nils-herrmann/clean_scirepeval huggingface dataset:
A fuzzy-logic based optical sensor for online weld defect-detection

This paper describes an intelligent optical sensor for real time defect detection in gas metal arc welding processes. The sensor measures the radiations emitted by the plasma surrounding the welding arc, and analyzes the information in real time to determine an index of local quality of the weld. The data processing algorithm encompasses a Kalman filter to reduce the heavy amount of noise affecting the measured signals, and an intelligent fuzzy system to assess the degree of acceptability of the weld. The fuzzy system is also able to detect the risk of specific problems (e.g., anomalies in the current, voltage or speed of the arc, contamination with other materials, holes) and the position of defects along the welding line. In an extensive experimental comparison, the fuzzy system outperforms a former version of the detection algorithm based on a statistical approach.
Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition

Machine learning is enabling a myriad innovations, including new algorithms for cancer diagnosis and self-driving cars. The broad use of machine learning makes it important to understand the extent to which machine-learning algorithms are subject to attack, particularly when used in applications where physical security or safety is at risk.
 In this paper, we focus on facial biometric systems, which are widely used in surveillance and access control. We define and investigate a novel class of attacks: attacks that are physically realizable and inconspicuous, and allow an attacker to evade recognition or impersonate another individual. We develop a systematic method to automatically generate such attacks, which are realized through printing a pair of eyeglass frames. When worn by the attacker whose image is supplied to a state-of-the-art face-recognition algorithm, the eyeglasses allow her to evade being recognized or to impersonate another individual. Our investigation focuses on white-box face-recognition systems, but we also demonstrate how similar techniques can be used in black-box scenarios, as well as to avoid face detection.
Vision-Based Autonomous Landing of an Unmanned Aerial Vehicle

We present the design and implementation of a realtime, vision-based landing algorithm for an autonomous helicopter. The helicopter is required to navigate from an initial position to a final position in a partially known environment based on GPS and vision, locate a landing target (a helipad of a known shape) and land on it. We use vision for precise target detection and recognition. The helicopter updates its landing target parameters based on vision and uses an on board behavior-based controller to follow a path to the landing site. We present results from flight trials in the field which demonstrate that our detection, recognition and control algorithms are accurate and repeatable.
A novel RGB-D SLAM algorithm based on points and plane-patches

In this work, we present a novel RGB-D SLAM algorithm. The novelty of the proposed algorithm lies in the use of both feature points and plane patches for pose estimation. A plane patch is defined as a small-sized patch constructed by using a feature point with small curvature. The feature points with small curvature are called plane points. The remaining feature points are classified as either smooth points which represent smooth surfaces or structural points which represent edges or corners of some objects. Two criteria (coplanarity and overlap) are used to match the plane patches from two frames. The proposed algorithm is able to weight various types of feature points in order to improve the robustness of SLAM algorithms in favouring plane patches but not the isolated feature points, and maintain the real-time performance of SLAM algorithms in favouring small plane patches, but not the large size of planes. We evaluate the proposed algorithm on multiple benchmark datasets. A large scale experiment is also presented to show the robustness of our algorithm.
The Mediating Role of Customer Trust on Customer Loyalty

The review of the literature reveals various relationships between the constructs of social identity, customer trust, and customer loyalty. Such results lead to questioning the nature of the conceptual structure that would include these constructs as well as their interrelationships in a structural model. From this perspective, the purpose of this study is to evaluate the mediating role of customer trust on customer loyalty in presence of corporate social identity in the context of financial institutions. Using data collected from 1296 customers-members of credit unions, the results of this study help to legitimize the idea that customer trust intervenes as a mediating variable that enhances the impact of corporate identity, corporate image and the reputation of the firm on customer loyalty. The implications of the study are discussed from both the research and managerial perspectives.
Developing a Methodology of Structuring and Layering Technological Information in Patent Documents through Natural Language Processing

Since patents contain various types of objective technological information, they are used to identify the characteristics of technology fields. Text mining in patent analysis is employed in various fields such as trend analysis and technology classification, and knowledge flow among technologies. However, since keyword-based text mining has the limitation whereby, when screening useful keywords, it frequently omits meaningful keywords, analyzers therefore need to repeat the careful scrutiny of the derived keywords to clarify the meaning of keywords. In this research, we structure meaningful keyword sets related to technological information from patent documents; then we layer the keywords, depending on the level of information. This research involves two steps. First, the characteristics of technological information are analyzed by reviewing the patent law and investigating the description of patent documents. Second, the technological information is structured by considering the information types, and the keywords in each type are layered through natural language processing. Consequently, the structured and layered keyword set does not omit useful keywords and the analyzer can easily understand the meaning of each keyword.
A Methodology for Increasing Business Process Maturity in Public Sector

Non-profit organizations such as public sector organizations have some specifics, which make renovation projects significantly different. It is clear that radical changes in the execution of business processes and in the organizational structures are not suitable for the public sector because they are not possible for many, also political, reasons. A business process change in public sector mostly means unification of business processes, automation of some activities and elimination of some unnecessary ones. Organizational changes are achievable only to a certain limit. Therefore classic business process renovation methodologies have to be adapted. The proposed methodology that is presented in the paper has been successfully applied in a process change project at one of the Slovene Ministries, which is presented as a case study. Activities, techniques, and tools are proposed for each of the six traditional business process change project’s stages. Problems and topics to which special attention has to be paid are also discussed.
Mind Reading: Neural Mechanisms of Theory of Mind and Self-Perspective

Human self-consciousness as the metarepresentation of ones own mental states and the so-called theory of mind (TOM) capacity, which requires the ability to model the mental states of others, are closely related higher cognitive functions. We address here the issue of whether taking the self-perspective (SELF) or modeling the mind of someone else (TOM) employ the same or differential neural mechanisms. A TOM paradigm was used and extended to include stimulus material that involved TOM and SELF capacities in a two-way factorial design. A behavioral study in 42 healthy volunteers showed that TOM and SELF induced differential states of mind: subjects assigned correctly first or third person pronouns when providing responses to the stimuli. Following the behavioral study, we used functional magnetic resonance imaging (fMRI) in eight healthy, right-handed males to study the common and differential neural mechanisms underlying TOM and SELF. The main factor TOM led to increased neural activity in the anterior cingulate cortex and left temporopolar cortex. The main factor SELF led to increased neural activity in the right temporoparietal junction and in the anterior cingulate cortex. A significant interaction of both factors TOM and SELF was observed in the right prefrontal cortex. These divergent neural activations in response to TOM and SELF suggest that these important differential mental capacities of human self-consciousness are implemented at least in part in distinct brain regions.
YouTube-8M: A Large-Scale Video Classification Benchmark

Many recent advancements in Computer Vision are attributed to large datasets. Open-source software packages for Machine Learning and inexpensive commodity hardware have reduced the barrier of entry for exploring novel approaches at scale. It is possible to train models over millions of examples within a few days. Although large-scale datasets exist for image understanding, such as ImageNet, there are no comparable size video classification datasets. In this paper, we introduce YouTube-8M, the largest multi-label video classification dataset, composed of∼8 million videos—500K hours of video—annotated with a vocabulary of 4800 visual entities. To get the videos and their (multiple) labels, we used a YouTube video annotation system, which labels videos with the main topics in them. While the labels are machine-generated, they have high-precision and are derived from a variety of human-based signals including metadata and query click signals, so they represent an excellent target for content-based annotation approaches. We filtered the video labels (Knowledge Graph entities) using both automated and manual curation strategies, including asking human raters if the labels are visually recognizable. Then, we decoded each video at one-frame-per-second, and used a Deep CNN pretrained on ImageNet to extract the hidden representation immediately prior to the classification layer. Finally, we compressed the frame features and make both the features and video-level labels available for download. The dataset contains frame-level features for over 1.9 billion video frames and 8 million videos, making it the largest public multi-label video dataset. We trained various (modest) classification models on the dataset, evaluated them using popular evaluation metrics, and report them as baselines. Despite the size of the dataset, some of our models train to convergence in less than a day on a single machine using the publicly-available TensorFlow framework. We plan to release code for training a basic TensorFlow model and for computing metrics. We show that pre-training on large data generalizes to other datasets like Sports-1M and ActivityNet. We achieve state-of-the-art on ActivityNet, improving mAP from 53.8% to 77.6%. We hope that the unprecedented scale and diversity of YouTube-8M will lead to advances in video understanding and representation learning.
Prepubertal hidradenitis suppurativa: two case reports and review of the literature.

Hidradenitis suppurativa (HS) is a chronic suppurative scarring disease of apocrine sweat gland-bearing skin in the axillary, anogenital, and, rarely, the breast and scalp regions. Females are more commonly affected than males and it is usually seen at puberty or later. We report two girls with prepubertal hidradenitis suppurativa whose initial presentation predated any signs of puberty. This early onset is very rare and its etiology remains unknown. Severe disease can be seen in prepubertal children and surgical intervention is effective in these cases.
Fast moving object detection with non-stationary background

The detection of moving objects under a free-moving camera is a difficult problem because the camera and object motions are mixed together and the objects are often detected into the separated components. To tackle this problem, we propose a fast moving object detection method using optical flow clustering and Delaunay triangulation as follows. First, we extract the corner feature points using Harris corner detector and compute optical flow vectors at the extracted corner feature points. Second, we cluster the optical flow vectors using K-means clustering method and reject the outlier feature points using Random Sample Consensus algorithm. Third, we classify each cluster into the camera and object motion using its scatteredness of optical flow vectors. Fourth, we compensate the camera motion using the multi-resolution block-based motion propagation method and detect the objects using the background subtraction between the previous frame and the motion compensated current frame. Finally, we merge the separately detected objects using Delaunay triangulation. The experimental results using Carnegie Mellon University database show that the proposed moving object detection method outperforms the existing other methods in terms of detection accuracy and processing time.
Accurate Singular Values of Bidiagonal Matrices

C Abstract om puting the singular valu es of a bidiagonal m atrix is the fin al phase of the stan dard algo-w rithm for the singular valu e decom position of a general m atrix. We present a ne w algorithm hich com putes all the singular valu es of a bidiagonal m atrix to high relative accuracy indepen-p dent of their m agn itudes. In contrast, the stan dard algorithm for bidiagonal m atrice s m ay com ute sm all singular valu es with no relative accuracy at all. Num erical experim ents sh ow th at K the new algorithm is com parable in speed to the stan dard algorithm , an d frequently faster. The stan dard algorithm for com puting the singular valu e decom position (SVD) of a gen-1 eral real m atrix A has two phases [7]:) Com pute orthogonal m atrices P an d Q su ch that B = P AQ is in bidiagonal form , i.e. 1 1 1 T 1. 2 has nonzero entries only on its diagonal an d first su perdiagonal) Com pute orthogonal m atrices P an d Q su ch that Σ = P BQ is diagonal an d nonnega-t i 2 2 2 T 2 ive. The diagonal entries σ of Σ are the singular values of A. We will take them to be-sorted in decreasing order: σ ≥ σ. The colum ns of Q = Q Q are the right singular vec i i + 1 1 2 t 1 2 ors, an d the colum ns of P= P P are the left singular vectors. n This process takes O (n) operations, where n is the dim ension of A. This is true eve 3 though Phase 2 is iterative, since it converges quickly in practice. The error an alysis o f this r u com bined procedure has a widely accepted conclusion , an d provided neither overflow no nderflow occurs m ay be su m m arized as follows: The com puted singular valu es σ differ from the true singular valu es of A by no m ore t 1 i han p (n). ε. A , where A = σ is the 2-norm of A, ε is the m ach ine precision, an d p (n) is a T slowly growing function of the dim ension n …
Sliding mode control of the DC-DC flyback converter with zero steady-state error

In this paper a novel approach for controlling the output voltage of the isolated flyback converter is presented using sliding mode controller. Due to non-minimum-phase nature of the converter and presence of a right-half-plan-zero in voltage transfer function, indirect regulation of the output voltage is applied. In the designed controller, simultaneous use of output voltage and transformer current feedbacks improves dynamic response of the controller. The sliding mode controller is developed so that in the final control law, integral of the output voltage error is present and consequently the output voltage error will be zero in the steady-state. Also, in order to measure transformer magnetizing current, simple and efficient method is proposed. Finally, in order to evaluate response of the controller, flyback converter is a simulated using MATLAB / Simulink.
Detecting Type and Size of Road Crack with the Smartphone

Detecting crack type and crack size is crucial for road maintenance and management. Mobile crowd sensing is a new way to collect the information of cracks on roads. We propose a system named CrackDetector to detect cracks and estimate their types and size with smart phone in this paper. The type of a crack (i.e., horizontal crack, vertical crack, net crack) is determined by a coordinate transmission method based on three directions, including the direction of the crack in the photo (estimated through an image processing method), the 3D shooting direction (i.e., the rotation of the smartphone while photographing) and the direction of the road (obtained from the OpenStreetMap road network). In order to estimate the size of a crack (i.e., the width and the length of the crack), we use the camera’s convex lens imaging theory and readings of the accelerometer and the magnetometer sensor. We collected pictures of 152 cracks with a specifically-developed android application by 8 volunteers. Experimental results show that our approach achieves an accuracy of 90.1% in crack type detection. Meanwhile, we get a 3.2cm RMSE error when estimating the crack width and a 13.2cm RMSE error when estimating the crack length.
Easily Extendable Compact Planar UWB MIMO Antenna Array

This letter presents a highly isolated compact four-element planar ultrawideband (UWB) multiple-input–multiple-output (MIMO) antenna array configuration. The main advantages of the proposed array configuration are that it requires no isolation/decoupling circuit and the configuration is easily extendable to larger size array. The array consists of novel miniaturized slotted annular ring monopole antenna and each element in the array is placed orthogonal to its adjacent elements. The fabricated structure provides good impedance bandwidth matching and high isolation between elements over the range from 3 to 15 GHz. The absence of decoupling circuit results in overall compact size of the proposed design. The prototypes are fabricated and tested. The simulated and measured results are in good agreement. Moreover, the envelope correlation coefficient and channel capacity loss of the array are calculated, which shows good MIMO performance. The proposed monopole antenna structure supports multielement UWB MIMO antenna array design with easy extension of elements and without any decoupling circuit. An example of eight-element array is also investigated.
Idiopathic isolated clitoromegaly: A report of two cases

Clitoromegaly is a frequent congenital malformation, but acquired clitoral enlargement is relatively rare.  Two acquired clitoromegaly cases treated in Ataturk Training Hospital, Izmir, Turkey are presented.  History from both patients revealed clitoromegaly over the last three years. Neither gynecological nor systemic abnormalities were detected in either patient. Karyotype analyses and hormonal tests were normal. Abdominal and gynaecological ultrasound did not show any cystic lesion or other abnormal finding. Computerized tomography scan of the adrenal glands was normal. Clitoroplasty with preservation of neurovascular pedicles was performed for the treatment of clitoromegaly.  The patients were diagnosed as "idiopathic isolated" clitoromegaly. To the best of our knowledge, there has been no detailed report about idiopathic clitoromegaly in the literature.
Classifying Data Streams with Skewed Class Distributions and Concept Drifts

Classification is an important data analysis tool that uses a model built from historical data to predict class labels for new observations. More and more applications are featuring data streams, rather than finite stored data sets, which are a challenge for traditional classification algorithms. Concept drifts and skewed distributions, two common properties of data stream applications, make the task of learning in streams difficult. The authors aim to develop a new approach to classify skewed data streams that uses an ensemble of models to match the distribution over under-samples of negatives and repeated samples of positives.
Aberrant Behavior Detection in Time Series for Network Monitoring

The open-source software RRDtool and Cricket provide a solution to the problem of collecting, storing, and visualizing service network time series data for the real-time monitoring task. However, simultaneously monitoring all service network time series of interest is an impossible task even for the accomplished network technician. The solution is to integrate a mathematical model for automatic aberrant behavior detection in time series into the monitoring software. While there are many such models one might choose, the primary goal should be a model compatible with real-time monitoring. At WebTV, the solution was to integrate a model based on exponential smoothing and Holt-Winters forecasting into the Cricket/RRDtool architecture. While perhaps not optimal, this solution is flexible, efficient, and effective as a tool for automatic aberrant behavior detection.
Examining the benefits and challenges of using audience response systems: A review of the literature

0360-1315/$ see front matter Crown Copyright 2 doi:10.1016/j.compedu.2009.05.001 * Corresponding author.  Audience response systems (ARSs) permit students to answer electronically displayed multiple choice questions using a remote control device. All responses are instantly presented, in chart form, then reviewed and discussed by the instructor and the class. A brief history of ARSs is offered including a discussion of the 26 labels used to identify this technology. Next a detailed review of 67 peer-reviewed papers from 2000 to 2007 is offered presenting the benefits and challenges associated with the use of an ARS. Key benefits for using ARSs include improvements to the classroom environment (increases in attendance, attention levels, participation and engagement), learning (interaction, discussion, contingent teaching, quality of learning, learning performance), and assessment (feedback, formative, normative). The biggest challenges for teachers in using ARSs are time needed to learn and set up the ARS technology, creating effective ARS questions, adequate coverage of course material, and ability to respond to instantaneous student feedback. Student challenges include adjusting to a new method of learning, increased confusion when multiple perspectives are discussed, and negative reactions to being monitored. It is concluded that more systematic, detailed research is needed in a broader range of contexts.
The Role of Haptics in Medical Training Simulators: A Survey of the State of the Art

This review paper discusses the role of haptics within virtual medical training applications, particularly, where it can be used to aid a practitioner to learn and practice a task. The review summarizes aspects to be considered in the deployment of haptics technologies in medical training. First, both force/torque and tactile feedback hardware solutions that are currently produced commercially and in academia are reviewed, followed by the available haptics-related software and then an in-depth analysis of medical training simulations that include haptic feedback. The review is summarized with scrutiny of emerging technologies and discusses future directions in the field.