nils-herrmann/clean_scirepeval huggingface dataset:
Geocoding location expressions in Twitter messages: A preference learning method

Resolving location expressions in text to the correct physical location, also known as geocoding or grounding, is complicated by the fact that so many places around the world share the same name. Correct resolution is made even more difficult when there is little context to determine which place is intended, as in a 140-character Twitter message, or when location cues from different sources conflict, as may be the case among different metadata fields of a Twitter message. We used supervised machine learning to weigh the different fields of the Twitter message and the features of a world gazetteer to create a model that will prefer the correct gazetteer candidate to resolve the extracted expression. We evaluated our model using the F1 measure and compared it to similar algorithms. Our method achieved results higher than state-of-the-art competitors.
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting

Timely accurate traffic forecast is crucial for urban traffic control and guidance. Due to the high nonlinearity and complexity of traffic flow, traditional methods cannot satisfy the requirements of mid-and-long term prediction tasks and often neglect spatial and temporal dependencies. In this paper, we propose a novel deep learning framework, Spatio-Temporal Graph Convolutional Networks (STGCN), to tackle the time series prediction problem in traffic domain. Instead of applying regular convolutional and recurrent units, we formulate the problem on graphs and build the model with complete convolutional structures, which enable much faster training speed with fewer parameters. Experiments show that our model STGCN effectively captures comprehensive spatio-temporal correlations through modeling multi-scale traffic networks and consistently outperforms state-of-the-art baselines on various real-world traffic datasets.
Prognostics of Combustion Instabilities from Hi-speed Flame Video using A Deep Convolutional Selective Autoencoder

The thermo-acoustic instabilities arising in combustion processes cause significant deterioration and safety issues in various human-engineered systems such as land and air based gas turbine engines. The phenomenon is described as selfsustaining and having large amplitude pressure oscillations with varying spatial scales of periodic coherent vortex shedding. Early detection and close monitoring of combustion instability are the keys to extending the remaining useful life (RUL) of any gas turbine engine. However, such impending instability to a stable combustion is extremely difficult to detect only from pressure data due to its sudden (bifurcationtype) nature. Toolchains that are able to detect early instability occurrence have transformative impacts on the safety and performance of modern engines. This paper proposes an endto-end deep convolutional selective autoencoder approach to capture the rich information in hi-speed flame video for instability prognostics. In this context, an autoencoder is trained to selectively mask stable flame and allow unstable flame image frames. Performance comparison is done with a wellknown image processing tool, conditional random field that is trained to be selective as well. In this context, an informationtheoretic threshold value is derived. The proposed framework is validated on a set of real data collected from a laboratory scale combustor over varied operating conditions where it is shown to effectively detect subtle instability features as a combustion process makes transition from stable to unstable region. ∗corresponding author Adedotun Akintayo et al.
Identification of Refused Bequest Code Smells

Accumulated technical debt can be alleviated by means of refactoring application aiming at architectural improvement. A prerequisite for wide scale refactoring application is the automated identification of the corresponding refactoring opportunities, or code smells. One of the major architectural problems that has received limited attention is the so called 'Refused Bequest' which refers to inappropriate use of inheritance in object-oriented systems. This code smell occurs when subclasses do not take advantage of the inherited behavior, implying that replacement by delegation should be used instead. In this paper we propose a technique for the identification of Refused Bequest code smells whose major novelty lies in the intentional introduction of errors in the inherited methods. The essence of inheritance is evaluated by exercising the system's functionality through the corresponding unit tests in order to reveal whether inherited methods are actually employed by clients. Based on the results of this approach and other structural information, an indication of the smell strength on a 'thermometer' is obtained. The proposed approach has been implemented as an Eclipse plug in.
Which Process Metrics Can Significantly Improve Defect Prediction Models? An Empirical Study

The knowledge about the software metrics which serve as defect indicators is vital for the efficient allocation of resources for quality assurance. It is the process metrics, although sometimes difficult to collect, which have recently become popular with regard to defect prediction. However, in order to identify rightly the process metrics which are actually worth collecting, we need the evidence validating their ability to improve the product metric-based defect prediction models. This paper presents an empirical evaluation in which several process metrics were investigated in order to identify the ones which significantly improve the defect prediction models based on product metrics. Data from a wide range of software projects (both, industrial and open source) were collected. The predictions of the models that use only product metrics (simple models) were compared with the predictions of the models which used product metrics, as well as one of the process metrics under scrutiny (advanced models). To decide whether the improvements were significant or not, statistical tests were performed and effect sizes were calculated. The advanced defect prediction models trained on a data set containing product metrics and additionally Number of Distinct Committers (NDC) were significantly better than the simple models without NDC, while the effect size was medium and the probability of superiority (PS) of the advanced models over simple ones was high (p = .016, r = .29, PS = .76), which is a substantial finding useful in defect prediction. A similar result with slightly smaller PS was achieved by the advanced models trained on a data set containing product metrics and additionally all of the investigated process metrics (p = .038, r = -.29, PS = .68). The advanced models trained on a data set containing product metrics and additionally Number of Modified Lines (NML) were significantly better than the simple models without NML, but the effect size was small (p = .038, r = .06). Hence, it is reasonable to recommend the NDC process metric in building the defect prediction models.
Classification of Fingerprint Images

Automatic fingerprint identification is one of the most important biometric technology. In order to efficiently match fingerprints in a large database, an indexing scheme is necessary. Fingerprint classification, which refers to assigning a fingerprint image into a number of pre-specified classes, provides a feasible indexing mechanism. In practice, however, large intraclass and small interclass variations in global pattern configuration and poor quality of fingerprint images make the classification problem very difficult. A fingerprint classification algorithm requires a robust feature extractor which should be able to reliablely extract salient features from input images. We present a fingerprint classification algorithm with an improved feature extraction algorithm and a novel classification scheme. This algorithm has been tested on the NIST-4 fingerprint database. For the 4,000 images in this database, error rates of 12.5% for the five-class problem and 7.7% for the four-class problem have been achieved. With a 20% reject rate (which eliminates most of the poor quality images in the database), the error of the four-class problem drops to 2.4%.
Gender identity and sexual orientation in women with borderline personality disorder.

In the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, text revision (DSM-IV-TR) (and earlier editions), a disturbance in "identity" is one of the defining features of borderline personality disorder (BPD). Gender identity, a person's sense of self as a male or a female, constitutes an important aspect of identity formation, but this construct has rarely been examined in patients with BPD.



In the present study, the presence of gender identity disorder or confusion was examined in women diagnosed with BPD.



We used a validated dimensional measure of gender dysphoria. Recalled gender identity and gender role behavior from childhood was also assessed with a validated dimensional measure, and current sexual orientation was assessed by two self-report measures.



A consecutive series of 100 clinic-referred women (mean age, 34 years) with BPD participated in the study. The women were diagnosed with BPD using the International Personality Disorder Exam-BPD Section.



None of the women with BPD met the criterion for caseness on the dimensional measure of gender dysphoria. Women who self-reported either a bisexual or a homosexual sexual orientation had a significantly higher score on the dimensional measure of gender dysphoria than the women who self-reported a heterosexual sexual orientation, and they also recalled significantly more cross-gender behavior during childhood. Results were compared with a previous study on a diagnostically heterogeneous group of women with other clinical problems.



The importance of psychosexual assessment in the clinical evaluation of patients with BPD is discussed.
Comparison of generic versus specific quality-of-life scales for mesh hernia repairs.

With the use of mesh shown to considerably reduce recurrence rates for hernia repair and the subsequent improvement in clinical outcomes, focus has now been placed on quality-of-life outcomes in patients undergoing these repairs, specifically, as they relate to the mesh prosthesis. Traditionally, quality of life after hernia surgery, like many other medical conditions, has been tested using the generic SF-36 survey. The SF-36 quality-of-life survey, although well studied and validated, may not be ideal for patients undergoing hernia repairs. We propose a new quality-of-life survey, the Carolinas Comfort Scale (CCS), pertaining specifically to patients undergoing hernia repair with mesh; our goal was to test the validity and reliability of this survey.



The CCS questionnaire was mailed to 1,048 patients to assess its acceptability, responsiveness, and psychometric properties. The survey sample included patients who were at least 6 months out after hernia repair with mesh. Patients were asked to fill out the CCS and the generic SF-36 questionnaires, four questions comparing the two surveys, and their overall satisfaction relating to their hernia repair and mesh.



The reliability of the CCS was confirmed by Cronbach's alpha coefficient (0.97). Test-retest validity was supported by the correlation found between two different administrations of the CCS; both Spearman's correlation coefficient and the kappa coefficient were important for each question of the CCS. Assessment of its discriminant validity showed that both the mean and median scores for satisfied patients were considerably lower than those for dissatisfied patients. Concurrent validity was demonstrated by the marked correlations found between the CCS and SF-36 questionnaire scales. When comparing the two surveys, 72% of patients preferred the CCS questionnaire, 80% believed it was easier to understand, 66% thought it was more reflective of their condition, and 69% said they would rather fill it out over the SF-36.



The CCS better assesses quality of life and satisfaction of patients who have undergone surgical hernia repair than the generic SF-36.
TextDraw: a prototype for gestural typesetting

This paper considers a new approach that allows for the creation of typographic works through gestural interaction. It describes a prototype application called TextDraw, which is designed to introduce new techniques for typesetting opened up by gesture and tangible manipulation. Previous work explored the implications of tangible-surface interaction in the context of Surface Games; specifically, how the combination of multi-touch and tangible objects can extend the paradigm of board games in new directions. TextDraw also looks toward tangible-surface interaction and the idea that associating functions to gestures combined with new tangible tools will result in radically new methodologies for design, artistic and poetic expression of typographic works. Furthermore, while artworks created with TextDraw are presented, this paper focuses on the introduction / implication of our prototype.
Towards Continuous and Passive Authentication via Touch Biometrics: An Experimental Study on Smartphones

Current smartphones generally cannot continuously authenticate users during runtime. This poses severe security and privacy threats: A malicious user can manipulate the phone if bypassing the screen lock. To solve this problem, our work adopts a continuous and passive authentication mechanism based on a user’s touch operations on the touchscreen. Such a mechanism is suitable for smartphones, as it requires no extra hardware or intrusive user interface. We study how to model multiple types of touch data and perform continuous authentication accordingly. As a first attempt, we also investigate the fundamentals of touch operations as biometrics by justifying their distinctiveness and permanence. A onemonth experiment is conducted involving over 30 users. Our experiment results verify that touch biometrics can serve as a promising method for continuous and passive authentication.
Stressed speech emotion recognition using feature fusion of teager energy operator and MFCC

In this paper, a novel feature fusion of Teager Energy Operator (TEO) and Mel Frequency Cepstral Coefficients (MFCC), as Teager-MFCC (T-MFCC) feature extraction technique, is used to recognize the stressed emotions from speech signal. TEO is a non-linear feature of speech, basically designed to identify the stressed emotions. The Gaussian Mixture Model (GMM) is used for classification of these different stressed emotions with reference to neutral speech. It is found that the proposed method achieves better performance compared to the existing feature extraction technique like MFCC.
Learning to Track Multiple Targets

Monocular multiple-object tracking is a fundamental yet under-addressed computer vision problem. In this paper, we propose a novel learning framework for tracking multiple objects by detection. First, instead of heuristically defining a tracking algorithm, we learn that a discriminative structure prediction model from labeled video data captures the interdependence of multiple influence factors. Given the joint targets state from the last time step and the observation at the current frame, the joint targets state at the current time step can then be inferred by maximizing the joint probability score. Second, our detection results benefit from tracking cues. The traditional detection algorithms need a nonmaximal suppression postprocessing to select a subset from the total detection responses as the final output and a large number of selection mistakes are induced, especially under a congested circumstance. Our method integrates both detection and tracking cues. This integration helps to decrease the postprocessing mistake risk and to improve performance in tracking. Finally, we formulate the entire model training into a convex optimization problem and estimate its parameters using the cutting plane optimization. Experiments show that our method performs effectively in a large variety of scenarios, including pedestrian tracking in crowd scenes and vehicle tracking in congested traffic.
The capacity of low-density parity-check codes under message-passing decoding

In this paper we present a general method for determining the capacity of low-density paritycheck codes under message-passing decoding when used over any binary-input memoryless channel with discrete or continuous output alphabets. Transmitting at rates below this capacity, a randomly chosen element of the given ensemble will achieve an arbitrarily small target probability of error with a probability that approaches one exponentially fast in the length of the code. (By concatenating with an appropriate outer code one can achieve a probability of error that approaches zero exponentially fast in the length of the code with arbitrarily small loss in rate.) Conversely, transmitting at rates above this capacity the probability of error is bounded away from zero by a strictly positive constant which is independent of the length of the code and of the number of iterations performed. Our results are based on the observation that the concentration of the performance of the decoder around its average performance, as observed by Luby et. al.  in the case of a binary symmetric channel and a binary message passing algorithm, is a general phenomenon. For the particularly important case of belief-propagation decoders, we provide an effective algorithm to determine the corresponding capacity to any desired degree of accuracy. The ideas presented in this paper are broadly applicable and extensions of the general method to low-density parity-check codes over larger alphabets, turbo codes, and other concatenated coding schemes are outlined. Index Terms – low-density parity-check codes, turbo codes, message-passing decoders, iterative decoding, belief-propagation, turbo decoding
New support vector algorithms with parametric insensitive/margin model

In this paper, a modification of v-support vector machines (v-SVM) for regression and classification is described, and the use of a parametric insensitive/margin model with an arbitrary shape is demonstrated. This can be useful in many cases, especially when the noise is heteroscedastic, that is, the noise strongly depends on the input value x. Like the previous v-SVM, the proposed support vector algorithms have the advantage of using the parameter 0 < or = v < or = 1 for controlling the number of support vectors. To be more precise, v is an upper bound on the fraction of training errors and a lower bound on the fraction of support vectors. The algorithms are analyzed theoretically and experimentally.
Salt resistance genes revealed by functional metagenomics from brines and moderate-salinity rhizosphere within a hypersaline environment

Hypersaline environments are considered one of the most extreme habitats on earth and microorganisms have developed diverse molecular mechanisms of adaptation to withstand these conditions. The present study was aimed at identifying novel genes from the microbial communities of a moderate-salinity rhizosphere and brine from the Es Trenc saltern (Mallorca, Spain), which could confer increased salt resistance to Escherichia coli. The microbial diversity assessed by pyrosequencing of 16S rRNA gene libraries revealed the presence of communities that are typical in such environments and the remarkable presence of three bacterial groups never revealed as major components of salt brines. Metagenomic libraries from brine and rhizosphere samples, were transferred to the osmosensitive strain E. coli MKH13, and screened for salt resistance. Eleven genes that conferred salt resistance were identified, some encoding for well-known proteins previously related to osmoadaptation such as a glycerol transporter and a proton pump, whereas others encoded proteins not previously related to this function in microorganisms such as DNA/RNA helicases, an endonuclease III (Nth) and hypothetical proteins of unknown function. Furthermore, four of the retrieved genes were cloned and expressed in Bacillus subtilis and they also conferred salt resistance to this bacterium, broadening the spectrum of bacterial species in which these genes can function. This is the first report of salt resistance genes recovered from metagenomes of a hypersaline environment.
Analysis of large-scale scalar data using hixels

One of the greatest challenges for today's visualization and analysis communities is the massive amounts of data generated from state of the art simulations. Traditionally, the increase in spatial resolution has driven most of the data explosion, but more recently ensembles of simulations with multiple results per data point and stochastic simulations storing individual probability distributions are increasingly common. This paper introduces a new data representation for scalar data, called hixels, that stores a histogram of values for each sample point of a domain. The histograms may be created by spatial down-sampling, binning ensemble values, or polling values from a given distribution. In this manner, hixels form a compact yet information rich approximation of large scale data. In essence, hixels trade off data size and complexity for scalar-value “uncertainty”. Based on this new representation we propose new feature detection algorithms using a combination of topological and statistical methods. In particular, we show how to approximate topological structures from hixel data, extract structures from multi-modal distributions, and render uncertain isosurfaces. In all three cases we demonstrate how using hixels compares to traditional techniques and provide new capabilities to recover prominent features that would otherwise be either infeasible to compute or ambiguous to infer. We use a collection of computer tomography data and large scale combustion simulations to illustrate our techniques.
CNN vs. SIFT for Image Retrieval: Alternative or Complementary?

In the past decade, SIFT is widely used in most vision tasks such as image retrieval. While in recent several years, deep convolutional neural networks (CNN) features achieve the state-of-the-art performance in several tasks such as image classification and object detection. Thus a natural question arises: for the image retrieval task, can CNN features substitute for SIFT? In this paper, we experimentally demonstrate that the two kinds of features are highly complementary. Following this fact, we propose an image representation model, complementary CNN and SIFT (CCS), to fuse CNN and SIFT in a multi-level and complementary way. In particular, it can be used to simultaneously describe scene-level, object-level and point-level contents in images. Extensive experiments are conducted on four image retrieval benchmarks, and the experimental results show that our CCS achieves state-of-the-art retrieval results.