nils-herrmann/clean_scirepeval huggingface dataset:
Adverse drug reactions - examples of detection of rare events using databases.

It is recognised that randomised controlled trials are not feasible for capturing rare adverse events. There is an increasing trend towards observational research methodologies using large population-based health databases. These databases offer more scope for adequate sample sizes, allowing for comprehensive patient characterisation and assessment of the associated factors. While direct causality cannot be established and confounders cannot be ignored, databases present an opportunity to explore and quantify rare events. The use of databases for the detection of rare adverse events in the following conditions, sudden death associated with attention deficit hyperactivity disorder (ADHD) treatment, retinal detachment associated with the use of fluoroquinolones and toxic epidermal necrolysis associated with drug exposure, are discussed as examples. In general, rare adverse events tend to have immediate and important clinical implications and may be life-threatening. An understanding of the causative factors is therefore important, in addition to the research methodologies and database platforms that enable the undertaking of the research.
Shake-Shake regularization

The method introduced in this paper aims at helping deep learning practitioners faced with an overfit problem. The idea is to replace, in a multi-branch network, the standard summation of parallel branches with a stochastic affine combination. Applied to 3-branch residual networks, shake-shake regularization improves on the best single shot published results on CIFAR-10 and CIFAR100 by reaching test errors of 2.86% and 15.85%. Experiments on architectures without skip connections or Batch Normalization show encouraging results and open the door to a large set of applications. Code is available at https://github.com/xgastaldi/shake-shake.
Audio signal processing by neural networks

In this paper a review of architectures suitable for nonlinear real-time audio signal processing is presented. The computational and structural complexity of neural networks (NNs) represent in fact, the main drawbacks that can hinder many practical NNs multimedia applications. In particular e,cient neural architectures and their learning algorithm for real-time on-line audio processing are discussed. Moreover, applications in the -elds of (1) audio signal recovery, (2) speech quality enhancement, (3) nonlinear transducer linearization, (4) learning based pseudo-physical sound synthesis, are brie1y presented and discussed.
A Quorum-Based Group Mutual Exclusion Algorithm for a Distributed System with Dynamic Group Set

The group mutual exclusion problem extends the traditional mutual exclusion problem by associating a type (or a group) with each critical section. In this problem, processes requesting critical sections of the same type can execute their critical sections concurrently. However, processes requesting critical sections of different types must execute their critical sections in a mutually exclusive manner. We present a distributed algorithm for solving the group mutual exclusion problem based on the notion of surrogate-quorum. Intuitively, our algorithm uses the quorum that has been successfully locked by a request as a surrogate to service other compatible requests for the same type of critical section. Unlike the existing quorum-based algorithms for group mutual exclusion, our algorithm achieves a low message complexity of O(q) and a low (amortized) bit-message complexity of O(bqr), where q is the maximum size of a quorum, b is the maximum number of processes from which a node can receive critical section requests, and r is the maximum size of a request while maintaining both synchronization delay and waiting time at two message hops. As opposed to some existing quorum-based algorithms, our algorithm can adapt without performance penalties to dynamic changes in the set of groups. Our simulation results indicate that our algorithm outperforms the existing quorum-based algorithms for group mutual exclusion by as much as 45 percent in some cases. We also discuss how our algorithm can be extended to satisfy certain desirable properties such as concurrent entry and unnecessary blocking freedom.
IMPROVING CONSTRUCTION MANAGEMENT PRACTICE IN SAUDI ARABIA WITH THE LAST PLANNER SYSTEM : A CASE STUDY

Construction management suffers from many problems which need to be solved or better understood. The research described in this paper evaluates the effectiveness of implementing the Last Planner System (LPS) to improve construction planning practice and enhance site management in the Saudi construction industry. To do so, LPS was implemented in two large state-owned construction projects through an action research process. The data collection methods employed included interviews, observations and a survey questionnaire. The findings identify major benefits covering many aspects of project management, including improved construction planning, enhanced site management and better communication and coordination between the parties involved. The fact that the structural work in one of the projects studied was completed two weeks ahead of schedule provides evidence of improvement of the specific site construction planning practices. The paper also describes barriers to the realisation the full potential of LPS, including the involvement of many subcontractors and people’s commitment and attitude to time.
A Joint Model for 2D and 3D Pose Estimation from a Single Image

We introduce a novel approach to automatically recover 3D human pose from a single image. Most previous work follows a pipelined approach: initially, a set of 2D features such as edges, joints or silhouettes are detected in the image, and then these observations are used to infer the 3D pose. Solving these two problems separately may lead to erroneous 3D poses when the feature detector has performed poorly. In this paper, we address this issue by jointly solving both the 2D detection and the 3D inference problems. For this purpose, we propose a Bayesian framework that integrates a generative model based on latent variables and discriminative 2D part detectors based on HOGs, and perform inference using evolutionary algorithms. Real experimentation demonstrates competitive results, and the ability of our methodology to provide accurate 2D and 3D pose estimations even when the 2D detectors are inaccurate.
Customer Loyalty Enhancement of Online-to-Offline Marketing in Beauty Industry

In the competitive and low entrance barrier beauty industry, customer loyalty is a critical factor for business success. Research literature of customer relationship management recommends various factors contributing to customer loyalty in the general setting, however, there are insufficient studies empirically weigh the importance of each critical factor for the beauty industry. This study investigates and ranks empirically the critical factors, which contributes to customer loyalty of Online-to-Offline (O2O) marketing in the beauty industry. Our result shows that customer satisfaction, customer switching costs, customer trust, corporate image and customer value positively influence customer loyalty of O2O marketing and in the order of decreasing importance. Attributes contributing to the five critical factors have also been studied and ranked. Findings of this study can help the beauty industry to develop an effective O2O marketing plan and hence customer loyalty can be enhanced through the process of implementing targeted marketing activities.
Leveraging Track Relationships for Web Service Recommendation

Existing Web services recommendation approaches are based on usage statistics or QoS properties, leaving aside the evolution of the services' ecosystem. These approaches do not always capture new or more recent users' preferences resulting in recommendations with possibly obsolete or less relevant services. In this paper, we describe a novel Web services recommendation approach where the services' ecosystem is represented as a heterogeneous multi-graph, and edges may have different semantics. The recommendation process relies on data mining techniques to suggest services "of interest" to a user.
High Resolution Direct Position Determination of Radio Frequency Sources

The most common methods for localization of radio frequency transmitters are based on two processing steps. In the first step, parameters such as angle of arrival or time of arrival are estimated at each base station independently. In the second step, the estimated parameters are used to determine the location of the transmitters. The direct position determination approach advocates using the observations from all the base stations together in order to estimate the locations in a single step. This single-step method is known to outperform two-step methods when the signal-to-noise ratio is low. In this paper, we propose a direct-position-determination-based method for localization of multiple emitters that transmit unknown signals. The method does not require knowledge of the number of emitters. It is based on minimum-variance-distortionless-response considerations to achieve a high resolution estimator that requires only a two-dimensional search for planar geometry, and a three-dimensional search for the general case.
Cognitive big data: survey and review on big data research and its implications. What is really "new" in big data?

Cognitive big data: survey and review on big data research and its implications. What is really ‘new’ in big data? Artur Lugmayr Björn Stockleben Christoph Scheib Mathew Mailaparampil Article information: To cite this document: Artur Lugmayr Björn Stockleben Christoph Scheib Mathew Mailaparampil , (2017)," Cognitive big data: survey and review on big data research and its implications. What is really ‘new’ in big data? ", Journal of Knowledge Management, Vol. 21 Iss 1 pp. Permanent link to this document: http://dx.doi.org/10.1108/JKM-07-2016-0307
Childhood trauma associated with smaller hippocampal volume in women with major depression.

Smaller hippocampal volume has been reported only in some but not all studies of unipolar major depressive disorder. Severe stress early in life has also been associated with smaller hippocampal volume and with persistent changes in the hypothalamic-pituitary-adrenal axis. However, prior hippocampal morphometric studies in depressed patients have neither reported nor controlled for a history of early childhood trauma. In this study, the volumes of the hippocampus and of control brain regions were measured in depressed women with and without childhood abuse and in healthy nonabused comparison subjects.



Study participants were 32 women with current unipolar major depressive disorder-21 with a history of prepubertal physical and/or sexual abuse and 11 without a history of prepubertal abuse-and 14 healthy nonabused female volunteers. The volumes of the whole hippocampus, temporal lobe, and whole brain were measured on coronal MRI scans by a single rater who was blind to the subjects' diagnoses.



The depressed subjects with childhood abuse had an 18% smaller mean left hippocampal volume than the nonabused depressed subjects and a 15% smaller mean left hippocampal volume than the healthy subjects. Right hippocampal volume was similar across the three groups. The right and left hippocampal volumes in the depressed women without abuse were similar to those in the healthy subjects.



A smaller hippocampal volume in adult women with major depressive disorder was observed exclusively in those who had a history of severe and prolonged physical and/or sexual abuse in childhood. An unreported history of childhood abuse in depressed subjects could in part explain the inconsistencies in hippocampal volume findings in prior studies in major depressive disorder.
Treatment of symptomatic macromastia in a breast unit

Patients suffering from symptomatic macromastia are usually underserved, as they have to put up with very long waiting lists and are usually selected under restrictive criteria. The Oncoplastic Breast Surgery subspeciality requires a cross-specialty training, which is difficult, in particular, for trainees who have a background in general surgery, and not easily available. The introduction of reduction mammaplasty into a Breast Cancer Unit as treatment for symptomatic macromastia could have a synergic effect, making the scarce therapeutic offer at present available to these patients, who are usually treated in Plastic Departments, somewhat larger, and accelerating the uptake of oncoplastic training as a whole and, specifically, the oncoplastic breast conserving procedures based on the reduction mammaplasty techniques such as displacement conservative techniques and onco-therapeutic mammaplasty. This is a retrospective study analyzing the outcome of reduction mammaplasty for symptomatic macromastia in our Breast Cancer Unit.



A cohort study of 56 patients who underwent bilateral reduction mammaplasty at our Breast Unit between 2005 and 2009 were evaluated; morbidity and patient satisfaction were considered as end points. Data were collected by reviewing medical records and interviewing patients.



Eight patients (14.28%) presented complications in the early postoperative period, two of them being reoperated on. The physical symptoms disappeared or significantly improved in 88% of patients and the degree of satisfaction with the care process and with the overall outcome were really high.



Our experience of the introduction of reduction mammaplasty in our Breast Cancer Unit has given good results, enabling us to learn the use of different reduction mammaplasty techniques using several pedicles which made it possible to perform oncoplastic breast conserving surgery. In our opinion, this management policy could bring clear advantages both to patients (large-breasted and those with a breast cancer) and surgeons.
Assessing clinical handover between paramedics and the trauma team.

The aim of effective clinical handover is seamless transfer of information between care providers. Handover between paramedics and the trauma team provides challenges in ensuring that information loss does not occur. Handover is often time-pressured and paramedics' clinical notes are often delayed in reaching the trauma team. Documentation by trauma team members must be accurate. This study evaluated information loss and discordance as patients were transferred from the scene of an incident to the Trauma Centre.



Twenty-five trauma patients presenting by ambulance to a tertiary Emergency and Trauma Centre were randomly selected. Audiotaped (pre-hospital) and videotaped (in-hospital) handover was compared with written documentation.



In the pre-hospital setting 171/228 (75%) of data items handed over by paramedics to the trauma team were documented and in the in-hospital handover 335/498 (67%) of information was documented. Information least likely to be documented by trauma team members (1) in the pre-hospital setting related to treatment provided and (2) in the in-hospital setting related to signs and symptoms. While 79% of information was subsequently documented by paramedics, 9% (n=59) of information was not documented either by trauma team members or paramedics and constitutes information loss. Information handed over was not congruent with documentation on seven occasions. Discrepancies included a patient's allergy status and sites of injury (n=2). Demographic details were most likely to be documented but not handed over by paramedics.



By documenting where deficits in handover occur we can identify points of vulnerability and strategies to capture this information.
NUS-WIDE: a real-world web image database from National University of Singapore

This paper introduces a web image dataset created by NUS's Lab for Media Search. The dataset includes: (1) 269,648 images and the associated tags from Flickr, with a total of 5,018 unique tags; (2) six types of low-level features extracted from these images, including 64-D color histogram, 144-D color correlogram, 73-D edge direction histogram, 128-D wavelet texture, 225-D block-wise color moments extracted over 5x5 fixed grid partitions, and 500-D bag of words based on SIFT descriptions; and (3) ground-truth for 81 concepts that can be used for evaluation. Based on this dataset, we highlight characteristics of Web image collections and identify four research issues on web image annotation and retrieval. We also provide the baseline results for web image annotation by learning from the tags using the traditional k-NN algorithm. The benchmark results indicate that it is possible to learn effective models from sufficiently large image dataset to facilitate general image retrieval.
Amalgamation of VLSI & Signal processing Technologies for the Implementation and Enhancement in Wireless Communication

Wireless communication system is a heavy dense composition of signal processing techniques with semiconductor technologies. With the ever increasing system capacity and data rate, VLSI design and implementation method for wireless communications becomes more challenging, which urges researchers in signal processing to provide new architectures and efficient algorithms to meet low power and high performance requirements. This paper presents a survey of recent research, a development in VLSI architecture and signal processing algorithms with emphasis on wireless communication systems. It is shown that while contemporary signal processing can be directly applied to the communication hardware design including ASIC, SoC, and FPGA, much work remains to realize its full potential. It is concluded that an integrated combination of VLSI and signal processing technologies will provide more complete solutions.
Understanding Pervasive Games through Gameplay Design Patterns

This paper reports on a cluster analysis of pervasive games through a bottom-up approach based upon 120 game examples. The basis for the clustering algorithm relies on the identification of pervasive gameplay design patterns for each game from a set of 75 possible patterns. The resulting hierarchy presents a view of the design space of pervasive games, and details of clusters and novel gameplay features are described. The paper concludes with a view over how the clusters relate to existing genres and models of pervasive games.
Mining event-related brain dynamics

This article provides a new, more comprehensive view of event-related brain dynamics founded on an information-based approach to modeling electroencephalographic (EEG) dynamics. Most EEG research focuses either on peaks 'evoked' in average event-related potentials (ERPs) or on changes 'induced' in the EEG power spectrum by experimental events. Although these measures are nearly complementary, they do not fully model the event-related dynamics in the data, and cannot isolate the signals of the contributing cortical areas. We propose that many ERPs and other EEG features are better viewed as time/frequency perturbations of underlying field potential processes. The new approach combines independent component analysis (ICA), time/frequency analysis, and trial-by-trial visualization that measures EEG source dynamics without requiring an explicit head model.
Reproductive tissue-specific expression profiling and genetic variation across a 19 gene bovine β-defensin cluster

β-defensins are small cationic peptides, with potent immunoregulatory and antimicrobial activity which are produced constitutively and inducibly by eukaryotic cells. This study profiles the expression of a cluster of 19 novel defensin genes which spans 320 kb on chromosome 13 in Bos taurus. It also assesses the genetic variation in these genes between two divergently selected cattle breeds. Using quantitative real-time PCR (qRT-PCR), all 19 genes in this cluster were shown to be expressed in the male genital tract and 9 in the female genital tract, in a region-specific manner. These genes were sequenced in Norwegian Red (NR) and Holstein-Friesian (HF) cattle for population genetic analysis. Of the 17 novel single nucleotide polymorphisms (SNPs) identified, 7 were non-synonymous, 6 synonymous and 4 outside the protein coding region. Significant frequency differences in SNPs in bovine β-defensins (BBD) 115, 117, 121, and 122 were detected between the two breeds, which was also reflected at the haplotype level (P < 0.05). There was clear segregation of the haplotypes into two blocks on chromosome 13 in both breeds, presumably due to historical recombination. This study documents genetic variation in this β-defensin gene cluster between Norwegian Red and Holstein-Friesian cattle which may result from divergent selection for production and fertility traits in these two breeds. Regional expression in the epididymis and fallopian tube suggests a potential reproductive-immunobiology role for these genes in cattle.
Selection of third party software in Off-The-Shelf-based software development - An interview study with industrial practitioners

The success of software development using third party components highly depends on the ability to select a suitable component for the intended application. The evidence shows that there is limited knowledge about current industrial OTS selection practices. As a result, there is often a gap between theory and practice, and the proposed methods for supporting selection are rarely adopted in the industrial practice. This paper’s goal is to investigate the actual industrial practice of component selection in order to provide an initial empirical basis that allows the reconciliation of research and industrial endeavors. The study consisted of semi-structured interviews with 23 employees from 20 different software-intensive companies that mostly develop web information system applications. It provides qualitative information that mpirical study ff-The-Shelf-based software development omponent selection help to further understand these practices, and emphasize some aspects that have been overlooked by researchers. For instance, although the literature claims that component repositories are important for locating reusable components; these are hardly used in industrial practice. Instead, other resources that have not received considerable attention are used with this aim. Practices and potential market niches for software-intensive companies have been also identified. The results are valuable from both the research and the industrial perspectives as they provide a basis for formulating well-substantiated hypotheses veme and more effective impro
TESTING DEFECT PREDICTION MODEL-A PRACTICAL APPROACH

Software defects prediction aims to reduce software testing efforts by guiding the testers through the defect classification of software systems. Defect predictors are widely used in many organizations to predict software defects in order to save time, improve quality, testing and for better planning of the resources to meet the timelines. The application of statistical software testing defect prediction model in a real life setting is extremely difficult because it requires more number of data variables and metrics and also historical defect data to predict the next releases or new similar type of projects. This paper explains our statistical model, how it will accurately predict the defects for upcoming software releases or projects. We have used 20 past release data points of software project, 5 parameters and build a model by applying descriptive statistics, correlation and multiple linear regression models with 95% confidence intervals (CI). In this appropriate multiple linear regression model the R-square value was 0.91 and its Standard Error is 5.90%. The Software testing defect prediction model is now being used to predict defects at various testing projects and operational releases. We have found 90.76% precision between actual and predicted defects.