nils-herrmann/clean_scirepeval huggingface dataset:
Collection statistics for fast duplicate document detection

We present a new algorithm for duplicate document detection thatuses collection statistics. We compare our approach with thestate-of-the-art approach using multiple collections. Thesecollections include a 30 MB 18,577 web document collectiondeveloped by Excite@Home and three NIST collections. The first NISTcollection consists of 100 MB 18,232 LA-Times documents, which isroughly similar in the number of documents to theExcite&at;Home collection. The other two collections are both 2GB and are the 247,491-web document collection and the TREC disks 4and 5---528,023 document collection. We show that our approachcalled I-Match, scales in terms of the number of documents andworks well for documents of all sizes. We compared our solution tothe state of the art and found that in addition to improvedaccuracy of detection, our approach executed in roughly one-fifththe time.
High Power Factor AC–DC LED Driver With Film Capacitors

In this paper, a new method is proposed to eliminate electrolytic capacitors in a two-stage ac-dc light-emitting diode (LED) driver. DC-biased sinusoidal or square-wave LED driving-current can help to reduce the power imbalance between ac input and dc output. In doing so, film capacitors can be adopted to improve LED driver's lifetime. The relationship between the peak-to-average ratio of the pulsating current in LEDs and the storage capacitance according to given storage capacitance is derived. Using the proposed “zero-low-level square-wave driving current” scheme, the storage capacitance in the LED driver can be reduced to 52.7% comparing with that in the driver using constant dc driving current. The input power factor is almost unity, which complies with lighting equipment standards such as IEC-1000-3-2 for Class C equipments. The voltage across the storage capacitors is analyzed and verified during the whole pulse width modulation dimming range. For the ease of dimming and implementation, a 50 W LED driver with zero-low-level square-wave driving current is built and the experimental results are presented to verify the proposed methods.
A comparison of IoT application layer protocols through a smart parking implementation

Several IoT protocols have been introduced in order to provide an efficient communication for resource-ronstrained applications. However, their performance is not as yet well understood. To address this issue, we evaluated and compared four communication protocols, namely, CoAP, MQTT, XMPP, and WebSocket. For this, we implemented a smart parking application using open source software for these protocols and measured their response time by varying the traffic load.
Informative joints based human action recognition using skeleton contexts

The launching of Microsoft Kinect with skeleton tracking technique opens up new potentials for skeleton based human action recognition. However, the 3D human skeletons, generated via skeleton tracking from the depth map sequences, are generally very noisy and unreliable. In this paper, we introduce a robust informative joints based human action recognition method. Inspired by the instinct of the human vision system, we analyze the mean contributions of human joints for each action class via differential entropy of the joint locations. There is significant difference between most of the actions, and the contribution ratio is highly in accordance with common sense. We present a novel approach named skeleton context to measure similarity between postures and exploit it for action recognition. The similarity is calculated by extracting the multi-scale pairwise position distribution for each informative joint. Then feature sets are evaluated in a bag-of-words scheme using a linear CRFs. We report experimental results and validate the method on two public action dataset. Experiments results have shown that the proposed approach is discriminative for similar human action recognition and well adapted to the intra-class variation. & 2015 Elsevier B.V. All rights reserved.
GAIN SCHEDULING CONTROL FOR THE HYDRAULIC ACTUATION OF THE HYQ ROBOT LEG

This paper focuses on a theoretical and experimental investigation on the control of a valve actuated hydraulic system driving a biologically inspired robotic leg. The study is part of a more complex project aiming at the design of a power and cognitive autonomous quadruped robot for outdoor operations called HyQ. The first leg prototype has currently two hydraulically activated degrees of freedom (DOF) actuating the hip and knee joints. Hydraulic actuation has been chosen in lieu of electrical, due to its high power-to-weight ratio and fast dynamic response, to meet the specifications in terms of performance and dimensions. The actuation system is composed of a proportional valve and an asymmetric cylinder (one for each DOF). Non-linear and linear system modelling and identification have been undertaken. The measured response in the time domain has been compared with the results of numerical simulation both in the linear and non-linear cases. An initial control implementation has been carried out. A gain scheduling algorithm has been implemented where PID controller gains are adapted according to the joint position within a walking trajectory cycle. The performance of this adaptive controller has been compared with a conventional PID control scheme.
Event-related desynchronization and movement-related cortical potentials on the ECoG and EEG.

Event-related desynchronization (ERD) 2.0 sec before and 1.0 sec after movement in the frequency bands of 8-10, 10-12, 12-20 and 20-30 Hz and movement-related cortical potentials (MRCPs) to self-paced movements were studied from subdural recordings over the central region in 3 patients, and from scalp-recorded EEGs in 20 normal volunteers. In direct cortical recordings, the peak ERD response and peak MRCP amplitude to self-paced finger movements were maximal over recording sites in the contralateral hand motor representations. The topography and time of onset of the ERD response to finger and foot movements suggest that the ERD responses in the 8-10 Hz and 10-12 Hz bands are more somatotopically restricted than the responses in the higher frequency bands. The power recovery and subsequent overshoot in the different frequency bands occurred in an orderly fashion with the faster frequencies recovering earlier. The ERD responses on the scalp-recorded EEGs were of lower magnitude and more widely distributed than those occurring on the subdural recordings. Across the population, there was no relation between the magnitude of the ERD response in any of the frequency bands studied and the peak amplitude of the negative slope (pNS') and the frontal peak of the motor potential (fpMP) of the MRCPs. MRCPs and ERD responses originate in similar cortical regions and share some common timing features, but the magnitude and spatial distribution of the two responses appear to be independent of each other, which suggests that the physiological mechanisms governing these two events are different and may represent different aspects of motor cortex activation. Differences in the timing and topographical features of the ERD responses in the various frequency bands also suggest a distinct functional significance for the various spectral components of the electrical activity in the motor cortex.
Exploratory Data Mining and Data Cleaning

It sounds good when knowing the exploratory data mining and data cleaning in this website. This is one of the books that many people looking for. In the past, many people ask about this book as their favourite book to read and collect. And now, we present hat you need quickly. It seems to be so happy to offer you this famous book. It will not become a unity of the way for you to get amazing benefits at all. But, it will serve something that will let you get the best time and moment to spend for reading the book.
Three-Dimensional SAR Focusing From Multipass Signals Using Compressive Sampling

Three-dimensional synthetic aperture radar (SAR) image formation provides the scene reflectivity estimation along azimuth, range, and elevation coordinates. It is based on multipass SAR data obtained usually by nonuniformly spaced acquisition orbits. A common 3-D SAR focusing approach is Fourier-based SAR tomography, but this technique brings about image quality problems because of the low number of acquisitions and their not regular spacing. Moreover, attained resolution in elevation is limited by the overall acquisitions baseline extent. In this paper, a novel 3-D SAR data imaging based on Compressive Sampling theory is presented. It is shown that since the image to be focused has usually a sparse representation along the elevation direction (i.e., only few scatterers with different elevation are present in the same range-azimuth resolution cell), it suffices to have a small number of measurements to construct the 3-D image. Furthermore, the method allows super-resolution imaging, overcoming the limitation imposed by the overall baseline span. Tomographic imaging is performed by solving an optimization problem which enforces sparsity through ℓ1-norm minimization. Numerical results on simulated and real data validate the method and have been compared with the truncated singular value decomposition technique.
An analysis of young people's use of and attitudes toward cell phones

Cell phones are a pervasive new communication technology, especially among college students. This paper examines college students cell phone usage from a behavioral and psychological perspective. Utilizing both qualitative (focus groups) and quantitative (survey) approaches, the study suggests these individuals use the devices for a variety of purposes: to help them feel safe, for financial benefits, to manage time efficiently, to keep in touch with friends and family members, et al. The degree to which the individuals are dependent on the cell phones and what they view as the negatives of their utilization are also examined. The findings suggest people have various feelings and attitudes toward cell phone usage. This study serves as a foundation on which future studies will be built.
Design, Implementation, and Evaluation of a Point Cloud Codec for Tele-Immersive Video

We present a generic and real-time time-varying point cloud codec for 3D immersive video. This codec is suitable for mixed reality applications in which 3D point clouds are acquired at a fast rate. In this codec, intra frames are coded progressively in an octree subdivision. To further exploit inter-frame dependencies, we present an inter-prediction algorithm that partitions the octree voxel space in  $N \times N \times N$  macroblocks ( $N=8,16,32$ ). The algorithm codes points in these blocks in the predictive frame as a rigid transform applied to the points in the intra-coded frame. The rigid transform is computed using the iterative closest point algorithm and compactly represented in a quaternion quantization scheme. To encode the color attributes, we defined a mapping of color per vertex attributes in the traversed octree to an image grid and use legacy image coding method based on JPEG. As a result, a generic compression framework suitable for real-time 3D tele-immersion is developed. This framework has been optimized to run in real time on commodity hardware for both the encoder and decoder. Objective evaluation shows that a higher rate-distortion performance is achieved compared with available point cloud codecs. A subjective study in a state-of-the-art mixed reality system shows that introduced prediction distortions are negligible compared with the original reconstructed point clouds. In addition, it shows the benefit of reconstructed point cloud video as a representation in the 3D virtual world. The codec is available as open source for integration in immersive and augmented communication applications and serves as a base reference software platform in JTC1/SC29/WG11 (MPEG) for the further development of standardized point-cloud compression solutions.
Unsupervised Adaptation for Deep Stereo

Recent ground-breaking works have shown that deep neural networks can be trained end-to-end to regress dense disparity maps directly from image pairs. Computer generated imagery is deployed to gather the large data corpus required to train such networks, an additional fine-tuning allowing to adapt the model to work well also on real and possibly diverse environments. Yet, besides a few public datasets such as Kitti, the ground-truth needed to adapt the network to a new scenario is hardly available in practice. In this paper we propose a novel unsupervised adaptation approach that enables to fine-tune a deep learning stereo model without any ground-truth information. We rely on off-the-shelf stereo algorithms together with state-of-the-art confidence measures, the latter able to ascertain upon correctness of the measurements yielded by former. Thus, we train the network based on a novel loss-function that penalizes predictions disagreeing with the highly confident disparities provided by the algorithm and enforces a smoothness constraint. Experiments on popular datasets (KITTI 2012, KITTI 2015 and Middlebury 2014) and other challenging test images demonstrate the effectiveness of our proposal.
Sparsely connected autoencoder

This work proposes to learn autoencoders with sparse connections. Prior studies on autoencoders enforced sparsity on the neuronal activity; these are different from our proposed approach - we learn sparse connections. Sparsity in connections helps in learning (and keeping) the important relations while trimming the irrelevant ones. We have tested the performance of our proposed method on two tasks - classification and denoising. For classification we have compared against stacked autneencoders, contractive autoencoders, deep belief network, sparse deep neural network and optimal brain damage neural network; the denoising performance was compared against denoising autoencoder and sparse (activity) autoencoder. In both the tasks our proposed method yields superior results.
Learning approximate neural estimators for wireless channel state information

Estimation is a critical component of synchronization in wireless and signal processing systems. There is a rich body of work on estimator derivation, optimization, and statistical characterization from analytic system models which are used pervasively today. We explore an alternative approach to building estimators which relies principally on approximate regression using large datasets and large computationally efficient artificial neural network models capable of learning non-linear function mappings which provide compact and accurate estimates. For single carrier PSK modulation, we explore the accuracy and computational complexity of such estimators compared with the current gold-standard analytically derived alternatives. We compare performance in various wireless operating conditions and consider the trade offs between the two different classes of systems. Our results show the learned estimators can provide improvements in areas such as short-time estimation and estimation under non-trivial real world channel conditions such as fading or other non-linear hardware or propagation effects.
Chapter 5 Discourse , power and access

One of the crucial tasks of Critical Discourse Analysis (CDA) is to account for the relationships between discourse and social power. More specifically, such an analysis should describe and explain how power abuse is enacted, reproduced or legitimised by the text and talk of dominant groups or institutions. Within the framework of such an account of discursively mediated dominance and inequality this chapter focuses on an important dimension of such dominance, that is, patterns of access to discourse. A critical analysis of properties of access to public discourse and communication presupposes insight into more general political, sociocultural and economic aspects of dominance. This chapter merely gives a succinct summary of this broader conceptual framework. Leaving acide a detailed discussion of numerous philosophical and theoretical complexities, the major presuppositions of this framework are, for example, the following (see, e.g., Clegg, 1989; Lukes, 1974; 1986; Wrong, 1979):
Emotion recognition: the role of facial movement and the relative importance of upper and lower areas of the face.

In order to investigate the role of facial movement in the recognition of emotions, faces were covered with black makeup and white spots. Video recordings of such faces were played back so that only the white spots were visible. The results demonstrated that moving displays of happiness, sadness, fear, surprise, anger and disgust were recognized more accurately than static displays of the white spots at the apex of the expressions. This indicated that facial motion, in the absence of information about the shape and position of facial features, is informative about these basic emotions. Normally illuminated dynamic displays of these expressions, however, were recognized more accurately than displays of moving spots. The relative effectiveness of upper and lower facial areas for the recognition of these six emotions was also investigated using normally illuminated and spots-only displays. In both instances the results indicated that different facial regions are more informative for different emitions. The movement patterns characterizing the various emotional expressions as well as common confusions between emotions are also discussed.
Integrated Design of Flight Simulator Motion Cueing Systems

A forward design process applicable to the specification of flight simulator cueing systems is presented in this paper. This process is based on the analysis of the pilotvehicle control loop by using a pilot model incorporating both visual and vestibular feedback, and the aircraft dynamics. After substituting the model for the simulated aircraft, the analysis tools are used to adjust the washout filter parameters with the goal of restoring pilot control behaviour. This process allows the specification of the motion cueing algorithm. Then, based on flight files representative for the operational flight envelope, the required motion system space is determined. The motion-base geometry is established based on practical limitations, as well as criteria for the stability of the platform with respect to singular conditions. With this process the characteristics of the aircraft, the tasks to be simulated, and the missions themselves are taken into account in defining the simulator motion cueing system.
Metaphor in Idiom Comprehension

Psycholinguistic research has shown that people’s tacit knowledge of conceptual metaphors, such as ANGER IS HEATED FLUID IN A CONTAINER, partly motivates how they make sense of idiomatic phrases like blow your stack and flip your lid. But do people quickly access conceptual metaphors each time an idiom is encountered in discourse? The present studies used a priming method to examine the role of conceptual metaphors in immediate idiom comprehension. Experiment 1 showed that people access conceptual metaphors when understanding idioms, but significantly less so when processing literal paraphrases of idioms. Experiment 2 demonstrated that people access the appropriate conceptual metaphors, such as ANGER IS HEAT, when processing some idioms, such as blow your stack, but not when they read idioms, such as jump down your throat, which have similar figurative meanings that are motivated by different conceptual metaphors (e.g., ANGER IS ANIMAL BEHAVIOR). The findings from these studies provide important evidence on the constraining role that common patterns of metaphoric thought have in figurative language understanding.
Robust recognition of chess-boards under deformation

Current methods for formation of detected chess-board vertices into a grid structure tend to be weak in situations with a warped grid, and false and missing vertex-features. In this paper we present a highly robust, yet efficient, scheme suitable for inference of regular 2D square mesh structure from vertices recorded both during projection of a chess-board pattern onto 3D objects, and in the more simple case of camera calibration. Examples of the method's performance in a lung function measuring application, observing chess-boards projected on to patients' chests, are given. The method presented is resilient to significant surface deformation, and tolerates inexact vertex-feature detection. This robustness results from the scheme's novel exploitation of feature orientation information.
A hierarchical method for traffic sign classification with support vector machines

Traffic sign classification is an important function for driver assistance systems. In this paper, we propose a hierarchical method for traffic sign classification. There are two hierarchies in the method: the first one classifies traffic signs into several super classes, while the second one further classifies the signs within their super classes and provides the final results. Two perspective adjustment methods are proposed and performed before the second hierarchy, which significantly improves the classification accuracy. Experimental results show that the proposed method gets an accuracy of 99.52% on the German Traffic Sign Recognition Benchmark (GTSRB), which outperforms the state-of-the-art method. In addition, it takes about 40 ms to process one image, making it suitable for realtime applications.
Improved normal and shear tactile force sensor performance via Least Squares Artificial Neural Network (LSANN)

This paper presents a new approach to the characterization of tactile array sensors that aims to reduce the computational time needed for convergence to obtain a useful estimator for normal and shear forces. This is achieved by breaking up the sensor characterization into two parts: a linear regression portion using multivariate least squares regression, and a nonlinear regression portion using a neural network as a multi-input, multi-output function approximator. This procedure has been termed Least Squares Artificial Neural Network (LSANN). By applying LSANN on the 2nd generation MIT Cheetah footpad, the convergence speed for the estimator of the normal and shear forces is improved by 59.2% compared to using only the neural network alone. The normalized root mean squared error between the two methods are nearly identical at 1.17% in the normal direction, and 8.30% and 10.14% in the shear directions. This approach could have broader implications in greatly reducing the amount of time needed to train a contact force estimator for a large number of tactile sensor arrays (i.e. in robotic hands and skin).
Expressing Emotions Through Color, Sound, and Vibration with an Appearance-Constrained Social Robot

Many researchers are now dedicating their efforts to studying interactive modalities such as facial expressions, natural language, and gestures. This phenomenon makes communication between robots and individuals become more natural. However, many robots currently in use are appearance constrained and not able to perform facial expressions and gestures. In addition, although humanoid-oriented techniques are promising, they are time and cost consuming, which leads to many technical difficulties in most research studies. To increase interactive efficiency and decrease costs, we alternatively focus on three interaction modalities and their combinations, namely color, sound, and vibration. We conduct a structured study to evaluate the effects of the three modalities on a human's emotional perception towards our simple-shaped robot "Maru." Our findings offer insights into human-robot affective interactions, which can be particularly useful for appearance-constrained social robots. The contribution of this work is not so much the explicit parameter settings but rather deepening the understanding of how to express emotions through the simple modalities of color, sound, and vibration while providing a set of recommended expressions that HRI researchers and practitioners could readily employ.