nils-herrmann/clean_scirepeval huggingface dataset:
Real-time saliency-aware video abstraction

Existing real-time automatic video abstraction systems rely on local contrast only for identifying perceptually important information and abstract imagery by reducing contrast in low-contrast regions while artificially increasing contrast in higher contrast regions. These methods, however, may fail to accentuate an object against its background for the images with objects of low contrast over background of high contrast. To solve this problem, we propose a progressive abstraction method based on a region-of-interest function derived from an elaborate perception model. Visual contents in perceptually salient regions are emphasized, whereas the background is abstracted appropriately. In addition, the edge-preserving smoothing and line drawing algorithms in this paper are guided by a vector field which describes the flow of salient features of the input image. The whole pipeline can be executed automatically in real time on the GPU, without requiring any user intervention. Several experimental examples are shown to demonstrate the effectiveness of our approach.
VizWiz: nearly real-time answers to visual questions

Visual information pervades our environment. Vision is used to decide everything from what we want to eat at a restaurant and which bus route to take to whether our clothes match and how long until the milk expires. Individually, the inability to interpret such visual information is a nuisance for blind people who often have effective, if inefficient, work-arounds to overcome them. Collectively, however, they can make blind people less independent. Specialized technology addresses some problems in this space, but automatic approaches cannot yet answer the vast majority of visual questions that blind people may have. VizWiz addresses this shortcoming by using the Internet connections and cameras on existing smartphones to connect blind people and their questions to remote paid workers' answers. VizWiz is designed to have low latency and low cost, making it both competitive with expensive automatic solutions and much more versatile.
OpenSoT: A whole-body control library for the compliant humanoid robot COMAN

A fundamental aspect of controlling humanoid robots lies in the capability to exploit the whole body to perform tasks. This work introduces a novel whole body control library called OpenSoT. OpenSoT is combined with joint impedance control to create a framework that can effectively generate complex whole body motion behaviors for humanoids according to the needs of the interaction level of the tasks. OpenSoT gives an easy way to implement tasks, constraints, bounds and solvers by providing common interfaces. We present the mathematical foundation of the library and validate it on the compliant humanoid robot COMAN to execute multiple motion tasks under a number of constraints. The framework is able to solve hierarchies of tasks of arbitrary complexity in a robust and reliable way.
Layered foil as an alternative to litz wire: Multiple methods for equal current sharing among layers

Litz wire is useful for high-frequency power but has high cost, limited thermal conductivity, and decreased effectiveness above 1 MHz. Multiple parallel layers of foil have the potential to overcome these limitations, but techniques are needed to enforce current sharing between the foil layers, as is accomplished by twisting in litz wire. Four strategies for this include already known techniques for interchanging foil layer positions, which are reviewed, and three new approaches: 1) balancing flux linkage by adjusting spacing between layers, 2) using capacitance between layers to provide ballast impedance, and 3) adding miniature balancing transformers to the foil terminations. The methods are analyzed and their scope of applicability is compared.
Investigation of Speech Separation as a Front-End for Noise Robust Speech Recognition

Recently, supervised classification has been shown to work well for the task of speech separation. We perform an in-depth evaluation of such techniques as a front-end for noise-robust automatic speech recognition (ASR). The proposed separation front-end consists of two stages. The first stage removes additive noise via time-frequency masking. The second stage addresses channel mismatch and the distortions introduced by the first stage; a non-linear function is learned that maps the masked spectral features to their clean counterpart. Results show that the proposed front-end substantially improves ASR performance when the acoustic models are trained in clean conditions. We also propose a diagonal feature discriminant linear regression (dFDLR) adaptation that can be performed on a per-utterance basis for ASR systems employing deep neural networks and HMM. Results show that dFDLR consistently improves performance in all test conditions. Surprisingly, the best average results are obtained when dFDLR is applied to models trained using noisy log-Mel spectral features from the multi-condition training set. With no channel mismatch, the best results are obtained when the proposed speech separation front-end is used along with multi-condition training using log-Mel features followed by dFDLR adaptation. Both these results are among the best on the Aurora-4 dataset.
ELECTROMAGNETIC DESIGN AND ANALYSIS OF A NOVEL MAGNETIC-GEAR-INTEGRATED WIND POWER GENERATOR USING TIME-STEPPING FINITE ELEMENT METHOD

This paper presents a novel permanent-magnet (PM) machine for wind power generation. In order to achieve high power/torque density as well as get rid of the nuisances aroused by the mechanical gearbox, a coaxial magnetic gear (CMG) is engaged. Different from the existing integrated machine in which armature windings are deployed in the inner bore of the CMG as an individual part, stator windings are directly inserted among the slots between the ferromagnetic segments in this proposed machine. Thus, it can offer several merits, such as simpler mechanical structure, better utilization of PM materials and lower manufacturing cost. Moreover, by artfully designing the connection of the armature windings, the electromagnetic coupling between the windings and the outer rotor PMs can be dramatically decreased, and the electromechanical energy conversion can be achieved by the field interaction between the inner rotor PMs and the armature windings.  February 2011, Scheduled 8 February 2011  Linni Jian (ln.jian@siat.ac.cn).
Video Summarization Using Singular Value Decomposition

In this paper, we propose a novel technique for video summarization based on the Singular Value Decomposition (SVD). For the input video sequence, we create a feature-frame matrix A, and perform the SVD on it. From this SVD, we are able to not only derive the reened feature space to better cluster visually similar frames, but also deene a metric to measure the amount of visual content contained in each frame cluster using its degree of visual changes. Then, in the reened feature space, we nd the most static frame cluster, deene it as the content unit, and use the content value computed from it as the threshold to cluster the rest of the frames. Based on this clustering result, either the optimal set of keyframes, or a summarized motion video with the user speciied time length can be generated to support diierent user requirements for video browsing and content overview. Our approach ensures that the summarized video representation contains little redundancy , and gives equal attention to the same amount of contents.
Optimized Execution of Business Processes on Blockchain

Blockchain technology enables the execution of collaborative business processes involving untrusted parties without requiring a central authority. Specifically, a process model comprising tasks performed by multiple parties can be coordinated via smart contracts operating on the blockchain. The consensus mechanism governing the blockchain thereby guarantees that the process model is followed by each party. However, the cost required for blockchain use is highly dependent on the volume of data recorded and the frequency of data updates by smart contracts. This paper proposes an optimized method for executing business processes on top of commodity blockchain technology. The paper presents a method for compiling a process model into a smart contract that encodes the preconditions for executing each task in the process using a space-optimized data structure. The method is empirically compared to a previously proposed baseline by replaying execution logs, including one from a real-life business process, and measuring resource consumption.
Intrinsic parameter calibration procedure for a (high-distortion) fish-eye lens camera with distortion model and accuracy estimation

-This paper presents a calibration procedure for a fish-eye lens (a high-distortion lens) mounted on a CCD TV camera. The method is designed to account for the differences in images acquired via a distortion-free lens camera setup and the images obtained by a fish-eye lens camera. The calibration procedure essentially defines a mapping between points in the world coordinate system and their corresponding point locations in the image plane. This step is important for applications in computer vision which involve quantitative measurements. The objective of this mapping is to estimate the internal parameters of the camera, including the effective focal length, one-pixel width on the image plane, image distortion center, and distortion coefficients. The number of parameters to be calibrated is reduced by using a calibration pattern with equally spaced dots and assuming a pin-hole model camera behavior for the image center, thus assuming negligible distortion at the image distortion center. Our method employs a non-finear transformation between points in the world coordinate system and their corresponding location on the image plane. A Lagrangian minimization method is used to determine the coefficients of the transformation. The validity and effectiveness of our calibration and distortion correction procedure are confirmed by application of this procedure on real images.  Camera calibration Lens distortion Intrinsic camera parameters Fish-eye lens Optimization
Adversarial training for multi-context joint entity and relation extraction

Adversarial training (AT) is a regularization method that can be used to improve the robustness of neural network methods by adding small perturbations in the training data. We show how to use AT for the tasks of entity recognition and relation extraction. In particular, we demonstrate that applying AT to a general purpose baseline model for jointly extracting entities and relations, allows improving the stateof-the-art effectiveness on several datasets in different contexts (i.e., news, biomedical, and real estate data) and for different languages (English and Dutch).
mmWave mobile broadband (MMB): Unleashing the 3–300GHz spectrum

Almost all cellular mobile communications including first generation analog systems, second generation digital systems, third generation WCDMA, and fourth generation OFDMA systems use Ultra High Frequency (UHF) band of radio spectrum with frequencies in the range of 300MHz-3GHz. This band of spectrum is becoming increasingly crowded due to spectacular growth in mobile data and other related services. The portion of the RF spectrum above 3GHz has largely been uxexploited for commercial mobile applications. In this paper, we reason why wireless community should start looking at 3–300GHz spectrum for mobile broadband applications. We discuss propagation and device technology challenges associated with this band as well as its unique advantages such as spectrum availability and small component sizes for mobile applications.
Part level transfer regularization for enhancing exemplar SVMs

Exemplar SVMs (E-SVMs, Malisiewicz et al., ICCV 2011), where an SVM is trained with only a single positive sample, have found applications in the areas of object detection and content-based image retrieval (CBIR), amongst others. In this paper we introduce a method of part based transfer regularization that boosts the performance of E-SVMs, with a negligible additional cost. This enhanced E-SVM (EE-SVM) improves the generalization ability of E-SVMs by softly forcing it to be constructed from existing classifier parts cropped from previously learned classifiers. In CBIR applications, where the aim is to retrieve instances of the same object class in a similar pose, the EE-SVM is able to tolerate increased levels of intra-class variation, including occlusions and truncations, over E-SVM, and thereby increases precision and recall. In addition to transferring parts, we introduce a method for transferring the statistics between the parts and also show that there is an equivalence between transfer regularization and feature augmentation for this problem and others, with the consequence that the new objective function can be optimized using standard libraries. EE-SVM is evaluated both quantitatively and qualitatively on the PASCAL VOC 2007 and ImageNet datasets for pose specific object retrieval. It achieves a significant performance improvement over E-SVMs, with greater suppression of negative detections and increased recall, whilst maintaining the same ease of training and testing.
Elements of Wireless Power Transfer Essential to High Power Charging of Heavy Duty Vehicles

Scaling wireless charging to power levels suitable for heavy duty passenger vehicles and mass transit bus requires indepth assessment of wireless power transfer (WPT) architectures, component sizing and stress, package size, electrical insulation requirements, parasitic loss elements, and cost minimization. It is demonstrated through an architecture comparison that the voltage rating of the power inverter semiconductors will be higher for inductor-capacitor-capacitor (LCC) than for a more conventional Series-Parallel (S-P) tuning. Higher voltage at the source inverter dc bus facilitates better utilization of the semiconductors, hence lower cost. Electrical and thermal stress factors of the passive components are explored, in particular the compensating capacitors and coupling coils. Experimental results are presented for a prototype, precommercial, 10 kW wireless charger designed for heavy duty (HD) vehicle application. Results are in good agreement with theory and validate a design that minimizes component stress.
On the Recursive Neural Networks for Relation Extraction and Entity Recognition

Recently there has been a surge of interest in neural architectures for complex structured learning tasks. Along this track, we are addressing the supervised task of relation extraction and named-entity recognition via recursive neural structures and deep unsupervised feature learning. Our models are inspired by several recent works in deep learning for natural language. We have extended the previous models, and evaluated them in various scenarios, for relation extraction and namedentity recognition. In the models, we avoid using any external features, so as to investigate the power of representation instead of feature engineering. We implement the models and proposed some more general models for future work. We will briefly review previous works on deep learning and give a brief overview of recent progresses relation extraction and named-entity recognition.
Improving Aggregated Search Coherence

Aggregated search is that task of blending results from different search services, or verticals, into the core web results. Aggregated search coherence is the extent to which results from different sources focus on similar senses of an ambiguous or underspecified query. Prior research studied the effect of aggregated search coherence on search behavior and found that the query-senses in the vertical results can affect user interaction with the web results. In this work, we develop and evaluate algorithms for vertical results selection—deciding which results from a particular vertical to display. Results from a large-scale user study suggest that algorithms that improve the level of coherence between the vertical and web results influence users to make more productive decisions with respect to the web results—to engage with the web results when at least one of them is relevant and, to a lesser extent, to avoid engaging with the web results otherwise.
Predicting Air Compressor Failures with Echo State Networks

Modern vehicles have increasing amounts of data streaming continuously on-board their controller area networks. These data are primarily used for controlling the vehicle and for feedback to the driver, but they can also be exploited to detect faults and predict failures. The traditional diagnostics paradigm, which relies heavily on human expert knowledge, scales poorly with the increasing amounts of data generated by highly digitised systems. The next generation of equipment monitoring and maintenance prediction solutions will therefore require a different approach, where systems can build up knowledge (semi-)autonomously and learn over the lifetime of the equipment. A key feature in such systems is the ability to capture and encode characteristics of signals, or groups of signals, on-board vehicles using different models. Methods that do this robustly and reliably can be used to describe and compare the operation of the vehicle to previous time periods or to other similar vehicles. In this paper two models for doing this, for a single signal, are presented and compared on a case of on-road failures caused by air compressor faults in city buses. One approach is based on histograms and the other is based on echo state networks. It is shown that both methods are sensitive to the expected changes in the signal’s characteristics and work well on simulated data. However, the histogram model, despite being simpler, handles the deviations in real data better than the echo state network.
A design theory for digital platforms supporting online communities: a multiple case study Open

This research proposes and validates a design theory for digital platforms that support online communities (DPsOC). It addresses ways in which digital platforms can effectively support social interactions in online communities. Drawing upon prior literature on IS design theory, online communities, and platforms, we derive an initial set of propositions for designing effective DPsOC. Our overarching proposition is that three components of digital platform architecture (core, interface, and complements) should collectively support the mix of the three distinct types of social interaction structures of online community (information sharing, collaboration, and collective action). We validate the initial propositions and generate additional insights by conducting an in-depth analysis of an European digital platform for elderly care assistance. We further validate the propositions by analyzing three widely used digital platforms, including Twitter, Wikipedia, and Liquidfeedback, and we derive additional propositions and insights that can guide DPsOC design. We discuss the implications of this research for research and practice.  February 2015; doi:10.1057/jit.2014.37
Decision Tree Based Predictive Models for Breast Cancer Survivability on Imbalanced Data

Based on imbalanced data, the predictive models for 5year survivability of breast cancer using decision tree are proposed. After data preprocessing from SEER breast cancer datasets, it is obviously that the category of data distribution is imbalanced. Under-sampling is taken to make up the disadvantage of the performance of models caused by the imbalanced data. The performance of the models is evaluated by AUC under ROC curve, accuracy, specificity and sensitivity with 10-fold stratified cross-validation. The performance of models is best while the distribution of data is approximately equal. Bagging algorithm is used to build an integration decision tree model for predicting breast cancer survivability.  data;decision tree;predictive breast cancer survivability;10-fold stratified cross-validation;bagging algorithm
A Preliminary Evaluation of the Leap Motion Sensor as Controller of New Digital Musical Instruments

The introduction of new gesture interfaces has been expanding the possibilities of creating new Digital Musical Instruments (DMIs). Leap Motion Controller was recently launched promising fine-grained hand sensor capabilities. This paper proposes a preliminary study and evaluation of this new sensor for building new DMIs. Here, we list a series of gestures, recognized by the device, which could be theoretically used for playing a large number of musical instruments. Then, we present an analysis of precision and latency of these gestures as well as a first case study integrating Leap Motion with a virtual music keyboard.
Tracking control for multi-agent consensus with an active leader and variable topology

In this paper, we consider a multi-agent consensus problem with an active leader and variable interconnection topology. The state of the considered leader not only keeps changing but also may not be measured. To track such a leader, a neighbor-based local controller together with a neighbor-based state-estimation rule is given for each autonomous agent. Then we prove that, with the proposed control scheme, each agent can follow the leader if the (acceleration) input of the active leader is known, and the tracking error is estimated if the input of the leader is unknown.
Sleep problems and their relation to cognitive factors, anxiety, and depressive symptoms in children and adolescents.

Existing research indicates sleep problems to be prevalent in youth with internalizing disorders. However, childhood sleep problems are common in the general population and few data are available examining unique relationships between sleep, specific types of anxiety and depressive symptoms among non-clinical samples of children and adolescents.



The presence of sleep problems was examined among a community sample of children and adolescents (N=175) in association with anxiety and depressive symptoms, age, and gender. Based on emerging findings from the adult literature we also examined associations between cognitive biases and sleep problems.



Overall findings revealed significant associations between sleep problems and both anxiety and depressive symptoms, though results varied by age. Depressive symptoms showed a greater association with sleep problems among adolescents, while anxiety symptoms were generally associated with sleep problems in all youth. Cognitive factors (cognitive errors and control beliefs) linked with anxiety and depression also were associated with sleep problems among adolescents, though these correlations were no longer significant after controlling for internalizing symptoms.



Results are discussed in terms of their implications for research and treatment of sleep and internalizing disorders in youth.
Semantic Representations for Domain Adaptation: A Case Study on the Tree Kernel-based Method for Relation Extraction

We study the application of word embeddings to generate semantic representations for the domain adaptation problem of relation extraction (RE) in the tree kernelbased method. We systematically evaluate various techniques to generate the semantic representations and demonstrate that they are effective to improve the generalization performance of a tree kernel-based relation extractor across domains (up to 7% relative improvement). In addition, we compare the tree kernel-based and the feature-based method for RE in a compatible way, on the same resources and settings, to gain insights into which kind of system is more robust to domain changes. Our results and error analysis shows that the tree kernel-based method outperforms the feature-based approach.