nils-herrmann/clean_scirepeval huggingface dataset:
Robust force control of Series Elastic Actuators using Sliding Mode Control and Disturbance Observer

In this paper, a new robust force controller is proposed for Series Elastic Actuators (SEAs) by using conventional Sliding Mode Control (SMC) and Disturbance Observer (DOb). The position measurement of the actuator's link is subtracted from the desired deflection of the spring so that the force control goal is defined as the desired position of the motor; i.e., the force control is simply performed by designing a position controller at motor side. However, the position accuracy of the motor is influenced by the dynamics of the actuator and environment. Since they cannot be easily identified in practice, the dynamics of the actuator's link and environment are considered as unknown disturbances in the design of the proposed controller. In order to improve the robustness, conventional SMC-based robust force controller is designed without considering the unknown disturbances. Although the robust force control can be theoretically performed by using the conventional SMC-based controller, it suffers from chattering in real implementations. Conventional DOb-based robust motion controller is designed at motor side so that not only the disturbances are cancelled by feeding-back their estimations but also the control signal of SMC-based robust force controller is lowered. Hence, a simple yet efficient robust force controller is designed for SEAs. The validity of the proposed robust force controller is verified by giving experimental results.
The humble Bayesian: model checking from a fully Bayesian perspective.

Gelman and Shalizi (2012) criticize what they call the 'usual story' in Bayesian statistics: that the distribution over hypotheses or models is the sole means of statistical inference, thus excluding model checking and revision, and that inference is inductivist rather than deductivist. They present an alternative hypothetico-deductive approach to remedy both shortcomings. We agree with Gelman and Shalizi's criticism of the usual story, but disagree on whether Bayesian confirmation theory should be abandoned. We advocate a humble Bayesian approach, in which Bayesian confirmation theory is the central inferential method. A humble Bayesian checks her models and critically assesses whether the Bayesian statistical inferences can reasonably be called upon to support real-world inferences.
Evaluating the Usefulness and the Ease of Use of a Web-based Inspection Data Collection Tool

This paper contributes a valid and reliable measurement instrument in the form of a questionnaire to determine, from a user’s perspective, the usefulness and ease of use of a Web-based Inspection Process Support tool (WIPS) that we developed for inspection data collection. The questionnaire is built upon the work of Fred Davis on perceived usefulness, ease of use, and usage of information technology. To validate the questionnaire and its underlying model as well as to evaluate WIPS, we performed a controlled experiment with computer science students as subjects. The subjects performed inspection of a code module and used WIPS for collecting defect and effort data. Once they had completed the code inspection, they filled out the usefulness and ease of use questionnaire. Our experimental results provide empirical evidence that the questionnaire is a reliable measurement instrument (cronbach alpha: 0.84 for usefulness; 0.82 for ease of use). Factor analysis revealed that the questionnaire items discriminate between two different concepts: usefulness and ease of use. Since WIPS received high ratings for both concepts, we can conclude that the subjects consider WIPS useful and easy to use. These results, together with the fact that usefulness was significantly correlated to selfpredicted future usage, imply that our subjects preferred WIPS over paper-based forms for inspection data collection. We are aware that a single experiment does not provide conclusive evidence. Hence, we consider our results as a baseline against which other researchers can compare their results when utilizing the presented questionnaire. However, its use is not limited to inspection tools, but applies to the usefulness/ ease of use evaluation of tools and techniques in general. To demonstrate this, we present preliminary results of a controlled experiment we are currently performing with professional software developers in an industrial setting to evaluate different inspection techniques.
Ordinary magic. Resilience processes in development.

The study of resilience in development has overturned many negative assumptions and deficit-focused models about children growing up under the threat of disadvantage and adversity. The most surprising conclusion emerging from studies of these children is the ordinariness of resilience. An examination of converging findings from variable-focused and person-focused investigations of these phenomena suggests that resilience is common and that it usually arises from the normative functions of human adaptational systems, with the greatest threats to human development being those that compromise these protective systems. The conclusion that resilience is made of ordinary rather than extraordinary processes offers a more positive outlook on human development and adaptation, as well as direction for policy and practice aimed at enhancing the development of children at risk for problems and psychopathology.
Harnessing Context Sensing to Develop a Mobile Intervention for Depression

Mobile phone sensors can be used to develop context-aware systems that automatically detect when patients require assistance. Mobile phones can also provide ecological momentary interventions that deliver tailored assistance during problematic situations. However, such approaches have not yet been used to treat major depressive disorder.



The purpose of this study was to investigate the technical feasibility, functional reliability, and patient satisfaction with Mobilyze!, a mobile phone- and Internet-based intervention including ecological momentary intervention and context sensing.



We developed a mobile phone application and supporting architecture, in which machine learning models (ie, learners) predicted patients' mood, emotions, cognitive/motivational states, activities, environmental context, and social context based on at least 38 concurrent phone sensor values (eg, global positioning system, ambient light, recent calls). The website included feedback graphs illustrating correlations between patients' self-reported states, as well as didactics and tools teaching patients behavioral activation concepts. Brief telephone calls and emails with a clinician were used to promote adherence. We enrolled 8 adults with major depressive disorder in a single-arm pilot study to receive Mobilyze! and complete clinical assessments for 8 weeks.



Promising accuracy rates (60% to 91%) were achieved by learners predicting categorical contextual states (eg, location). For states rated on scales (eg, mood), predictive capability was poor. Participants were satisfied with the phone application and improved significantly on self-reported depressive symptoms (beta(week) = -.82, P < .001, per-protocol Cohen d = 3.43) and interview measures of depressive symptoms (beta(week) = -.81, P < .001, per-protocol Cohen d = 3.55). Participants also became less likely to meet criteria for major depressive disorder diagnosis (b(week) = -.65, P = .03, per-protocol remission rate = 85.71%). Comorbid anxiety symptoms also decreased (beta(week) = -.71, P < .001, per-protocol Cohen d = 2.58).



Mobilyze! is a scalable, feasible intervention with preliminary evidence of efficacy. To our knowledge, it is the first ecological momentary intervention for unipolar depression, as well as one of the first attempts to use context sensing to identify mental health-related states. Several lessons learned regarding technical functionality, data mining, and software development process are discussed.
An Embarrassingly Simple Approach to Visual Domain Adaptation

We show that it is possible to achieve high-quality domain adaptation without explicit adaptation. The nature of the classification problem means that when samples from the same class in different domains are sufficiently close, and samples from differing classes are separated by large enough margins, there is a high probability that each will be classified correctly. Inspired by this, we propose an embarrassingly simple yet effective approach to domain adaptation—only the class mean is used to learn class-specific linear projections. Learning these projections is naturally cast into a linear-discriminant-analysis-like framework, which gives an efficient, closed form solution. Furthermore, to enable to application of this approach to unsupervised learning, an iterative validation strategy is developed to infer target labels. Extensive experiments on cross-domain visual recognition demonstrate that, even with the simplest formulation, our approach outperforms existing non-deep adaptation methods and exhibits classification performance comparable with that of modern deep adaptation methods. An analysis of potential issues effecting the practical application of the method is also described, including robustness, convergence, and the impact of small sample sizes.
Detailed Real-Time Urban 3D Reconstruction from Video

The paper presents a system for automatic, geo-registered, real-time 3D reconstruction from video of urban scenes. The system collects video streams, as well as GPS and inertia measurements in order to place the reconstructed models in geo-registered coordinates. It is designed using current state of the art real-time modules for all processing steps. It employs commodity graphics hardware and standard CPU’s to achieve real-time performance. We present the main considerations in designing the system and the steps of the processing pipeline. Our system extends existing algorithms to meet the robustness and variability necessary to operate out of the lab. To account for the large dynamic range of outdoor videos the processing pipeline estimates global camera gain changes in the feature tracking stage and efficiently compensates for these in stereo estimation without impacting the real-time performance. The required accuracy for many applications is achieved with a two-step stereo reconstruction process exploiting the redundancy across frames. We show results on real video sequences comprising hundreds of thousands of frames.
Determining the effectiveness of prompts for self-regulated learning in problem-solving scenarios

Cognitive scientists have studied internal cognitive structures, processes, and systems for decades in order to understand how they function in human learning. In order to solve challenging tasks in problem situations, learners not only have to perform cognitive activities, e.g., activating existing cognitive structures or organizing new information, they also have to set specific goals, plan their activities, monitor their performance during the problem-solving process, and evaluate the efficiency of their actions. This paper reports an experimental study with 98 participants where effective instructional interventions for self-regulated learning within problemsolving processes are investigated. Furthermore, an automated assessment and analysis methodology for determining the quality of learning outcomes is introduced. The results indicate that generic prompts are an important aid for developing cognitive structures while solving problems.
Unrealistic optimism about susceptibility to health problems: Conclusions from a community-wide sample

A mailed questionnaire was used to obtain comparative risk judgments for 32 different hazards from a random sample of 296 individuals living in central New Jersey. The results demonstrate that an optimistic bias about susceptibility to harm-a tendency to claim that one is less at risk than one's peers—is not limited to any particular age, sex, educational, or occupational group. It was found that an optimistic bias is often introduced when people extrapolate from their past experience to estimate their future vulnerability. Thus, the hazards most likely to elicit unrealistic optimism are those associated with the belief (often incorrect) that if the problem has not yet appeared, it is unlikely to occur in the future. Optimistic biases also increase with the perceived preventability of a hazard and decrease with perceived frequency and personal experience. Other data presented illustrate the inconsistent relationships between personal risk judgments and objective risk factors.
Foreground object detection from videos containing complex background

This paper proposes a novel method for detection and segmentation of foreground objects from a video which contains both stationary and moving background objects and undergoes both gradual and sudden "once-off" changes. A Bayes decision rule for classification of background and foreground from selected feature vectors is formulated. Under this rule, different types of background objects will be classified from foreground objects by choosing a proper feature vector. The stationary background object is described by the color feature, and the moving background object is represented by the color co-occurrence feature. Foreground objects are extracted by fusing the classification results from both stationary and moving pixels. Learning strategies for the gradual and sudden "once-off" background changes are proposed to adapt to various changes in background through the video. The convergence of the learning process is proved and a formula to select a proper learning rate is also derived. Experiments have shown promising results in extracting foreground objects from many complex backgrounds including wavering tree branches, flickering screens and water surfaces, moving escalators, opening and closing doors, switching lights and shadows of moving objects.
Automatic ballistocardiogram (BCG) beat detection using a template matching approach

This paper suggests a beat detection method for ballistocardiogram (BCG) from an unconstrained cardiac signal monitoring devices. A fiducial peak point of BCG is an I-J-K complex which corresponds with ventricle contraction and Electrocardiogram (ECG) QRS complex. The goal of the method is extraction of J peak without ECG synchronization. The detection method is based on a “template matching” rule evaluated using a correlation function in a local moving-window procedure. The total beat detection algorithm operates in two stages, template definition stage and beat detection stage with defined template in previous stage. In the first stage, the BCG template is constructed by the expert with an empirical analysis of BCG signal and measurement device. In the second stage, the correlation function calculates an accuracy of template with BCG signal using a local moving-window. The data analysis has been performed on the subjects tested at Seoul National University Hospital Sleep Medicine Center and presents 95.16% of sensitivity and 94.76% of positive predictivity value for the J peak detection.
The forgotten ones? The validity of consideration and initiating structure in leadership research.

This study provided a meta-analysis of the relationship of the Ohio State leadership behaviors--Consideration and Initiating Structure--with leadership. Overall, 163 independent correlations for Consideration and 159 correlations for Initiating Structure were analyzed. Results revealed that both Consideration (.48) and Initiating Structure (.29) have moderately strong, nonzero relations with leadership outcomes. Consideration was more strongly related to follower satisfaction (leader satisfaction, job satisfaction), motivation, and leader effectiveness, and Initiating Structure was slightly more strongly related to leader job performance and group-organization performance. Validities did vary by leadership measure, but in most cases validities generalized regardless of the measure used. Overall, the results provide important support for the validity of Initiating Structure and Consideration in leadership research.
Distributed Wireless Power Transfer With Energy Feedback

Energy beamforming (EB) is a key technique for achieving efficient radio-frequency transmission enabled wireless energy transfer (WET). By optimally designing the waveforms from multiple energy transmitters (ETs) over the wireless channels, they can be constructively combined at the energy receiver (ER) to achieve an EB gain that scales with the number of ETs. However, the optimal design of EB waveforms requires accurate channel state information (CSI) at the ETs, which is challenging to obtain practically, especially in a distributed system with ETs at separate locations. In this paper, we study practical and efficient channel training methods to achieve optimal EB in a distributed WET system. We propose two protocols with and without centralized coordination, respectively, where distributed ETs either sequentially or in parallel adapt their transmit phases based on a low-complexity energy feedback from the ER. The energy feedback only depends on the received power level at the ER, where each feedback indicates one particular transmit phase that results in the maximum harvested power over a set of previously used phases. Simulation results show that the two proposed training protocols converge very fast in practical WET systems even with a large number of distributed ETs, while the protocol with sequential ET phase adaptation is also analytically shown to converge to the optimal EB design with perfect CSI by increasing the training time. Numerical results are also provided to evaluate the performance of the proposed distributed EB and training designs as compared to other benchmark schemes.
Group signatures with verifier-local revocation

Group signatures have recently become important for enabling privacy-preserving attestation in projects such as Microsoft's ngscb effort (formerly Palladium). Revocation is critical to the security of such systems. We construct a short group signature scheme that supports Verifier-Local Revocation (VLR). In this model, revocation messages are only sent to signature verifiers (as opposed to both signers and verifiers). Consequently there is no need to contact individual signers when some user is revoked. This model is appealing for systems providing attestation capabilities. Our signatures are as short as standard RSA signatures with comparable security. Security of our group signature (in the random oracle model) is based on the Strong Diffie-Hellman assumption and the Decision Linear assumption in bilinear groups. We give a precise model for VLR group signatures and discuss its implications.
The structure of creative cognition in the human brain

Creativity is a vast construct, seemingly intractable to scientific inquiry-perhaps due to the vague concepts applied to the field of research. One attempt to limit the purview of creative cognition formulates the construct in terms of evolutionary constraints, namely that of blind variation and selective retention (BVSR). Behaviorally, one can limit the "blind variation" component to idea generation tests as manifested by measures of divergent thinking. The "selective retention" component can be represented by measures of convergent thinking, as represented by measures of remote associates. We summarize results from measures of creative cognition, correlated with structural neuroimaging measures including structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), and proton magnetic resonance spectroscopy (1H-MRS). We also review lesion studies, considered to be the "gold standard" of brain-behavioral studies. What emerges is a picture consistent with theories of disinhibitory brain features subserving creative cognition, as described previously (Martindale, 1981). We provide a perspective, involving aspects of the default mode network (DMN), which might provide a "first approximation" regarding how creative cognition might map on to the human brain.
DC-DC converters for electric vehicle applications

An integral part of any modern day electric vehicle is power electronic circuits (PECs) comprising of DC-AC inverters and DC-DC converters. A DC-AC inverter supplies the high power electric motor and utility loads such as air-conditioning system, whereas a DC-DC converter supplies conventional low-power, low-voltage loads. However, the need for high power bidirectional DC- DC converters in future electric vehicles has led to the development of many new topologies of DC-DC converters. This paper presents an overview of state-of-the-art DC-DC converters used in battery electric vehicles (BEVs), hybrid electric vehicles (HEVs), and fuel cell vehicles (FCVs). Several DC-DC converters such as isolated, nonisolated, half-bridge, full-bridge, unidirectional and bidirectional topologies, and their applications in electric vehicles are presented.
Biomedical Image Segmentation Using Fully Convolutional Networks on TrueNorth

With the rapid growth of medical and biomedical image data, energy-efficient solutions for analyzing such image data that can be processed fast and accurately on platforms with low power budget are highly desirable. This paper uses segmenting glial cells in brain microscopy images as a case study to demonstrate how to achieve biomedical image segmentation with significant energy saving and minimal comprise in accuracy. Specifically, we design, train, implement, and evaluate Fully Convolutional Networks (FCNs) for biomedical image segmentation on IBM's neurosynaptic DNN processor — TrueNorth (TN). Comparisons in terms of accuracy and energy dissipation of TN with that of a low power NVIDIA TX2 mobile GPU platform have been conducted. Experimental results show that TN can offer at least two orders of magnitude improvement in energy efficiency when compared to TX2 GPU for the same workload.
Time-frequency Techniques in Biomedical Signal Analysis

This review outlines the method ological fundamentals of the most frequently used non-parametric time-frequency analysis techniques in biomedicine and their main properties, as well as providing decision aids concerning their applications.  The short-term Fourier transform (STFT), the Gabor transform (GT), the S-transform (ST), the continuous Morlet wavelet transform (CMWT), and the Hilbert transform (HT) are introduced as linear transforms by using a unified concept of the time-frequency representation which is based on a standardized analytic signal. The Wigner-Ville dis tribution (WVD) serves as an example of the ‘quadratic transforms’ class. The combination of WVD and GT with the matching pursuit (MP) decomposition and that of the HT with the empirical mode decomposition (EMD) are explained; these belong to the class of signal-adaptive approaches.  Similarities between linear transforms are demonstrated and differences with regard to the time-frequency resolution and interference (cross) terms are presented in detail. By means of simulated signals the effects of different time-frequency resolutions of the GT, CMWT, and WVD as well as the resolution-related properties of the inter ference (cross) terms are shown. The methodinherent drawbacks and their consequences for the application of the time-frequency techniques are demonstrated by instantaneous amplitude, frequency and phase measures and related time-frequency representations (spectrogram, scalogram, timefrequency distribution, phase-locking maps) of measured magnetoencephalographic (MEG) signals.  The appropriate selection of a method and its parameter settings will ensure readability of the time-frequency representations and reliability of results. When the time-frequency characteristics of a signal strongly correspond with the time-frequency resolution of the analysis then a method may be considered ‘optimal’. The MP-based signal-adaptive approaches are preferred as these provide an appropriate time-frequency resolution for all frequencies while simultaneously reducing interference (cross) terms.