nils-herrmann/clean_scirepeval huggingface dataset:
Aerodynamic Performance of a Corrugated Dragonfly Airfoil Compared with Smooth Airfoils at Low Reynolds Numbers

An experimental study was conducted to investigate the flow behavior around a corrugated dragonfly airfoil compared with a traditional, streamlined airfoil and a flat plate. The experimental study was conducted at the chord Reynolds number of ReC =34,000, i.e., the regime where Micro-Air-Vehicles (MAV) usually operate, to explore the potential applications of such bio-inspired airfoils for MAV designs. The measurement results demonstrated clearly that the corrugated dragonfly airfoil has much better performance over the streamlined airfoil and the flat plate in preventing large-scale flow separation and airfoil stall at the test low Reynolds number level. The detailed PIV measurements near the noses of the airfoils elucidated underlying physics about why the corrugated dragonfly airfoil could suppress flow separation and airfoil stall at low Reynolds numbers: Instead of having laminar separation, the protruding corners of the corrugated dragonfly airfoil were found to be acting as “turbulators” to generate unsteady vortices to promote the transition of the boundary layer from laminar to turbulent rapidly. The unsteady vortex structures trapped in the valleys of the corrugated cross section could pump high-speed fluid from outside to near wall regions to provide sufficient energy for the boundary layer to overcome the adverse pressure gradient, thus, discourage flow separations and airfoil stall.
Heterogeneous image feature integration via multi-modal spectral clustering

In recent years, more and more visual descriptors have been proposed to describe objects and scenes appearing in images. Different features describe different aspects of the visual characteristics. How to combine these heterogeneous features has become an increasing critical problem. In this paper, we propose a novel approach to unsupervised integrate such heterogeneous features by performing multi-modal spectral clustering on unlabeled images and unsegmented images. Considering each type of feature as one modal, our new multi-modal spectral clustering (MMSC) algorithm is to learn a commonly shared graph Laplacian matrix by unifying different modals (image features). A non-negative relaxation is also added in our method to improve the robustness and efficiency of image clustering. We applied our MMSC method to integrate five types of popularly used image features, including SIFT, HOG, GIST, LBP, CENTRIST and evaluated the performance by two benchmark data sets: Caltech-101 and MSRC-v1. Compared with existing unsupervised scene and object categorization methods, our approach always achieves superior performances measured by three standard clustering evaluation metrices.
Human behavior analysis in video surveillance: A Social Signal Processing perspective

The analysis of human activities is one of the most intriguing and important open issues for the automated video surveillance community. Since few years ago, it has been handled following a mere Computer Vision and Pattern Recognition perspective, where an activity corresponded to a temporal sequence of explicit actions (run, stop, sit, walk, etc.). Even under this simplistic assumption, the issue is hard, due to the strong diversity of the people appearance, the number of individuals considered (we may monitor single individuals, groups, crowd), the variability of the environmental conditions (indoor/outdoor, different weather conditions), and the kinds of sensors employed. More recently, the automated surveillance of human activities has been faced considering a new perspective, that brings in notions and principles from the social, affective, and psychological literature, and that is called Social Signal Processing (SSP). SSP employs primarily nonverbal cues, most of them are outside of conscious awareness, like face expressions and gazing, body posture and gestures, vocal characteristics, relative distances in the space and the like. This paper is the first review analyzing this new trend, proposing a structured snapshot of the state of the art and envisaging novel challenges in the surveillance domain where the cross-pollination of Computer Science technologies and Sociology theories may offer valid investigation strategies.
The OpenGrm open-source finite-state grammar software libraries

In this paper, we present a new collection of open-source software libraries that provides command line binary utilities and library classes and functions for compiling regular expression and context-sensitive rewrite rules into finite-state transducers, and for n-gram language modeling. The OpenGrm libraries use the OpenFst library to provide an efficient encoding of grammars and general algorithms for building, modifying and applying models.
Taxonomy of information security risk assessment (ISRA)

Information is a perennially significant business asset in all organizations. Therefore, it must be protected as any other valuable asset. This is the objective of information security, and an information security program provides this kind of protection for a company’s information assets and for the company as a whole. One of the best ways to address information security problems in the corporate world is through a risk-based approach. In this paper, we present a taxonomy of security risk assessment drawn from 125 papers published from 1995 to May 2014. Organizations with different size may face problems in selecting suitable risk assessment methods that satisfy their needs. Although many risk-based approaches have been proposed, most of them are based on the old taxonomy, avoiding the need for considering and applying the important criteria in assessing risk raised by rapidly changing technologies and the attackers knowledge level. In this paper, we discuss the key features of risk assessment that should be included in an information security management system. We believe that our new risk assessment taxonomy helps organizations to not only understand the risk assessment better by comparing different new concepts but also select a suitable way to conduct the risk assessment properly. Moreover, this taxonomy will open up interesting avenues for future research in the growing field of security risk assessment.
Science vs Conspiracy: Collective Narratives in the Age of Misinformation

The large availability of user provided contents on online social media facilitates people aggregation around shared beliefs, interests, worldviews and narratives. In spite of the enthusiastic rhetoric about the so called collective intelligence unsubstantiated rumors and conspiracy theories-e.g., chemtrails, reptilians or the Illuminati-are pervasive in online social networks (OSN). In this work we study, on a sample of 1.2 million of individuals, how information related to very distinct narratives-i.e. main stream scientific and conspiracy news-are consumed and shape communities on Facebook. Our results show that polarized communities emerge around distinct types of contents and usual consumers of conspiracy news result to be more focused and self-contained on their specific contents. To test potential biases induced by the continued exposure to unsubstantiated rumors on users' content selection, we conclude our analysis measuring how users respond to 4,709 troll information-i.e. parodistic and sarcastic imitation of conspiracy theories. We find that 77.92% of likes and 80.86% of comments are from users usually interacting with conspiracy stories.
A historical review on lateral and longitudinal control of autonomous vehicle motions

In this research, brief survey of literature on current developments in the field of lateral and longitudinal control of autonomous vehicle motions. The paper categorizes the motions of vehicle to: car following, lane keeping, lane changing, subsequently, longitudinal control (brake and throttle), lateral control (steering) and integration of these controls for autonomous vehicles was investigated. Also different equipment and approaches to control of motions in each field was proposed.
Generalized neural network for nonsmooth nonlinear programming problems

In 1988 Kennedy and Chua introduced the dynamical canonical nonlinear programming circuit (NPC) to solve in real time nonlinear programming problems where the objective function and the constraints are smooth (twice continuously differentiable) functions. In this paper, a generalized circuit is introduced (G-NPC), which is aimed at solving in real time a much wider class of nonsmooth nonlinear programming problems where the objective function and the constraints are assumed to satisfy only the weak condition of being regular functions. G-NPC, which derives from a natural extension of NPC, has a neural-like architecture and also features the presence of constraint neurons modeled by ideal diodes with infinite slope in the conducting region. By using the Clarke's generalized gradient of the involved functions, G-NPC is shown to obey a gradient system of differential inclusions, and its dynamical behavior and optimization capabilities, both for convex and nonconvex problems, are rigorously analyzed in the framework of nonsmooth analysis and the theory of differential inclusions. In the special important case of linear and quadratic programming problems, salient dynamical features of G-NPC, namely the presence of sliding modes , trajectory convergence in finite time, and the ability to compute the exact optimal solution of the problem being modeled, are uncovered and explained in the developed analytical framework.
A Web-based Platform for Collection of Human-Chatbot Interactions

Over recent years, the world has seen multiple uses for conversational agents. Chatbots has been implemented into ecommerce systems, such as Amazon Echo's Alexa . Businesses and organizations like Facebook are also implementing bots into their applications. While a number of amazing chatbot platform exists, there are still difficulties in creating data-driven-systems as they large amount of data is needed for development and training. This paper we describe an advanced platform for evaluating and annotating human-chatbot interactions, its main features and goals, as well as the future plans we have for it.
Strangers Intrusion Detection-Detecting Spammers and Fake Profiles in Social Networks Based on Topology Anomalies

Today’s social networks are plagued by numerous types of malicious profiles which can range from socialbots to sexual predators. We present a novel method for the detection of these malicious profiles by using the social network’s own topological features only. Reliance on these features alone ensures that the proposed method is generic enough to be applied on a range of social networks. The algorithm has been evaluated on several social networks and was found to be effective in detecting various types of malicious profiles. We believe this method is a valuable step in the increasing battle against social network spammers, socialbots, and sexual predictors.
Direction of Arrival Estimation Using Directive Antennas in Uniform Circular Arrays

The effect of directional antenna elements in uniform circular arrays (UCAs) for direction of arrival (DOA) estimation is studied in this paper. While the vast majority of previous work assumes isotropic antenna elements or omnidirectional dipoles, this work demonstrates that improved DOA estimation accuracy and increased bandwidth is achievable with appropriately-designed directional antennas. The Cramer-Rao Lower Bound (CRLB) is derived for UCAs with directional antennas and is compared to isotropic antennas for 4- and 8-element arrays using a theoretical radiation pattern. The directivity that minimizes the CRLB is identified and microstrip patch antennas approximating the optimal theoretical gain pattern are designed to compare the resulting DOA estimation accuracy with a UCA using dipole antenna elements. Simulation results show improved DOA estimation accuracy and robustness using microstrip patch antennas as opposed to conventional dipoles. Additionally, it is shown that the bandwidth of a UCA for DOA estimation is limited only by the broadband characteristics of the directional antenna elements and not by the electrical size of the array as is the case with omnidirectional antennas.
DIRT @SBT@discovery of inference rules from text

In this paper, we propose an unsupervised method for discovering inference rules from text, such as "X is author of Y ≈ X wrote Y", "X solved Y ≈ X found a solution to Y", and "X caused Y ≈ Y is triggered by X". Inference rules are extremely important in many fields such as natural language processing, information retrieval, and artificial intelligence in general. Our algorithm is based on an extended version of Harris' Distributional Hypothesis, which states that words that occurred in the same contexts tend to be similar. Instead of using this hypothesis on words, we apply it to paths in the dependency trees of a parsed corpus.
Statistical Inverse Ray Tracing for Image-Based 3D Modeling

This paper proposes a new formulation and solution to image-based 3D modeling (aka “multi-view stereo”) based on generative statistical modeling and inference. The proposed new approach, named statistical inverse ray tracing, models and estimates the occlusion relationship accurately through optimizing a physically sound image generation model based on volumetric ray tracing. Together with geometric priors, they are put together into a Bayesian formulation known as Markov random field (MRF) model. This MRF model is different from typical MRFs used in image analysis in the sense that the ray clique, which models the ray-tracing process, consists of thousands of random variables instead of two to dozens. To handle the computational challenges associated with large clique size, an algorithm with linear computational complexity is developed by exploiting, using dynamic programming, the recursive chain structure of the ray clique. We further demonstrate the benefit of exact modeling and accurate estimation of the occlusion relationship by evaluating the proposed algorithm on several challenging data sets.
Triangle mesh-based edge detection and its application to surface segmentation and adaptive surface smoothing

Triangle meshes are widely used in representing surfaces in computer vision and computer graphics. Although 2D image processingbased edge detection techniques have been popular in many application areas, they are not well developed for surfaces represented by triangle meshes. This paper proposes a robust edge detection algorithm for triangle meshes and its applications to surface segmentation and adaptive surface smoothing. The proposed edge detection technique is based on eigen analysis of the surface normal vector field in a geodesic window. To compute the edge strength of a certain vertex, the neighboring vertices in a specified geodesic distance are involved. Edge information are used further to segment the surfaces with watershed algorithm and to achieve edgepreserved, adaptive surface smoothing. The proposed algorithm is novel in robustly detecting edges on triangle meshes against noise. The 3D watershed algorithm is an extension from previous work. Experimental results on surfaces reconstructed from multi-view real range images are presented.
Geometric Consistency for Self-Supervised End-to-End Visual Odometry

With the success of deep learning based approaches in tackling challenging problems in computer vision, a wide range of deep architectures have recently been proposed for the task of visual odometry (VO) estimation. Most of these proposed solutions rely on supervision, which requires the acquisition of precise ground-truth camera pose information, collected using expensive motion capture systems or high-precision IMU/GPS sensor rigs. In this work, we propose an unsupervised paradigm for deep visual odometry learning. We show that using a noisy teacher, which could be a standard VO pipeline, and by designing a loss term that enforces geometric consistency of the trajectory, we can train accurate deep models for VO that do not require ground-truth labels. We leverage geometry as a self-supervisory signal and propose "Composite Transformation Constraints (CTCs)", that automatically generate supervisory signals for training and enforce geometric consistency in the VO estimate. We also present a method of characterizing the uncertainty in VO estimates thus obtained. To evaluate our VO pipeline, we present exhaustive ablation studies that demonstrate the efficacy of end-to-end, self-supervised methodologies to train deep models for monocular VO. We show that leveraging concepts from geometry and incorporating them into the training of a recurrent neural network results in performance competitive to supervised deep VO methods.
Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey

This paper presents a comprehensive review of 196 studies which employ operational research (O.R.) and artificial intelligence (A.I.) techniques in the assessment of bank performance. Several key issues in the literature are highlighted. The paper also points to a number of directions for future research. We first discuss numerous applications of data envelopment analysis which is the most widely applied O.R. technique in the field. Then we discuss applications of other techniques such as neural networks, support vector machines, and multicriteria decision aid that have also been used in recent years, in bank failure prediction studies and the assessment of bank creditworthiness and underperformance.
SiC-SIT Circuit Breakers With Controllable Interruption Voltage for 400-V DC Distribution Systems

400-V direct current (400-V dc) distribution systems are promising candidate of highly efficient and reliable distribution systems for data centers. To realize the 400-V dc distribution systems, development of methods for high-speed overcurrent protection is one of the important issues. In this paper, as a solution to this problem, a semiconductor dc circuit breaker using SiC static induction transistors (SiC-SIT's) is investigated. The SiC-SIT's has extremely low on-state resistance and very large safe operating area (SOA). These properties are attractive in the application of the circuit breakers for 400-V dc distribution systems. A novel control method of the gate voltage waveform to reduce the transient overvoltage and resonance during the interruption process is proposed. The effectiveness of the proposed method is confirmed by actual operation tests employing an experimental prototype of the dc distribution system. All of the result is confirmed by the fabricated SiC-SIT circuit breaker prototype.
Discrete Dielectric Reflectarray and Lens for E-Band With Different Feed

This letter presents the design and results of low-loss discrete dielectric flat reflectarray and lens for E-band. Using two different kinds of feed, 3-D-pyramidal (wideband) horn and 2 × 2 planar microstrip array (narrowband) antenna, the radiation performances of the two collimating structures are investigated. The discrete lens is optimized to cover the frequencies 71-86 GHz (71-76- and 81-86-GHz bands), while the discrete reflectarray is optimized to cover the 71-76-GHz band. The presented designs utilize the principle of perforated dielectric substrate using a square lattice of drilled holes of different radii and can be fabricated using standard printed circuit board (PCB) technology. The discrete lens has 41 × 41 unit cells and thickness of 6.35 mm, while the reflectarray has 40 × 40 unit cells and thickness of 3.24 mm. A good impedance matching ( |S11|<; -10 dB) and peak gain of 34 ±1 dB with maximum aperture efficiency of 44.6% are achieved over 71-86 GHz for the lens case. On the other hand, reflectarray with peak gain of 32 ±1 dB and aperture efficiency of 41.9% are achieved for 71-76-GHz band.
Impact of Data Normalization on Stock Index Forecasting

Forecasting the behavior of the financial market is a nontrivial task that relies on the discovery of strong empirical regularities in observations of the system. These regularities are often masked by noise and the financial time series often have nonlinear and non-stationary behavior. With the rise of artificial intelligence technology and the growing interrelated markets of the last two decades offering unprecedented trading opportunities, technical analysis simply based on forecasting models is no longer enough. To meet the trading challenge in today’s global market, technical analysis must be redefined. Before using the neural network models some issues such as data preprocessing, network architecture and learning parameters are to be considered. Data normalization is a fundamental data preprocessing step for learning from data before feeding to the Artificial Neural Network (ANN). Finding an appropriate method to normalize time series data is one of the most critical steps in a data mining process. In this paper we considered two ANN models and two neuro-genetic hybrid models for forecasting the closing prices of Indian stock market. The present pursuit evaluates impact of various normalization methods on four intelligent forecasting models i.e. a simple ANN model trained with gradient descent (ANN-GD), genetic algorithm (ANN-GA), and a functional link artificial neural network model trained with GD (FLANN-GD) and genetic algorithm (FLANN-GA). The present study is applied on daily closing price of Bombay stock exchange (BSE) and several empirical as well as experimental result shows that these models can be promising tools for the Indian stock market forecasting and the prediction performance of the models are strongly influenced by the data preprocessing method used.
Geometry-Based Camera Calibration Using Five-Point Correspondences From a Single Image

As an essential step in many computer vision tasks, camera calibration has been studied extensively. In this paper, we propose a novel calibration technique that, based on geometric analysis, camera parameters can be estimated effectively and accurately from just one view of only five corresponding points. Our core contribution is the geometric analysis for deriving the basic equations to realize camera calibration from four coplanar corresponding points and a fifth noncoplanar one. The position, orientation, and focal length of a zooming camera can be directly estimated with unique solution. The estimated parameters are further optimized by the bundle adjustment technique. The proposed calibration method is examined and evaluated on both computer simulated data and real images. The experimental results confirm the validity of the proposed method that camera parameters can be estimated with sufficient accuracy using just five-point correspondences from a single image, even in the presence of image noise.