nils-herrmann/clean_scirepeval huggingface dataset:
Automatic Identification of Defect Patterns in Semiconductor Wafer Maps Using Spatial Correlogram and Dynamic Time Warping

A wafer map is a graphical illustration of the locations of defective chips on a wafer. Defective chips are likely to exhibit a spatial dependence across the wafer map, which contains useful information on the process of integrated circuit (IC) fabrication. An analysis of wafer map data helps to better understand ongoing process problems. This paper proposes a new methodology in which spatial correlogram is used for the detection of the presence of spatial autocorrelations and for the classification of defect patterns on the wafer map. After the detection of spatial autocorrelation based on our proposed spatial randomness test using spatial correlogram, the dynamic time warping algorithm which provides nonlinear alignments between two sequences to find optimal warping path is adopted for the automatic classification of spatial patterns based on spatial correlogram. We also develop generalized join-count (JC)-based statistics and then propose a procedure to determine the optimal weights of JC-based statistics. The proposed method is illustrated using real-life examples and simulated data sets. The experimental results show that our method is robust to random noise and has a robust performance regardless of defect location and size.
A Multi-Stage Strategy to Perspective Rectification for Mobile Phone Camera-Based Document Images

Document images captured by a mobile phone camera often have perspective distortions. Efficiency and accuracy are two important issues in designing a rectification system for such perspective documents. In this paper, we propose a new perspective rectification system based on vanishing point detection. This system achieves both the desired efficiency and accuracy using a multi-stage strategy: at the first stage, document boundaries and straight lines are used to compute vanishing points; at the second stage, text baselines and block aligns are utilized; and at the last stage, character tilt orientations are voted for the vertical vanishing point. A profit function is introduced to evaluate the reliability of detected vanishing points at each stage. If vanishing points at one stage are reliable, then rectification is ended at that stage. Otherwise, our method continues to seek more reliable vanishing points in the next stage. We have tested this method with more than 400 images including paper documents, signboards and posters. The image acceptance rate is more than 98.5% with an average speed of only about 60 ms.
A Recurrent Neural Network Solution for Predicting Driver Intention at Unsignalized Intersections

In this letter, we present a system capable of inferring intent from observed vehicles traversing an unsignalized intersection, a task critical for the safe driving of autonomous vehicles, and beneficial for advanced driver assistance systems. We present a prediction method based on recurrent neural networks that takes data from a Lidar-based tracking system similar to those expected in future smart vehicles. The model is validated on a roundabout, a popular style of unsignalized intersection in urban areas. We also present a very large naturalistic dataset recorded in a typical intersection during two days of operation. This comprehensive dataset is used to demonstrate the performance of the algorithm introduced in this letter. The system produces excellent results, giving a significant 1.3-s prediction window before any potential conflict occurs.
A comprehensive view of Hadoop research - A systematic literature review

In recent years, the valuable knowledge that can be retrieved from petabyte scale datasets – known as Big Data – led to the development of solutions to process information based on parallel and distributed computing. Lately, Apache Hadoop has attracted strong attention due to its applicability to Big Data processing.  The support of Hadoop by the research community has provided the development of new features to the framework. Recently, the number of publications in journals and conferences about Hadoop has increased consistently, which makes it difficult for researchers to comprehend the full body of research and areas that require further investigation.  We conducted a systematic literature review to assess research contributions to Apache Hadoop. Our objective was to identify gaps, providing motivation for new research, and outline collaborations to Apache Hadoop and its ecosystem, classifying and quantifying the main topics addressed in the literature.  Our analysis led to some relevant conclusions: many interesting solutions developed in the studies were never incorporated into the framework; most publications lack sufficient formal documentation of the experiments conducted by authors, hindering their reproducibility; finally, the systematic review presented in this paper demonstrates that Hadoop has evolved into a solid platform to process large datasets, but we were able to spot promising areas and suggest topics for future research within the framework. & 2014 Elsevier Ltd. All rights reserved.
Automated testing of GUI applications: Models, tools, and controlling flakiness

System testing of applications with graphical user interfaces (GUIs) such as web browsers, desktop or mobile apps, is more complex than testing from the command line. Specialized tools are needed to generate and run test cases, models are needed to quantify behavioral coverage, and changes in the environment, such as the operating system, virtual machine or system load, as well as starting states of the executions, impact the repeatability of the outcome of tests making tests appear flaky. In this tutorial, we present an overview of the state of the art in GUI testing, consisting of both lectures and demonstrations on various platforms (desktop, web and mobile applications), using an open source testing tool, GUITAR. We show how to setup a system under test, how to extract models without source code, and how to then use those models to generate and replay test cases. We then present a lecture on the various factors that may cause flakiness in the execution of GUI-centric software, and hence impact the results of analyses and experiments based on such software. We end with a demonstration of a community resource for sharing GUI testing artifacts aimed at controlling these factors. This tutorial targets both researchers who develop techniques for testing GUI software, and practitioners from industry who want to learn more about model-based GUI testing or who run and rerun GUI tests and often find their runs are flaky.
Processing billions of RDF triples on a single machine using streaming and sorting

We consider the feasibility of processing billions of RDF triples on a single commodity machine using streaming and sorting techniques and focusing on RDF processing tasks relevant for Linked Data consumption: data filtering and transformation, RDFS inference, owl:sameAs smushing and statistics extraction. To investigate this research question we built RDFpro (rdf processor), an open source tool that provides streaming and sorting-based processors for the considered tasks and allows their sequential and parallel composition in complex pipelines. an empirical evaluation of RDFpro in four application scenario---dataset analysis, filtering, merging and massaging---shows the effectiveness of the tool and allows to positively answer our research question.
A Micropower Chopper—CDS Operational Amplifier

A low-power spur-free precision amplifier, which uses input chopping and correlated double sampling for demodulation, is presented. This circuit employs an AC coupling between the first and the second stage that removes the first stage offset without causing ripple. The input rail-to-rail circuit, fabricated in a mixed 0.18-0.5 μm CMOS technology, operates with supply ranging from 1.8 V to 5 V. The circuit achieves a simulated 168-dB DC gain with an overall current consumption of 14.4 μA. The measured offset voltage over the available samples results in a distribution with 2-μV standard deviation. The obtained input noise density at low-frequency equal to 37 nV/√Hz gives a 5.5 noise efficiency factor.
Image Feature Representation of the Subband Power Distribution for Robust Sound Event Classification

The ability to automatically recognize a wide range of sound events in real-world conditions is an important part of applications such as acoustic surveillance and machine hearing. Our approach takes inspiration from both audio and image processing fields, and is based on transforming the sound into a two-dimensional representation, then extracting an image feature for classification. This provided the motivation for our previous work on the spectrogram image feature (SIF). In this paper, we propose a novel method to improve the sound event classification performance in severe mismatched noise conditions. This is based on the subband power distribution (SPD) image - a novel two-dimensional representation that characterizes the spectral power distribution over time in each frequency subband. Here, the high-powered reliable elements of the spectrogram are transformed to a localized region of the SPD, hence can be easily separated from the noise. We then extract an image feature from the SPD, using the same approach as for the SIF, and develop a novel missing feature classification approach based on a nearest neighbor classifier (kNN). We carry out comprehensive experiments on a database of 50 environmental sound classes over a range of challenging noise conditions. The results demonstrate that the SPD-IF is both discriminative over the broad range of sound classes, and robust in severe non-stationary noise.
Fuzzy semantic distance measures between ontological concepts

An emphasis has been placed on the use of ontologies for representing application domain knowledge. Determining a degree or measure of semantic similarity, semantic distance, or semantic relatedness between concepts from different systems or domains, is becoming an increasingly important task. This paper presents a brief overview of such measures between concepts within ontological representations and provides several examples of such measures found in the research literature. These measures are then examined within the framework of fuzzy set similarity measures. The use of a semantic similarity measure between elements that are part of a domain for which an ontological structure exists is explored in order to extend standard fuzzy set compatibility measures.
Long Short Term Memory Recurrent Neural Network Classifier for Intrusion Detection

Due to the advance of information and communication techniques, sharing information through online has been increased. And this leads to creating the new added value. As a result, various online services were created. However, as increasing connection points to the internet, the threats of cyber security have also been increasing. Intrusion detection system(IDS) is one of the important security issues today. In this paper, we construct an IDS model with deep learning approach. We apply Long Short Term Memory(LSTM) architecture to a Recurrent Neural Network(RNN) and train the IDS model using KDD Cup 1999 dataset. Through the performance test, we confirm that the deep learning approach is effective for IDS.
Hidden Markov Measure Field Models for Image Segmentation

Parametric image segmentation consists of finding a label field that defines a partition of an image into a set of nonoverlapping regions and the parameters of the models that describe the variation of some property within each region. A new Bayesian formulation for the solution of this problem is presented, based on the key idea of using a doubly stochastic prior model for the label field, which allows one to find exact optimal estimators for both this field and the model parameters by the minimization of a differentiable function. An efficient minimization algorithm and comparisons with existing methods on synthetic images are presented, as well as examples of realistic applications to the segmentation of Magnetic Resonance volumes and to motion segmentation.
Structural equation modeling: strengths, limitations, and misconceptions.

Because structural equation modeling (SEM) has become a very popular data-analytic technique, it is important for clinical scientists to have a balanced perception of its strengths and limitations. We review several strengths of SEM, with a particular focus on recent innovations (e.g., latent growth modeling, multilevel SEM models, and approaches for dealing with missing data and with violations of normality assumptions) that underscore how SEM has become a broad data-analytic framework with flexible and unique capabilities. We also consider several limitations of SEM and some misconceptions that it tends to elicit. Major themes emphasized are the problem of omitted variables, the importance of lower-order model components, potential limitations of models judged to be well fitting, the inaccuracy of some commonly used rules of thumb, and the importance of study design. Throughout, we offer recommendations for the conduct of SEM analyses and the reporting of results.
Indian Paper Currency Authentication System using Image processing

Over the past few years, as a result of the great technological advances in color printing, duplicating and scanning, counterfeiting problems have become more and more serious. In the past, only the printing house has the ability to make counterfeit paper currency, but today it is possible for any person to print counterfeit bank notes simply by using a computer and a laser printer at house. Therefore the issue of efficiently distinguishing counterfeit banknotes from genuine ones via automatic machines has become more and more important. Counterfeit notes are a problem of almost every country but India has been hit really hard and has become a very acute problem. There is a need to design a system that is helpful in recognition of paper currency notes with fast speed and in less time. This proposed system describes an approach for verification of Indian currency banknotes. The currency will be verified by using image processing techniques. In this article, six characteristic features are extracted. The approach consists of a number of components including image processing, edge detection, image segmentation, characteristic extraction, comparing images. The characteristics extraction is performed on the image of the currency and it is compared with the characteristics of the genuine currency. The Sobel operator with gradient magnitude is used for characteristic extraction. Paper currency recognition with good accuracy and high processing speed has great importance for banking system. MATLAB is used to extract the characteristic features of paper currency. The proposed method has advantages of simplicity and high speed. The result will be whether currency is genuine or counterfeit.
Gender Representation in Cinematic Content: A Multimodal Approach

The goal of this paper is to enable an objective understanding of gender portrayals in popular films and media through multimodal content analysis. An automated system for analyzing gender representation in terms of screen presence and speaking time is developed. First, we perform independent processing of the video and the audio content to estimate gender distribution of screen presence at shot level, and of speech at utterance level. A measure of the movie's excitement or intensity is computed using audiovisual features for every scene. This measure is used as a weighting function to combine the gender-based screen/speaking time information at shot/utterance level to compute gender representation for the entire movie. Detailed results and analyses are presented on seventeen full length Hollywood movies.
Recipes for the linear analysis of EEG

In this paper, we describe a simple set of "recipes" for the analysis of high spatial density EEG. We focus on a linear integration of multiple channels for extracting individual components without making any spatial or anatomical modeling assumptions, instead requiring particular statistical properties such as maximum difference, maximum power, or statistical independence. We demonstrate how corresponding algorithms, for example, linear discriminant analysis, principal component analysis and independent component analysis, can be used to remove eye-motion artifacts, extract strong evoked responses, and decompose temporally overlapping components. The general approach is shown to be consistent with the underlying physics of EEG, which specifies a linear mixing model of the underlying neural and non-neural current sources.
SLAM-Loop Closing with Visually Salient Features

Within the context of Simultaneous Localisation and Mapping (SLAM), “loop closing” is the task of deciding whether or not a vehicle has, after an excursion of arbitrary length, returned to a previously visited area. Reliable loop closing is both essential and hard. It is without doubt one of the greatest impediments to long term, robust SLAM. This paper illustrates how visual features, used in conjunction with scanning laser data, can be used to a great advantage. We use the notion of visual saliency to focus the selection of suitable (affine invariant) image-feature descriptors for storage in a database. When queried with a recently taken image the database returns the capture time of matching images. This time information is used to discover loop closing events. Crucially this is achieved independently of estimated map and vehicle location. We integrate the above technique into a SLAM algorithm using delayed vehicle states and scan matching to form interpose geometric constraints. We present initial results using this system to close loops (around 100m) in an indoor environment.
Life skill interventions with homeless youth, domestic violence victims and adults with mental illness.

This paper presents three exploratory studies of life skills interventions (employment, money management or food/nutrition) with 73 homeless individuals from four shelters and supportive housing programs located in the urban Midwest for youth, victims of domestic violence and adults with mental illness. The Ansell Casey Life Skills Assessment was administered prior to the eight group and individual sessions. Quizzes and posttests indicated clinical change in all groups, with statistical significance in the domestic violence group. The intervention implementation, challenges encountered, and strategies developed for implementing shelter-based interventions are discussed. Recommendations for successfully providing collaborative university-shelter clinical interventions are provided.
Replacing a Swiss ball for an exercise bench causes variable changes in trunk muscle activity during upper limb strength exercises

The addition of Swiss balls to conventional exercise programs has recently been adopted. Swiss balls are an unstable surface which may result in an increased need for force output from trunk muscles to provide adequate spinal stability or balance. The aim of the study was to determine whether the addition of a Swiss ball to upper body strength exercises results in consistent increases in trunk muscle activation levels.



The myoelectric activity of four trunk muscles was quantified during the performance of upper body resistance exercises while seated on both a stable (exercise bench) and labile (swiss ball) surface. Participants performed the supine chest press, shoulder press, lateral raise, biceps curl and overhead triceps extension. A repeated measures ANOVA with post-hoc Tukey test was used to determine the influence of seated surface type on muscle activity for each muscle.



There was no statistically significant (p < .05) difference in muscle activity between surface conditions. However, there was large degree of variability across subjects suggesting that some individuals respond differently to surface stability. These findings suggest that the incorporation of swiss balls instead of an exercise bench into upper body strength training regimes may not be justified based only on the belief that an increase spinal stabilizing musculature activity is inherent. Biomechanically justified ground based exercises have been researched and should form the basis for spinal stability training as preventative and therapeutic exercise training regimes.



Selected trunk muscle activity during certain upper limb strength training exercises is not consistently influenced by the replacement of an exercise bench with a swiss ball.
Trait Emotional Intelligence Theory

In addition to trait EI, Cherniss identifies three other EI models whose main limitations must be succinctly mentioned, not least because they provided the impetus for the development of the trait EI model. Bar-On’s (1997) model is predicated on the problematic assumption that emotional intelligence (or ‘‘ability’’ or ‘‘competence’’ or ‘‘skill’’ or ‘‘potential’’—terms that appear to be used interchangeably in his writings) can be validly assessed through self-report questions of the type ‘‘It is easy for me to understand my emotions.’’ Psychometrically, as pointed out in Petrides and Furnham (2001), this is not a viable position because such self-report questions can only be tapping into self-perceptions rather than into abilities or competencies. This poses a fundamental threat to the validity of this model, far more serious than the pervasive faking problem noted by several authors (e.g., Grubb & McDaniel, 2008). Goleman’s (1995) model is difficult to evaluate scientifically because of its reliance on
Optimal power control in interference-limited fading wireless channels with outage-probability specifications

We propose a new method of power control for interference limited wireless networks with Rayleigh fading of both the desired and interference signals. Our method explictly takes into account the statistical variation of both the received signal and interference power, and optimally allocates power subject to constraints on the probability of fading induced outage for each transmitter/receiver pair. We establish several results for this type of problem. For the case in which the only constraints are those on the outage probabilities, we give a fast iterative method for finding the optimal power allocation. We establish tight bounds that relate the outage probability caused by channel fading to the signal-to-interference margin calculated when the statistical variation of the signal and intereference powers are ignored. This allows us to show that well-known methods for allocating power, based on Perron-Frobenius eigenvalue theory, can be used to determine power allocations that are provably close to achieving optimal (i.e., minimal) outage probability. In the most general case, which includes bounds on powers and other constraints, we show that the power control problem can be posed as a geometric program, which is a special type of optimization problem that can be transformed to a nonlinear convex optimization by a change of variables, and therefore solved globally and efficiently by recently developed interior-point methods.