nils-herrmann/clean_scirepeval huggingface dataset:
Rough set methods in feature selection and recognition

We present applications of rough set methods for feature selection in pattern recognition. We emphasize the role of the basic constructs of rough set approach in feature selection, namely reducts and their approximations, including dynamic reducts. In the overview of methods for feature selection we discuss feature selection criteria, including the rough set based methods. Our algorithm for feature selection is based on an application of a rough set method to the result of principal components analysis (PCA) used for feature projection and reduction. Finally, the paper presents numerical results of face and mammogram recognition experiments using neural network, with feature selection based on proposed PCA and rough set methods.
Ultrastructural analysis of 3 hyaluronic acid soft-tissue fillers using scanning electron microscopy.

Although hyaluronic acid (HA) specifications such as molecular weight and particle size are fairly well characterized, little information about HA ultrastructural and morphologic characteristics has been reported in clinical literature.



To examine uniformity of HA structure, the effects of extrusion, and lidocaine dilution of 3 commercially available HA soft-tissue fillers.



Using scanning electron microscopy and energy-dispersive x-ray analysis, investigators examined the soft-tissue fillers at various magnifications for ultrastructural detail and elemental distributions.



All HAs contained oxygen, carbon, and sodium, but with uneven distributions. Irregular particulate matter was present in RES but BEL and JUV were largely particle free. Spacing was more uniform in BEL than JUV and JUV was more uniform than RES. Lidocaine had no apparent effect on morphology; extrusion through a 30-G needle had no effect on ultrastructure.



Descriptions of the ultrastructural compositions and nature of BEL, JUV, and RES are helpful for matching the areas to be treated with the HA soft-tissue filler architecture. Lidocaine and extrusion through a 30-G needle exerted no influence on HA structure. Belotero Balance shows consistency throughout the syringe and across manufactured lots.
MagicMeeting: A Collaborative Tangible Augmented Reality System

We describe an augmented reality (AR) system that allows multiple participants to interact with 2D and 3D data using tangible user interfaces. The system features face-to-face communication, collaborative viewing and manipulation of 3D models, and seamless access to 2D desktop applications within the shared 3D space. All virtual content, including 3D models and 2D desktop windows, is attached to tracked physical objects in order to leverage the efficiencies of natural two-handed manipulation. The presence of 2D desktop space within 3D facilitates data exchange between the two realms, enables control of 3D information by 2D applications, and generally increases productivity by providing access to familiar tools. We present a general concept for a collaborative tangible AR system, including a comprehensive set of interaction techniques, a distributed hardware setup, and a component-based software architecture that can be flexibly configured using XML. We show the validity of our concept with an implementation of an application scenario from the automotive industry.
A Machine Learning Model for Stock Market Prediction

Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on a financial exchange. The successful prediction of a stock's future price will maximize investor’s gains. This paper proposes a machine learning model to predict stock market price. The proposed algorithm integrates Particle swarm optimization (PSO) and least square support vector machine (LS-SVM). The PSO algorithm is employed to optimize LS-SVM to predict the daily stock prices. Proposed model is based on the study of stocks historical data and technical indicators. PSO algorithm selects best free parameters combination for LS-SVM to avoid over-fitting and local minima problems and improve prediction accuracy. The proposed model was applied and evaluated using thirteen benchmark financials datasets and compared with artificial neural network with Levenberg-Marquardt (LM) algorithm. The obtained results showed that the proposed model has better prediction accuracy and the potential of PSO algorithm in optimizing LS-SVM.
Semiotic Engineering Methods for Scientific Research in HCI

in printed reviews, without the prior permission of the publisher. Semiotic Engineering Methods for Scientific Research in HCI. Clarisse Sieckenius de Souza. order to produce usable concepts, models and methods for the benefit of non-. research projects at the intersection between semiotics and HCI. In order to be of any scientific value, semiotic engineering must provide. www2.serg.inf.puc-rio.br/docs/MonteiroDeSouzaLeitao2013.pdf. To date, there hasnt been enough empirical research in HCI exploring this complex phenomenon. usable concepts, models and methods for the benefit of nonsemiotician [2]. In order to be of any scientific value, semiotic engineering must provide. Dept. of Computer Science/Center for Human Machine Interaction, University of. In my experience, semiotics can be useful for the HCI-field, but the purely analytic. cated techniques for montage — the temporal succession of shots — involving rhythm. But semiotic research need not to be confined to the user interface.
Bayesian estimation supersedes the t test.

Bayesian estimation for 2 groups provides complete distributions of credible values for the effect size, group means and their difference, standard deviations and their difference, and the normality of the data. The method handles outliers. The decision rule can accept the null value (unlike traditional t tests) when certainty in the estimate is high (unlike Bayesian model comparison using Bayes factors). The method also yields precise estimates of statistical power for various research goals. The software and programs are free and run on Macintosh, Windows, and Linux platforms.
Spam filtering for short messages

We consider the problem of content-based spam filtering for short text messages that arise in three contexts: mobile (SMS) communication, blog comments, and email summary information such as might be displayed by a low-bandwidth client. Short messages often consist of only a few words, and therefore present a challenge to traditional bag-of-words based spam filters. Using three corpora of short messages and message fields derived from real SMS, blog, and spam messages, we evaluate feature-based and compression-model-based spam filters. We observe that bag-of-words filters can be improved substantially using different features, while compression-model filters perform quite well as-is. We conclude that content filtering for short messages is surprisingly effective.
Representing Objects, Relations, and Sequences

Vector symbolic architectures (VSAs) are high-dimensional vector representations of objects (e.g., words, image parts), relations (e.g., sentence structures), and sequences for use with machine learning algorithms. They consist of a vector addition operator for representing a collection of unordered objects, a binding operator for associating groups of objects, and a methodology for encoding complex structures. We first develop constraints that machine learning imposes on VSAs; for example, similar structures must be represented by similar vectors. The constraints suggest that current VSAs should represent phrases (“The smart Brazilian girl”) by binding sums of terms, in addition to simply binding the terms directly. We show that matrix multiplication can be used as the binding operator for a VSA, and that matrix elements can be chosen at random. A consequence for living systems is that binding is mathematically possible without the need to specify, in advance, precise neuron-to-neuron connection properties for large numbers of synapses. A VSA that incorporates these ideas, Matrix Binding of Additive Terms (MBAT), is described that satisfies all constraints. With respect to machine learning, for some types of problems appropriate VSA representations permit us to prove learnability rather than relying on simulations. We also propose dividing machine (and neural) learning and representation into three stages, with differing roles for learning in each stage. For neural modeling, we give representational reasons for nervous systems to have many recurrent connections, as well as for the importance of phrases in language processing. Sizing simulations and analyses suggest that VSAs in general, and MBAT in particular, are ready for real-world applications.
Novel wideband orthomode transducer for 70–95GHz

This paper presents a novel orthomode transducer (OMT) with the dimension of WR-10 waveguide. The internal structure of the OMT is in the shape of Y so we named it a Y-junction OMT, it contain one square waveguide port with the dimension 2.54mm × 2.54mm and two WR-10 rectangular waveguide ports with the dimension of 1.27mm × 2.54mm. The operating frequency band of OMT is 70-95GHz (more than 30% bandwidth) with simulated insertion loss <;-0.3dB and cross polarization better than -40dB throughout the band for both TE10 and TE01 modes.
Towards a Metrics Suite for Object Oriented Design

While software metrics are a generally desirable feature in the software management functions of project planning and project evaluation, they are of especial importance with a new technology such as the object-oriented approach. This is due to the significant need to train software engineers in generally accepted object-oriented principles. This paper presents theoretical work that builds a suite of metrics for object-oriented design. In particular, these metrics are based upon measurement theory and are informed by the insights of experienced object-oriented software developers. The proposed metrics are formally evaluated against a widelyaccepted list of software metric evaluation criteria.
RAP: protecting commodity wi-fi networks from rogue access points

We first give a comprehensive taxonomy of rouge access points (APs), which includes a new class of rouge APs never addressed in the literature before. Then, we propose an efficient rogue AP protection system termed as RAP for commodity Wi-Fi networks. In RAP, novel techniques are introduced to detect rouge APs and to improve network resilience. Our system has the following nice properties: i) it requires neither specialized hardware nor modification to existing standards; ii) the proposed mechanism can be integrated with an AP in a plugin manner; iii) it provides a cost-effective security enhancement to Wi-Fi networks by incorporating free but mature software tools; iv) it can protect the network from adversaries capable of using customized equipment and violating the IEEE 802.11 standard.
Skin melanoma segmentation using recurrent and convolutional neural networks

Skin melanoma is one of the highly addressed health problems in many countries. Dermatologists diagnose melanoma by visual inspections of mole using clinical assessment tools such as ABCD. However, computer vision tools have been introduced to assist in quantitative analysis of skin lesions. Deep learning is one of the trending machine learning techniques that have been successfully utilized to solve many difficult computer vision tasks. We proposed using a hybrid method that utilizes two popular deep learning methods: convolutional and recurrent neural networks. The proposed method was trained using 900 images and tested on 375 images. Images were obtained from “Skin Lesion Analysis Toward Melanoma Detection” challenge which was hosted by ISBI 2016 conference. We achieved segmentation average accuracy of 0.98 and Jaccard index of 0.93. Results were compared with other state-of-the-art methods, including winner of ISBI 2016 challenge for skin melanoma segmentation, along with the same evaluation criteria.
Priming ditransitive structures in comprehension

Many studies have shown evidence for syntactic priming during language production (e.g., Bock, 1986). It is often assumed that comprehension and production share similar mechanisms and that priming also occurs during comprehension (e.g., Pickering & Garrod, 2004). Research investigating priming during comprehension (e.g., Branigan, Pickering, & McLean, 2005; Scheepers & Crocker, 2004) has mainly focused on syntactic ambiguities that are very different from the meaning-equivalent structures used in production research. In two experiments, we investigated whether priming during comprehension occurs in ditransitive sentences similar to those used in production research. When the verb was repeated between prime and target, we observed a priming effect similar to that in production. However, we observed no evidence for priming when the verbs were different. Thus, priming during comprehension occurs for very similar structures as priming during production, but in contrast to production, the priming effect is completely lexically dependent.
Dual Use of Mobile IT and Work-to-Life Conflict in the Context of IT Consumerization

Using the same technologies for both work and private life is an intensifying phenomenon. Mostly driven by the availability of consumer IT in the marketplace, individuals—more often than not—are tempted to use privately-owned IT rather than enterprise IT in order to get their job done. However, this dual-use of technologies comes at a price. It intensifies the blurring of the boundaries between work and private life—a development in stark contrast to the widely spread desire of employees to segment more clearly between their two lives. If employees cannot follow their segmentation preference, it is proposed that this misfit will result in work-to-life conflict (WtLC). This paper investigates the relationship between organizational encouragement for dual use and WtLC. Via a quantitative survey, we find a significant relationship between the two concepts. In line with boundary theory, the effect is stronger for people that strive for work-life segmentation.
Grower or shower? Predictors of change in penile length from the flaccid to erect state

In colloquial English, a “grower” is a man whose phallus expands significantly in length from the flaccid  to the erect state; a “shower” is a man whose phallus does not demonstrate such expansion. We sought to investigate various factors that might predict a man being either a grower or a shower. A retrospective review of 274 patients who underwent penile duplex Doppler ultrasound (PDDU) for erectile dysfunction between 2011 and 2013 was performed. Penile length was measured, both in the flaccid state prior to intracavernosal injection (ICI) of a vasodilating agent (prostaglandin E1), and at peak erection during PDDU. The collected data included patient demographics, vascular, and anatomic parameters. The median change in penile length from flaccid to erect state was 4.0  cm (1.0–7.0), and was used as a cut-off value defining a grower (≥4.0 cm) or a shower (4.0 cm). A total of 73 men (26%) fit the definition of a grower (mean change in length of 5.3 cm [SD 0.5]) and 205 (74%) were showers (mean change in length of 3.1 cm [SD 0.9]). There were no differences between the groups with regards to race, smoking history, co-morbidities, erectile function, flaccid penile length, degree of penile rigidity after ICI, or PDDU findings. Growers were significantly younger (mean age 47.5 vs. 55.9 years, p   < 0.001), single (37% vs. 23%, p  =  0.031), received less vasodilator dose (10.3  mcg vs. 11.0  mcg, p  =  0.038) and had a larger erect phallus (15.5 cm vs. 13.1 cm, p <   0.001). On multivariate analysis, only younger age was significantly predictive of being a grower (p < 0.001). These results suggest that younger age and single status could be predictors of a man being a grower, rather than a shower. Larger, multicultural and multinational studies are needed to confirm these results.
Explaining Deep Convolutional Neural Networks on Music Classification

Deep convolutional neural networks (CNNs) have been actively adopted in the field of music information retrieval, e.g. genre classification, mood detection, and chord recognition. However, the process of learning and prediction is little understood, particularly when it is applied to spectrograms. We introduce auralisation of a CNN to understand its underlying mechanism, which is based on a deconvolution procedure introduced in . Auralisation of a CNN is converting the learned convolutional features that are obtained from deconvolution into audio signals. In the experiments and discussions, we explain trained features of a 5-layer CNN based on the deconvolved spectrograms and auralised signals. The pairwise correlations per layers with varying different musical attributes are also investigated to understand the evolution of the learnt features. It is shown that in the deep layers, the features are learnt to capture textures, the patterns of continuous distributions, rather than shapes of lines.
Cross-Media Shared Representation by Hierarchical Learning with Multiple Deep Networks

Inspired by the progress of deep neural network (DNN) in single-media retrieval, the researchers have applied the DNN to cross-media retrieval. These methods are mainly two-stage learning: the first stage is to generate the separate representation for each media type, and the existing methods only model the intra-media information but ignore the inter-media correlation with the rich complementary context to the intra-media information. The second stage is to get the shared representation by learning the cross-media correlation, and the existing methods learn the shared representation through a shallow network structure, which cannot fully capture the complex cross-media correlation. For addressing the above problems, we propose the cross-media multiple deep network (CMDN) to exploit the complex cross-media correlation by hierarchical learning. In the first stage, CMDN jointly models the intra-media and intermedia information for getting the complementary separate representation of each media type. In the second stage, CMDN hierarchically combines the inter-media and intra-media representations to further learn the rich cross-media correlation by a deeper two-level network strategy, and finally get the shared representation by a stacked network style. Experiment results show that CMDN achieves better performance comparing with several state-of-the-art methods on 3 extensively used cross-media datasets.
Retinomorphic Event-Based Vision Sensors: Bioinspired Cameras With Spiking Output

State-of-the-art image sensors suffer from significant limitations imposed by their very principle of operation. These sensors acquire the visual information as a series of “snapshot” images, recorded at discrete points in time. Visual information gets time quantized at a predetermined frame rate which has no relation to the dynamics present in the scene. Furthermore, each recorded frame conveys the information from all pixels, regardless of whether this information, or a part of it, has changed since the last frame had been acquired. This acquisition method limits the temporal resolution, potentially missing important information, and leads to redundancy in the recorded image data, unnecessarily inflating data rate and volume. Biology is leading the way to a more efficient style of image acquisition. Biological vision systems are driven by events happening within the scene in view, and not, like image sensors, by artificially created timing and control signals. Translating the frameless paradigm of biological vision to artificial imaging systems implies that control over the acquisition of visual information is no longer being imposed externally to an array of pixels but the decision making is transferred to the single pixel that handles its own information individually. In this paper, recent developments in bioinspired, neuromorphic optical sensing and artificial vision are presented and discussed. It is suggested that bioinspired vision systems have the potential to outperform conventional, frame-based vision systems in many application fields and to establish new benchmarks in terms of redundancy suppression and data compression, dynamic range, temporal resolution, and power efficiency. Demanding vision tasks such as real-time 3-D mapping, complex multiobject tracking, or fast visual feedback loops for sensory-motor action, tasks that often pose severe, sometimes insurmountable, challenges to conventional artificial vision systems, are in reach using bioinspired vision sensing and processing techniques.
Sketch-based image retrieval on a large scale database

The paper presents a simple and effective sketch-based algorithm for large scale image retrieval. One of the main challenges in image retrieval is to localize a region in an image which would be matched with the query image in contour. To tackle this problem, we use the human perception mechanism to identify two types of regions in one image: the first type of region (the main region) is defined by a weighted center of image features, suggesting that we could retrieve objects in images regardless of their sizes and positions. The second type of region, called region of interests (ROI), is to find the most salient part of an image, and is helpful to retrieve images with objects similar to the query in a complicated scene. So using the two types of regions as candidate regions for feature extraction, our algorithm could increase the retrieval rate dramatically. Besides, to accelerate the retrieval speed, we first extract orientation features and then organize them in a hierarchal way to generate global-to-local features. Based on this characteristic, a hierarchical database index structure could be built which makes it possible to retrieve images on a very large scale image database online. Finally a real-time image retrieval system on 4.5 million database is developed to verify the proposed algorithm. The experiment results show excellent retrieval performance of the proposed algorithm and comparisons with other algorithms are also given.
3D Scene Flow Estimation with a Piecewise Rigid Scene Model

3D scene flow estimation aims to jointly recover dense geometry and 3D motion from stereoscopic image sequences, thus generalizes classical disparity and 2D optical flow estimation. To realize its conceptual benefits and overcome limitations of many existing methods, we propose to represent the dynamic scene as a collection of rigidly moving planes, into which the input images are segmented. Geometry and 3D motion are then jointly recovered alongside an over-segmentation of the scene. This piecewise rigid scene model is significantly more parsimonious than conventional pixel-based representations, yet retains the ability to represent real-world scenes with independent object motion. It, furthermore, enables us to define suitable scene priors, perform occlusion reasoning, and leverage discrete optimization schemes toward stable and accurate results. Assuming the rigid motion to persist approximately over time additionally enables us to incorporate multiple frames into the inference. To that end, each view holds its own representation, which is encouraged to be consistent across all other viewpoints and frames in a temporal window. We show that such a view-consistent multi-frame scheme significantly improves accuracy, especially in the presence of occlusions, and increases robustness against adverse imaging conditions. Our method currently achieves leading performance on the KITTI benchmark, for both flow and stereo.