nils-herrmann/clean_scirepeval huggingface dataset:
Explicit dynamics process simulation of linear coil winding for electric drives production

Slowly but steadily, more and more electric vehicles push onto the consumer market. For a cost efficient production of electrical engines, in first-class quality and in sufficient quantity, it is indispensable to understand the process of coil winding. Thereby the prediction of the wire behavior is one of the key challenges. Therefore, a detailed model is built to investigate wire behavior during the linear winding process. The finite element based simulation tool LS-DYNA serves as explicit dynamics tool. To represent the high dynamic process of winding within this simulation, some first adaptions have to be made. This means, that dynamic influences such as rotational speed or acceleration of the coil body are definable. Within process simulation, the given boundary conditions are applied to the model. The material properties of the wire under scrutiny are validated by a tensile test and by the values out of datasheets in previous research. In order to achieve the best convergence, different contact algorithms are selected for each individual contact behavior. Furthermore, specific adjustments to the mesh are necessary to gain significant results. State of the art in coil winding is an experimental procedure, which delivers adequate process parameters and, thus, expertise in winding technology. Nevertheless, there are a lot of different, interacting parameters, which have to be optimized in terms of boundary conditions. The simulation model of winding process, in which varying parameters can be optimized pertaining to the optimal winding result, calls for extensive research in this field. The generated model enables the user not only to influence the process parameters but also to modify the geometry of a winding body. To make the simulation scientifically sound, it is validated by previous experiments and simulations
Adaptation Regularization: A General Framework for Transfer Learning

Domain transfer learning, which learns a target classifier using labeled data from a different distribution, has shown promising value in knowledge discovery yet still been a challenging problem. Most previous works designed adaptive classifiers by exploring two learning strategies independently: distribution adaptation and label propagation. In this paper, we propose a novel transfer learning framework, referred to as Adaptation Regularization based Transfer Learning (ARTL), to model them in a unified way based on the structural risk minimization principle and the regularization theory. Specifically, ARTL learns the adaptive classifier by simultaneously optimizing the structural risk functional, the joint distribution matching between domains, and the manifold consistency underlying marginal distribution. Based on the framework, we propose two novel methods using Regularized Least Squares (RLS) and Support Vector Machines (SVMs), respectively, and use the Representer theorem in reproducing kernel Hilbert space to derive corresponding solutions. Comprehensive experiments verify that ARTL can significantly outperform state-of-the-art learning methods on several public text and image datasets.
Still looking: investigating seamless gaze-supported selection, positioning, and manipulation of distant targets

We investigate how to seamlessly bridge the gap between users and distant displays for basic interaction tasks, such as object selection and manipulation. For this, we take advantage of very fast and implicit, yet imprecise gaze- and head-directed input in combination with ubiquitous smartphones for additional manual touch control. We have carefully elaborated two novel and consistent sets of gaze-supported interaction techniques based on touch-enhanced gaze pointers and local magnification lenses. These conflict-free sets allow for fluently selecting and positioning distant targets. Both sets were evaluated in a user study with 16 participants. Overall, users were fastest with a touch-enhanced gaze pointer for selecting and positioning an object after some training. While the positive user feedback for both sets suggests that our proposed gaze- and head-directed interaction techniques are suitable for a convenient and fluent selection and manipulation of distant targets, further improvements are necessary for more precise cursor control.
Learning Factors Analysis - A General Method for Cognitive Model Evaluation and Improvement

A cognitive model is a set of production rules or skills encoded in intelligent tutors to model how students solve problems. It is usually generated by brainstorming and iterative refinement between subject experts, cognitive scientists and programmers. In this paper we propose a semi-automated method for improving a cognitive model called Learning Factors Analysis that combines a statistical model, human expertise and a combinatorial search. We use this method to evaluate an existing cognitive model and to generate and evaluate alternative models. We present improved cognitive models and make suggestions for improving the intelligent tutor based on those models.
DARPA's Nitride Electronic NeXt Generation Technology Program

DARPA/MTO has sponsored electronics programs to exploit the unique combination of high intrinsic breakdown voltages, high electron saturation velocities, and large sheet carrier densities of the nitride material system. The NEXT program is pushing III-N-based HEMTs toward its operating frequency (size) scaling limits by simultaneously minimizing carrier transit time, maximizing electron density, reducing access resistances and optimizing parasitic capacitances with innovative epitaxial structures and dielectric heterointerfaces. The Phase I goals of the NEXT program are to demonstrate 300 GHz D-mode and 200 GHz E-mode HEMTs while maintaining the breakdown voltage and transistor cutoff frequency product of more than 5 THz•Volt. The final goal of the NEXT program is to enable a 1000-transistor, high-yield, 500 GHz E/D-mode GaN technology for mixed signal applications.
Semi-Supervised Word Sense Disambiguation Using Word Embeddings in General and Specific Domains

One of the weaknesses of current supervised word sense disambiguation (WSD) systems is that they only treat a word as a discrete entity. However, a continuous-space representation of words (word embeddings) can provide valuable information and thus improve generalization accuracy. Since word embeddings are typically obtained from unlabeled data using unsupervised methods, this method can be seen as a semi-supervised word sense disambiguation approach. This paper investigates two ways of incorporating word embeddings in a word sense disambiguation setting and evaluates these two methods on some SensEval/SemEval lexical sample and all-words tasks and also a domain-specific lexical sample task. The obtained results show that such representations consistently improve the accuracy of the selected supervised WSD system. Moreover, our experiments on a domainspecific dataset show that our supervised baseline system beats the best knowledge-based systems by a large margin.
FLOWGUARD: building robust firewalls for software-defined networks

Software-Defined Networking (SDN) introduces significant granularity, visibility and flexibility to networking, but at the same time brings forth new security challenges. One of the fundamental challenges is to build robust firewalls for protecting OpenFlow-based networks where network states and traffic are frequently changed. To address this challenge, we introduce FlowGuard, a comprehensive framework, to facilitate not only accurate detection but also effective resolution of firewall policy violations in dynamic OpenFlow-based networks. FlowGuard checks network flow path spaces to detect firewall policy violations when network states are updated. In addition, FlowGuard conducts automatic and real-time violation resolutions with the help of several innovative resolution strategies designed for diverse network update situations. We also implement our framework and demonstrate the efficacy and efficiency of the proposed detection and resolution approaches in FlowGuard through experiments with a real-world network topology.
Online branding

The role of brands and branding in the new economy that is characterised by digitisation and globalisation are attracting considerable attention. Taking the organisational perspective the challenges for branding in online environments relate to: the message capacity of Web pages, the need to integrate branding and marketing communications across different channels, the trend towards organisational value propositions, brands as search keys, the opportunity to link and develop brand positions, globalisation, and the increased engagement of the public sector with branding. In the context of the brand experience, key themes are customer control, customisation and customer relationships, the help yourself nature of the medium, the increasing emphasis on experience, and the opportunity offered by m-commerce to revolutionise the brand experience. An online brand development strategy includes the following stages: setting the context for the brand, deciding on brand objectives and message; developing a brand specification; developing a brand design, creating the Web site and other communications using the brand, launching and promoting the brand, building the brand experience, and finally, reviewing, evolving and protecting the brand.
Fit-sphere unwrapping and performance analysis of 3D fingerprints.

To solve problems associated with conventional 2D fingerprint acquisition processes including skin deformations and print smearing, we developed a noncontact 3D fingerprint scanner employing structured light illumination that, in order to be backwards compatible with existing 2D fingerprint recognition systems, requires a method of unwrapping the 3D scans into 2D equivalent prints. For the latter purpose of virtually flattening a 3D print, this paper introduces a fit-sphere unwrapping algorithm. Taking advantage of detailed 3D information, the proposed method defuses the unwrapping distortion by controlling the distances between neighboring points. Experimental results will demonstrate the high quality and recognition performance of the 3D unwrapped prints versus traditionally collected 2D prints. Furthermore, by classifying the 3D database into high- and low-quality data sets, we demonstrate that the relationship between quality and recognition performance holding for conventional 2D prints is achieved for 3D unwrapped fingerprints.
Acoustic sequences in non-human animals: a tutorial review and prospectus.

Animal acoustic communication often takes the form of complex sequences, made up of multiple distinct acoustic units. Apart from the well-known example of birdsong, other animals such as insects, amphibians, and mammals (including bats, rodents, primates, and cetaceans) also generate complex acoustic sequences. Occasionally, such as with birdsong, the adaptive role of these sequences seems clear (e.g. mate attraction and territorial defence). More often however, researchers have only begun to characterise - let alone understand - the significance and meaning of acoustic sequences. Hypotheses abound, but there is little agreement as to how sequences should be defined and analysed. Our review aims to outline suitable methods for testing these hypotheses, and to describe the major limitations to our current and near-future knowledge on questions of acoustic sequences. This review and prospectus is the result of a collaborative effort between 43 scientists from the fields of animal behaviour, ecology and evolution, signal processing, machine learning, quantitative linguistics, and information theory, who gathered for a 2013 workshop entitled, 'Analysing vocal sequences in animals'. Our goal is to present not just a review of the state of the art, but to propose a methodological framework that summarises what we suggest are the best practices for research in this field, across taxa and across disciplines. We also provide a tutorial-style introduction to some of the most promising algorithmic approaches for analysing sequences. We divide our review into three sections: identifying the distinct units of an acoustic sequence, describing the different ways that information can be contained within a sequence, and analysing the structure of that sequence. Each of these sections is further subdivided to address the key questions and approaches in that area. We propose a uniform, systematic, and comprehensive approach to studying sequences, with the goal of clarifying research terms used in different fields, and facilitating collaboration and comparative studies. Allowing greater interdisciplinary collaboration will facilitate the investigation of many important questions in the evolution of communication and sociality.
In-Ear EEG Biometrics for Feasible and Readily Collectable Real-World Person Authentication

The use of electroencephalogram (EEG) as a biometrics modality has been investigated for about a decade; however, its feasibility in real-world applications is not yet conclusively established, mainly due to the issues with collectability and reproducibility. To this end, we propose a readily deployable EEG biometrics system based on a “one-fits-all” viscoelastic generic in-ear EEG sensor (collectability), which does not require skilled assistance or cumbersome preparation. Unlike most existing studies, we consider data recorded over multiple recording days and for multiple subjects (reproducibility) while, for rigour, the training and test segments are not taken from the same recording days. A robust approach is considered based on the resting state with eyes closed paradigm, the use of both parametric (autoregressive model) and non-parametric (spectral) features, and supported by simple and fast cosine distance, linear discriminant analysis, and support vector machine classifiers. Both the verification and identification forensics scenarios are considered and the achieved results are on par with the studies based on impractical on-scalp recordings. Comprehensive analysis over a number of subjects, setups, and analysis features demonstrates the feasibility of the proposed ear-EEG biometrics, and its potential in resolving the critical collectability, robustness, and reproducibility issues associated with current EEG biometrics.
Secondary-Side-Regulated Soft-Switching Full-Bridge Three-Port Converter Based on Bridgeless Boost Rectifier and Bidirectional Converter for Multiple Energy Interface

A systematic method for deriving soft-switching three-port converters (TPCs), which can interface multiple energy, is proposed in this paper. Novel full-bridge (FB) TPCs featuring single-stage power conversion, reduced conduction loss, and low-voltage stress are derived. Two nonisolated bidirectional power ports and one isolated unidirectional load port are provided by integrating an interleaved bidirectional Buck/Boost converter and a bridgeless Boost rectifier via a high-frequency transformer. The switching bridges on the primary side are shared; hence, the number of active switches is reduced. Primary-side pulse width modulation and secondary-side phase shift control strategy are employed to provide two control freedoms. Voltage and power regulations over two of the three power ports are achieved. Furthermore, the current/voltage ripples on the primary-side power ports are reduced due to the interleaving operation. Zero-voltage switching and zero-current switching are realized for the active switches and diodes, respectively. A typical FB-TPC with voltage-doubler rectifier developed by the proposed method is analyzed in detail. Operation principles, control strategy, and characteristics of the FB-TPC are presented. Experiments have been carried out to demonstrate the feasibility and effectiveness of the proposed topology derivation method.
A computational model for periodic pattern perception based on frieze and wallpaper groups

We present a computational model for periodic pattern perception based on the mathematical theory of crystallographic groups. In each N-dimensional Euclidean space, a finite number of symmetry groups can characterize the structures of an infinite variety of periodic patterns. In 2D space, there are seven frieze groups describing monochrome patterns that repeat along one direction and 17 wallpaper groups for patterns that repeat along two linearly independent directions to tile the plane. We develop a set of computer algorithms that "understand" a given periodic pattern by automatically finding its underlying lattice, identifying its symmetry group, and extracting its representative motifs. We also extend this computational model for near-periodic patterns using geometric AIC. Applications of such a computational model include pattern indexing, texture synthesis, image compression, and gait analysis.
How Do Users Like This Feature? A Fine Grained Sentiment Analysis of App Reviews

App stores allow users to submit feedback for downloaded apps in form of star ratings and text reviews. Recent studies analyzed this feedback and found that it includes information useful for app developers, such as user requirements, ideas for improvements, user sentiments about specific features, and descriptions of experiences with these features. However, for many apps, the amount of reviews is too large to be processed manually and their quality varies largely. The star ratings are given to the whole app and developers do not have a mean to analyze the feedback for the single features. In this paper we propose an automated approach that helps developers filter, aggregate, and analyze user reviews. We use natural language processing techniques to identify fine-grained app features in the reviews. We then extract the user sentiments about the identified features and give them a general score across all reviews. Finally, we use topic modeling techniques to group fine-grained features into more meaningful high-level features. We evaluated our approach with 7 apps from the Apple App Store and Google Play Store and compared its results with a manually, peer-conducted analysis of the reviews. On average, our approach has a precision of 0.59 and a recall of 0.51. The extracted features were coherent and relevant to requirements evolution tasks. Our approach can help app developers to systematically analyze user opinions about single features and filter irrelevant reviews.
Demographics, behavior problems, and psychosexual characteristics of adolescents with gender identity disorder or transvestic fetishism.

This study provided a descriptive and quantitative comparative analysis of data from an assessment protocol for adolescents referred clinically for gender identity disorder (n = 192; 105 boys, 87 girls) or transvestic fetishism (n = 137, all boys). The protocol included information on demographics, behavior problems, and psychosexual measures. Gender identity disorder and transvestic fetishism youth had high rates of general behavior problems and poor peer relations. On the psychosexual measures, gender identity disorder patients had considerably greater cross-gender behavior and gender dysphoria than did transvestic fetishism youth and other control youth. Male gender identity disorder patients classified as having a nonhomosexual sexual orientation (in relation to birth sex) reported more indicators of transvestic fetishism than did male gender identity disorder patients classified as having a homosexual sexual orientation (in relation to birth sex). The percentage of transvestic fetishism youth and male gender identity disorder patients with a nonhomosexual sexual orientation self-reported similar degrees of behaviors pertaining to transvestic fetishism. Last, male and female gender identity disorder patients with a homosexual sexual orientation had more recalled cross-gender behavior during childhood and more concurrent cross-gender behavior and gender dysphoria than did patients with a nonhomosexual sexual orientation. The authors discuss the clinical utility of their assessment protocol.
Curbing the roofline: a scalable and flexible architecture for CNNs on FPGA

Convolutional Neural Networks (CNNs) have reached outstanding results in several complex visual recognition tasks, such as classification and scene parsing. CNNs are composed of multiple filtering layers that perform 2D convolutions over input images. The intrinsic parallelism in such a computation kernel makes it suitable to be effectively accelerated on parallel hardware. In this paper we propose a highly flexible and scalable architectural template for acceleration of CNNs on FPGA devices, based on the cooperation between a set of software cores and a parallel convolution engine that communicate via a tightly coupled L1 shared scratchpad. Our accelerator structure, tested on a Xilinx Zynq XC-Z7045 device, delivers peak performance up to 80 GMAC/s, corresponding to 100 MMAC/s for each DSP slice in the programmable fabric. Thanks to the flexible architecture, convolution operations can be scheduled in order to reduce input/output bandwidth down to 8 bytes per cycle without degrading the performance of the accelerator in most of the meaningful use-cases.
DisplaySkin: Exploring Pose-Aware Displays on a Flexible Electrophoretic Wristband

Mobile devices can provide people with contextual information. This information may benefit a primary activity, assuming it is easily accessible. In this paper, we present DisplaySkin, a pose-aware device with a flexible display circling the wrist. DisplaySkin creates a kinematic model of a user's arm and uses it to place information in view, independent of body pose. In doing so, DisplaySkin aims to minimize the cost of accessing information without being intrusive. We evaluated our pose-aware display with a rotational pointing task, which was interrupted by a notification on DisplaySkin. Results show that a pose-aware display reduces the time required to respond to notifications on the wrist.
Iterative Instance Segmentation

Existing methods for pixel-wise labelling tasks generally disregard the underlying structure of labellings, often leading to predictions that are visually implausible. While incorporating structure into the model should improve prediction quality, doing so is challenging - manually specifying the form of structural constraints may be impractical and inference often becomes intractable even if structural constraints are given. We sidestep this problem by reducing structured prediction to a sequence of unconstrained prediction problems and demonstrate that this approach is capable of automatically discovering priors on shape, contiguity of region predictions and smoothness of region contours from data without any a priori specification. On the instance segmentation task, this method outperforms the state-of-the-art, achieving a mean APr of 63:6% at 50% overlap and 43:3% at 70% overlap.