nils-herrmann/clean_scirepeval huggingface dataset:
Online Dictionary Learning on Symmetric Positive Definite Manifolds with Vision Applications

Symmetric Positive Definite (SPD) matrices in the form of region covariances are considered rich descriptors for images and videos. Recent studies suggest that exploiting the Riemannian geometry of the SPD manifolds could lead to improved performances for vision applications. For tasks involving processing large-scale and dynamic data in computer vision, the underlying model is required to progressively and efficiently adapt itself to the new and unseen observations. Motivated by these requirements, this paper studies the problem of online dictionary learning on the SPD manifolds. We make use of the Stein divergence to recast the problem of online dictionary learning on the manifolds to a problem in Reproducing Kernel Hilbert Spaces, for which, we develop efficient algorithms by taking into account the geometric structure of the SPD manifolds. To our best knowledge, our work is the first study that provides a solution for online dictionary learning on the SPD manifolds. Empirical results on both large-scale image classification task and dynamic video processing tasks validate the superior performance of our approach as compared to several state-of-the-art algorithms.
Revising the Structural Framework for Marketing Management

Special thanks to Robert Skipper and Aaron Hyman for their assistance on an earlier version of this manuscript. Also thanks to Shaun McQuitty, Robin Peterson, Chuck Pickett, Kevin Shanahan, and the Journal of Business Research editors and reviewers, for their helpful comments. An earlier version of this manuscript won the Shaw Award for best paper presented at 2001 Society for Marketing Advances conference. An abridged version of this manuscript has been accepted for publication in Journal of Business Research.
Body Image and Personality in Aesthetic Plastic Surgery : A Case-Control Study

The amount of research on the relationship between plastic surgery and psychological features, such as personality disorders and indexes of body image dissatisfaction, has increased significantly in the last years.  The aim of the present study was to examine these psychological features among Italian patients who underwent aesthetic plastic surgery, testing the mediating role of the mass media influence on the relationship between them and the choice of aesthetic plastic surgery. The Personality Diagnostic Questionnaire 4+ (PDQ-4+) and the Body Uneasiness Test (BUT) were administered to patients who underwent aesthetic plastic surgery (N = 111) and participants who had no history of aesthetic plastic surgical procedures (N = 149).  Results showed that aesthetic patients reported higher indexes of body image disturbance than controls. No significant differences between aesthetic participants and controls were found in all three cluster B personality disorders. Moreover, the effect of body image dissatisfaction on the choice to undergo aesthetic plastic surgery was partially mediated by the influence of mass media.  In conclusion, the present study confirmed the importance of body dissatisfaction as a predictor of the choice to undergo aesthetic surgery and highlighted the influence of media messages regarding physical appearance.
A continuous measure of phasic electrodermal activity

Electrodermal activity is characterized by the superposition of what appear to be single distinct skin conductance responses (SCRs). Classic trough-to-peak analysis of these responses is impeded by their apparent superposition. A deconvolution approach is proposed, which separates SC data into continuous signals of tonic and phasic activity. The resulting phasic activity shows a zero baseline, and overlapping SCRs are represented by predominantly distinct, compact impulses showing an average duration of less than 2 s. A time integration of the continuous measure of phasic activity is proposed as a straightforward indicator of event-related sympathetic activity. The quality and benefit of the proposed measure is demonstrated in an experiment with short interstimulus intervals as well as by means of a simulation study. The advances compared to previous decomposition methods are discussed.
A Hybrid Model to Detect Malicious Executables

We present a hybrid data mining approach to detect malicious executables. In this approach we identify important features of the malicious and benign executables. These features are used by a classifier to learn a classification model that can distinguish between malicious and benign executables. We construct a novel combination of three different kinds of features: binary n-grams, assembly n-grams, and library function calls. Binary features are extracted from the binary executables, whereas assembly features are extracted from the disassembled executables. The function call features are extracted from the program headers. We also propose an efficient and scalable feature extraction technique. We apply our model on a large corpus of real benign and malicious executables. We extract the above mentioned features from the data and train a classifier using support vector machine. This classifier achieves a very high accuracy and low false positive rate in detecting malicious executables. Our model is compared with other feature-based approaches, and found to be more efficient in terms of detection accuracy and false alarm rate.
MORPH: a longitudinal image database of normal adult age-progression

This paper details MORPH a longitudinal face database developed for researchers investigating all facets of adult age-progression, e.g. face modeling, photo-realistic animation, face recognition, etc. This database contributes to several active research areas, most notably face recognition, by providing: the largest set of publicly available longitudinal images; longitudinal spans from a few months to over twenty years; and, the inclusion of key physical parameters that affect aging appearance. The direct contribution of this data corpus for face recognition is highlighted in the evaluation of a standard face recognition algorithm, which illustrates the impact that age-progression, has on recognition rates. Assessment of the efficacy of this algorithm is evaluated against the variables of gender and racial origin. This work further concludes that the problem of age-progression on face recognition (FR) is not unique to the algorithm used in this work
A Review on Position/Speed Sensorless Control for Permanent-Magnet Synchronous Machine-Based Wind Energy Conversion Systems

Owing to the advantages of higher efficiency, greater reliability, and better grid compatibility, the direct-drive permanent-magnet synchronous generator (PMSG)-based variable-speed wind energy conversion systems (WECSs) have drawn the highest attention from both academia and industry in the last few years. Applying mechanical position/speed sensorless control to direct-drive PMSG-based WECSs will further reduce the cost and complexity, while enhancing the reliability and robustness of the WECSs. This paper reviews the state-of-the-art and highly applicable mechanical position/speed sensorless control schemes for PMSG-based variable-speed WECSs. These include wind speed sensorless control schemes, generator rotor position and speed sensorless vector control schemes, and direct torque and direct power control schemes for a variety of direct-drive PMSG-based WECSs.
Workshop on novelty and diversity in recommender systems - DiveRS 2011

Novelty and diversity have been identified as key dimensions of recommendation utility in real scenarios, and a fundamental research direction to keep making progress in the field. Yet recommendation novelty and diversity remain a largely open area for research. The DiveRS workshop gathered researchers and practitioners interested in the role of these dimensions in recommender systems. The workshop seeks to advance towards a better understanding of what novelty and diversity are, how they can improve the effectiveness of recommendation methods and the utility of their outputs. The workshop pursued the identification of open problems, relevant research directions, and opportunities for innovation in the recommendation business.
Novel Anti-forensics Approaches for Smart Phones

Anti-forensics is defined as a method undertaken to thwart the digital investigation process conducted by forensic investigators. In this paper we introduce the design and implementation of three novel anti-forensic approaches for data deletion and manipulation on three of the market-leading forensics tools. The evaluation results of our proposed anti-forensic approaches on a typical Android cell phone device demonstrate the severe limitations of current forensic tools and conclude the critical need for modifications and/or redesigns of these contemporary forensic tools to maintain accuracy and legitimacy of forensic data acquisition and analysis.
A Conditional Random Field model for font forgery detection

Nowadays, document forgery is becoming a real issue. A large amount of documents that contain critical information as payment slips, invoices or contracts, are constantly subject to fraudster manipulation because of the lack of security regarding this kind of document. Previously, a system to detect fraudulent documents based on its intrinsic features has been presented. It was especially designed to retrieve copy-move forgery and imperfection due to fraudster manipulation. However, when a set of characters is not present in the original document, copy-move forgery is not feasible. Hence, the fraudster will use a text toolbox to add or modify information in the document by imitating the font or he will cut and paste characters from another document where the font properties are similar. This often results in font type errors. Thus, a clue to detect document forgery consists of finding characters, words or sentences in a document with font properties different from their surroundings. To this end, we present in this paper an automatic forgery detection method based on document font features. Using the Conditional Random Field a measurement of probability that a character belongs to a specific font is made by comparing the character font features to a knowledge database. Then, the character is classified as a genuine or a fake one by comparing its probability to belong to a certain font type with those of the neighboring characters.
CSI Neural Network: Using Side-channels to Recover Your Artificial Neural Network Information

Machine learning has become mainstream across industries. Numerous examples proved the validity of it for security applications. In this work, we investigate how to reverse engineer a neural network by using only power side-channel information. To this end, we consider a multilayer perceptron as the machine learning architecture of choice and assume a non-invasive and eavesdropping attacker capable of measuring only passive side-channel leakages like power consumption, electromagnetic radiation, and reaction time. We conduct all experiments on real data and common neural net architectures in order to properly assess the applicability and extendability of those attacks. Practical results are shown on an ARM CORTEX-M3 microcontroller. Our experiments show that the side-channel attacker is capable of obtaining the following information: the activation functions used in the architecture, the number of layers and neurons in the layers, the number of output classes, and weights in the neural network. Thus, the attacker can effectively reverse engineer the network using sidechannel information. Next, we show that once the attacker has the knowledge about the neural network architecture, he/she could also recover the inputs to the network with only a single-shot measurement. Finally, we discuss several mitigations one could use to thwart such attacks.
Matchmaker: constructing constrained texture maps

Texture mapping enhances the visual realism of 3D models by adding fine details. To achieve the best results, it is often necessary to force a correspondence between some of the details of the texture and the features of the model.The most common method for mapping texture onto 3D meshes is to use a planar parameterization of the mesh. This, however, does not reflect any special correspondence between the mesh geometry and the texture. The Matchmaker algorithm presented here forces user-defined feature correspondence for planar parameterization of meshes. This is achieved by adding positional constraints to the planar parameterization. Matchmaker allows users to introduce scores of constraints while maintaining a valid one-to-one mapping between the embedding and the 3D surface. Matchmaker's constraint mechanism can be used for other applications requiring parameterization besides texture mapping, such as morphing and remeshing.Matchmaker begins with an unconstrained planar embedding of the 3D mesh generated by conventional methods. It moves the constrained vertices to the required positions by matching a triangulation of these positions to a triangulation of the planar mesh formed by paths between constrained vertices. The matching triangulations are used to generate a new parameterization that satisfies the constraints while minimizing the deviation from the original 3D geometry.
A deep learning approach for detecting and correcting highlights in endoscopic images

The image of an object changes dramatically depending on the lightning conditions surrounding that object. Shadows, reflections and highlights can make the object very difficult to be recognized for an automatic system. Additionally, images used in medical applications, such as endoscopic images and videos contain a large amount of such reflective components. This can pose an extra difficulty for experts to analyze such type of videos and images. It can then be useful to detect — and possibly correct — the locations where those highlights happen. In this work we designed a Convolutional Neural Network for that task. We trained such a network using a dataset that contains groundtruth highlights showing that those reflective elements can be learnt and thus located and extracted. We then used that trained network to localize and correct the highlights in endoscopic images from the El Salvador Atlas Gastrointestinal videos obtaining promising results.
Strong higher order mutation-based test data generation

This paper introduces SHOM, a mutation-based test data generation approach that combines Dynamic Symbolic Execution and Search Based Software Testing. SHOM targets strong mutation adequacy and is capable of killing both first and higher order mutants. We report the results of an empirical study using 17 programs, including production industrial code from ABB and Daimler and open source code as well as previously studied subjects. SHOM achieved higher strong mutation adequacy than two recent mutation-based test data generation approaches, killing between 8% and 38% of those mutants left unkilled by the best performing previous approach.
Circuit Modeling for Rectangular Printed Disc Monopole Antenna with Slot for UWB System

In this paper, a circuit model is done for rectangular printed disc monopole antenna with slot for ultra wideband application. For this circuit modeling, patch cavity model consists of series resistor and inductor which represent the surface resistance and surface inductance with the combination of LC circuit as the resonant circuit is introduced. Moreover, a slot is etched on the patch antenna and represented by a series LC circuit for its circuit model. The result is then repeated with modification done on the slot and also the circuit model. Finally return loss result for both antenna structure and circuit model were compared and discussed.
A Comparison of Affine Region Detectors

The paper gives a snapshot of the state of the art in affine covariant region detectors, and compares their performance on a set of test images under varying imaging conditions. Six types of detectors are included: detectors based on affine normalization around Harris  (Mikolajczyk and  Schmid, 2002; Schaffalitzky and  Zisserman, 2002) and Hessian points  (Mikolajczyk and  Schmid, 2002), a detector of ‘maximally stable extremal regions', proposed by Matas et al. (2002); an edge-based region detector  (Tuytelaars and Van Gool, 1999) and a detector based on intensity extrema (Tuytelaars and Van Gool, 2000), and a detector of ‘salient regions', proposed by Kadir, Zisserman and Brady (2004). The performance is measured against changes in viewpoint, scale, illumination, defocus and image compression. The objective of this paper is also to establish a reference test set of images and performance software, so that future detectors can be evaluated in the same framework.
Fast and robust keypoint detection in unstructured 3-D point clouds

In robot perception, as well as in other areas of 3-D computer vision, keypoint detection is the first major step for an efficient and accurate 3-D perception of the environment. Thus, a fast and robust algorithm for an automatic identification of keypoints in unstructured 3-D point clouds is essential. The presented algorithm is designed to be highly parallelizable and can be implemented on modern GPUs for fast execution. The computation is based on a convolution of a voxel based representation of the point cloud and a voxelized integral volume. The generation of the voxel-based representation neither requires additional surface information or normals nor needs to approximate them. The proposed approach is robust against noise up to the mean distance between the 3-D points. In addition, the algorithm provides moderate scale invariance, i. e., it can approximate keypoints for lower resolution versions of the input point cloud. This is particularly useful, if keypoints are supposed to be used with any local 3-D point cloud descriptor to recognize or classify point clouds at different scales. We evaluate our approach in a direct comparison with state-of-the-art keypoint detection algorithms in terms of repeatability and computation time.
Predicting the Success of Online Petitions Leveraging Multidimensional Time-Series

Applying classical time-series analysis techniques to online content is challenging, as web data tends to have data quality issues and is often incomplete, noisy, or poorly aligned. In this paper, we tackle the problem of predicting the evolution of a time series of user activity on the web in a manner that is both accurate and interpretable, using related time series to produce a more accurate prediction. We test our methods in the context of predicting signatures for online petitions using data from thousands of petitions posted on The Petition Site—one of the largest platforms of its kind. We observe that the success of these petitions is driven by a number of factors, including promotion through social media channels and on the front page of the petitions platform. We propose an interpretable model that incorporates seasonality, aging effects, self-excitation, and external effects. The interpretability of the model is important for understanding the elements that drives the activity of an online content. We show through an extensive empirical evaluation that our model is significantly better at predicting the outcome of a petition than state-of-the-art techniques.
Automatic Calibration of Spinning Actuated Lidar Internal Parameters

Actuated lidar, where a scanning lidar is combined with an actuation mechanism to scan a 3D volume rather than a single line, has been used heavily on a wide variety of field robotics applications. Common examples of actuated lidar include spinning/rolling and nodding/pitching configurations. Due to construction of actuated lidar, the center of rotation of the lidar mirror may not coincide with the center of rotation of the actuation mechanism. In order to triangulate a precise point cloud representation of the environment, the centers of rotation must be brought into alignment using a suitable calibration procedure. We refer to this problem as estimating the internal parameters of actuated lidar. In this work, we focus on spinning/rolling lidar and present a fully automated algorithm for calibration using generic scenes without the need for specialized calibration targets. The algorithm is evaluated on a range of real and synthetic data and is shown to be robust, accurate and has a large basin of convergence.
Ability of Experience Design Elements to Elicit Emotions and Loyalty Behaviors

Experience design, an approach to create emotional connection with guests or customers through careful planning of tangible and intangible service elements, has gained popularity in many hospitality and retail businesses. With ever-increasing competition, service providers seek to develop loyalty by aggressively designing, continuously innovating, and managing their customer experiences. This article explores the relationship between different service elements designed to create enhanced experience and customer loyalty. In addition, it looks at emotional responses as mediating factors between the physical and relational elements and loyalty behaviors. A model is proposed and tested with a VIP hospitality tent for an internationally renowned touring circus. Results of the study indicate that while a few design elements directly affect loyalty behavior, the relationship between most design elements and loyalty behavior is strongly mediated by eliciting certain types of emotional behavior. This connection has implications for the focus of service managers’ efforts in different environments.  Emotion and Loyalty Modeling, Experience, and Service Design.
Hough-transform based algorithm for the automatic invariant recognition of rectangular chocolates . Detection of defective pieces

By means of this relation, any binary point (x,y) in image space X-Y is mapped into a sinusoidal in the accumulator space ρ-θ , which is discretized in cells of coordinates (ρ,θ) . The main feature of the HT is that the N sinusoidals corresponding to a set of N collinear points (xi,yi) meet at a point (ρo,θo) in accumulator space, the values of ρo and θo characterizing the original straight line in the image. Every sinusoidal passing by (ρ,θ) contributes with 1 to a counting stored in that location. The counting stored in every cell of the accumulator corresponds to the number of points that are contained in a straight line characterized by (ρ,θ) in eq. (1).
Crowd detection and management using cascade classifier on ARMv8 and OpenCV-Python

The steady increase in population and overcrowding has become an unavoidable factor in any public gathering or on the street during any festive occasions. The intelligent monitoring technology has been developing in recent years and human tracking has made a lot of progress. In this paper, we propose a method to manage the crowd by keeping in track the count of the people in the scene. In our study, we develop a system using Raspberry Pi 3 board that consists of ARMv8 CPU that detects the human heads and provide a count of humans in the region using OpenCV-Python. A Haar cascade classifier is trained for human head detection. Human tracking is achieved by indicating the direction of movement of the person. The results of the analysis will be helpful in managing the crowd in any area with high density of crowds.