nils-herrmann/clean_scirepeval huggingface dataset:
Combining and benchmarking methods of foetal ECG extraction without maternal or scalp electrode data.

Despite significant advances in adult clinical electrocardiography (ECG) signal processing techniques and the power of digital processors, the analysis of non-invasive foetal ECG (NI-FECG) is still in its infancy. The Physionet/Computing in Cardiology Challenge 2013 addresses some of these limitations by making a set of FECG data publicly available to the scientific community for evaluation of signal processing techniques.The abdominal ECG signals were first preprocessed with a band-pass filter in order to remove higher frequencies and baseline wander. A notch filter to remove power interferences at 50 Hz or 60 Hz was applied if required. The signals were then normalized before applying various source separation techniques to cancel the maternal ECG. These techniques included: template subtraction, principal/independent component analysis, extended Kalman filter and a combination of a subset of these methods (FUSE method). Foetal QRS detection was performed on all residuals using a Pan and Tompkins QRS detector and the residual channel with the smoothest foetal heart rate time series was selected.The FUSE algorithm performed better than all the individual methods on the training data set. On the validation and test sets, the best Challenge scores obtained were E1 = 179.44, E2 = 20.79, E3 = 153.07, E4 = 29.62 and E5 = 4.67 for events 1-5 respectively using the FUSE method. These were the best Challenge scores for E1 and E2 and third and second best Challenge scores for E3, E4 and E5 out of the 53 international teams that entered the Challenge. The results demonstrated that existing standard approaches for foetal heart rate estimation can be improved by fusing estimators together. We provide open source code to enable benchmarking for each of the standard approaches described.
GPTIPS 2: an open-source software platform for symbolic data mining

GPTIPS is a free, open source MATLAB based software platform for symbolic data mining (SDM). It uses a ‘multigene’ variant of the biologically inspired machine learning method of genetic programming (MGGP) as the engine that drives the automatic model discovery process. Symbolic data mining is the process of extracting hidden, meaningful relationships from data in the form of symbolic equations. In contrast to other data-mining methods, the structural transparency of the generated predictive equations can give new insights into the physical systems or processes that generated the data. Furthermore – this transparency makes the models very easy to deploy outside of MATLAB. The rationale behind GPTIPS is to reduce the technical barriers to using, understanding, visualising and deploying GP based symbolic models of data, whilst at the same time remaining highly customisable and delivering robust numerical performance for ‘power users’. In this chapter, notable new features of the latest version of the software GPTIPS 2 are discussed with these aims in mind. Additionally, a simplified variant of the MGGP high level gene crossover mechanism is proposed. It is demonstrated that the new functionality of GPTIPS 2 (a) facilitates the discovery of compact symbolic relationships from data using multiple approaches, e.g. using novel ‘gene-centric’ visualisation analysis to mitigate horizontal bloat and reduce complexity in multigene symbolic regression models (b) provides numerous methods for visualising the properties of symbolic models (c) emphasises the generation of graphically navigable ‘libraries’ of models that are optimal in terms of the Pareto trade off surface of model performance and complexity and (d) expedites 'real world' applications by the simple, rapid and robust deployment of symbolic models outside the software environment they were developed in.
The meaning and use of the area under a receiver operating characteristic (ROC) curve.

A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect differences in the accuracy of diagnostic techniques.
BEST PRACTICE GUIDELINE FOR THE CFD SIMULATION OF FLOWS IN THE URBAN ENVIRONMENT

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Tracking technical debt — An exploratory case study

The technical debt metaphor is increasingly being used to describe the effect of delaying certain software maintenance tasks on software projects. Practitioners understand intuitively how technical debt can turn into a serious problem if it is left unattended. However, it remains unknown how serious the problem is and whether explicit measurement and management of technical debt is useful. In this paper, we explore the effect of technical debt by tracking a single delayed maintenance task in a real software project throughout its lifecycle and simulate how explicit technical debt management might have changed project outcomes. The results from this study demonstrate how and to what extent technical debt affects software projects. The study also sheds light on the research methodologies that can be used to investigate the technical debt management problem.
An analysis of unreliability and asymmetry in low-power wireless links

Experimental studies have demonstrated that the behavior of real links in low-power wireless networks (such as wireless sensor networks) deviates to a large extent from the ideal binary model used in several simulation studies. In particular, there is a large transitional region in wireless link quality that is characterized by significant levels of unreliability and asymmetry, significantly impacting the performance of higher-layer protocols. We provide a comprehensive analysis of the root causes of unreliability and asymmetry. In particular, we derive expressions for the distribution, expectation, and variance of the packet reception rate as a function of distance, as well as for the location and extent of the transitional region. These expressions incorporate important environmental and radio parameters such as the path loss exponent and shadowing variance of the channel, and the modulation, encoding, and hardware variance of the radios.
Rough Set Theory in analyzing the attributes of combination values for the insurance market

Based on Rough Set Theory, this research addresses the effect of attributes/features on the combination values of decisions that insurance companies make to satisfy customers’ needs. Attributes impact on combination values by yielding sets with fewer objects (such as one or two objects), which increases both the lower and upper approximations. It also increases the decision rules, and degrades the precision of decisions. Our approach redefines the value set of attributes through expert knowledge by reducing the independent data set and reclassifying it. This approach is based on an empirical study. The results demonstrate that the redefined combination values of attributes can contribute to the precision of decisions in insurance marketing. Following an empirical analysis, we use a hit test that incorporates 50 validated sample data into the decision rule so that the hit rate reaches 100%. The results of the empirical study indicate that the generated decision rules can cover all new data. Consequently, we believe that the effects of attributes on combination values can be fully applied in research into insurance marketing.
Surveying Theories and Philosophies of Mathematics Education

Any theory of thinking or teaching or learning rests on an underlying philosophy of knowledge. Mathematics education is situated at the nexus of two fields of inquiry, namely mathematics and education. However, numerous other disciplines interact with these two fields, which compound the complexity of developing theories that define mathematics education (Sriraman 2009a). We first address the issue of clarifying a philosophy of mathematics education before attempting to answer whether theories of mathematics education are constructible. In doing so we draw on the foundational writings of Lincoln and Guba (1994), in which they clearly posit that any discipline within education, in our case mathematics education, needs to clarify for itself the following questions:
A review on Video Quality Assessment

With the advancement in technology, more and more video content are available on Internet. Video content such as news, sports, entertainment plays a very important role in everybody's life. But before reaching at the user end due to processing and transmission the videos may get distorted. In this paper various artifacts, distortions which gets introduced in videos are discussed. The assessment of quality of video depends upon the type of distortion. In this paper we also discussed two Video Quality Assessment techniques namely: Subjective and Objective assessment. These methods provide the users QoE (Quality of experience) and QoS (Quality of service). However, subjective assessment is considered to the easiest method for evaluation as the evaluation is done with the help of users. But it is very time consuming and tedious. So, most of modern applications are inclined to use the objective assessment. Different VQA performance metrics used for objective assessment are analyzed.
Learning A Deep $\ell_\infty$ Encoder for Hashing

We investigate the `∞-constrained representation which demonstrates robustness to quantization errors, utilizing the tool of deep learning. Based on the Alternating Direction Method of Multipliers (ADMM), we formulate the original convex minimization problem as a feed-forward neural network, named Deep `∞ Encoder, by introducing the novel Bounded Linear Unit (BLU) neuron and modeling the Lagrange multipliers as network biases. Such a structural prior acts as an effective network regularization, and facilitates the model initialization. We then investigate the effective use of the proposed model in the application of hashing, by coupling the proposed encoders under a supervised pairwise loss, to develop a Deep Siamese `∞ Network, which can be optimized from end to end. Extensive experiments demonstrate the impressive performances of the proposed model. We also provide an in-depth analysis of its behaviors against the competitors.
Semi-Dense Depth Interpolation using Deep Convolutional Neural Networks

With advances of recent technologies, augmented reality systems and autonomous vehicles gained a lot of interest from academics and industry. Both these areas rely on scene geometry understanding, which usually requires depth map estimation. However, in case of systems with limited computational resources, such as smartphones or autonomous robots, high resolution dense depth map estimation may be challenging. In this paper, we study the problem of semi-dense depth map interpolation along with low resolution depth map upsampling. We present an end-to-end learnable residual convolutional neural network architecture that achieves fast interpolation of semi-dense depth maps with different sparse depth distributions: uniform, sparse grid and along intensity image gradient. We also propose a loss function combining classical mean squared error with perceptual loss widely used in intensity image super-resolution and style transfer tasks. We show that with some modifications, this architecture can be used for depth map super-resolution. Finally, we evaluate our results on both synthetic and real data, and consider applications for autonomous vehicles and creating AR/MR video games.
Functional genomic analysis of the AUXIN RESPONSE FACTOR gene family members in Arabidopsis thaliana: unique and overlapping functions of ARF7 and ARF19.

The AUXIN RESPONSE FACTOR (ARF) gene family products, together with the AUXIN/INDOLE-3-ACETIC ACID proteins, regulate auxin-mediated transcriptional activation/repression. The biological function(s) of most ARFs is poorly understood. Here, we report the identification and characterization of T-DNA insertion lines for 18 of the 23 ARF gene family members in Arabidopsis thaliana. Most of the lines fail to show an obvious growth phenotype except of the previously identified arf2/hss, arf3/ett, arf5/mp, and arf7/nph4 mutants, suggesting that there are functional redundancies among the ARF proteins. Subsequently, we generated double mutants. arf7 arf19 has a strong auxin-related phenotype not observed in the arf7 and arf19 single mutants, including severely impaired lateral root formation and abnormal gravitropism in both hypocotyl and root. Global gene expression analysis revealed that auxin-induced gene expression is severely impaired in the arf7 single and arf7 arf19 double mutants. For example, the expression of several genes, such as those encoding members of LATERAL ORGAN BOUNDARIES domain proteins and AUXIN-REGULATED GENE INVOLVED IN ORGAN SIZE, are disrupted in the double mutant. The data suggest that the ARF7 and ARF19 proteins play essential roles in auxin-mediated plant development by regulating both unique and partially overlapping sets of target genes. These observations provide molecular insight into the unique and overlapping functions of ARF gene family members in Arabidopsis.
A Hybrid Text Classification Approach Using KNN And SVM

Text classification is the process of assigning text documents based on certain categories. A classifier is used to define the appropriate class for each text document based on the input algorithm used for classification. Due to the emerging trends in the field of internet and computers ,billions of text data are processed at a given time and so there is a need for organizing these data to provide easy storage and accessing .Many text classification approaches were developed for effectively solving the problem of identifying and classifying these data .In this project a new text document classifier is proposed by integrating the nearest neighbor classification approach with the support vector machine(SVM) training algorithm. The proposed SVM-NN approach aims to reduce the impact of parameters in classification accuracy. In the training stage, the SVM is utilized to reduce the training samples for each of the available categories to their support vectors (SVs).The SVs from different categories are used as the training data of nearest neighbor classification algorithm in which the similarity measures or distance function is used to calculate the which class does the testing data belongs and which also reduce time consumption.  Machine Learning,Text document classification, Nearest Neighbor, Support Vector Machine, Distance function
Annotation of Linguistic and Conceptual Metaphor

The study of metaphor dates back to the times of Aristotle and touches on various aspects of human reasoning and multiple disciplines. Since the first inquiries, the theory of metaphor has evolved significantly under the influence of linguistic and psychological findings [3, 63, 31, 29, 25, 15, 4, 17, 18], and the establishment of the fields of artificial intelligence [2, 45], cognitive science [23] and neuroscience [13]. Following Aristotle’s Poetics, it is widely acknowledged across these disciplines that metaphor is based on analogy [14, 31, 21, 45, 11] and arises when one concept is viewed in terms of the properties of another. Humans often use metaphor to describe abstract concepts through reference to more concrete or physical experiences. Below are some examples of metaphor.
Semantic Monte-Carlo localization in changing environments using RGB-D cameras

The localization with respect to a prior map is a fundamental requirement for mobile robots. The commonly used adaptive monte carlo localization (AMCL) can be found on most of the mobile robots ranging from small cleaning robots to large AGVs. While achieving accurate pose estimates in static environments, this algorithm tends to fail in the presence of significant changes. Recently published extensions and alternatives to AMCL observe the environment over longer times while building complex spatio-temporal models. Our approach, in contrast, utilizes object recognition and prior semantic maps to enable robust localization. It exploits the fact that putative changes in the environment can be predicted based on prior semantic knowledge. Our system is experimentally evaluated in a warehouse environment being subject to frequent changes. This emphasizes the importance of our algorithm for challenging industrial applications.
Why do metaphors seem deeper than similes ? In

Figurative expressions in metaphor form (e .g., Marriage is a journey) seem stronger and deeper than expressions in simile foam (e .g., Marriage is like a journey) . We ran a study to examine the nature of these judgments. Participants read short paragraphs describing either object attributes or relational structure and then made a forced choice of the grammatical form of a figurative expression mentioning the target concept referred to in the passages. The results showed that the metaphor form was chosen more often (I) for expressions with conventional bases, and (2) when figurative statements followed contexts containing relational information . We speculate about a possible linkage between conventionalization and relationality .
2D and 3D Convolutional Neural Network Fusion for Predicting the Histological Grade of Hepatocellular Carcinoma

Preoperative Knowledge of the histological grade of hepatocellular carcinoma (HCC) is significant for patient management and prognosis in clinical practice. Recent studies reported that 3D Convolutional Neural Network (CNN) outperformed 2D CNN for lesion characterization. Since 2D and 3D deep feature derived from CNN embed different spatial information of neoplasm, we hypothesize that the performance of lesion characterization might be improved if taking full advantage of both 2D and 3D characterization. In this work, we propose a 2D and 3D CNN fusion architecture to integrate both 2D and 3D spatial information of neoplasm for predicting the histological grade of HCC. Specifically, correlated and individual component analysis (CICA) is performed to fuse the 2D deep features in three orthogonal views and the 3D deep feature in volumetric images of HCC. Experimental results of 46 clinical patients with HCCs demonstrate several encouraging features of the proposed 2D and 3D deep feature fusion framework as follows: (1) Fusion of 2D and 3D deep feature using CICA outperforms 2D or 3D deep feature for predicting the histological grade of HCC. (2) Fusion of 2D deep features derived from three orthogonal views using CICA yields better results than those of 3D deep feature. (3) CICA is better than the conventional concatenation and the correlation learning model for deep feature fusion.
Comparison of Strategies for Multi-step-Ahead Prediction of Time Series Using Neural Network

If the one-step forecasting of a time series is already a challenging task, performing multi-step ahead forecasting is more difficult. Several approaches that deal with this complex problem have been proposed in literature: recursive (or iterated) strategy, direct strategy, combination of both the recursive and direct strategies, called DirREC, the Multi-Input Multi-Output (MIMO) strategy, and the last strategy, called DirMO which aims to preserve the advantageous aspects of both the Direct and MIMO strategies. This paper aims to review existing strategies for multi-step ahead forecasting using neural networks and compare their performances empirically. To attain such an objective, we performed several experiments of these different strategies on three datasets: NN3 competition dataset, the Vietnam composite stock price index (VNINDEX) and the closing prices of the FPT stock. The most consistent findings are that the DirREC strategy is better than all the other strategies for multi-step ahead forecasting using neural network.
Wideband differential antenna in package with superstrate structure at 77GHz

This paper presents a novel antenna in package solution with superstrate structure at 77 GHz for automotive radar applications. Two edge-coupled patches are driven by a differential signal which supports seamless integration with differential output MMICs in a single package. The antenna has a wide bandwidth (17 GHz) and stable gain. The antenna prototypes are fabricated using Embedded Wafer Level Ball Grid Array (eWLB) packaging technology. The measurement results show that the EIRP of the package (chip + antenna) reaches 11 dBm from 75 GHz to 80 GHz. This concept is suitable for a wide area of millimeter-wave front-end applications.
Agile Practitioners’ Understanding of Security Requirements: Insights from a Grounded Theory Analysis

A 2017 systematic review on engineering non-functional requirements in agile projects revealed a number of published proposals for approaching security requirements in agile settings. While these proposals acknowledge the urgent need for methods to systematically engineer security requirements in agile projects, they were designed mostly in academic settings. Very little empirical evaluation of these proposals happened in real-life contexts. In turn, little is known about how agile developers understand security requirements and how they devise their coping strategies regarding these requirements. This position paper presents a qualitative analysis that sought to discover how agile practitioners reason about security requirements, what contextual factors they consider important for shaping the pro-cess of coping with security requirements in agile projects, and what these strategies are. We conclude with some implications for practice and research.
A Review of Implantable Antennas for Wireless Biomedical Devices

In this review paper, research progress on implantable antennas for wireless biomedical devices is discussed and summarized. An implantable antenna is a key component for radio frequency linked telemetry as many challenges arise. An implantable antenna needs to meet requirements such as compact size, operating bandwidth, sufficient radiation efficiency, and patient safety. The purpose of this paper is to give an overview of the current progress and achievements and address the challenges for implantable antenna design. Firstly, the overview of the requirements related to the implantable antenna design is provided. Then simulation and test methods for implantable antenna design are examined. Different antenna types, operating frequency bands and design environments are reviewed. Finally recent research topicss on implantable antennas are introduced.