nils-herrmann/clean_scirepeval huggingface dataset:
More than a neuroanatomical representation in The Creation of Adam by Michelangelo Buonarroti, a representation of the Golden Ratio.

Michelangelo Buonarroti (1475-1564) was a master anatomist as well as an artistic genius. He dissected numerous cadavers and developed a profound understanding of human anatomy. Among his best-known artworks are the frescoes painted on the ceiling of the Sistine Chapel (1508-1512), in Rome. Currently, there is some debate over whether the frescoes merely represent the teachings of the Catholic Church at the time or if there are other meanings hidden in the images. In addition, there is speculation regarding the image of the brain embedded in the fresco known as "The Creation of Adam," which contains anatomic features of the midsagittal and lateral surfaces of the brain. Within this context, we report our use of Image Pro Plus Software 6.0 to demonstrate mathematical evidence that Michelangelo painted "The Creation of Adam" using the Divine Proportion/Golden Ratio (GR) (1.6). The GR is classically associated with greater structural efficiency and is found in biological structures and works of art by renowned artists. Thus, according to the evidence shown in this article, we can suppose that the beauty and harmony recognized in all Michelangelo's works may not be based solely on his knowledge of human anatomical proportions, but that the artist also probably knew anatomical structures that conform to the GR display greater structural efficiency. It is hoped that this report will at least stimulate further scientific and scholarly contributions to this fascinating topic, as the study of these works of art is essential for the knowledge of the history of Anatomy.
Rethinking stress: the role of mindsets in determining the stress response.

This article describes 3 studies that explore the role of mindsets in the context of stress. In Study 1, we present data supporting the reliability and validity of an 8-item instrument, the Stress Mindset Measure (SMM), designed to assess the extent to which an individual believes that the effects of stress are either enhancing or debilitating. In Study 2, we demonstrate that stress mindsets can be altered by watching short, multimedia film clips presenting factual information biased toward defining the nature of stress in 1 of 2 ways (stress-is-enhancing vs. stress-is-debilitating). In Study 3, we demonstrate the effect of stress mindset on physiological and behavioral outcomes, showing that a stress-is-enhancing mindset is associated with moderate cortisol reactivity and high desire for feedback under stress. Together, these 3 studies suggest that stress mindset is a distinct and meaningful variable in determining the stress response.
Factors Affecting Customer Satisfaction of Mobile Services in Yemen Hashed

The aim of this study is to find out the customer’s satisfaction with Yemeni Mobile service providers. Th is study examined the relationship between perceived quality, perceived value, customer expectation, and corporate image with customer satisfaction. The result of this study is based on data gathered online from 118 academic staff in public universit ies in Yemen. The study found that the relationship between perceived value, perceived quality and corporate image have a significant positive influence on customer satisfaction, whereas customer expectation has positive but without statistical significance.
Using Mise-En-Scène Visual Features based on MPEG-7 and Deep Learning for Movie Recommendation

Item features play an important role in movie recommender systems, where recommendations can be generated by using explicit or implicit preferences of users on traditional features (attributes) such as tag, genre, and cast. Typically, movie features are human-generated, either editorially (e.g., genre and cast) or by leveraging the wisdom of the crowd (e.g., tag), and as such, they are prone to noise and are expensive to collect. Moreover, these features are often rare or absent for new items, making it difficult or even impossible to provide good quality recommendations. In this paper, we show that user’s preferences on movies can be better described in terms of the Mise-en-Scène features, i.e., the visual aspects of a movie that characterize design, aesthetics and style (e.g., colors, textures). We use both MPEG-7 visual descriptors and Deep Learning hidden layers as example of mise-en-scène features that can visually describe movies. Interestingly, mise-en-scène features can be computed automatically from video files or even from trailers, offering more flexibility in handling new items, avoiding the need for costly and error-prone human-based tagging, and providing good scalability. We have conducted a set of experiments on a large catalogue of 4K movies. Results show that recommendations based on mise-en-scène features consistently provide the best performance with respect to richer sets of more traditional features, such as genre and tag.
Exploring Customer Preferences with Probabilistic Topics Models

Customer preference learning and recommendation for (e-)commerce is a widely researched problem where a number of different solutions have been proposed. In this study we propose and implement a novel approach to the problem of extracting and modelling user preferences in commerce using latent topic models. We explore the use of probabilistic topic models on transaction itemsets considering both single one-time actions and customers’ shopping history. We conclude that the extracted latent models not only provide insight to the consumer behaviour but also can effectively support an item recommender system.
MPEG DASH SRD: spatial relationship description

This paper presents the Spatial Representation Description (SRD) feature of the second amendment of MPEG DASH standard part 1, 23009-1:2014 . SRD is an approach for streaming only spatial sub-parts of a video to display devices, in combination with the form of adaptive multi-rate streaming that is intrinsically supported by MPEG DASH. The SRD feature extends the Media Presentation Description (MPD) of MPEG DASH by describing spatial relationships between associated pieces of video content. This enables the DASH client to select and retrieve only those video streams at those resolutions that are relevant to the user experience. The paper describes the design principles behind SRD, the different possibilities it enables and examples of how SRD was used in different experiments on interactive streaming of ultra-high resolution video.
Measurement of DC Currents in the Power Grid by Current Transformer

DC currents in power grids are mainly caused by geomagnetic activity especially during magnetic storms. It is desirable to monitor these currents to prevent saturation of transformers, which may cause blackout. However, adding DC current sensors to existing installations would be very costly. We suggest to convert some of the existing current transformers to fluxgate DC current sensors by injecting AC excitation current into their secondary winding. We successfully tested this concept on 500 A current transformer. The achievable accuracy is 10% for DC currents and 1.5% for AC currents, which is sufficient for protection and monitoring purposes. We analyze the DC current sensitivity dependence on the (changing) grid impedance, and we show that the sensitivity can be stabilized by controlling the secondary voltage component at the excitation frequency. Excitation current injected into the grid also depends on the grid impedance, but for realistic conditions it is below 2 A.
An Isolated, bridgeless, quasi-resonant ZVS-switching, buck-boost single-stage AC-DC converter with power factor correction (PFC)

The number of power supplies connected to the public household power grid is constantly increasing. For a good mains voltage quality, the harmonic input currents are limited in standards. Many applications also require a lower output voltage with isolation. Today, this is mostly obtained via a multi-stage converter, consisting of a diode bridge rectifier, a boost PFC and a DC-DC converter with galvanic isolation. For higher efficiency, a bridgeless PFC-stage can be used, but it nevertheless requires a DC-DC converter for isolation. In order to further increase efficiency and decrease component count, a single-stage concept can be used. The Cuk “True Bridgeless PFC” rectifier system, for example, can perform all the requirements in a single stage. A simple application and realization of a converter topology is also quite important, especially in consumer electronics, where costs are crucial. In this paper, a new single stage topology with a different operation principle is presented, focusing on ease of use in hardware realization.
Local Histograms for Design of Transfer Functions in Direct Volume Rendering

Direct volume rendering (DVR) is of increasing diagnostic value in the analysis of data sets captured using the latest medical imaging modalities. The deployment of DVR in everyday clinical work, however, has so far been limited. One contributing factor is that current transfer function (TF) models can encode only a small fraction of the user's domain knowledge. In this paper, we use histograms of local neighborhoods to capture tissue characteristics. This allows domain knowledge on spatial relations in the data set to be integrated into the TF. As a first example, we introduce partial range histograms in an automatic tissue detection scheme and present its effectiveness in a clinical evaluation. We then use local histogram analysis to perform a classification where the tissue-type certainty is treated as a second TF dimension. The result is an enhanced rendering where tissues with overlapping intensity ranges can be discerned without requiring the user to explicitly define a complex, multidimensional TF
GRAM-CNN: a deep learning approach with local context for named entity recognition in biomedical text

Best performing named entity recognition (NER) methods for biomedical literature are based on hand-crafted features or task-specific rules, which are costly to produce and difficult to generalize to other corpora. End-to-end neural networks achieve state-of-the-art performance without hand-crafted features and task-specific knowledge in non-biomedical NER tasks. However, in the biomedical domain, using the same architecture does not yield competitive performance compared with conventional machine learning models.



We propose a novel end-to-end deep learning approach for biomedical NER tasks that leverages the local contexts based on n-gram character and word embeddings via Convolutional Neural Network (CNN). We call this approach GRAM-CNN. To automatically label a word, this method uses the local information around a word. Therefore, the GRAM-CNN method does not require any specific knowledge or feature engineering and can be theoretically applied to a wide range of existing NER problems. The GRAM-CNN approach was evaluated on three well-known biomedical datasets containing different BioNER entities. It obtained an F1-score of 87.26% on the Biocreative II dataset, 87.26% on the NCBI dataset and 72.57% on the JNLPBA dataset. Those results put GRAM-CNN in the lead of the biological NER methods. To the best of our knowledge, we are the first to apply CNN based structures to BioNER problems.


Availability and implementation
The GRAM-CNN source code, datasets and pre-trained model are available online at: https://github.com/valdersoul/GRAM-CNN.



andyli@ece.ufl.edu or aconesa@ufl.edu.



Supplementary data are available at Bioinformatics online.
Img2UML: A System for Extracting UML Models from Images

In this paper, we demonstrate our Img2UML system tool. This system tool eliminates the gap between pixel-based diagram and engineering model, that it supports the extraction of the UML class model from images and produces an XMI file of the UML model. In addition to this, Img2UML offers a repository of UML class models of images that have been collected from the Internet. This project has both industrial and academic aims: for industry, this tool proposals a method that enables the updating of software design documentation (that typically contains UML images). For academia, this system unlocks a corpus of UML models that are publicly available, but not easily analyzable for scientific studies.
What is an Innovation Ecosystem ? By

One expects there to be a conceptual analogy between an innovation ecosystem and the biological ecosystems observed in nature. The biological ecosystem is a system that includes all living organisms (biotic factors) in an area as well as its physical environments (abiotic factors) functioning together as a unit. It is characterized by one or more equilibrium states, where a relatively stable set of conditions exist to maintain a population or nutrient exchange at desirable levels. The ecosystem has certain functional characteristics that specifically regulate change or maintain the stability of a desired equilibrium state.
Equal Opportunity Objectification ? The Sexualization of Men and Women on the Cover of Rolling Stone

A number of journalists and scholars have pointed to the sexual objectification of women and men in popular media to argue that Western culture has become ‘‘sexualized’’ or even ‘‘pornified.’’ Yet it is not clear whether men or women have become more frequently—or more intensely sexualized—over time. In a longitudinal content analysis of images of women and men on more than four decades of Rolling Stone magazine covers (1967–2009), we begin to answer such questions. Using a unique analytical framework that allows us to measure both the frequency and intensity of sexualization, we find that sexualized images of men and women have increased, though women continue to be more frequently sexualized than men. Yet our most striking finding is the change in how women—but not men—are sexualized. Women are increasingly likely to be ‘‘hypersexualized,’’ but men are not. These findings not only document changes in the sexualization of men and women in popular culture over time, they also point to a narrowing of the culturally acceptable ways for ‘‘doing’’ femininity as presented in popular media.
Model Predictive-Based Maximum Power Point Tracking for Grid-Tied Photovoltaic Applications Using a Z-Source Inverter

This paper presents a model predictive-based maximum power point tracking (MPPT) method for a photovoltaic energy harvesting system based on a single-stage grid-tied Z-source inverter. First, it provides a brief review of Z-source inverters, MPPT methods and model predictive control. Next, it introduces the proposed model predictive-based MPPT method. Finally, it provides experimental results to verify the theoretical outcomes.
The overview of reviews: unique challenges and opportunities when research syntheses are the principal elements of new integrative scholarship.

In the past two decades, a new form of scholarship has appeared in which researchers present an overview of previously conducted research syntheses on the same topic. In these efforts, research syntheses are the principal units of evidence. Overviews of reviews introduce unique problems that require unique solutions. This article describes what methods overviewers have developed or have adopted from other forms of scholarship. These methods concern how to (a) define the broader problem space of an overview, (b) conduct literature searches that specifically look for research syntheses, (c) address the overlap in evidence in related reviews, (d) evaluate the quality of both primary research and research syntheses, (e) integrate the outcomes of research syntheses, especially when they produce discordant results, (f) conduct a second-order meta-analysis, and (g) present findings. The limitations of overviews are also discussed, especially with regard to the age of the included evidence.
Growing random forest on deep convolutional neural networks for scene categorization

Breakthrough performances have been achieved in computer vision by utilizing deep neural networks. In this paper we propose to use random forest to classify image representations obtained by concatenating multiple layers of learned features of deep convolutional neural networks for scene classification. Specifically, we first use deep convolutional neural networks pre-trained on the large-scale image database Places to extract features from scene images. Then, we concatenate multiple layers of features of the deep neural networks as image representations. After that, we use random forest as the classifier for scene classification. Moreover, to reduce feature redundancy in image representations we derived a novel feature selection method for selecting features that are suitable for random forest classification. Extensive experiments are conducted on two benchmark datasets, i.e. MIT-Indoor and UIUC-Sports. Obtained results demonstrated the effectiveness of the proposed method. The contributions of the paper are as follows. First, by extracting multiple layers of deep neural networks, we can explore more information of image contents for determining their categories. Second, we proposed a novel feature selection method that can be used to reduce redundancy in features obtained by deep neural networks for classification based on random forest. In particular, since deep learning methods can be used to augment expert systems by having the systems essentially training themselves, and the proposed framework is general, which can be easily extended to other intelligent systems that utilize deep learning methods, the proposed method provide a potential way for improving performances of other expert Email address: shuangb@bjtu.edu.cn (Shuang Bai) Preprint submitted to Expert Systems with Applications October 17, 2016
A General Framework for Regularized, Similarity-Based Image Restoration

Any image can be represented as a function defined on a weighted graph, in which the underlying structure of the image is encoded in kernel similarity and associated Laplacian matrices. In this paper, we develop an iterative graph-based framework for image restoration based on a new definition of the normalized graph Laplacian. We propose a cost function, which consists of a new data fidelity term and regularization term derived from the specific definition of the normalized graph Laplacian. The normalizing coefficients used in the definition of the Laplacian and associated regularization term are obtained using fast symmetry preserving matrix balancing. This results in some desired spectral properties for the normalized Laplacian such as being symmetric, positive semidefinite, and returning zero vector when applied to a constant image. Our algorithm comprises of outer and inner iterations, where in each outer iteration, the similarity weights are recomputed using the previous estimate and the updated objective function is minimized using inner conjugate gradient iterations. This procedure improves the performance of the algorithm for image deblurring, where we do not have access to a good initial estimate of the underlying image. In addition, the specific form of the cost function allows us to render the spectral analysis for the solutions of the corresponding linear equations. In addition, the proposed approach is general in the sense that we have shown its effectiveness for different restoration problems, including deblurring, denoising, and sharpening. Experimental results verify the effectiveness of the proposed algorithm on both synthetic and real examples.
Practical and secure nearest neighbor search on encrypted large-scale data

Nearest neighbor search (or k-nearest neighbor search in general) is one of the most fundamental queries on massive datasets, and it has extensive applications such as pattern recognition, statistical classification, graph algorithms, Location-Based Services and online recommendations. With the raising trend of outsourcing massive sensitive datasets to public clouds, it is urgent for companies and organizations to demand fast and secure nearest neighbor search solutions over their outsourced data, but without revealing privacy to untrusted clouds. However, existing solutions for secure nearest neighbor search still face significant limitations, which make them far from practice. In this paper, we propose a new searchable encryption scheme, which can efficiently and securely enable nearest neighbor search over encrypted data on untrusted clouds. Specifically, we modify the search algorithm of nearest neighbors with tree structures (e.g., R-trees), where the modified algorithm adapts to lightweight cryptographic primitives (e.g., Order-Preserving Encryption) without affecting the original faster-than-linear search complexity. As a result, we address all the limitations in the previous works while still maintaining correctness and security. Moreover, our design is general, which can be used for secure k-nearest neighbor search, and it is compatible with other similar tree structures. Our experimental results on Amazon EC2 show that our scheme is extremely practical over massive datasets.
Socket Shield Technique for Ridge Preservation : A Case Report

Yu-Hwa Pan, DDS, MS Chair, Department of General Dentistry, Chang-Gung Memorial Hospital, Taipei, Taiwan Abstract e "socket shield technique" has demonstrated the potential in preventing buccal tissue from resorption in animal and clinical studies. It is assumed that retaining a root fragment a ached to the buccal bone plate in this technique can avoid tissue alteration after tooth extraction. This article presents a 58-year-old healthy man with a failing upper right second premolar which would be replaced by an implant-supported single crown. Leaving a partial root fragment at buccal side in combination with immediate implant placement lingual to the retained fragment was performed. Four months a er implant placement, clinical examination showed healthy peri-implant so tissue and the ridge was well preserved. A de nitive metal ceramic crown was fabricated and cemented on a titanium abutment. The prosthesis successfully restored the function of the patient. A maximum amount of horizontal resorption at the buccal side was 0.72mm. Applying socket shield technique and immediate implant placement may be a feasible treatment option in case with high esthetic concern.
Integrating Gaussian mixtures into deep neural networks: Softmax layer with hidden variables

In the hybrid approach, neural network output directly serves as hidden Markov model (HMM) state posterior probability estimates. In contrast to this, in the tandem approach neural network output is used as input features to improve classic Gaussian mixture model (GMM) based emission probability estimates. This paper shows that GMM can be easily integrated into the deep neural network framework. By exploiting its equivalence with the log-linear mixture model (LMM), GMM can be transformed to a large softmax layer followed by a summation pooling layer. Theoretical and experimental results indicate that the jointly trained and optimally chosen GMM and bottleneck tandem features cannot perform worse than a hybrid model. Thus, the question “hybrid vs. tandem” simplifies to optimizing the output layer of a neural network. Speech recognition experiments are carried out on a broadcast news and conversations task using up to 12 feed-forward hidden layers with sigmoid and rectified linear unit activation functions. The evaluation of the LMM layer shows recognition gains over the classic softmax output.