nils-herrmann/clean_scirepeval huggingface dataset:
Identifying Emotional and Informational Support in Online Health Communities

A large number of online health communities exist today, helping millions of people with social support during difficult phases of their lives when they suffer from serious diseases. Interactions between members in these communities contain discussions on practical problems faced by people during their illness such as depression, side-effects of medications, etc and answers to those problems provided by other members. Analyzing these interactions can be helpful in getting crucial information about the community such as dominant health issues, identifying sentimental effects of interactions on individual members and identifying influential members. In this paper, we analyze user messages of an online cancer support community, Cancer Survivors Network (CSN), to identity the two types of social support present in them: emotional support and informational support. We model the task as a binary classification problem. We use several generic and novel domain-specific features. Experimental results show that we achieve high classification performance. We, then, use the classifier to predict the type of support in CSN messages and analyze the posting behaviors of regular members and influential members in CSN in terms of the type of support they provide in their messages. We find that influential members generally provide more emotional support as compared to regular members in CSN.
Spatially Nonuniform Scaling Functions for High Contrast Images

An algorithm is presented that scales the pixel intensities of a computer generated greyscale image so that they are all displayable on a standard CRT. This scaling is spatially nonuniform over the image in that different pixels with the same intensity in the original image may have different intensities in the resulting image. The goal of this scaling transformation is to produce an image on the CRT that perceptually mimics the calculated image, while staying within the physical limitations of the CRT.
A High-Speed Brain Speller using steady-State Visual evoked potentials

Implementing a complex spelling program using a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) remains a challenge due to difficulties in stimulus presentation and target identification. This study aims to explore the feasibility of mixed frequency and phase coding in building a high-speed SSVEP speller with a computer monitor. A frequency and phase approximation approach was developed to eliminate the limitation of the number of targets caused by the monitor refresh rate, resulting in a speller comprising 32 flickers specified by eight frequencies (8-15 Hz with a 1 Hz interval) and four phases (0°, 90°, 180°, and 270°). A multi-channel approach incorporating Canonical Correlation Analysis (CCA) and SSVEP training data was proposed for target identification. In a simulated online experiment, at a spelling rate of 40 characters per minute, the system obtained an averaged information transfer rate (ITR) of 166.91 bits/min across 13 subjects with a maximum individual ITR of 192.26 bits/min, the highest ITR ever reported in electroencephalogram (EEG)-based BCIs. The results of this study demonstrate great potential of a high-speed SSVEP-based BCI in real-life applications.
Android-based attendance management system

Until today, most lecturers in universities are found still using the conventional methods of taking students' attendance either by calling out the student names or by passing around an attendance sheet for students to sign confirming their presence. In addition to the time-consuming issue, such method is also at higher risk of having students cheating about their attendance, especially in a large classroom. Therefore a method of taking attendance by employing an application running on the Android platform is proposed in this paper. This application, once installed can be used to download the students list from a designated web server. Based on the downloaded list of students, the device will then act like a scanner to scan each of the student cards one by one to confirm and verify the student's presence. The device's camera will be used as a sensor that will read the barcode printed on the students' cards. The updated attendance list is then uploaded to an online database and can also be saved as a file to be transferred to a PC later on. This system will help to eliminate the current problems, while also promoting a paperless environment at the same time. Since this application can be deployed on lecturers' own existing Android devices, no additional hardware cost is required.
Analysis of LLC Resonant Converter considering effects of parasitic components

Nowadays the trend of power supply market is more inclined to high switching frequency, high efficiency and high power density. To meet this trend, resonant power supply holds more attraction, because it can be operated in high switching frequency with high efficiency. There are many resonant power supplies such as Series-Resonant Converter (SRC), Parallel-Resonant Converter (PRC) and Series-Parallel Resonant Converter (SPRC). Among them, LLC Resonant Converter has a lot of advantages over the conventional SRC and PRC considering relatively narrow switching frequency variation over wide input and load variation and Zero-Voltage-Switching for entire load range. Therefore, the LLC Resonant Converter has been widely used and discussed. However, the conventional analysis of LLC Resonant Converter with Fundamental Harmonic Approximation (FHA) can not explain the practical operation of LLC Resonant Converter. To overcome this limitation, in this paper, analysis and design of the LLC Resonant Converter including parasitic components which are affecting converter operation are proposed using a traditional analysis based on FHA. The effect of each parasitic component is analyzed with simulation results and the design guideline standing on this analysis will be described. Moreover, the experimental results of prototype designing on the basis of the analysis are shown to demonstrate the proposed analysis and design guideline.
Parts Of Speech Tagging for Indian Languages: A Literature Survey

Part of speech (POS) tagging is the process of assigning the part of speech tag or other lexical class marker to each and every word in a sentence. In many Natural Language Processing applications such as word sense disambiguation, information retrieval, information processing, parsing, question answering, and machine translation, POS tagging is considered as the one of the basic necessary tool. Identifying the ambiguities in language lexical items is the challenging objective in the process of developing an efficient and accurate POS Tagger. Literature survey shows that, for Indian languages, POS taggers were developed only in Hindi, Bengali, Panjabi and Dravidian languages. Some POS taggers were also developed generic to the Hindi, Bengali and Telugu languages. All proposed POS taggers were based on different Tagset, developed by different organization and individuals. This paper addresses the various developments in POS-taggers and POS-tagset for Indian language, which is very essential computational linguistic tool needed for many natural language processing (NLP) applications.
Learning behaviors via human-delivered discrete feedback: modeling implicit feedback strategies to speed up learning

For real-world applications, virtual agents must be able to learn new behaviors from non-technical users. Positive and negative feedback are an intuitive way to train new behaviors, and existing work has presented algorithms for learning from such feedback. That work, however, treats feedback as numeric reward to be maximized, and assumes that all trainers provide feedback in the same way. In this work, we show that users can provide feedback in many different ways, which we describe as “training strategies.” Specifically, users may not always give explicit feedback in response to an action, and may be more likely to provide explicit reward than explicit punishment, or vice versa, such that the lack of feedback itself conveys information about the behavior. We present a probabilistic model of trainer feedback that describes how a trainer chooses to provide explicit reward and/or explicit punishment and, based on this model, develop two novel learning algorithms (SABL and I-SABL) which take trainer strategy into account, and can therefore learn from cases where no feedback is provided. Through online user studies we demonstrate that these algorithms can learn with less feedback than algorithms based on a numerical interpretation of feedback. Furthermore, we conduct an empirical analysis of the training strategies employed by users, and of factors that can affect their choice of strategy.
Robobarista: Object Part based Transfer of Manipulation Trajectories from Crowd-sourcing in 3D Pointclouds

There is a large variety of objects and appliances in human environments, such as stoves, coffee dispensers, juice extractors, and so on. It is challenging for a roboticist to program a robot for each of these object types and for each of their instantiations. In this work, we present a novel approach to manipulation planning based on the idea that many household objects share similarly-operated object parts. We formulate the manipulation planning as a structured prediction problem and design a deep learning model that can handle large noise in the manipulation demonstrations and learns features from three different modalities: point-clouds, language and trajectory. In order to collect a large number of manipulation demonstrations for different objects, we developed a new crowd-sourcing platform called Robobarista. We test our model on our dataset consisting of 116 objects with 249 parts along with 250 language instructions, for which there are 1225 crowd-sourced manipulation demonstrations. We further show that our robot can even manipulate objects it has never seen before.  Robotics and Learning, Crowd-sourcing, Manipulation
Deep Epitomic Convolutional Neural Networks

Deep convolutional neural networks have recently proven ex tremely competitive in challenging image recognition tasks. This paper propose s th epitomic convolution as a new building block for deep neural networks. An ep itomic convolution layer replaces a pair of consecutive convolution and max-po oling layers found in standard deep convolutional neural networks. The main vers ion of the proposed model uses mini-epitomes in place of filters and computes res ponses invariant to small translations by epitomic search instead of max-poo ling over image positions. The topographic version of the proposed model uses large epitomes to learn filter maps organized in translational topographies. We show that error backpropagation can successfully learn multiple epitomic laye rs in a supervised fashion. The effectiveness of the proposed method is assessed in image classification tasks on standard benchmarks. Our experiments on Imagenet i ndicate improved recognition performance compared to standard convolution al neural networks of similar architecture. Our models pre-trained on Imagenet p erform excellently on Caltech-101. We also obtain competitive image classificati on results on the smallimage MNIST and CIFAR-10 datasets.
PARAM: A Model Checker for Parametric Markov Models

We present PARAM 1.0, a model checker for parametric discrete-time Markov chains (PMCs). PARAM can evaluate temporal properties of PMCs and certain extensions of this class. Due to parametricity, evaluation results are polynomials or rational functions. By instantiating the parameters in the result function, one can cheaply obtain results for multiple individual instantiations, based on only a single more expensive analysis. In addition, it is possible to post-process the result function symbolically using for instance computer algebra packages, to derive optimum parameters or to identify worst cases.
Bandit Based Monte-Carlo Planning

For large state-space Markovian Decision Problems MonteCarlo planning is one of the few viable approaches to find near-optimal solutions. In this paper we introduce a new algorithm, UCT, that applies bandit ideas to guide Monte-Carlo planning. In finite-horizon or discounted MDPs the algorithm is shown to be consistent and finite sample bounds are derived on the estimation error due to sampling. Experimental results show that in several domains, UCT is significantly more efficient than its alternatives.
Wireless Network Information Flow: A Deterministic Approach

In a wireless network with a single source and a single destination and an arbitrary number of relay nodes, what is the maximum rate of information flow achievable? We make progress on this long standing problem through a two-step approach. First, we propose a deterministic channel model which captures the key wireless properties of signal strength, broadcast and superposition. We obtain an exact characterization of the capacity of a network with nodes connected by such deterministic channels. This result is a natural generalization of the celebrated max-flow min-cut theorem for wired networks. Second, we use the insights obtained from the deterministic analysis to design a new quantize-map-and-forward scheme for Gaussian networks. In this scheme, each relay quantizes the received signal at the noise level and maps it to a random Gaussian codeword for forwarding, and the final destination decodes the source's message based on the received signal. We show that, in contrast to existing schemes, this scheme can achieve the cut-set upper bound to within a gap which is independent of the channel parameters. In the case of the relay channel with a single relay as well as the two-relay Gaussian diamond network, the gap is 1 bit/s/Hz. Moreover, the scheme is universal in the sense that the relays need no knowledge of the values of the channel parameters to (approximately) achieve the rate supportable by the network. We also present extensions of the results to multicast networks, half-duplex networks, and ergodic networks.
APPARENT PERIODS OF A BUILDING . I : FOURIER ANALYSISa By

This and the companion paper present an analysis of the amplitude and time-dependent changes of the apparent frequency of a seven-story reinforced-concrete hotel building in Van Nuys, Calif. Data of recorded response to 12 earthquakes are used, representing very small, intermediate, and large excitations (peak ground velocity, vmax = 0.6 2 11, 23, and 57 cm/s, causing no minor and major damage). This paper presents a description of the building structure, foundation, and surrounding soil, the strong motion data used in the analysis, the soil-structure interaction model assumed, and results of Fourier analysis of the recorded response. The results show that the apparent frequency changes form one earthquake to another. The general trend is a reduction with increasing amplitudes of motion. The smallest values (measured during the damaging motions) are 0.4 and 0.5 Hz for the longitudinal and transverse directions. The largest values are 1.1 and 1.4 Hz, respectively, determined from response to ambient noise after the damage occurred. This implies 64% reduction of the system frequency, or a factor '3 change, from small to large response amplitudes, and is interpreted to be caused by nonlinearities in the soil.
Listening in: practices surrounding iTunes music sharing

This paper presents a descriptive account of the social practices surrounding the iTunes music sharing of 13 participants in one organizational setting. Specifically, we characterize adoption, critical mass, and privacy; impression management and access control; the musical impressions of others that are created as a result of music sharing; the ways in which participants attempted to make sense of the dynamic system; and implications of the overlaid technical, musical, and corporate topologies. We interleave design implications throughout our results and relate those results to broader themes in a music sharing design space.
Clustering improved grid map registration using the normal distribution transform

Grid map registration is an important field in mobile robotics. Applications in which multiple robots are involved benefit from multiple aligned grid maps as they provide an efficient exploration of the environment in parallel. In this paper, a normal distribution transform (NDT)-based approach for grid map registration is presented. For simultaneous mapping and localization approaches on laser data, the NDT is widely used to align new laser scans to reference scans. The original grid quantization-based NDT results in good registration performances but has poor convergence properties due to discontinuities of the optimization function and absolute grid resolution. This paper shows that clustering techniques overcome disadvantages of the original NDT by significantly improving the convergence basin for aligning grid maps. A multi-scale clustering method results in an improved registration performance which is shown on real world experiments on radar data.
Solar photovoltaic array fed Luo converter based BLDC motor driven water pumping system

This paper deals with the solar photovoltaic (SPV) array fed water-pumping system using a Luo converter as an intermediate DC-DC converter and a permanent magnet brushless DC (BLDC) motor to drive a centrifugal water pump. Among the different types of DC-DC converters, an elementary Luo converter is selected in order to extract the maximum power available from the SPV array and for safe starting of BLDC motor. The elementary Luo converter with reduced components and single semiconductor switch has inherent features of reducing the ripples in its output current and possessing a boundless region for maximum power point tracking (MPPT). The electronically commutated BLDC motor is used with a voltage source inverter (VSI) operated at fundamental frequency switching thus avoiding the high frequency switching losses resulting in a high efficiency of the system. The SPV array is designed such that the power at rated DC voltage is supplied to the BLDC motor-pump under standard test condition and maximum switch utilization of Luo converter is achieved which results in efficiency improvement of the converter. Performances at various operating conditions such as starting, dynamic and steady state behavior are analyzed and suitability of the proposed system is demonstrated using MATLAB/Simulink based simulation results.
Design of Dual-Band Bandpass Filter Using DGS With Controllable Second Passband

In this letter, the application of a variable characteristic impedance transmission line that can be used to design a dual-band bandpass filter (BPF) is presented. The proposed filter offers a fixed first passband and a controllable second passband. The tuning of the second passband is achieved by varying the characteristic impedance of the shunt open stub of stub loaded resonator (SLR) with the help of a defected ground structure (DGS) transmission line and varactor diodes. In order to validate the theoretical predictions and simulated results, a two stage dual-band BPF with three transmission zeros was implemented and experimentally verified.
Pulsed power-driven high-power microwave sources

The advent of pulsed power technology in the 1960s has enabled the development of very high peak power sources of electromagnetic radiation in the microwave and millimeter wave bands of the electromagnetic spectrum. Such sources have applications in plasma physics, particle acceleration techniques, fusion energy research, high-power radars, and communications, to name just a few. This article describes recent ongoing activity in this field in both Russia and the United States. The overview of research in Russia focuses on high-power microwave (HPM) sources that are powered using SINUS accelerators, which were developed at the Institute of High Current Electronics. The overview of research in the United States focuses more broadly on recent accomplishments of a multidisciplinary university research initiative on HPM sources, which also involved close interactions with Department of Defense laboratories and industry. HPM sources described in this article have generated peak powers exceeding several gigawatts in pulse durations typically on the order of 100 ns in frequencies ranging from about 1 GHz to many tens of gigahertz.
Pathway selection mechanism of a screw drive in-pipe robot in T-branches

Pipelines are important infrastructures in today's society. To avoid leakages in these pipelines, efficient robotic pipe inspections are required, and to this date, various types of in-pipe robots have been developed. Some of them can select their path at a T-branch at the expense of additional actuators. However, fewer actuators are better in terms of size reduction, energy conservation, production cost, and maintenance. To reduce the number of actuators, a screw drive mechanism with only one actuator has been developed for propelling an in-pipe robot through straight pipes and elbow pipes. Based on this screw drive mechanism, in this paper, we develop a novel robot that uses only two motors and can select pathways. The robot has three locomotion modes: screw driving, steering, and rolling modes. These modes enable the robot to navigate not only through straight pipes but also elbow pipes and T-branches. We performed experiments to verify the validity of the proposed mechanism.
Personal Dead-reckoning System for GPS-denied Environments

This paper introduces a positioning system for walking persons, called "Personal Dead-reckoning" (PDR) system. The PDR system does not require GPS, beacons, or landmarks. The system is therefore useful in GPS-denied environments, such as inside buildings, tunnels, or dense forests. Potential users of the system are military and security personnel as well as emergency responders. The PDR system uses a 6-DOF inertial measurement unit (IMU) attached to the user's boot. The IMU provides rate-of-rotation and acceleration measurements that are used in real-time to estimate the location of the user relative to a known starting point. In order to reduce the most significant errors of this IMU-based system-caused by the bias drift of the accelerometers-we implemented a technique known as "Zero Velocity Update" (ZUPT). With the ZUPT technique and related signal processing algorithms, typical errors of our system are about 2% of distance traveled for short walks. This typical PDR system error is largely independent of the gait or speed of the user. When walking continuously for several minutes, the error increases gradually beyond 2%. The PDR system works in both 2-dimensional (2-D) and 3-D environments, although errors in Z-direction are usually larger than 2% of distance traveled. Earlier versions of our system used an unpractically large IMU. In the most recent version we implemented a much smaller IMU. This paper discussed specific problems of this small IMU, our measures for eliminating these problems, and our first experimental results with the small IMU under different conditions.
Data mining for customer service support

In traditional customer service support of a manufacturing environment, a customer service database usually stores two types of service information: (1) unstructured customer service reports record machine problems and its remedial actions and (2) structured data on sales, employees, and customers for day-to-day management operations. This paper investigates how to apply data mining techniques to extract knowledge from the database to support two kinds of customer service activities: decision support and machine fault diagnosis. A data mining process, based on the data mining tool DBMiner, was investigated to provide structured management data for decision support. In addition, a data mining technique that integrates neural network, case-based reasoning, and rule-based reasoning is proposed; it would search the unstructured customer service records for machine fault diagnosis. The proposed technique has been implemented to support intelligent fault diagnosis over the World Wide Web.