nils-herrmann/clean_scirepeval huggingface dataset:
A process prediction model based on Cuckoo algorithm for abrasive waterjet machining

Cuckoo is a new evolutionary algorithm which is inspired by lifestyle of bird family. This study proposes Cuckoo algorithm for prediction of the surface roughness of Abrasive Water Jet (AWJ). Several prediction models with different initial eggs were developed and analyzed to investigate the best predicted surface roughness value. The paired sample t-test was used to demonstrate the validity of the results. Throughout this study, it was evidence that the Cuckoo algorithm could improve machining performances of the AWJ. The result shows that the more initial eggs were considered, a much lower predicted value of surface roughness was obtained. When the initial eggs increase, the value of the best parameters become nearer to the goal point. It was found that Cuckoo algorithm is capable for giving an improved surface roughness as it outperformed the results of two established computational techniques, artificial neural network and support vector machine.
Cross-Language Authorship Attribution

This paper presents a novel task of cross-language authorship attribution (CLAA), an extension of authorship attribution task to multilingual settings: given data labelled with authors in language X , the objective is to determine the author of a document written in language Y , where X 6= Y . We propose a number of cross-language stylometric features for the task of CLAA, such as those based on sentiment and emotional markers. We also explore an approach based on machine translation (MT) with both lexical and cross-language features. We experimentally show that MT could be used as a starting point to CLAA, since it allows good attribution accuracy to be achieved. The cross-language features provide acceptable accuracy while using jointly with MT, though do not outperform lexical
Sustained attention in children with specific language impairment (SLI).

Information-processing limitations have been associated with language problems in children with specific language impairment (SLI). These processing limitations may be associated with limitations in attentional capacity, even in the absence of clinically significant attention deficits. In this study, the authors examined the performance of 4- to 6-year-old children with SLI and their typically developing (TD) peers on a visual sustained attention task. It was predicted that the children with SLI would demonstrate lower levels of performance in the absence of clinically significant attention deficits.



A visual continuous performance task (CPT) was used to assess sustained attention in 13 children with SLI (M = 62.07 months) and 13 TD age-matched controls (M = 62.92 months). All children were screened for normal vision, hearing, and attention. Accuracy (d') and response time were analyzed to see if this sustained attention task could differentiate between the 2 groups.



The children with SLI were significantly less accurate but not significantly slower than the TD children on this test of visual sustained attention.



Children with SLI may have reduced capacity for sustained attention in the absence of clinically significant attention deficits that, over time, could contribute to language learning difficulties.
UWB at SemEval-2016 Task 1: Semantic Textual Similarity using Lexical, Syntactic, and Semantic Information

We present our UWB system for Semantic Textual Similarity (STS) task at SemEval 2016. Given two sentences, the system estimates the degree of their semantic similarity. We use state-of-the-art algorithms for the meaning representation and combine them with the best performing approaches to STS from previous years. These methods benefit from various sources of information, such as lexical, syntactic, and semantic. In the monolingual task, our system achieve mean Pearson correlation 75.7% compared with human annotators. In the cross-lingual task, our system has correlation 86.3% and is ranked first among 26 systems.
Online Learning and Sequential Anomaly Detection in Trajectories.

Detection of anomalous trajectories is an important problem in the surveillance domain. Various algorithms based on learning of normal trajectory patterns have been proposed for this problem. Yet, these algorithms typically suffer from one or more limitations: They are not designed for sequential analysis of incomplete trajectories or online learning based on an incrementally updated training set. Moreover, they typically involve tuning of many parameters, including ad-hoc anomaly thresholds, and may therefore suffer from overfitting and poorly-calibrated alarm rates. In this article, we propose and investigate the Sequential Hausdorff Nearest-Neighbour Conformal Anomaly Detector (SHNN-CAD) for online learning and sequential anomaly detection in trajectories. This is a parameter-light algorithm that offers a well-founded approach to the calibration of the anomaly threshold. The discords algorithm, originally proposed by Keogh et al, is another parameter-light anomaly detection algorithm that has previously been shown to have good classification performance on a wide range of time-series datasets, including trajectory data. We implement and investigate the performance of SHNN-CAD and the discords algorithm on four different labelled trajectory datasets. The results show that SHNN-CAD achieves competitive classification performance with minimum parameter tuning during unsupervised online learning and sequential anomaly detection in trajectories.
miRBase: annotating high confidence microRNAs using deep sequencing data

We describe an update of the miRBase database (http://www.mirbase.org/), the primary microRNA sequence repository. The latest miRBase release (v20, June 2013) contains 24 521 microRNA loci from 206 species, processed to produce 30 424 mature microRNA products. The rate of deposition of novel microRNAs and the number of researchers involved in their discovery continue to increase, driven largely by small RNA deep sequencing experiments. In the face of these increases, and a range of microRNA annotation methods and criteria, maintaining the quality of the microRNA sequence data set is a significant challenge. Here, we describe recent developments of the miRBase database to address this issue. In particular, we describe the collation and use of deep sequencing data sets to assign levels of confidence to miRBase entries. We now provide a high confidence subset of miRBase entries, based on the pattern of mapped reads. The high confidence microRNA data set is available alongside the complete microRNA collection at http://www.mirbase.org/. We also describe embedding microRNA-specific Wikipedia pages on the miRBase website to encourage the microRNA community to contribute and share textual and functional information.
SemEval-2015 Task 3: Answer Selection in Community Question Answering

Community Question Answering (cQA) provides new interesting research directions to the traditional Question Answering (QA) field, e.g., the exploitation of the interaction between users and the structure of related posts. In this context, we organized SemEval2015 Task 3 on Answer Selection in cQA, which included two subtasks: (a) classifying answers as good, bad, or potentially relevant with respect to the question, and (b) answering a YES/NO question with yes, no, or unsure, based on the list of all answers. We set subtask A for Arabic and English on two relatively different cQA domains, i.e., the Qatar Living website for English, and a Quran-related website for Arabic. We used crowdsourcing on Amazon Mechanical Turk to label a large English training dataset, which we released to the research community. Thirteen teams participated in the challenge with a total of 61 submissions: 24 primary and 37 contrastive. The best systems achieved an official score (macro-averaged F1) of 57.19 and 63.7 for the English subtasks A and B, and 78.55 for the Arabic subtask A.
Unsupervised word segmentation from noisy input

In this paper we present an algorithm for the unsupervised segmentation of a character or phoneme lattice into words. Using a lattice at the input rather than a single string accounts for the uncertainty of the character/phoneme recognizer about the true label sequence. An example application is the discovery of lexical units from the output of an error-prone phoneme recognizer in a zero-resource setting, where neither the lexicon nor the language model is known. Recently a Weighted Finite State Transducer (WFST) based approach has been published which we show to suffer from an issue: language model probabilities of known words are computed incorrectly. Fixing this issue leads to greatly improved precision and recall rates, however at the cost of increased computational complexity. It is therefore practical only for single input strings. To allow for a lattice input and thus for errors in the character/phoneme recognizer, we propose a computationally efficient suboptimal two-stage approach, which is shown to significantly improve the word segmentation performance compared to the earlier WFST approach.
Fast shadows and lighting effects using texture mapping

Generating images of texture mapped geometry requires projecting surfaces onto a two-dimensional screen. If this projection involves perspective, then a division must be performed at each pixel of the projected surface in order to correctly calculate texture map coordinates. We show how a simple extension to perspective-comect texture mapping can be used to create various lighting effects, These include arbitrary projection of two-dimensional images onto geometry, realistic spotlights, and generation of shadows using shadow maps. These effects are obtained in real time using hardware that performs correct texture mapping.
Deep Residual Networks and Weight Initialization

Residual Network (ResNet) is the state-of-the-art architecture that realizes successful training of really deep neural network. It is also known that good weight initialization of neural network avoids problem of vanishing/exploding gradients. In this paper, simplified models of ResNets are analyzed. We argue that goodness of ResNet is correlated with the fact that ResNets are relatively insensitive to choice of initial weights. We also demonstrate how batch normalization improves backpropagation of deep ResNets without tuning initial values of weights.
Dual marching cubes

We present the definition and computational algorithms for a new class of surfaces which are dual to the isosurface produced by the widely used marching cubes (MC) algorithm. These new isosurfaces have the same separating properties as the MC surfaces but they are comprised of quad patches that tend to eliminate the common negative aspect of poorly shaped triangles of the MC isosurfaces. Based upon the concept of this new dual operator, we describe a simple, but rather effective iterative scheme for producing smooth separating surfaces for binary, enumerated volumes which are often produced by segmentation algorithms. Both the dual surface algorithm and the iterative smoothing scheme are easily implemented.
Analysis and Optimization of Loss Functions for Multiclass, Top-k, and Multilabel Classification

Top-k error is currently a popular performance measure on large scale image classification benchmarks such as ImageNet and Places. Despite its wide acceptance, our understanding of this metric is limited as most of the previous research is focused on its special case, the top-1 error. In this work, we explore two directions that shed more light on the top-k error. First, we provide an in-depth analysis of established and recently proposed single-label multiclass methods along with a detailed account of efficient optimization algorithms for them. Our results indicate that the softmax loss and the smooth multiclass SVM are surprisingly competitive in top-k error uniformly across all k, which can be explained by our analysis of multiclass top-k calibration. Further improvements for a specific k are possible with a number of proposed top-k loss functions. Second, we use the top-k methods to explore the transition from multiclass to multilabel learning. In particular, we find that it is possible to obtain effective multilabel classifiers on Pascal VOC using a single label per image for training, while the gap between multiclass and multilabel methods on MS COCO is more significant. Finally, our contribution of efficient algorithms for training with the considered top-k and multilabel loss functions is of independent interest.
On-Road Vehicle Detection and Tracking Using MMW Radar and Monovision Fusion

With the potential to increase road safety and provide economic benefits, intelligent vehicles have elicited a significant amount of interest from both academics and industry. A robust and reliable vehicle detection and tracking system is one of the key modules for intelligent vehicles to perceive the surrounding environment. The millimeter-wave radar and the monocular camera are two vehicular sensors commonly used for vehicle detection and tracking. Despite their advantages, the drawbacks of these two sensors make them insufficient when used separately. Thus, the fusion of these two sensors is considered as an efficient way to address the challenge. This paper presents a collaborative fusion approach to achieve the optimal balance between vehicle detection accuracy and computational efficiency. The proposed vehicle detection and tracking design is extensively evaluated with a real-world data set collected by the developed intelligent vehicle. Experimental results show that the proposed system can detect on-road vehicles with 92.36% detection rate and 0% false alarm rate, and it only takes ten frames (0.16 s) for the detection and tracking of each vehicle. This system is installed on Kuafu-II intelligent vehicle for the fourth and fifth autonomous vehicle competitions, which is called “Intelligent Vehicle Future Challenge” in China.
Learning Representations and Generative Models for 3D Point Clouds

Three-dimensional geometric data offer an excellent domain for studying representation learning and generative modeling. In this paper, we look at geometric data represented as point clouds. We introduce a deep AutoEncoder (AE) network with state-of-the-art reconstruction quality and generalization ability. The learned representations outperform existing methods on 3D recognition tasks and enable shape editing via simple algebraic manipulations, such as semantic part editing, shape analogies and shape interpolation, as well as shape completion. We perform a thorough study of different generative models including GANs operating on the raw point clouds, significantly improved GANs trained in the fixed latent space of our AEs, and Gaussian Mixture Models (GMMs). To quantitatively evaluate generative models we introduce measures of sample fidelity and diversity based on matchings between sets of point clouds. Interestingly, our evaluation of generalization, fidelity and diversity reveals that GMMs trained in the latent space of our AEs yield the best results overall.
The design versus the analysis of observational studies for causal effects: parallels with the design of randomized trials.

For estimating causal effects of treatments, randomized experiments are generally considered the gold standard. Nevertheless, they are often infeasible to conduct for a variety of reasons, such as ethical concerns, excessive expense, or timeliness. Consequently, much of our knowledge of causal effects must come from non-randomized observational studies. This article will advocate the position that observational studies can and should be designed to approximate randomized experiments as closely as possible. In particular, observational studies should be designed using only background information to create subgroups of similar treated and control units, where 'similar' here refers to their distributions of background variables. Of great importance, this activity should be conducted without any access to any outcome data, thereby assuring the objectivity of the design. In many situations, this objective creation of subgroups of similar treated and control units, which are balanced with respect to covariates, can be accomplished using propensity score methods. The theoretical perspective underlying this position will be presented followed by a particular application in the context of the US tobacco litigation. This application uses propensity score methods to create subgroups of treated units (male current smokers) and control units (male never smokers) who are at least as similar with respect to their distributions of observed background characteristics as if they had been randomized. The collection of these subgroups then 'approximate' a randomized block experiment with respect to the observed covariates.
Programming industrial robots using advanced input-output devices: test-case example using a CAD package and a digital pen based on the Anoto technology

Interaction with robot systems for specification of manufacturing tasks and motions needs to be simple, to enable wide-spread use of robots in SMEs. In the best case, existing practices from manual work could be used, to smoothly let current employees start using robot technology as a natural part of their work. Our aim is to simplify the robot programming task by allowing the user to simply make technical drawings on a sheet of paper. Craftsman use paper and raw sketches for several situations; to share ideas, to get a better imagination or to remember the customer situation. Currently these sketches have either to be interpreted by the worker when producing the final product by hand, or transferred into CAD file using an according tool. The former means that no automation is included, the latter means extra work and much experience in using the CAD tool. Our approach is to use the digital pen and paper from Anoto as input devices for SME robotic tasks, thereby creating simpler and more user friendly alternatives for programming, parameterization and commanding actions. To this end, the basic technology has been investigated and fully working prototypes have been developed to explore the possibilities and limitation in the context of typical SME applications. Based on the encouraging experimental results, we believe that drawings on digital paper will, among other means of human-robot interaction, play an important role in manufacturing SMEs in the future. Index Terms — CAD, Human machine interfaces, Industrial Robots, Robot programming.
Survey of Various Homomorphic Encryption algorithms and Schemes

Homomorphic encryption is the encryption scheme which means the operations on the encrypted data. Homomorphic encryption can be applied in any system by using various public key algorithms. When the data is transferred to the public area, there are many encryption algorithms to secure the operations and the storage of the data. But to process data located on remote server and to preserve privacy, homomorphic encryption is useful that allows the operations on the cipher text, which can provide the same results after calculations as the working directly on the raw data. In this paper, the main focus is on public key cryptographic algorithms based on homomorphic encryption scheme for preserving security. The case study
VideoWhiteboard: video shadows to support remote collaboration

VideoWhiteboard is a prototype tool to support remote shared drawing activity. It provides a whiteboard-sized shared drawing space for collaborators who are located in remote sites. It allows each user to see the drawings and a shadow of the gestures of collaborators at the remote site. The development of VideoWhiteboard is based on empirical studies of collaborative drawing activity, including experiences in using the VideoDraw shared drawing prototype. VideoWhiteboard enables remote collaborators to work together much as if they were sharing a whiteboard, and in some ways allows them to work together even more closely than if they were in the same room.
Influence of hemolysis on routine clinical chemistry testing.

Preanalytical factors are the main source of variation in clinical chemistry testing and among the major determinants of preanalytical variability, sample hemolysis can exert a strong influence on result reliability. Hemolytic samples are a rather common and unfavorable occurrence in laboratory practice, as they are often considered unsuitable for routine testing due to biological and analytical interference. However, definitive indications on the analytical and clinical management of hemolyzed specimens are currently lacking. Therefore, the present investigation evaluated the influence of in vitro blood cell lysis on routine clinical chemistry testing.



Nine aliquots, prepared by serial dilutions of homologous hemolyzed samples collected from 12 different subjects and containing a final concentration of serum hemoglobin ranging from 0 to 20.6 g/L, were tested for the most common clinical chemistry analytes. Lysis was achieved by subjecting whole blood to an overnight freeze-thaw cycle.



Hemolysis interference appeared to be approximately linearly dependent on the final concentration of blood-cell lysate in the specimen. This generated a consistent trend towards overestimation of alanine aminotransferase (ALT), aspartate aminotransferase (AST), creatinine, creatine kinase (CK), iron, lactate dehydrogenase (LDH), lipase, magnesium, phosphorus, potassium and urea, whereas mean values of albumin, alkaline phosphatase (ALP), chloride, gamma-glutamyltransferase (GGT), glucose and sodium were substantially decreased. Clinically meaningful variations of AST, chloride, LDH, potassium and sodium were observed in specimens displaying mild or almost undetectable hemolysis by visual inspection (serum hemoglobin < 0.6 g/L). The rather heterogeneous and unpredictable response to hemolysis observed for several parameters prevented the adoption of reliable statistic corrective measures for results on the basis of the degree of hemolysis.



If hemolysis and blood cell lysis result from an in vitro cause, we suggest that the most convenient corrective solution might be quantification of free hemoglobin, alerting the clinicians and sample recollection.
To Give Is Better Than to Receive: The Benefits of Peer Review to the Reviewer's Own Writing.

Although peer review has been shown to be beneficial in many writing classrooms, the benefits of peer review to the reviewer, or the student giving feedback, has not been thoroughly investigated in second-language writing research. The purpose of this study is to determine which is more beneficial to improving student writing: giving or receiving peer feedback. The study was conducted at an intensive English institute with ninety-one students in nine writing classes at two proficiency levels. The ‘‘givers’’ reviewed anonymous papers but received no peer feedback over the course of the semester, while the ‘‘receivers’’ received feedback but did not review other students’ writing. An analysis in the gains in writing ability measured from writing samples collected at the beginning and end of the semester indicated that the givers, who focused solely on reviewing peers’ writing, made more significant gains in their own writing over the course of the semester than did the receivers, who focused solely on how to use peer feedback. Results also indicated that givers at the lower proficiency level made more gains than those at higher proficiency levels and that slightly more gains were observed on global than local aspects of writing.