nils-herrmann/clean_scirepeval huggingface dataset:
Evaluating the Readability of Text Simplification Output for Readers with Cognitive Disabilities

This paper presents an approach for automatic evaluation of the readability of text simplification output for readers with cognitive disabilities. First, we present our work towards the development of the EasyRead corpus, which contains easy-to-read documents created especially for people with cognitive disabilities. We then compare the EasyRead corpus to the simplified output contained in the LocalNews corpus (Feng, 2009), the accessibility of which has been evaluated through reading comprehension experiments including 20 adults with mild intellectual disability. This comparison is made on the basis of 13 disability-specific linguistic features. The comparison reveals that there are no major differences between the two corpora, which shows that the EasyRead corpus is to a similar reading level as the user-evaluated texts. We also discuss the role of Simple Wikipedia (Zhu et al., 2010) as a widely-used accessibility benchmark, in light of our finding that it is significantly more complex than both the EasyRead and the LocalNews corpora.
Deep multi-task and task-specific feature learning network for robust shape preserved organ segmentation

Fully convolutional network (FCN) has shown potency in segmenting heterogeneous objects from natural images with high run-time efficiency. This technique, however, is not able to produce continuous, smooth and shape-preserved regions consistently due to complex organ structures and occasional weak appearance information commonly observed in various anatomical structures in medical images. In this paper, we propose a deep end-to-end network with two task-specific branches to ensure continuousness, smoothness and shape-preservation in segmented structure without additionally sophisticated shape adjustment, e.g., dense conditional random fields. The novelties of the proposed method lie in three aspects. First, we formulate the organ segmentation as a multi-task learning process that combines both region and boundary identification tasks, which can alleviate spatially isolated segmentation errors. Second, we use boundary distance regression to ensure the smoothness of the segmented contours, instead of formulating boundary identification as a classification problem . Third, our deep network is designed to have a "Y" shape, i.e., the first half of the network is shared by both region and boundary identification tasks, while the second half is branched for each task independently. This architecture enables the task-specific feature learning for better region and boundary identification, and offers information for segmentation refinement based on a fusion scheme using energy functional. Extensive evaluations are conducted on a variety of applications across organs and modalities, e.g., MR femur, CT kidney, etc. Our proposed method shows better performance compared to the state-of-the-art methods.
Person Re-identification by Discriminatively Selecting Parts and Features

This paper presents a novel appearance-based method for person re-identification. The core idea is to rank and select different body parts on the basis of the discriminating power of their characteristic features. In our approach, we first segment the pedestrian images into meaningful parts, then we extract features from such parts as well as from the whole body and finally, we perform a salience analysis based on regression coefficients. Given a set of individuals, our method is able to estimate the different importance (or salience) of each body part automatically. To prove the effectiveness of our approach, we considered two standard datasets and we demonstrated through an exhaustive experimental section how our method improves significantly upon existing approaches, especially in multiple-shot scenarios.
The social brain in adolescence

The term 'social brain' refers to the network of brain regions that are involved in understanding others. Behaviour that is related to social cognition changes dramatically during human adolescence. This is paralleled by functional changes that occur in the social brain during this time, in particular in the medial prefrontal cortex and the superior temporal sulcus, which show altered activity during the performance of social cognitive tasks, such as face recognition and mental-state attribution. Research also indicates that, in humans, these parts of the social brain undergo structural development, including synaptic reorganization, during adolescence. Bringing together two relatively new and rapidly expanding areas of neuroscience — social neuroscience and the study of brain development during adolescence — will increase our understanding of how the social brain develops during adolescence.
A simplified design approach for radial power combiners

It is known that a radial power combiner is very effective in combining a large number of power amplifiers, where high efficiency (greater than 90%) over a relatively wide band can be achieved. However, its current use is limited due to its design complexity. In this paper, we develop a step-by-step design procedure, including both the initial approximate design formulas and suitable models for final accurate design optimization purposes. Based on three-dimensional electromagnetic modeling, predicted results were in excellent agreement with those measured. Practical issues related to the radial-combiner efficiency, its graceful degradation, and the effects of higher order package resonances are discussed here in detail
Building a Virtual HPC Cluster with Auto Scaling by the Docker

Solving the software dependency issue under the HPC environment has always been a difficult task for both computing system administrators and application scientists. This work would like to tackle the issue by introducing the modern container technology, the Docker, to be specific. By integrating the auto-scaling feature of service discovery with the light-weight virtualization tool, the Docker, the construction of a virtual cluster on top of physical cluster hardware is attempted. Thus, through the isolation of computing environment, a remedy of software dependency of HPC environment is possible. HPC; Docker; auto-scaling; service discovery; MPI; Consul;
Avramenko Diode Circuit Topology for Microwave Energy Harvesting in Goubau Line and Wireless Mediums

Wireless energy harvesting at microwave frequencies is a known challenge. The transmission medium to support delivery of a wireless energy is normally an insulator that conducts mainly a displacement current. However, Nikola Tesla has hinted that an electric energy can be transmitted on the surface of a natural medium in the same manner as it propagates in a single-wire transmission line. Armed with this knowledge, we have successfully employed a single-wire transmission methodology to deliver a wireless power over a distance in excess of 2.5 m. The proposed methodology involves the use of modified Avramenko diode configuration (ADC), which is known to be able to rectify an ac power in a single-wire transmission line into a dc voltage. The rectified voltage of an ADC, though small, can be enormously multiplied to boost the yield of the power of the harvested output. The results of our investigation suggest two facts: 1) the RF ground does not need to have any physical connection with the remote power source and 2) the output voltage is weakly dependent of the input current, proving the feasibility of harvesting energy from a transmission medium of high characteristic impedance. The proposed methodology is applicable to virtually all transmission mediums, including a single-wire transmission line, free space, and the air-ground interface.
Measuring the quality of web content using factual information

Nowadays, many decisions are based on information found in the Web. For the most part, the disseminating sources are not certified, and hence an assessment of the quality and credibility of Web content became more important than ever. With factual density we present a simple statistical quality measure that is based on facts extracted from Web content using Open Information Extraction. In a first case study, we use this measure to identify featured/good articles in Wikipedia. We compare the factual density measure with word count, a measure that has successfully been applied to this task in the past. Our evaluation corroborates the good performance of word count in Wikipedia since featured/good articles are often longer than non-featured. However, for articles of similar lengths the word count measure fails while factual density can separate between them with an F-measure of 90.4%. We also investigate the use of relational features for categorizing Wikipedia articles into featured/good versus non-featured ones. If articles have similar lengths, we achieve an F-measure of 86.7% and 84% otherwise.
Resting-state brain networks: literature review and clinical applications

This review focuses on resting-state functional connectivity, a functional MRI technique which allows the study of spontaneous brain activity generated under resting conditions. This approach is useful to explore the brain’s functional organization and to examine if it is altered in neurological or psychiatric diseases. Resting-state functional connectivity has revealed a number of networks which are consistently found in healthy subjects and represent specific patterns of synchronous activity. In this review, we examine the behavioral, physiological and neurological evidences relevant to this coherent brain activity and, in particular, to each network. The investigation of functional connectivity appears promising from a clinical perspective, considering the amount of evidence regarding the importance of spontaneous activity and that resting-state paradigms are inherently simple to implement. We also discuss some examples of existing clinical applications, such as in Alzheimer’s disease, and emerging possibilities such as in pre-operative mapping and disorders of consciousness.
BNS feature scaling: an improved representation over tf-idf for svm text classification

In the realm of machine learning for text classification, TF-IDF is the most widely used representation for real-valued feature vectors. However, IDF is oblivious to the training class labels and naturally scales some features inappropriately. We replace IDF with Bi-Normal Separation (BNS), which has been previously found to be excellent at ranking words for feature selection filtering. Empirical evaluation on a benchmark of 237 binary text classification tasks shows substantially better accuracy and F-measure for a Support Vector Machine (SVM) by using BNS scaling. A wide variety of other feature representations were later tested and found inferior, as well as binary features with no scaling. Moreover, BNS scaling yielded better performance without feature selection, obviating the need for feature selection.
OntoTrack: A semantic approach for ontology authoring

OntoTrack is an ontology authoring tool that combines a graph-based hierarchical layout and instant reasoning feedback within one single view. Currently OntoTrack can handle ontologies with an expressivity almost comparable to OWL Lite. The graphical representation provides an animated and zoomable subsumption graph with context sensitive features such as click-able miniature branches or selective detail views, together with drag-and-drop editing. Each editing step is instantly synchronised with an external reasoner in order to provide appropriate graphical feedback about relevant modelling consequences. A recent extention of OntoTrack provides an on-demand textual explanation for subsumption relationships between classes. This paper describes the key features of the current implementation and discusses future work, as well as some development issues. OntoTrack can be downloaded at http://www.informatik.uni-ulm.de/ki/ontotrack/.
Dual Quaternion Variational Integrator for Rigid Body Dynamic Simulation

We introduce a symplectic dual quaternion variational integrator(DQVI) for simulating single rigid body motion in all six degrees of freedom. Dual quaternion is used to represent rigid body kinematics and one-step Lie group variational integrator is used to conserve the geometric structure, energy and momentum of the system during the simulation. The combination of these two becomes the first Lie group variational integrator for rigid body simulation without decoupling translations and rotations. Newton-Raphson method is used to solve the recursive dynamic equation. This method is suitable for real-time rigid body simulations with high precision under large time step. DQVI respects the symplectic structure of the system with excellent long-term conservation of geometry structure, momentum and energy. It also allows the reference point and 6-by-6 inertia matrix to be arbitrarily defined, which is very convenient for a variety of engineering problems.
Generating Animatable 3D Virtual Humans from Photographs

We present an easy, practical and efficient full body cloning methodology. This system utilizes photos taken from the front, side and back of a person in any given imaging envir o ment without requiring a special background or a controlled illuminating condition. A seamless generic bo dy specified in the VRML H-Anim 1.1 format is used to generate an individualized virtual human. The system is c omposed of two major components: face-cloning and body-cloning. The face-cloning component uses feature poi nts n front and side images and then applies DFFD for shape modification. Next a fully automatic seamless text ur mapping is generated for 360 coloring on a 3D polygonal model. The body-cloning component has two step s: (i) feature points specification, which enables automatic silhouette detection in an arbitrary background (ii) two-stage body modification by using feature points and body silhouette respectively. The final integrated huma n odel has photo-realistic animatable face, hands, feet and body. The result can be visualized in any VRML compli ant browser.
2.4 GHz micro-strip patch antenna array with suppressed sidelobes

In this paper, we present the design and simulated results for a patch antenna array at 2.4 GHz. The antenna consists of one linear array with six series-fed patch antennas. In radar and smart-antenna applications it is very important to use antennas with high gain and low sidelobes in order to avoid the interference and to increase the gain of antenna array. The aim of this research is to design an antenna array that has suppressed sidelobes as much as possible in azimuth by using Kaiser-Bessel amplitude coefficients. Our designed antenna has a gain of 15.1 dB with an angular width of 14.6° at a frequency of 2.4 GHz. The measured 10 dB return loss bandwidth equals 18.7 MHz. Patch antenna array is analyzed using CST MICROWAVE STUDIO 2014.
Distributed Representations of Sentences and Documents

Many machine learning algorithms require the input to be represented as a fixed-length feature vector. When it comes to texts, one of the most common fixed-length features is bag-of-words. Despite their popularity, bag-of-words features have two major weaknesses: they lose the ordering of the words and they also ignore semantics of the words. For example, “powerful,” “strong” and “Paris” are equally distant. In this paper, we proposeParagraph Vector , an unsupervised algorithm that learns fixed-length feature representations from variable-length pieces of texts, such as sentences, paragraphs, and documents. Our algorithm represents each document by a dense vector which is trained to predict words in the document. Its construction gives our algorithm the potential to overcome the weaknesses of bag-ofwords models. Empirical results show that Paragraph Vectors outperform bag-of-words models as well as other techniques for text representations. Finally, we achieve new state-of-the-art results on several text classification and sentiment analysis tasks.
On the Euclidean distance of images

We present a new Euclidean distance for images, which we call image Euclidean distance (IMED). Unlike the traditional Euclidean distance, IMED takes into account the spatial relationships of pixels. Therefore, it is robust to small perturbation of images. We argue that IMED is the only intuitively reasonable Euclidean distance for images. IMED is then applied to image recognition. The key advantage of this distance measure is that it can be embedded in most image classification techniques such as SVM, LDA, and PCA. The embedding is rather efficient by involving a transformation referred to as standardizing transform (ST). We show that ST is a transform domain smoothing. Using the face recognition technology (FERET) database and two state-of-the-art face identification algorithms, we demonstrate a consistent performance improvement of the algorithms embedded with the new metric over their original versions.
Variability of Worked Examples and Transfer of Geometrical Problem-Solving Skills : A Cognitive-Load Approach

Four computer-based training strategies for geometrical problem solving in the domain of computer numerically controlled machinery programming were studied with regard to their effects on training performance, transfer performance, and cognitive load. A lowand a high-variability conventional condition, in which conventional practice problems had to be solved (followed by worked examples), were compared with a lowand a high-variability worked condition, in which worked examples had to be studied. Results showed that students who studied worked examples gained most from high-variability examples, invested less time and mental effort in practice, and attained better and less effort-demanding transfer performance than students who first attempted to solve conventional problems and then studied work examples.
Unsupervised document zone identification using probabilistic graphical models

Document zone identification aims to automatically classify sequences of text-spans (e.g. sentences) within a document into predefined zone categories. Current approaches to document zone identification mostly rely on supervised machine learning methods, which require a large amount of annotated data, which is often difficult and expensive to obtain. In order to overcome this bottleneck, we propose graphical models based on the popular Latent Dirichlet Allocation (LDA) model. The first model, which we call zoneLDA aims to cluster the sentences into zone classes using only unlabelled data. We also study an extension of zoneLDA called zoneLDAb, which makes distinction between common words and non-common words within the different zone types. We present results on two different domains: the scientific domain and the technical domain. For the latter one we propose a new document zone classification schema, which has been annotated over a collection of 689 documents, achieving a Kappa score of 85%. Overall our experiments show promising results for both of the domains, outperforming the baseline model. Furthermore, on the technical domain the performance of the models are comparable to the supervised approach using the same feature sets. We thus believe that graphical models are a promising avenue of research for automatic document zoning.
Tasks and scenario-based evaluation of information visualization techniques

Usability evaluation of an information visualization technique can only be done by the joint evaluation of both the visual representation and the interaction techniques. This work proposes task models as a key element for carrying out such evaluations in a structured way. We base our work on a taxonomy abstracting from rendering functions supported by information visualization techniques. CTTE is used to model these abstract visual tasks as well as to generate scenarios from this model for evaluation purposes. We conclude that the use of task models allows generating test scenarios which are more effective than informal and unstructured evaluations.
Learning to solve inverse problems using Wasserstein loss

We propose using the Wasserstein loss for training in inverse problems. In particular, we consider a learned primal-dual reconstruction scheme for ill-posed inverse problems using the Wasserstein distance as loss function in the learning. This is motivated by miss-alignments in training data, which when using standard mean squared error loss could severely degrade reconstruction quality. We prove that training with the Wasserstein loss gives a reconstruction operator that correctly compensates for miss-alignments in certain cases, whereas training with the mean squared error gives a smeared reconstruction. Moreover, we demonstrate these effects by training a reconstruction algorithm using both mean squared error and optimal transport loss for a problem in computerized tomography.
Neural Machine Translation of Rare Words with Subword Units

Neural machine translation (NMT) models typically operate with a fixed vocabulary, but translation is an open-vocabulary problem. Previous work addresses the translation of out-of-vocabulary words by backing off to a dictionary. In this paper, we introduce a simpler and more effective approach, making the NMT model capable of open-vocabulary translation by encoding rare and unknown words as sequences of subword units. This is based on the intuition that various word classes are translatable via smaller units than words, for instance names (via character copying or transliteration), compounds (via compositional translation), and cognates and loanwords (via phonological and morphological transformations). We discuss the suitability of different word segmentation techniques, including simple character ngram models and a segmentation based on the byte pair encoding compression algorithm, and empirically show that subword models improve over a back-off dictionary baseline for the WMT 15 translation tasks English→German and English→Russian by up to 1.1 and 1.3 BLEU, respectively.
A Survey of Distributed Mining of Data Streams

With advances in data collection and generation technologies, organizations and researchers are faced with the ever growing problem of how to manage and analyze large dynamic datasets. Environments that produce streaming sources of data are becoming common place. Examples include stock market, sensor, web click stream, and network data. In many instances, these environments are also equipped with multiple distributed computing nodes that are often located near the data sources. Analyzing and monitoring data in such environments requires data mining technology that is cognizant of the mining task, the distributed nature of the data, and the data influx rate. In this chapter, we survey the current state of the field and identify potential directions of future research.