nils-herrmann/clean_scirepeval huggingface dataset:
Strength Design Curves for Thin-Walled Sections Undergoing Distortional Buckling

Certain types of cold-formed sections, notably those composed of high-strength steel, some decking sections and racking sections may undergo a mode of buckling called distortional in which edge and intermediate stiffeners are inadequate to prevent lateral movement of the flanges which they support. The existing design methods in current standards and specifications are often not adequate to account for distortional buckling. This paper describes proposed strength design curves for thin-walled sections undergoing distortional buckling. The design curves are based on testing and the test data base is included in the paper. The influence of local buckling on distortional buckling is also discussed. A A¢ b be Fy M~ M y Z Z~ O'de O'di N O T A T I O N Gross sect ion area Effective par t of gross section area Pla te width Effective par t of plate width Yield stress used in design Ul t ima te m o m e n t capaci ty M o m e n t at first yield Gross section modu lus Effective sect ion modu lus Elastic d is tor t iona l buckl ing stress Inelast ic d is tor t iona l buckl ing stress
Contact-less Transfer of Energy by means of a Rotating Transformer

This paper examines the electrical properties of a rotating transformer used for contact-less transfer of energy to rotating equipment. Two winding layouts are analysed theoretically and experimentally. The reluctance modelling provides a deep understanding of how the geometry of the core and windings affect the electrical behaviour of the component. Theoretical calculations, measured results and finite element analysis are used to compare the proposed layouts. Basic design guidelines are given to adjust the leakage and magnetising inductances. The selection of a phaseshifted full bridge converter is suggested for applications at the power level of 1kW.
Adaptive Application Scheduling under Interference in Kubernetes

Containers are rapidly replacing Virtual Machines (VMs) as the compute instance in cloud-based deployments. The significantly lower overhead of deploying containers (compared to VMs) has often been cited as one reason for this. However, interference caused by the limited isolation in shared resources can impact into the performance of hosted applications. We develop a Reference Net-based model of resource management within Kubernetes, primarily to better characterise such performance issues. Our model makes use of data obtained from a Kubernetes deployment, and can be used as a basis to design scalable (and potentially interference-tolerant) applications that make use of Kubernetes.
FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-Based CNN Architecture

In this paper we address the problem of semantic labeling of indoor scenes on RGB-D data. With the availability of RGB-D cameras, it is expected that additional depth measurement will improve the accuracy. Here we investigate a solution how to incorporate complementary depth information into a semantic segmentation framework by making use of convolutional neural networks (CNNs). Recently encoder-decoder type fully convolutional CNN architectures have achieved a great success in the field of semantic segmentation. Motivated by this observation we propose an encoder-decoder type network, where the encoder part is composed of two branches of networks that simultaneously extract features from RGB and depth images and fuse depth features into the RGB feature maps as the network goes deeper. Comprehensive experimental evaluations demonstrate that the proposed fusion-based architecture achieves competitive results with the state-of-the-art methods on the challenging SUN RGB-D benchmark obtaining 76.27% global accuracy, 48.30% average class accuracy and 37.29% average intersectionover-union score.
Motion-aware noise filtering for deblurring of noisy and blurry images

Image noise can present a serious problem in motion deblurring. While most state-of-the-art motion deblurring algorithms can deal with small levels of noise, in many cases such as low-light imaging, the noise is large enough in the blurred image that it cannot be handled effectively by these algorithms. In this paper, we propose a technique for jointly denoising and deblurring such images that elevates the performance of existing motion deblurring algorithms. Our method takes advantage of estimated motion blur kernels to improve denoising, by constraining the denoised image to be consistent with the estimated camera motion (i.e., no high frequency noise features that do not match the motion blur). This improved denoising then leads to higher quality blur kernel estimation and deblurring performance. The two operations are iterated in this manner to obtain results superior to suppressing noise effects through regularization in deblurring or by applying denoising as a preprocess. This is demonstrated in experiments both quantitatively and qualitatively using various image examples.
A Uniplanar Compact Photonic-Bandgap ( UC-PBG ) Structure and Its Applications for Microwave Circuits

This paper presents a novel photonic bandgap (PBG) structure for microwave integrated circuits. This new PBG structure is a two-dimensional square lattice with each element consisting of a metal pad and four connecting branches. Experimental results of a microstrip on a substrate with the PBG ground plane displays a broad stopband, as predicted by finite-difference time-domain simulations. Due to the slow-wave effect generated by this unique structure, the period of the PBG lattice is only 0:1 0 at the cutoff frequency, resulting in the most compact PBG lattice ever achieved. In the passband, the measured slowwave factor ( =k0) is 1.2–2.4 times higher and insertion loss is at the same level compared to a conventional 50line. This uniplanar compact PBG (UC-PBG) structure can be built using standard planar fabrication techniques without any modification. Several application examples have also been demonstrated, including a nonleaky conductor-backed coplanar waveguide and a compact spurious-free bandpass filter. This UC-PBG structure should find wide applications for high-performance and compact circuit components in microwave and millimeter-wave integrated circuits.
WNtags: A Web-Based Tool For Image Labeling And Retrieval With Lexical Ontologies

Ever growing number of image documents available on the Internet continuously motivates research in better annotation models and more efficient retrieval methods. Formal knowledge representation of objects and events in pictures, their interaction as well as context complexity becomes no longer an option for a quality image repository, but a necessity. We present an ontologybased online image annotation tool WNtags and demonstrate its usefulness in several typical multimedia retrieval tasks using International Affective Picture System emotionally annotated image database. WNtags is built around WordNet lexical ontology but considers Suggested Upper Merged Ontology as the preferred labeling formalism. WNtags uses sets of weighted WordNet synsets as high-level image semantic descriptors and query matching is performed with word stemming and node distance metrics. We also elaborate our near future plans to expand image content description with induced affect as in stimuli for research of human emotion and attention.
Child abuse, neglect, and adult behavior: research design and findings on criminality, violence, and child abuse.

Using a prospective cohorts design, a large sample of physical and sexual abuse cases was compared to a matched control group. Overall, abused and neglected subjects had higher rates than did controls for adult criminality and arrests for violent offenses, but not for adult arrests for child abuse or neglect. Findings are discussed in the context of intergenerational transmission of violence, and directions for future research are suggested.
Skull stripping of MRI brain images using mathematical morphology

Skull stripping is a major phase in MRI brain imaging applications and it refers to the removal of its non-cerebral tissues. The main problem in skull-stripping is the segmentation of the non-cerebral and the intracranial tissues due to their homogeneity intensities. As morphology requires prior binarization of the image, this paper proposed mathematical morphology segmentation using double and Otsu's thresholding. The purpose is to identify robust threshold values to remove the non-cerebral tissue from MRI brain images. Ninety collected samples of T1-weighted, T2-weighted and FLAIR MRI brain images are used in the experiments. The results showed promising use of double threholding as a robust threshold value in handling intensity inhomogeneities compared to Otsu's thresholding.
Artificial intelligence applications in the intensive care unit.

To review the history and current applications of artificial intelligence in the intensive care unit.



The MEDLINE database, bibliographies of selected articles, and current texts on the subject.



The studies that were selected for review used artificial intelligence tools for a variety of intensive care applications, including direct patient care and retrospective database analysis.



All literature relevant to the topic was reviewed.



Although some of the earliest artificial intelligence (AI) applications were medically oriented, AI has not been widely accepted in medicine. Despite this, patient demographic, clinical, and billing data are increasingly available in an electronic format and therefore susceptible to analysis by intelligent software. Individual AI tools are specifically suited to different tasks, such as waveform analysis or device control.



The intensive care environment is particularly suited to the implementation of AI tools because of the wealth of available data and the inherent opportunities for increased efficiency in inpatient care. A variety of new AI tools have become available in recent years that can function as intelligent assistants to clinicians, constantly monitoring electronic data streams for important trends, or adjusting the settings of bedside devices. The integration of these tools into the intensive care unit can be expected to reduce costs and improve patient outcomes.
Virtual and augmented reality effects on K-12, higher and tertiary education students’ twenty-first century skills

The purpose of this review article is to present state-of-the-art approaches and examples of virtual reality/augmented reality (VR/AR) systems, applications and experiences which improve student learning and the generalization of skills to the real world. Thus, we provide a brief, representative and non-exhaustive review of the current research studies, in order to examine the effects, as well as the impact of VR/AR technologies on K-12, higher and tertiary education students’ twenty-first century skills and their overall learning. According to the literature, there are promising results indicating that VR/AR environments improve learning outcomes and present numerous advantages of investing time and financial resources in K-12, higher and tertiary educational settings. Technological tools such as VR/AR improve digital-age literacy, creative thinking, communication, collaboration and problem solving ability, which constitute the so-called twenty-first century skills, necessary to transform information rather than just receive it. VR/AR enhances traditional curricula in order to enable diverse learning needs of students. Research and development relative to VR/AR technology is focused on a whole ecosystem around smart phones, including applications and educational content, games and social networks, creating immersive three-dimensional spatial experiences addressing new ways of human–computer interaction. Raising the level of engagement, promoting self-learning, enabling multi-sensory learning, enhancing spatial ability, confidence and enjoyment, promoting student-centered technology, combination of virtual and real objects in a real setting and decreasing cognitive load are some of the pedagogical advantages discussed. Additionally, implications of a growing VR/AR industry investment in educational sector are provided. It can be concluded that despite the fact that there are various barriers and challenges in front of the adoption of virtual reality on educational practices, VR/AR applications provide an effective tool to enhance learning and memory, as they provide immersed multimodal environments enriched by multiple sensory features.
MapReduce-based fuzzy c-means clustering algorithm: implementation and scalability

The management and analysis of big data has been identified as one of the most important emerging needs in recent years. This is because of the sheer volume and increasing complexity of data being created or collected. Current clustering algorithms can not handle big data, and therefore, scalable solutions are necessary. Since fuzzy clustering algorithms have shown to outperform hard clustering approaches in terms of accuracy, this paper investigates the parallelization and scalability of a common and effective fuzzy clustering algorithm named Fuzzy C-Means (FCM) algorithm. The algorithm is parallelized using the MapReduce paradigm outlining how the Map and Reduce primitives are implemented. A validity analysis is conducted in order to show that the implementation works correctly achieving competitive purity results compared to state-of-the art clustering algorithms. Furthermore, a scalability analysis is conducted to demonstrate the performance of the parallel FCM implementation with increasing number of computing nodes used.
Techniques for the Automated Analysis of Musical Audio

This thesis presents work on automated analysis techniques for musical audio. A complete analysis of the content within an audio waveform is termed ‘transcription’ and can be used for intelligent, informed processing of the signal in a variety of applications. However, full transcription is beyond the ability of current methods and hence this thesis concerns subtasks of whole problem. A major part of musical analysis is the extraction of signal information from a time-frequency representation, often the short time Fourier transform or spectrogram. The ‘reassignment’ method is a technique for improving the clarity of these representations. It is comprehensively reviewed and its relationship to a number of classical instantaneous frequency measures is explicitly stated. Performance of reassignment as a sinusoidal frequency estimator is then assessed quantitatively for the first time. This is achieved in the framework of the Cramér-Rao bound. Reassignment is shown to be inferior to some other estimators for simple synthetic signals but achieves comparable results for real musical examples. Several extensions and uses of the reassignment method are then described. These include the derivation of reassignment measures extracted from the amplitude of the spectrogram, rather than the traditional phase-based method and the use of reassignment measures for signal classification. The second major area of musical analysis investigated in this thesis is beat tracking, where the aim is to extract both the tempo implied in the music and also its phase which is analagous to beat location. Three models for the beat in general music examples are described. These are cast in a state-space formulation within the Bayesian paradigm, which allows the use of Monte Carlo methods for inference of the model parameters. The first two models use pre-extracted note onsets and model tempo as either a linear Gaussian process or as Brownian motion. The third model also includes on-line detection of onsets, thus adding an extra layer of complexity. Sequential Monte Carlo algorithms, termed particle filters, are then used for the estimation of the data. The systems are tested on an extensive database, nearly three and a half hours in length and consisting of various styles of music. The results exceed the current published state of the art. The research presented here could form the early stages of a full transcription system, a proposal for which is also expounded. This would use a flow of contextual information from simpler, more global structures to aid the inference of more complex and detailed processes. The global structures present in the music (such as style, structure, tempo, etc.) still have their own uses, making this scheme instantly applicable to real problems.
Combining Bottom-Up, Top-Down, and Smoothness Cues for Weakly Supervised Image Segmentation

This paper addresses the problem of weakly supervised semantic image segmentation. Our goal is to label every pixel in a new image, given only image-level object labels associated with training images. Our problem statement differs from common semantic segmentation, where pixel-wise annotations are typically assumed available in training. We specify a novel deep architecture which fuses three distinct computation processes toward semantic segmentation &#x2013; namely, (i) the bottom-up computation of neural activations in a CNN for the image-level prediction of object classes, (ii) the top-down estimation of conditional likelihoods of the CNNs activations given the predicted objects, resulting in probabilistic attention maps per object class, and (iii) the lateral attention-message passing from neighboring neurons at the same CNN layer. The fusion of (i)-(iii) is realized via a conditional random field as recurrent network aimed at generating a smooth and boundary-preserving segmentation. Unlike existing work, we formulate a unified end-to-end learning of all components of our deep architecture. Evaluation on the benchmark PASCAL VOC 2012 dataset demonstrates that we outperform reasonable weakly supervised baselines and state-of-the-art approaches.
Anomaly Detection in Video Surveillance via Gaussian Process

In this paper, we propose a new approach for anomaly detection in video surveillance. This approach is based on a nonparametric Bayesian regression model built upon Gaussian process priors. It establishes a set of basic vectors describing motion patterns from low-level features via online clustering, and then constructs a Gaussian process regression model to approximate the distribution of motion patterns in kernel space. We analyze di®erent anomaly measure criterions derived from Gaussian process regression model and compare their performances. To reduce
Automatic Software Bug Triage System (BTS) Based on Latent Semantic Indexing and Support Vector Machine

A bug triage system is used for validation and allocation of bug reports to the most appropriate developers. An automatic bug triage system may reduce the software maintenance time and improve its quality by correct and timely assignment of new bug reports to the appropriate developers. In this paper, we present the techniques behind an automatic bug triage system, which is based on the categorization of bug reports. In order to obtain an automatic bug triage system we used these techniques and performed comparative experiments. We downloaded 1,983 resolved bug reports along with the developer activity data from the Mozilla open source project. We extracted the relevant features like report title, report summary etc., from each bug report, and extracted developer’s name who resolved the bug reports from the developers activity data. We processed the extracted textual data, and obtained the term-to-document matrix using parsing, filtering and term weighting methods. For term weighting methods we used simple term frequency and TF×IDF (term frequency inverse document frequency) methods. Furthermore, we reduced the dimensionality of the obtained term-to-document matrix by applying feature selection and latent semantic indexing methods. Finally we used seven different machine learning methods for the classification of bug reports. The best obtained bug triage system is based on latent semantic indexing and support vector machine having 44.4% classification accuracy. The average precision and recall values are 30% and 28%, respectively.
LNSC: A Security Model for Electric Vehicle and Charging Pile Management Based on Blockchain Ecosystem

The Internet of Energy (IoE) provides an effective networking technology for distributed green energy, which allows the connection of energy anywhere at any time. As an important part of the IoE, electric vehicles (EVs), and charging pile management are of great significance to the development of the IoE industry. Previous work has mainly focused on network performance optimization for its management, and few studies have considered the security of the management between EVs and charging piles. Therefore, this paper proposes a decentralized security model based on the lightning network and smart contract in the blockchain ecosystem; this proposed model is called the lightning network and smart contract (LNSC). The overall model involves registration, scheduling, authentication, and charging phases. The new proposed security model can be easily integrated with current scheduling mechanisms to enhance the security of trading between EVs and charging piles. Experimental results according to a realistic infrastructure are presented in this paper. These experimental results demonstrate that our scheme can effectively enhance vehicle security. Different performances of LNSC-based scheduling strategies are also presented.
The impact of staff turnover on software projects: the importance of understanding what makes software practitioners tick

In this paper we investigate the impact of staff turnover on software projects. In particular we investigate whether high staff turnover damages project success. We analyse data from an empirical study of 89 software practitioners to show that projects with high staff turnover are less successful. Furthermore our empirical data suggests a relationship between high staff turnover on projects and low staff motivation levels. We discuss factors which have been previously found to improve motivation levels and conclude that improving motivation levels can reduce staff turnover, which in turn increases project success.
Innovative information visualization of electronic health record data: a systematic review

This study investigates the use of visualization techniques reported between 1996 and 2013 and evaluates innovative approaches to information visualization of electronic health record (EHR) data for knowledge discovery.



An electronic literature search was conducted May-July 2013 using MEDLINE and Web of Knowledge, supplemented by citation searching, gray literature searching, and reference list reviews. General search terms were used to assure a comprehensive document search.



Beginning with 891 articles, the number of articles was reduced by eliminating 191 duplicates. A matrix was developed for categorizing all abstracts and to assist with determining those to be excluded for review. Eighteen articles were included in the final analysis.



Several visualization techniques have been extensively researched. The most mature system is LifeLines and its applications as LifeLines2, EventFlow, and LifeFlow. Initially, research focused on records from a single patient and visualization of the complex data related to one patient. Since 2010, the techniques under investigation are for use with large numbers of patient records and events. Most are linear and allow interaction through scaling and zooming to resize. Color, density, and filter techniques are commonly used for visualization.



With the burgeoning increase in the amount of electronic healthcare data, the potential for knowledge discovery is significant if data are managed in innovative and effective ways. We identify challenges discovered by previous EHR visualization research, which will help researchers who seek to design and improve visualization techniques.