITCH 'Search Tool for Interacting Chemicals' that integrates these disparate data sources for 430 000 chemicals into a single, easy to use resource. In addition to the increased scope of the database, we have implemented a new network view that gives the user the ability to view binding affinities of chemicals in the interaction network. This enables the user to get a quick overview of the potential effects of the chemical on its interaction partners. For each organism, STITCH provides a global network; however, not all proteins have the same pattern of spatial expression. Therefore, only a certain subset of interactions can occur simultaneously. In the new, fifth release of STITCH, we have implemented functionality to filter out the proteins and chemicals not associated with a given tissue. The STITCH database can be downloaded in full, accessed programmatically via an extensive API, or searched via a redesigned web interface at http:/stitch.embl.de.
Staple: Complementary Learners for Real Time Tracking
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Correlation Filter based trackers have recently achieved excellent performance, showing great robustness to challenging situations exhibiting motion blur and illumination changes. However, since the model that they learn depends strongly on the spatial layout of the tracked object, they are notoriously sensitive to deformation. Models based on colour statistics have complementary traits: they cope well with variation in shape, but suffer when illumination is not consistent throughout a sequence. Moreover, colour distributions alone can be insufficiently discriminative. In this paper, we show that a simple tracker combining complementary cues in a ridge regression framework can operate faster than 80 FPS and outperform not only all entries in the popular VOT14 competition, but also recent and far more sophisticated trackers according to multiple benchmarks.
Erlotinib and the Risk of Oral Cancer: The Erlotinib Prevention of Oral Cancer (EPOC) Randomized Clinical Trial.
Venue: JAMA oncology

Standard molecularly based strategies to predict and/or prevent oral cancer development in patients with oral premalignant lesions (OPLs) are lacking.  To test if the epidermal growth factor receptor inhibitor erlotinib would reduce oral cancer development in patients with high risk OPLs defined by specific loss of heterozygosity (LOH) profiles. Secondary objectives included prospective determination of LOH as a prognostic marker in OPLs.  The Erlotinib Prevention of Oral Cancer (EPOC) study was a randomized, placebo controlled, double bind trial. Accrual occurred from November 2006 through July 2012, with a median follow up time of 35 months in an ambulatory care setting in 5 US academic referral institutions. Patients with OPLs were enrolled in the protocol, and each underwent LOH profiling (N 379) they were classified as high risk (LOH positive) or low risk (LOH negative) patients based on their LOH profiles and oral cancer history. The randomized sample consisted of 150 LOH positive patients.  Oral erlotinib treatment (150 mg/d) or placebo for 12 months.  Oral cancer free survival (CFS)  A total of 395 participants were classified with LOH profiles, and 254 were classified LOH positive. Of these, 150 (59% were randomized, 75 each to the placebo and erlotinib groups. The 3 year CFS rates in placebo and erlotinib treated patients were 74% and 70% respectively (hazard ratio [HR] 1.27; 95% CI, 0.68 2.38; P .45) The 3 year CFS was significantly lower for LOH positive compared with LOH negative groups (74% vs 87% HR, 2.19; 95% CI, 1.25 3.83; P .01) Increased EGFR gene copy number correlated with LOH positive status (P .001) and lower CFS (P .01) The EGFR gene copy number was not predictive of erlotinib efficacy. Erlotinib induced skin rash was associated with improved CFS (P .01)  In this trial, LOH was validated as a marker of oral cancer risk and found to be associated with increased EGFR copy number (the target of the intervention) Erlotinib did not, however, improve CFS in high risk patients with LOH positive or high EGFR gene copy number OPLs. These results support incorporation of LOH testing as a prognostic tool in routine clinical practice but do not support erlotinib use in this setting.
The Effects of Ethical Sourcing On Organization Performance in the Energy Sector (A Case of Kenya Power Company Limited)

The research was carried out to determine the effect of ethical sourcing on the organization performance in the energy sector, a case of Kenya Power Company Limited. The essence of an efficient public sector procurement system no doubt is to create wealth and ensure good governance and transparency. Adhering to ethical policies, ethical codes and corporate social responsibilities by a corporate organization such as Kenya Power Company Limited (KPCL) enhances its financial performance and corporate image. The sampling technique used was stratified and simple random sampling. Quantitative data was collected using questionnaires from 72 respondents while qualitative data was collected using interviews. Data was analyzed using descriptive and qualitative methods. The relationship between variables was established using correlation analysis and the test of factors predicting independent variable was done through regression analysis. From the above findings it was concluded that Ethical Policies, Ethical Codes and CSR affect organizational performance. There is a strong correlation between the independent variables to the performance. A significant percentage (37.5% of the respondents agreed that policies, codes and CSR have a strong relationship with ethical sourcing which will ensure transparency, ethical standards and efficiency in the sourcing processes.
Analysis of the construction waste management performance in Hong Kong: the public and private sectors compared using big data

There is an ongoing debate concerning the disparity between the public and private sectors in relation to construction waste management (CWM) performance: some argue that CWM performance between the two sectors should have no difference since they are under the governance of the same set of CWM related regulations, while others argue that public sector clients should perform better as they are subject to greater social scrutiny. Previous studies comparing CWM performance have suffered from insufficient quality data, leaving the debate on the CWM performance disparity largely inconclusive. Informed by the Coase Invariant Theorem, this research empirically compares CWM performance between public and private projects. It does so by using big data in the form of 2 million waste disposal records generated from around 5700 projects undertaken in Hong Kong during 2011 and 2012. It is found that there is a notable CWM performance disparity between the public and private sectors, with contractors performing better in managing both inert and non inert waste in public projects than they do in private projects. Furthermore, the interviews and case studies conducted as part of the research suggest that CWM transaction costs are not high enough to incentivize contractors to manage waste conscientiously and therefore other institutional arrangements, such as promoting the value of environment protection leadership, are critical for achieving superior CWM performance. The research therefore supports the corollary of Coase Invariant Theorem, which asserts that certain forms of institutions would improve CWM performance by reducing transaction cost even though both sectors are subject to the same set of CWM related formal public policies.
Construction Waste Management Profiles, Practices, and Performance: A Cross Jurisdictional Analysis in Four Countries

Construction waste management (CWM) has received worldwide attention for some time. As a result, a plethora of research, investigating a wide array of CWM issues such as their profiles, practices, and performance, has been reported in individual economies around the globe. However, a cross jurisdictional comparison of these issues is limitedly presented in the literature despite its importance to benchmarking performance and identifying best CWM practices in the context of globalization whereby knowledge sharing has already transcended traditional country boundaries. The aim of this ex post facto research is to compare CWM profiles, practices, and performance in Australia, Europe (Europe refers to EU 27 member countries in the European Union, including Belgium, France, Germany, Italy, Luxembourg, Netherlands, Denmark, Great Britain, Ireland, Portugal, Spain, Greece, Austria, Finland, Sweden, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Slovakia, Slovenia, Malta, Cyprus, Bulgaria, and Romania. Hong Kong, and the United Kingdom at a national level, with a view to facilitating CWM knowledge sharing internationally. It does so by triangulating empirical data collected from various national statistical yearbooks with research papers and professional reports on CWM in these economies. It is found that in producing one million (US) dollars' work, construction contributes a volume of solid waste ranging from 28 to 121 tons among countries. Conscientious CWM practices can make a significant difference in reducing, reusing, or recycling construction waste, as evident in the large variation in the CWM performance. While it might be oversimplified to conclude that the best practices in one country can be applied in another, the research provides insightful references into sharing CWM knowledge across boundaries.
Dual plane multiple spatial watermarking with self encryption

Watermarking has established itself as a promising solution in the context of digital image copyright protection. Frequency domain watermarking is mainly preferred due to associated robustness and perceptual issues but requires a large amount of computation. On the other hand spatial domain watermarking is much faster but lacks when robustness is concerned. In this paper a block based spatial domain watermarking technique for color images is proposed that is fast and robust. Two way embedding introduced in the paper provides capability to previously estimate perceptual degradation in host image due to watermark embedding. Multiple watermarking in two component images is used to achieve better imperceptibility and better robustness. Self encryption introduced aims to remove dependency over external secret key however optional encryption with random secret keys is proposed for providing better security. Proposed embedding technique is an effort to achieve proper balance between robustness and quality degradation of image. Experiments suggest proposed method is robust against processing attacks such as spatial filtering transformations image enhancement and loss compression.
Wave energy absorption by a floating air filled bag

for the 31st Intl Workshop on Water Waves and Floating Bodies, Plymouth (US) 3 6 April 2016 Wave energy absorption by a floating air filled bag Adi Kurniawan, Deborah Greaves, Martyn Hann John Chaplin, Francis Farley Plymouth University, Plymouth PL4 8AA, UK University of Southampton, Southampton SO17 1BJ, UK E mail: adi.kurniawan@plymouth.ac.uk Highlights Measurements of capture widths and motions of a floating air filled bag subjected to regular incident waves and equipped with a power take off.
Emirati General Education Pre service Teachers' Preparedness for Diversity in Inclusive Classrooms

Today's general education classrooms continue to become increasingly diverse. Educational diversity and the high expectations for all learners have a profound impact on pre service teacher preparation. This paper explores Emirati general education pre service teachers' perception of preparedness to effectively instruct diverse learners including those with exceptionalities. Individual and focus group interviews and student teaching internship portfolios were used to examine Emirati pre service teachers' beliefs about their roles and responsibilities in the diverse classrooms of today. Findings shed light on the impact of teacher preparation on their perceived readiness, willingness, and abilities to meet the wide range of learner needs through inclusive practices. Moreover, several areas presented challenges to the pre service teachers during their teaching practice. The study also identified issues with current teacher preparation practices, program coordination or collaboration efforts, and practicum experience necessary for pre service teachers. Based on the findings, recommendations for reassessing the content and structure of higher education teacher preparation programs are discussed.
Implications of Shadow Bank Regulation for Monetary Policy at the Zero Lower Bounds

Counter to the credit channel of monetary transmission, monetary policy tightening induces a rise in lending by two di erent types of non bank nancial institutions (NBFI) shadow banks and investment funds. A monetary DSGE model is able to replicate the empirical facts when augmented with interme diaries that allow for regulatory arbitrage on the one hand, and household portfolio rebalancing on the other. Therefore NBFI reduce the e ectiveness of the bank lending channel, which posits a decrease in bank lending following monetary tightening. Given the pending regulation of the nancial system, I study how regulation of the shadow banking sector may a ect the monetary transmission mechanism, especially during a zero lower bound (ZLB) episode. I nd that bringing shadow banks back onto the balance sheets of commercial banks is bene cial for consumption smoothing. Alternatively, regulating them like investment funds results in a milder recession during, and a quicker escape from, the ZLB. This is because a large demand shock that moves the economy to the ZLB acts in a similar way to a monetary tightening due to the inability to lower the policy rate to the unconstrained level. Consequently, the bank lending channel becomes operational and its e ectiveness can be reduced via less reliance on deposit funding.
A System View of Financial Blockchains
Venue: 2016 IEEE Symposium on Service Oriented System Engineering (SOSE)

Blockchain (BC) has received significant attention recently. This paper presents system related issues for BCs for financial applications. This paper first presents the design of a BC without consideration of any application scenarios, and issues such as performance, security, performance and scalability lead to specific BC designs. Sample BC scenarios are analyzed and these lead to additional BC designs. Specifically, two new kinds of BC emerge: for storing information at transactional level, for storing account information. By splitting traditional BCs into these two BCs allow one to optimize the system with respect to scalability and privacy.