nils-herrmann/clean_scirepeval huggingface dataset:
Prefix and Suffix Invariant Dynamic Time Warping

While there exist a plethora of classification algorithms for most data types, there is an increasing acceptance that the unique properties of time series mean that the combination of nearest neighbor classifiers and Dynamic Time Warping (DTW) is very competitive across a host of domains, from medicine to astronomy to environmental sensors. While there has been significant progress in improving the efficiency and effectiveness of DTW in recent years, in this work we demonstrate that an underappreciated issue can significantly degrade the accuracy of DTW in real-world deployments. This issue has probably escaped the attention of the very active time series research community because of its reliance on static highly contrived benchmark datasets, rather than real world dynamic datasets where the problem tends to manifest itself. In essence, the issue is that DTW's eponymous invariance to warping is only true for the main "body" of the two time series being compared. However, for the "head" and "tail" of the time series, the DTW algorithm affords no warping invariance. The effect of this is that tiny differences at the beginning or end of the time series (which may be either consequential or simply the result of poor "cropping") will tend to contribute disproportionally to the estimated similarity, producing incorrect classifications. In this work, we show that this effect is real, and reduces the performance of the algorithm. We further show that we can fix the issue with a subtle redesign of the DTW algorithm, and that we can learn an appropriate setting for the extra parameter we introduced. We further demonstrate that our generalization is amiable to all the optimizations that make DTW tractable for large datasets.
Cardiac Limited Ultrasound Examination Techniques to Augment the Bedside Cardiac Physical Examination.

The current practice of physical diagnosis is dependent on physician skills and biases, inductive reasoning, and time efficiency. Although the clinical utility of echocardiography is well known, few data exist on how to integrate 2-dimensional screening "quick-look" ultrasound applications into a novel, modernized cardiac physical examination. We discuss the evidence basis behind ultrasound "signs" pertinent to the cardiovascular system and elemental in synthesis of bedside diagnoses and propose the application of a brief cardiac limited ultrasound examination based on these signs. An ultrasound-augmented cardiac physical examination can be taught in traditional medical education and has the potential to improve bedside diagnosis and patient care.
Learning Distributed Representations for Multilingual Text Sequences

We propose a novel approach to learning distributed representations of variable-length text sequences in multiple languages simultaneously. Unlike previous work which often derive representations of multi-word sequences as weighted sums of individual word vectors, our model learns distributed representations for phrases and sentences as a whole. Our work is similar in spirit to the recent paragraph vector approach but extends to the bilingual context so as to efficiently encode meaning-equivalent text sequences of multiple languages in the same semantic space. Our learned embeddings achieve state-of-theart performance in the often used crosslingual document classification task (CLDC) with an accuracy of 92.7 for English to German and 91.5 for German to English. By learning text sequence representations as a whole, our model performs equally well in both classification directions in the CLDC task in which past work did not achieve.
Prostate Segmentation using 2D Bridged U-net.

In this paper, we focus on three problems in deep learning based medical image segmentation. Firstly, U-net, as a popular model for medical image segmentation, is difficult to train when convolutional layers increase even though a deeper network usually has a better generalization ability because of more learnable parameters. Secondly, the exponential ReLU (ELU), as an alternative of ReLU, is not much different from ReLU when the network of interest gets deep. Thirdly, the Dice loss, as one of the pervasive loss functions for medical image segmentation, is not effective when the prediction is close to ground truth and will cause oscillation during training. To address the aforementioned three problems, we propose and validate a deeper network that can fit medical image datasets that are usually small in the sample size. Meanwhile, we propose a new loss function to accelerate the learning process and a combination of different activation functions to improve the network performance. Our experimental results suggest that our network is comparable or superior to state-of-the-art methods.
Data-driven comparison of spatio-temporal monitoring techniques

Monitoring marine ecosystems is challenging due to the dynamic and unpredictable nature of environmental phenomena. In this work we survey a series of techniques used in information gathering that can be used to increase experts' understanding of marine ecosystems through dynamic monitoring. To achieve this, an underwater glider simulator is constructed, and four different path planning algorithms are investigated: Boustrophendon paths, a gradient based approach, a Level-Sets method, and Sequential Bayesian Optimization. Each planner attempts to maximize the time the glider spends in an area where ocean variables are above a threshold value of interest. To emulate marine ecosystem sensor data, ocean temperatures are used. The planners are simulated 50 times each at random starting times and locations. After validation through simulation, we show that informed decision making improves performance, but more accurate prediction of ocean conditions would be necessary to benefit from long horizon lookahead planning.
Deep tessellated retinal image detection using Convolutional Neural Networks

Tessellation in fundus is not only a visible feature for aged-related and myopic maculopathy but also confuse retinal vessel segmentation. The detection of tessellated images is an inevitable processing in retinal image analysis. In this work, we propose a model using convolutional neural network for detecting tessellated images. The input to the model is pre-processed fundus image, and the output indicate whether this photograph has tessellation or not. A database with 12,000 colour retinal images is collected to evaluate the classification performance. The best tessellation classifier achieves accuracy of 97.73% and AUC value of 0.9659 using pretrained GoogLeNet and transfer learning technique.
KGEval: Accuracy Estimation of Automatically Constructed Knowledge Graphs

Automatic construction of large knowledge graphs (KG) by mining web-scale text datasets has received considerable attention recently. Estimating accuracy of such automatically constructed KGs is a challenging problem due to their size and diversity and has largely been ignored in prior research. In this work, we try to fill this gap by proposing KGEval. KGEval uses coupling constraints to bind facts and crowdsource those few that can infer large parts of the graph. We demonstrate that the objective optimized by KGEval is submodular and NP-hard, allowing guarantees for our approximation algorithm. Through experiments on real-world datasets, we demonstrate that KGEval best estimates KG accuracy compared to other baselines, while requiring significantly lesser number of human evaluations.
Reusable Model Transformation Patterns

This paper is a reflection of our experience with the specification and subsequent execution of model transformations in the QVT core and Relations languages. Since this technology for executing transformations written in high-level, declarative specification languages is of very recent date, we observe that there is little knowledge available on how to write such declarative model transformations. Consequently, there is a need for a body of knowledge on transformation engineering. With this paper we intend to make an initial contribution to this emerging discipline. Based on our experiences we propose a number of useful design patterns for transformation specification. In addition we provide a method for specifying such transformation patterns in QVT, such that others can add their own patterns to a catalogue and the body of knowledge can grow as experience is built up. Finally, we illustrate how these patterns can be used in the specification of complex transformations.
Early Cancer Detection in Blood Vessels Using Mobile Nanosensors

In this paper, we propose using mobile nanosensors (MNSs) for early stage anomaly detection. For concreteness, we focus on the detection of cancer cells located in a particular region of a blood vessel. These cancer cells produce and emit special molecules, so-called biomarkers, which are symptomatic for the presence of anomaly, into the cardiovascular system. Detection of cancer biomarkers with conventional blood tests is difficult in the early stages of a cancer due to the very low concentration of the biomarkers in the samples taken. However, close to the cancer cells, the concentration of the cancer biomarkers is high. Hence, detection is possible if a sensor with the ability to detect these biomarkers is placed in the vicinity of the cancer cells. Therefore, in this paper, we study the use of MNSs that are injected at a suitable injection site and can move through the blood vessels of the cardiovascular system, which potentially contain cancer cells. These MNSs can be activated by the biomarkers close to the cancer cells, where the biomarker concentration is sufficiently high. Eventually, the MNSs are collected by a fusion center (FC) where their activation levels are read and exploited to declare the presence of anomaly. We analytically derive the biomarker concentration in a network of interconnected blood vessels as well as the probability mass function of the MNSs' activation levels and validate the obtained results via particle-based simulations. Then, we derive the optimal decision rule for the FC regarding the presence of anomaly assuming that the entire network is known at the FC. Finally, for the FC, we propose a simple sum detector that does not require knowledge of the network topology. Our simulations reveal that while the optimal detector achieves a higher performance than the sum detector, both proposed detectors significantly outperform a benchmark scheme that uses fixed nanosensors at the FC.
Coattention-Based Multi-Perspective Matching Network for Machine Comprehension

Machine comprehension, the task for machine to understand the contextual meaning of a passage, is a central yet challenging problem in natural language processing. In this paper, we focus on the question answering task based on the SQuAD dataset and construct a system utilizes the power of coattention mechanism and multi-perspective context matching to formulate a powerful questionparagraph co-dependent encoder in order to aggregate the matching vectors from multiple perspectives to predict the answer span to the question. Experiment result on the test set of SQuAD shows that our model achieves a decent prediction result.
On the development of variable length Teager energy operator (VTEO)

Teager Energy Operator (TEO) proposed by Kaiser and Teager is based on a definition of energy required to generate the signal. TEO gives us the running estimate of energy as a function of amplitude and instantaneous frequency content of the signal. However, it considers three consecutive samples to calculate the energy estimate. In this paper, we suggests an alternative and generalized approach to TEO to calculate the instantaneous estimate of the energy where not only consecutive but other distant samples can also be incorporated in the calculation of running estimate of the energy and the number of samples taken to calculate energy can also be increased depending on our signal properties to better capture the energy content variations in the speech signal.
Cannabinoid signaling in health and disease.

Cannabis sativa has long been used for medicinal purposes. To improve safety and efficacy, compounds from C. sativa were purified or synthesized and named under an umbrella group as cannabinoids. Currently, several cannabinoids may be prescribed in Canada for a variety of indications such as nausea and pain. More recently, an increasing number of reports suggest other salutary effects associated with endogenous cannabinoid signaling including cardioprotection. The therapeutic potential of cannabinoids is therefore extended; however, evidence is limited and mechanisms remain unclear. In addition, the use of cannabinoids clinically has been hindered due to pronounced psychoactive side effects. This review provides an overview on the endocannabinoid system, including known physiological roles, and conditions in which cannabinoid receptor signaling has been implicated.
Skeletal class III malocclusion treated using a non-surgical approach supplemented with mini-implants: a case report.

We describe a 28-year-old man who sought orthodontic treatment complaining about the esthetics of his smile and difficulties associated with masticatory function. The patient had a straight facial profile, skeletal and dental class III relationship, anterior open bite and posterior crossbite. He refused orthognathic surgery and was therefore treated with camouflage orthodontics supplemented with the placement of one mini-implant in each side of the mandible to facilitate movement of the lower dentition distally, tooth-by-tooth. At the end of treatment, a class I molar relationship was obtained, with an ideal overjet and overbite and excellent intercuspation. Furthermore, the open bite and crossbite were corrected. Analysis 2 years after treatment revealed good stability of treatment outcome.
Personalized Prognostic Models for Oncology: A Machine Learning Approach

We have applied a little-known data transformation to subsets of the Surveillance, Epidemiology, and End Results (SEER) publically available data of the National Cancer Institute (NCI) to make it suitable input to standard machine learning classifiers. This transformation properly treats the right-censored data in the SEER data and the resulting Random Forest and Multi-Layer Perceptron models predict full survival curves. Treating the 6, 12, and 60 months points of the resulting survival curves as 3 binary classifiers, the 18 resulting classifiers have AUC values ranging from .765 to .885. Further evidence that the models have generalized well from the training data is provided by the extremely high levels of agreement between the random forest and neural network models predictions on the 6, 12, and 60 month binary classifiers.
Towards Evolutional Compression

Compressing convolutional neural networks (CNNs) is essential for transferring the success of CNNs to a wide variety of applications to mobile devices. In contrast to directly recognizing subtle weights or filters as redundant in a given CNN, this paper presents an evolutionary method to automatically eliminate redundant convolution filters. We represent each compressed network as a binary individual of specific fitness. Then, the population is upgraded at each evolutionary iteration using genetic operations. As a result, an extremely compact CNN is generated using the fittest individual. In this approach, either large or small convolution filters can be redundant, and filters in the compressed network are more distinct. In addition, since the number of filters in each convolutional layer is reduced, the number of filter channels and the size of feature maps are also decreased, naturally improving both the compression and speed-up ratios. Experiments on benchmark deep CNN models suggest the superiority of the proposed algorithm over the state-of-the-art compression methods.
Unsupervised Attention-guided Image-to-Image Translation

Current unsupervised image-to-image translation techniques struggle to focus their attention on individual objects without altering the background or the way multiple objects interact within a scene. Motivated by the important role of attention in human perception, we tackle this limitation by introducing unsupervised attention mechanisms that are jointly adversarially trained with the generators and discriminators. We demonstrate qualitatively and quantitatively that our approach attends to relevant regions in the image without requiring supervision, which creates more realistic mappings when compared to those of recent approaches.  Ours CycleGAN  RA  DiscoGAN  UNIT  DualGAN   By explicitly modeling attention, our algorithm is able to better alter the object of interest in unsupervised image-to-image translation tasks, without changing the background at the same time.
Surveying Aviation Professionals on the Security of the Air Traffic Control System

In this paper, we report findings from an exploratory study concerning the security of 15 different wireless technologies used in aviation. 242 aviation professionals and experts from 24 different countries completed an on-line questionnaire about their use and perceptions of each of these technologies. We examine the respondents’ familiarity with and reliance on each technology, with particular regard to their security. Furthermore, we analyse respondents’ perceptions of the possible impact of a wireless attack on the air traffic control system, from both a safety and a business point of view. We deepen these insights with statistical analysis comparing five different stakeholder groups: pilots, air traffic controllers, aviation authorities, aviation engineers, and private pilots.
What Is Being Played in the World? Mobile eSport Applications

In this study, the aim is to examine the most popular eSport applications at a global scale. In this context, the App Store and Google Play Store application platforms which have the highest number of users at a global scale were focused on. For this reason, the eSport applications included in these two platforms constituted the sampling of the present study. A data collection form was developed by the researcher of the study in order to collect the data in the study. This form included the number of the countries, the popularity ratings of the application, the name of the application, the type of it, the age limit, the rating of the likes, the company that developed it, the version and the first appearance date. The study was conducted with the Qualitative Research Method, and the Case Study design was made use of in this process; and the Descriptive Analysis Method was used to analyze the data. As a result of the study, it was determined that the most popular eSport applications at a global scale were football, which ranked the first, basketball, billiards, badminton, skateboarding, golf and dart. It was also determined that the popularity of the mobile eSport applications changed according to countries and according to being free or paid. It was determined that the popularity of these applications differed according to the individuals using the App Store and Google Play Store application markets. As a result, it is possible to claim that mobile eSport applications have a wide usage area at a global scale and are accepted widely. In addition, it was observed that the interest in eSport applications was similar to that in traditional sports. However, in the present study, a certain date was set, and the interest in mobile eSport applications was analyzed according to this specific date. In future studies, different dates and different fields like educational sciences may be set to analyze the interest in mobile eSport applications. In this way, findings may be obtained on the change of the interest in mobile eSport applications according to time. The findings of the present study and similar studies may have the quality of guiding researchers and system/software developers in terms of showing the present status of the topic and revealing the relevant needs.
Modular Brain Networks.

The development of new technologies for mapping structural and functional brain connectivity has led to the creation of comprehensive network maps of neuronal circuits and systems. The architecture of these brain networks can be examined and analyzed with a large variety of graph theory tools. Methods for detecting modules, or network communities, are of particular interest because they uncover major building blocks or subnetworks that are particularly densely connected, often corresponding to specialized functional components. A large number of methods for community detection have become available and are now widely applied in network neuroscience. This article first surveys a number of these methods, with an emphasis on their advantages and shortcomings; then it summarizes major findings on the existence of modules in both structural and functional brain networks and briefly considers their potential functional roles in brain evolution, wiring minimization, and the emergence of functional specialization and complex dynamics.
Automated UI & UX Framework

A consistent UI leaves an overall impression on user’s psychology, aesthetics and taste. Human–computer interaction (HCI) is the study of how humans interact with computer systems. Many disciplines contribute to HCI, including computer science, psychology, ergonomics, engineering, and graphic design. HCI is a broad term that covers all aspects of the way in which people interact with computers. In their daily lives, people are coming into contact with an increasing number of computer-based technologies. Some of these computer systems, such as personal computers, we use directly. We come into contact with other systems less directly — for example, we have all seen cashiers use laser scanners and digital cash registers when we shop. We have taken the same but in extensible line and made more solid justified by linking with other scientific pillars and concluded some of the best holistic base work for future innovations. It is done by inspecting various theories of Colour, Shape, Wave, Fonts, Design language and other miscellaneous theories in detail.  Karamvir Singh Rajpal, Mandeep Singh Rajpal, User Interface, User Experience, Design, Frontend, Neonex Technology,
Appearance of the fetal posterior fossa at 11 + 3 to 13 + 6 gestational weeks on transabdominal ultrasound examination.

To describe the sonographic appearance of the structures of the posterior cranial fossa in fetuses at 11 + 3 to 13 + 6 weeks of pregnancy and to determine whether abnormal findings of the brain and spine can be detected by sonography at this time.



This was a prospective study including 692 fetuses whose mothers attended Innsbruck Medical University Hospital for first-trimester sonography. In 3% (n = 21) of cases, measurement was prevented by fetal position. Of the remaining 671 cases, in 604 there was either a normal anomaly scan at 20 weeks or delivery of a healthy child and in these cases the transcerebellar diameter (TCD) and the anteroposterior diameter of the cisterna magna (CM), measured at 11 + 3 to 13 + 6 weeks, were analyzed. In 502 fetuses, the anteroposterior diameter of the fourth ventricle (4V) was also measured. In 25 fetuses, intra- and interobserver repeatability was calculated.



We observed a linear correlation between crown-rump length (CRL) and CM (CM = 0.0536 × CRL - 1.4701; R2 = 0.688), TCD (TCD = 0.1482 × CRL - 1.2083; R2 = 0.701) and 4V (4V = 0.0181 × CRL + 0.9186; R2 = 0.118). In three patients with posterior fossa cysts, measurements significantly exceeded the reference values. One fetus with spina bifida had an obliterated CM and the posterior border of the 4V could not be visualized.



Transabdominal sonographic assessment of the posterior fossa is feasible in the first trimester. Measurements of the 4V, the CM and the TCD performed at this time are reliable. The established reference values assist in detecting fetal anomalies. However, findings must be interpreted carefully, as some supposed malformations might be merely delayed development of brain structures.