nils-herrmann/clean_scirepeval huggingface dataset:
Efficiency-Improved High-Voltage Analog Power Amplifier for Driving Piezoelectric Actuators

A novel high-voltage analog power amplifier derived from a well-known complementary class B amplifier is presented in this paper. The amplifier offers improved efficiency when driving capacitive loads such as piezoelectric multilayer actuators, while maintaining a high level of signal quality, peak power and low electromagnetic radiation. The main drawback of a class B amplifier in comparison to other power amplifiers is low efficiency, resulting in extensive power-loss in the two complementary transistors of the amplifier stage. Power-loss can be reduced by minimizing the voltage drop across the collector-emitter path of the transistors when a high collector current occurs. The proposed circuit uses an additional capacitor to recover up to one half of the charge stored in the actuator whenever the actuator is being discharged. The capacitor offers an additional power supply rail next to the positive and negative supply voltage of the amplifier stage. Switching the power supply net of the power transistors between the different supply rails, in order to reduce the voltage drop across the transistors, minimizes power-loss. The development of the amplifier was triggered by the introduction of a new piezoelectric motor and the requirements concerning signal quality and electromagnetic radiation. Therefore, in this paper, the circuit is analyzed using application-specific sinusoidal control signals which are used to drive the motor. Following the analytical investigation of the circuit, the amplifier is tested in an experiment measuring power consumption with a pure reactive load, and the level of distortion caused by the amplifier. In the experiment, a power-loss reduction of 47% was achieved in comparison with a class B stage. The amplifier processed a peak power of 160 W. The proposed amplifier showed total harmonic distortion of THD = 0.60% maximum. Distortions of a pure class B amplifier in the same setup were measured at THD = 0.25% .
Factors influencing the composition of the intestinal microbiota in early infancy.

The aim of this study was to examine the contribution of a broad range of external influences to the gut microbiotic composition in early infancy.



Fecal samples from 1032 infants at 1 month of age, who were recruited from the KOALA Birth Cohort Study in the Netherlands, were subjected to quantitative real-time polymerase chain reaction assays for the enumeration of bifidobacteria, Escherichia coli, Clostridium difficile, Bacteroides fragilis group, lactobacilli, and total bacterial counts. Information on potential determinants of the gut microbiotic composition was collected with repeated questionnaires. The associations between these factors and the selected gut bacteria were analyzed with univariate and multivariate analyses.



Infants born through cesarean section had lower numbers of bifidobacteria and Bacteroides, whereas they were more often colonized with C difficile, compared with vaginally born infants. Exclusively formula-fed infants were more often colonized with E coli, C difficile, Bacteroides, and lactobacilli, compared with breastfed infants. Hospitalization and prematurity were associated with higher prevalence and counts of C difficile. Antibiotic use by the infant was associated with decreased numbers of bifidobacteria and Bacteroides. Infants with older siblings had slightly higher numbers of bifidobacteria, compared with infants without siblings.



The most important determinants of the gut microbiotic composition in infants were the mode of delivery, type of infant feeding, gestational age, infant hospitalization, and antibiotic use by the infant. Term infants who were born vaginally at home and were breastfed exclusively seemed to have the most "beneficial" gut microbiota (highest numbers of bifidobacteria and lowest numbers of C difficile and E coli).
Smartphone usage in the 21st century: who is active on WhatsApp?

Mounting evidence shows that smartphone usage heavily disrupts our work life and social activities. Moreover, it is possible that overuse could resemble addictive tendencies. A key contributing factor to smartphone overuse seems to be usage of the messaging application WhatsApp. Although WhatsApp is one of the most commonly used communication applications on smartphones, research in this area is scarce. Given the huge societal debate on the impact of smartphone usage on our daily lives, the present study undertook a large-scale investigation in order to provide numbers on smartphone usage generally—and use of WhatsApp in particular, with the aim of providing a basis for a scientific debate. In a large sample of N = 2,418 users, we recorded WhatsApp behaviour over a 4 week period. Our data show that use of WhatsApp accounted for 19.83% (= 32.11 min) of all smartphone behaviour (compare: Facebook only 9.38% = 15.19 min). The mean of general daily smartphone usage was 161.95 min. Females used WhatsApp for significantly longer periods of time than males and younger age was associated with longer duration of WhatsApp use. While the personality trait Extraversion was positively associated with daily WhatsApp use, Conscientiousness showed an inverse correlation with the length of daily WhatsApp use. The numbers on smartphone usage in the present study show that the smartphone dominates our daily life. In particular WhatsApp is a driving force, here. Given the length of daily smartphone and WhatsApp usage, more studies need to be conducted to better understand smartphone usage.
Can complex network metrics predict the behavior of NBA teams?

The United States National Basketball Association (NBA) is one of the most popular sports league in the world and is well known for moving a millionary betting market that uses the countless statistical data generated after each game to feed the wagers. This leads to the existence of a rich historical database that motivates us to discover implicit knowledge in it. In this paper, we use complex network statistics to analyze the NBA database in order to create models to represent the behavior of teams in the NBA. Results of complex network-based models are compared with box score statistics, such as points, rebounds and assists per game. We show the box score statistics play a significant role for only a small fraction of the players in the league. We then propose new models for predicting a team success based on complex network metrics, such as clustering coefficient and node degree. Complex network-based models present good results when compared to box score statistics, which underscore the importance of capturing network relationships in a community such as the NBA.
Design concept development of a spherical stepper for robotic applications

This communication presents the design concept of a new spherical stepper motor capable of three-degrees-of-freedom (DOF) motion in a single joint. The ball-joint-like motor has no singularities except at the boundary of the workspace and can perform isotropic manipulation in all three directions. Due to its relatively simple ball-like structure, undesired cross-coupling and centrifugal components of wrist rotor dynamics can be effectively minimized or eliminated. The spherical stepper motor has potential in robotic applications as a three-DOF shoulder or an eyeball as well as a wrist actuator. In particular, this work presents the systematic conceptualization of a spherical stepper and the feasibility of constructing the spherical stepper. By pointing out the significant differences between a three-DOF spherical motor and the conventional stepper motor, this work illustrates the fundamentals of the spherical stepper design for robotic applications. Along with the experimental data, an analytical approach based on the permeance formula was employed to predict the driving forces generated by a neodymium-iron (Nd-Fe) permanent magnet. The force-displacement curves provide useful information for rational spherical motor design and control.
Multi-Prediction Deep Boltzmann Machines

We introduce the multi-prediction deep Boltzmann machine (MP-DBM). The MPDBM can be seen as a single probabilistic model trained to maximize a variational approximation to the generalized pseudolikelihood, or as a family of recurrent nets that share parameters and approximately solve different inference problems. Prior methods of training DBMs either do not perform well on classification tasks or require an initial learning pass that trains the DBM greedily, one layer at a time. The MP-DBM does not require greedy layerwise pretraining, and outperforms the standard DBM at classification, classification with missing inputs, and mean field prediction
An Electric Field Pretouch system for grasping and co-manipulation

Pretouch sensing is longer range than contact, but shorter range than vision. The hypothesis motivating this work is that closed loop feedback based on short range but non-contact measurements can improve the reliability of manipulation. This paper presents a grasping system that is guided at short range by Electric Field (EF) Pretouch. We describe two sets of experiments. The first set of experiments involves human-to-robot and robot-to-human handoff, including the use of EF Pretouch to detect whether or not a human is also touching an object that the robot is holding, which we call the “co-manipulation state.” In the second set of experiments, the robot picks up standalone objects. We describe a number of techniques that servo the arm and fingers in order to both collect relevant geometrical information, and to actually perform the manipulation task.
Dataset Coverage for Testing Machine Learning Computer Programs

Machine learning programs are non-testable, and thus testing with pseudo oracles is recommended. Although metamorphic testing is effective for testing with pseudo oracles, identifying metamorphic properties has been mostly ad hoc. This paper proposes a systematic method to derive a set of metamorphic properties for machine learning classifiers, support vector machines. The proposal includes a new notion of test coverage for the machine learning programs; this test coverage provides a clear guideline for conducting a series of metamorphic testing.
Eye Contact Detection via Deep Neural Networks

With the presence of ubiquitous devices in our daily lives, effectively capturing and managing user attention becomes a critical device requirement. While gaze-tracking is typically employed to determine the user’s focus of attention, gaze-lock detection to sense eyecontact with a device is proposed in . This work proposes eye contact detection using deep neural networks, and makes the following contributions: (1) With a convolutional neural network (CNN) architecture, we achieve superior eye-contact detection performance as compared to [16] with minimal data pre-processing ; our algorithm is furthermore validated on multiple datasets, (2) Gaze-lock detection is improved by combining head pose and eye-gaze information consistent with social attention literature, and (3) We demonstrate gaze-locking on an Android mobile platform via CNN model compression.
Human and Automated CEFR-based Grading of Short Answers

This paper is concerned with the task of automatically assessing the written proficiency level of non-native (L2) learners of English. Drawing on previous research on automated L2 writing assessment following the Common European Framework of Reference for Languages (CEFR), we investigate the possibilities and difficulties of deriving the CEFR level from short answers to open-ended questions, which has not yet been subjected to numerous studies up to date. The object of our study is twofold: to examine the intricacy involved with both human and automated CEFR-based grading of short answers. On the one hand, we describe the compilation of a learner corpus of short answers graded with CEFR levels by three certified Cambridge examiners. We mainly observe that, although the shortness of the answers is reported as undermining a clear-cut evaluation, the length of the answer does not necessarily correlate with inter-examiner disagreement. On the other hand, we explore the development of a soft-voting system for the automated CEFR-based grading of short answers and draw tentative conclusions about its use in a computer-assisted testing (CAT) setting.
An Ethernet-Based Fronthaul Implementation with MAC/PHY Split LTE Processing

A testbed implementation for an Ethernet fronthaul transporting signals arising from a long-term evolution (LTE) functional subdivision ("split") at the media-access control (MAC)/physical layer (PHY) interface is presented. Based on open LTE base station software, the testbed demonstrates significant data rate reductions compared to current fronthaul implementations that rely on In- phase and Quadrature radio sample transportation and data rates that scale with cell load. All generated traffic flows are clearly distinguishable using appropriate packet headers. A selection of test cases and their corresponding results are presented to demonstrate the operation of the fronthaul and the performance of individual flows in terms of data rates and overheads.
Mining Roles with Multiple Objectives

With the growing adoption of Role-Based Access Control (RBAC) in commercial security and identity management products, how to facilitate the process of migrating a non-RBAC system to an RBAC system has become a problem with significant business impact. Researchers have proposed to use data mining techniques to discover roles to complement the costly top-down approaches for RBAC system construction. An important problem is how to construct RBAC systems with low complexity. In this article, we define the notion of weighted structural complexity measure and propose a role mining algorithm that mines RBAC systems with low structural complexity. Another key problem that has not been adequately addressed by existing role mining approaches is how to discover roles with semantic meanings. In this article, we study the problem in two primary settings with different information availability. When the only information is user-permission relation, we propose to discover roles whose semantic meaning is based on formal concept lattices. We argue that the theory of formal concept analysis provides a solid theoretical foundation for mining roles from a user-permission relation. When user-attribute information is also available, we propose to create roles that can be explained by expressions of user-attributes. Since an expression of attributes describes a real-world concept, the corresponding role represents a real-world concept as well. Furthermore, the algorithms we propose balance the semantic guarantee of roles with system complexity. Finally, we indicate how to create a hybrid approach combining top-down candidate roles. Our experimental results demonstrate the effectiveness of our approaches.
Accessibility Evaluation of Classroom Captions

Real-time captioning enables deaf and hard of hearing (DHH) people to follow classroom lectures and other aural speech by converting it into visual text with less than a five second delay. Keeping the delay short allows end-users to follow and participate in conversations. This article focuses on the fundamental problem that makes real-time captioning difficult: sequential keyboard typing is much slower than speaking. We first surveyed the audio characteristics of 240 one-hour-long captioned lectures on YouTube, such as speed and duration of speaking bursts. We then analyzed how these characteristics impact caption generation and readability, considering specifically our human-powered collaborative captioning approach. We note that most of these characteristics are also present in more general domains. For our caption comparison evaluation, we transcribed a classroom lecture in real-time using all three captioning approaches. We recruited 48 participants (24 DHH) to watch these classroom transcripts in an eye-tracking laboratory. We presented these captions in a randomized, balanced order. We show that both hearing and DHH participants preferred and followed collaborative captions better than those generated by automatic speech recognition (ASR) or professionals due to the more consistent flow of the resulting captions. These results show the potential to reliably capture speech even during sudden bursts of speed, as well as for generating “enhanced” captions, unlike other human-powered captioning approaches.
ENHANCING THE TOURISM EXPERIENCE THROUGH MOBILE AUGMENTED REALITY: CHALLENGES AND PROSPECTS

The paper discusses the use of Augmented Reality (AR) applications for the needs of tourism. It describes the technology’s evolution from pilot applications into commercial mobile applications. We address the technical aspects of mobile AR applications development, emphasizing on the technologies that render the delivery of augmented reality content possible and experientially superior. We examine the state of the art, providing an analysis concerning the development and the objectives of each application. Acknowledging the various technological limitations hindering AR’s substantial end-user adoption, the paper proposes a model for developing AR mobile applications for the field of tourism, aiming to release AR’s full potential within the field.
Discovering social circles in ego networks

People's personal social networks are big and cluttered, and currently there is no good way to automatically organize them. Social networking sites allow users to manually categorize their friends into social circles (e.g., “circles” on Google+, and “lists” on Facebook and Twitter). However, circles are laborious to construct and must be manually updated whenever a user's network grows. In this article, we study the novel task of automatically identifying users' social circles. We pose this task as a multimembership node clustering problem on a user's ego network, a network of connections between her friends. We develop a model for detecting circles that combines network structure as well as user profile information. For each circle, we learn its members and the circle-specific user profile similarity metric. Modeling node membership to multiple circles allows us to detect overlapping as well as hierarchically nested circles. Experiments show that our model accurately identifies circles on a diverse set of data from Facebook, Google+, and Twitter, for all of which we obtain hand-labeled ground truth.
MATLAB-based toolkit for an introductory course in SAR image processing

This paper documents the continuation of the work that started with a student paper presented at Radar04. The earlier paper was a description of a graphic user interface (GUI) for SAR data processing that was developed for classroom use, based on a previously published algorithm. This GUI and associated MATLAB toolbox has now been extended to include several other capabilities and has grown into a toolkit that can be used in an introductory course in SAR image processing. Besides the processing of raw SAR data, the GUI and toolbox now include additional teaching and research capabilities, to include additional types of speckle filters and image compression capability. Apart from this the user can choose from two different algorithms that help to extract the single look image from the raw SAR data. These algorithms were implemented in MATLAB, because of its wide availability, and "self documenting" capability. Thus, the resulting package is suitable for classroom use to illustrate the principles of SAR data processing. Results of the project is made available to the radar community for use by the other schools and institutions that desire a simple SAR processing capability. The MATLAB code has been extensively commented, so that it provides useful baseline for other class projects. Although the material presented is not cutting edge research, it does provide a much-needed tutorial introduction to SAR processing in the classroom. We have also begun developing C and Python based versions of this package so that users can make comparative performance measurements and understand how various implementations of the algorithms affect the end results. We would like to acknowledge the help received from Alaska Satellite Facility (ASF) and the Space Dynamics Laboratory (SDL) at Utah State University for this project.
Low Power Magnetic Full-Adder Based on Spin Transfer Torque MRAM

Power issues have become a major problem of CMOS logic circuits as technology node shrinks below 90 nm. In order to overcome this limitation, emerging logic-in-memory architecture based on nonvolatile memories (NVMs) are being investigated. Spin transfer torque (STT) magnetic random access memory (MRAM) is considered one of the most promising NVMs thanks to its high speed, low power, good endurance, and 3-D back-end integration. This paper presents a novel magnetic full-adder (MFA) design based on perpendicular magnetic anisotropy (PMA) STT-MRAM. It provides advantageous power efficiency and die area compared with conventional CMOS-only full adder (FA). Transient simulations have been performed to validate this design by using an industrial CMOS 40 nm design kit and an accurate STT-MRAM compact model including physical models and experimental measurements.
Automatic road network extraction from UAV image in mountain area

In this paper, we propose an approach to extract road network from unmanned aerial vehicle (UAV) images in mountain area. Firstly, a series images are arranged automatically based on their generation time and mosaiced to the zonal image along flight direction. Secondly, line segments are detected using a region growing algorithm. Thirdly, potential road segments are identified using parallel criteria. Each road's central axis is extracted from the region generated by the entire parallel line segments. Finally, we propose a novel algorithm to connect the broken road segments, remove short or pseudo segments with orientation- projection criteria.
Using neural networks in reliability prediction

It is shown that neural network reliability growth models have a significant advantage over analytic models in that they require only failure history as input and not assumptions about either the development environment or external parameters. Using the failure history, the neural-network model automatically develops its own internal model of the failure process and predicts future failures. Because it adjusts model complexity to match the complexity of the failure history, it can be more accurate than some commonly used analytic models. Results with actual testing and debugging data which suggest that neural-network models are better at endpoint predictions than analytic models are presented.<<ETX>>
Re-Active Learning: Active Learning with Relabeling

Active learning seeks to train the best classifier at the lowest annotation cost by intelligently picking the best examples to label. Traditional algorithms assume there is a single annotator and disregard the possibility of requesting additional independent annotations for a previously labeled example. However, relabeling examples is important, because all annotators make mistakes — especially crowdsourced workers, who have become a common source of training data. This paper seeks to understand the difference in marginal value between decreasing the noise of the training set via relabeling and increasing the size and diversity of the (noisier) training set by labeling new examples. We use the term re-active learning to denote this generalization of active learning. We show how traditional active learning methods perform poorly at re-active learning, present new algorithms designed for this important problem, formally characterize their behavior, and empirically show that our methods effectively make this