nils-herrmann/clean_scirepeval huggingface dataset:
Leveraging multi-criteria customer feedback for satisfaction analysis and improved recommendations

Travel websites and online booking platforms represent today’s major sources for customers when gathering information before a trip. In particular, community-provided customer reviews and ratings of various tourism services represent a valuable source of information for trip planning. With respect to customer ratings, many modern travel and tourism platforms – in contrast to several other e-commerce domains – allow customers to rate objects along multiple dimensions and thus to provide more fine-granular post-trip feedback on the booked accommodation or travel package. In this paper, we first show how this multi-criteria rating information can help to obtain a better understanding of factors driving customer satisfaction for different segments. For this purpose, we performed a Penalty-Reward Contrast analysis on a data set from a major tourism platform, which reveals that customer segments significantly differ in the way the formation of overall satisfaction can be explained. Beyond the pure identification of segment-specific satisfaction factors, we furthermore show how this fine-granular rating information can be exploited to improve the accuracy of rating-based recommender systems. In particular, we propose to utilize userand object-specific factor relevance weights which can be learned through linear regression. An empirical evaluation on datasets from different domains finally shows that our method helps us to predict the customer preferences more accurately and thus to develop better online recommendation services.
Communication of emotions in vocal expression and music performance: different channels, same code?

Many authors have speculated about a close relationship between vocal expression of emotions and musical expression of emotions. but evidence bearing on this relationship has unfortunately been lacking. This review of 104 studies of vocal expression and 41 studies of music performance reveals similarities between the 2 channels concerning (a) the accuracy with which discrete emotions were communicated to listeners and (b) the emotion-specific patterns of acoustic cues used to communicate each emotion. The patterns are generally consistent with K. R. Scherer's (1986) theoretical predictions. The results can explain why music is perceived as expressive of emotion, and they are consistent with an evolutionary perspective on vocal expression of emotions. Discussion focuses on theoretical accounts and directions for future research.
Application of knowledge-based approaches in software architecture: A systematic mapping study

Knowledge management technologies have been employed across software engineering activities for more than two decades. Knowledge-based approaches can be used to facilitate software architecting activities (e.g., architectural evaluation). However, there is no comprehensive understanding on how various knowledge-based approaches (e.g., knowledge reuse) are employed in software architecture.  This work aims to collect studies on the application of knowledge-based approaches in software architecture and make a classification and thematic analysis on these studies, in order to identify the gaps in the existing application of knowledge-based approaches to various architecting activities, and promising research directions.  A systematic mapping study is conducted for identifying and analyzing the application of knowledge-based approaches in software architecture, covering the papers from major databases, journals, conferences, and workshops, published between January 2000 and March 2011.  Fifty-five studies were selected and classified according to the architecting activities they contribute to and the knowledge-based approaches employed. Knowledge capture and representation (e.g., using an ontology to describe architectural elements and their relationships) is the most popular approach employed in architecting activities. Knowledge recovery (e.g., documenting past architectural design decisions) is an ignored approach that is seldom used in software architecture. Knowledge-based approaches are mostly used in architectural evaluation, while receive the least attention in architecture impact analysis and architectural implementation.  The study results show an increased interest in the application of knowledge-based approaches in software architecture in recent years. A number of knowledge-based approaches, including knowledge capture and representation, reuse, sharing, recovery, and reasoning, have been employed in a spectrum of architecting activities. Knowledge-based approaches have been applied to a wide range of application domains, among which ‘‘Embedded software’’ has received the most attention.
SRPGAN: Perceptual Generative Adversarial Network for Single Image Super Resolution

Single image super resolution (SISR) is to reconstruct a high resolution image from a single low resolution image. The SISR task has been a very attractive research topic over the last two decades. In recent years, convolutional neural network (CNN) based models have achieved great performance on SISR task. Despite the breakthroughs achieved by using CNN models, there are still some problems remaining unsolved, such as how to recover high frequency details of high resolution images. Previous CNN based models always use a pixel wise loss, such as l2 loss. Although the high resolution images constructed by these models have high peak signal-to-noise ratio (PSNR), they often tend to be blurry and lack high-frequency details, especially at a large scaling factor. In this paper, we build a super resolution perceptual generative adversarial network (SRPGAN) framework for SISR tasks. In the framework, we propose a robust perceptual loss based on the discriminator of the built SRPGAN model. We use the Charbonnier loss function to build the content loss and combine it with the proposed perceptual loss and the adversarial loss. Compared with other state-of-the-art methods, our method has demonstrated great ability to construct images with sharp edges and rich details. We also evaluate our method on different benchmarks and compare it with previous CNN based methods. The results show that our method can achieve much higher structural similarity index (SSIM) scores on most of the benchmarks than the previous state-of-art methods.
Development of a Multi-fingered Robot Hand with Softness-changeable Skin Mechanism

This paper develops a multi-fingered robot hand with skin mechanism enabling softness change. We show how the softness of the skin affects grasping and manipulation. Elastic skin provides stable grasping while precise manipulation is lost. Hard skin provides precise manipulation while stability of grasp is lost. We will try to change the softness of the skin according to situation, object, and so on. In this paper, we develop a novel human like robot hand with softnesschangeable skin mechanism.
Meaningful change and responsiveness in common physical performance measures in older adults.

To estimate the magnitude of small meaningful and substantial individual change in physical performance measures and evaluate their responsiveness.



Secondary data analyses using distribution- and anchor-based methods to determine meaningful change.



Secondary analysis of data from an observational study and clinical trials of community-dwelling older people and subacute stroke survivors.



Older adults with mobility disabilities in a strength training trial (n=100), subacute stroke survivors in an intervention trial (n=100), and a prospective cohort of community-dwelling older people (n=492).



Gait speed, Short Physical Performance Battery (SPPB), 6-minute-walk distance (6MWD), and self-reported mobility.



Most small meaningful change estimates ranged from 0.04 to 0.06 m/s for gait speed, 0.27 to 0.55 points for SPPB, and 19 to 22 m for 6MWD. Most substantial change estimates ranged from 0.08 to 0.14 m/s for gait speed, 0.99 to 1.34 points for SPPB, and 47 to 49 m for 6MWD. Based on responsiveness indices, per-group sample sizes for clinical trials ranged from 13 to 42 for substantial change and 71 to 161 for small meaningful change.



Best initial estimates of small meaningful change are near 0.05 m/s for gait speed, 0.5 points for SPPB, and 20 m for 6MWD and of substantial change are near 0.10 m/s for gait speed, 1.0 point for SPPB, and 50 m for 6MWD. For clinical use, substantial change in these measures and small change in gait speed and 6MWD, but not SPPB, are detectable. For research use, these measures yield feasible sample sizes for detecting meaningful change.
Using the Balanced Scorecard as a Strategic Management System

for competition that is based on information, their ability to exploit intangible assets has become far more decisive than their ability to invest in and manage physical assets. Several years ago, in recognition of this change, we introduced a concept we called the balanced scorecard. The balanced scorecard supplemented traditional fi nancial measures with criteria that measured performance from three additional perspectives – those of customers, internal business processes, and learning and growth.  It therefore enabled companies to track fi nancial results while simultaneously monitoring progress in building the capabilities and acquiring the intangible assets they would need for future growth. The scorecard wasn’t Editor In 1992, Robert S. Kaplan and David P. Norton’s concept of the balanced scorecard revolutionized conventional thinking about performance metrics. By going beyond traditional measures of fi nancial performance, the concept has given a generation of managers a better understanding of how their companies are really doing. These nonfi nancial metrics are so valuable mainly because they predict future fi nancial performance rather than simply report what’s already happened. This article, fi rst published in 1996, describes how the balanced scorecard can help senior managers systematically link current actions with tomorrow’s goals, focusing on that place where, in the words of the authors, “the rubber meets the sky.” Using the Balanced Scorecard as a Strategic Management System
Signals in tactile afferents from the fingers eliciting adaptive motor responses during precision grip

While human subjects lift small objects using the precision grip between the tips of the fingers and thumb the ratio between the grip force and the load force (i.e. the vertical lifting force) is adapted to the friction between the object and the skin. The present report provides direct evidence that signals in tactile afferent units are utilized in this adaptation. Tactile afferent units were readily excited by small but distinct slips between the object and the skin revealed as vibrations in the object. Following such afferent slip responses the force ratio was upgraded to a higher, stable value which provided a safety margin to prevent further slips. The latency between the onset of the a slip and the appearance of the ratio change (74 ±9 ms) was about half the minimum latency for intended grip force changes triggered by cutaneous stimulation of the fingers. This indicated that the motor responses were automatically initiated. If the subjects were asked to very slowly separate their thumb and the opposing finger while the object was held in air, grip force reflexes originating from afferent slip responses appeared to counteract the voluntary command, but the maintained upgrading of the force ratio was suppressed. In experiments with weak electrical cutaneous stimulation delivered through the surfaces of the object it was established that tactile input alone could trigger the upgrading of the force ratio. Although, varying in responsiveness, each of the three types of tactile units which exhibit a pronounced dynamic sensitivity (FA I, FA II and SA I units) could reliably signal these slips. Similar but generally weaker afferent responses, sometimes followed by small force ratio changes, also occurred in the FA I and the SA I units in the absence of detectable vibrations events. In contrast to the responses associated with clear vibratory events, the weaker afferent responses were probably caused by localized frictional slips, i.e. slips limited to small fractions of the skin area in contact with the object. Indications were found that the early adjustment to a new frictional condition, which may appear soon (ca. 0.1–0.2 s) after the object is initially gripped, might depend on the vigorous responses in the FA I units during the initial phase of the lifts (see Westling and Johansson 1987). The role of the tactile input in the adaptation of the force coordination to the frictional condition is discussed.
Projective Feature Learning for 3D Shapes with Multi-View Depth Images

Feature learning for 3D shapes is challenging due to the lack of natural paramterization for 3D surface models. We adopt the multi-view depth image representation and propose Multi-View Deep Extreme Learning Machine (MVD-ELM) to achieve fast and quality projective feature learning for 3D shapes. In contrast to existing multiview learning approaches, our method ensures the feature maps learned for different views are mutually dependent via shared weights and in each layer, their unprojections together form a valid 3D reconstruction of the input 3D shape through using normalized convolution kernels. These lead to a more accurate 3D feature learning as shown by the encouraging results in several applications. Moreover, the 3D reconstruction property enables clear visualization of the learned features, which further demonstrates the meaningfulness of our feature learning.
Reuse in the wild: an empirical and ethnographic study of organizational content reuse

We present a large-scale study of content reuse networks in a large and highly hierarchical organization. In our study, we combine analysis of a collection of presentations produced by employees with interviews conducted throughout the organization and a survey to study presentation content reuse. Study results show a variety of information needs and behaviors related to content reuse as well as a need for a personalized and socially-integrated networking tool for enabling easy access to previously generated presentation material. In this paper we describe our findings and outline a set of requirements for an effective content reuse facility.
An Efficient Image Matching Method for Multi-View Stereo

Most existing Multi-View Stereo (MVS) algorithms employ the image matching method using Normalized Cross-Correlation (NCC) to estimate the depth of an object. The accuracy of the estimated depth depends on the step size of the depth in NCC-based window matching. The step size of the depth must be small for accurate 3D reconstruction, while the small step significantly increases computational cost. To improve the accuracy of depth estimation and reduce the computational cost, this paper proposes an efficient image matching method for MVS. The proposed method is based on Phase-Only Correlation (POC), which is a high-accuracy image matching technique using the phase components in Fourier transforms. The advantages of using POC are (i) the correlation function is obtained only by one window matching and (ii) the accurate sub-pixel displacement between two matching windows can be estimated by fitting the analytical correlation peak model of the POC function. Thus, using POC-based window matching for MVS makes it possible to estimate depth accurately from the correlation function obtained only by one window matching. Through a set of experiments using the public MVS datasets, we demonstrate that the proposed method performs better in terms of accuracy and computational cost than the conventional method.
An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson's disease

In this paper, we explore the potential of extreme learning machine (ELM) and kernel ELM (KELM) for early diagnosis of Parkinson’s disease (PD). In the proposed method, the key parameters including the number of hidden neuron and type of activation function in ELM, and the constant parameter C and kernel parameter γ in KELM are investigated in detail. With the obtained optimal parameters, ELM and KELM manage to train the optimal predictive models for PD diagnosis. In order to further improve the performance of ELM and KELM models, feature selection techniques are implemented prior to the construction of the classification models. The effectiveness of the proposed method has been rigorously evaluated against the PD data set in terms of classification accuracy, sensitivity, specificity and the area under the ROC (receiver operating characteristic) curve (AUC). Compared to the existing methods in previous studies, the proposed method has achieved very promising classification accuracy via 10-fold cross-validation (CV) analysis, with the highest accuracy of 96.47% and average accuracy of 95.97% over 10 runs of 10-fold CV.
High performance GaN single-chip frontend for compact X-band AESA systems

A next generation of AESA antennas will be challenged with the need for lower size, weight, power and cost (SWAP-C). This leads to enhanced demands especially with regard to the integration density of the RF-part inside a T/R module. The semiconductor material GaN has proven its capacity for high power amplifiers, robust receive components as well as switch components for separation of transmit and receive mode. This paper will describe the design and measurement results of a GaN-based single-chip T/R module frontend (HPA, LNA and SPDT) using UMS GH25 technology and covering the frequency range from 8 GHz to 12 GHz. Key performance parameters of the frontend are 13 W minimum transmit (TX) output power over the whole frequency range with peak power up to 17 W. The frontend in receive (RX) mode has a noise figure below 3.2 dB over the whole frequency range, and can survive more than 5 W input power. The large signal insertion loss of the used SPDT is below 0.9 dB at 43 dBm input power level.
A prototype electrical actuator for aircraft flaps and slats

This paper considers the electrical actuation of aircraft wing surfaces, with particular emphasis upon flap systems. It discusses existing electro-hydraulic systems and proposes an electrical alternative, examining the potential system benefits in terms of increased functionality, maintenance and life cycle costs. The paper then progresses to describe a full scale actuation demonstrator of the flap system, including the high speed electrical drive, step down gearbox and flaps. Detailed descriptions are given of the fault tolerant motor, power electronics, control architecture and position sensor systems, along with a range of test results, demonstrating the system in operation
Representation Learning on Graphs: Methods and Applications

Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks. The primary challenge in this domain is finding a way to represent, or encode, graph structure so that it can be easily exploited by machine learning models. Traditionally, machine learning approaches relied on user-defined heuristics to extract features encoding structural information about a graph (e.g., degree statistics or kernel functions). However, recent years have seen a surge in approaches that automatically learn to encode graph structure into low-dimensional embeddings, using techniques based on deep learning and nonlinear dimensionality reduction. Here we provide a conceptual review of key advancements in this area of representation learning on graphs, including matrix factorization-based methods, random-walk based algorithms, and graph convolutional networks. We review methods to embed individual nodes as well as approaches to embed entire (sub)graphs. In doing so, we develop a unified framework to describe these recent approaches, and we highlight a number of important applications and directions for future work.
The Serial Safety Net: Efficient Concurrency Control on Modern Hardware

Concurrency control (CC) algorithms must trade off strictness for performance, with serializable schemes generally paying high cost---both in runtime overhead such as contention on lock tables, and in wasted efforts by aborting transactions---to prevent anomalies. We propose the serial safety net (SSN), a serializability-enforcing certifier for modern hardware with substantial core count and large main memory. SSN can be applied with minimal overhead on top of various CC schemes that offer higher performance but admit anomalies, e.g., snapshot isolation and read committed.
 We demonstrate the efficiency, accuracy and robustness of SSN using a memory-optimized OLTP engine with different CC schemes. We find that SSN is a promising approach to serializability with low abort rates and robust performance for various workloads.
Data Mining for Imbalanced Datasets: An Overview

A dataset is imbalanced if the classification categories are not approximately equally represented. Recent years brought increased interest in applying machine learning techniques to difficult "real-world" problems, many of which are characterized by imbalanced data. Additionally the distribution of the testing data may differ from that of the training data, and the true misclassification costs may be unknown at learning time. Predictive accuracy, a popular choice for evaluating performance of a classifier, might not be appropriate when the data is imbalanced andlor the costs of different errors vary markedly. In this Chapter, we discuss some of the sampling techniques used for balancing the datasets, and the performance measures more appropriate for mining imbalanced datasets.
MULTI-MODAL BACKGROUND SUBTRACTION USING GAUSSIAN MIXTURE MODELS

Background subtraction is a common first step in the field of video processing and it is used to reduce the effective image size in subsequent processing steps by segmenting the mostly static background from the moving or changing foreground. In this paper previous approaches towards background modeling are extended to handle videos accompanied by information gained from a novel 2D/3D camera. This camera contains a color and a PMD chip which operates on the Time-of-Flight operating principle. The background is estimated using the widely spread Gaussian mixture model in color as well as in depth and amplitude modulation. A new matching function is presented that allows for better treatment of shadows and noise and reduces block artifacts. Problems and limitations to overcome the problem of fusing high resolution color information with low resolution depth data are addressed and the approach is tested with different parameters on several scenes and the results are compared to common and widely accepted methods.
A modified Gabor filter design method for fingerprint image enhancement

Fingerprint image enhancement is an essential preprocessing step in fingerprint recognition applications. In this paper, we propose a novel filter design method for fingerprint image enhancement, primarily inspired from the traditional Gabor filter (TGF). The previous fingerprint image enhancement methods based on TGF banks have some drawbacks in their image-dependent parameter selection strategy, which leads to artifacts in some cases. To address this issue, we develop an improved version of the TGF, called the modified Gabor filter (MGF). Its parameter selection scheme is image-independent. The remarkable advantages of our MGF over the TGF consist in preserving fingerprint image structure and achieving image enhancement consistency. Experimental results indicate that the proposed MGF enhancement algorithm can reduce the FRR of a fingerprint matcher by approximately 2% at a FAR of 0.01%.