nils-herrmann/clean_scirepeval huggingface dataset:
Self-biasing low power adaptive photoreceptor

This report describes a self-biased 5-transistor, 2-capacitor, adaptive logarithmic photoreceptor circuit. The photoreceptor uses its photocurrent, multiplied by a constant factor, to bias its forward amplifier. As a result, power consumption scales with illumination and takes on low values under typical illumination levels. This scaling enables solar powered operation, because power consumption scales with available power. As in earlier circuits, the photoreceptor has an adaptive logarithmic response: it has a bandpass characteristic with low gain for DC (including mismatch) and a higher gain for transient changes in contrast. The gain-bandwidth product is higher than for a passive architecture by a desired multiple. Stability criteria, sharing the bias among many photoreceptors, and measurements from a fabricated chip are shown.
Low-Cost Printed Chipless RFID Humidity Sensor Tag for Intelligent Packaging

This paper presents a fully-printed chipless radio frequency identification sensor tag for short-range item identification and humidity monitoring applications. The tag consists of two planar inductor-capacitor resonators operating wirelessly through inductive coupling. One resonator is used to encode ID data based on frequency spectrum signature, and another one works as a humidity sensor, utilizing a paper substrate as a sensing material. The sensing performances of three paper substrates, including commercial packaging paper, are investigated. The use of paper provides excellent sensitivity and reasonable response time to humidity. The cheap and robust packaging paper, particularly, exhibits the largest sensitivity over the relative humidity range from 20% to 70%, which offers the possibility of directly printing the sensor tag on traditional packages to make the package intelligent at ultralow cost.
Multi-Dimensional, Phrase-Based Summarization in Text Cubes

To systematically analyze large numbers of textual documents, it is often desirable to manage documents (and their metadata) in a multi-dimensional text database (Text Cube). Such structure provides flexibility of understanding local information with different granularities. Moreover, the contextualized analysis derived from cube structure often yields comparative insights. To quickly digest the content of subsets of documents in the multi-dimensional context, we study the problem of phrase-based summarization of a subset of documents of interest. We propose a new phrase ranking measure to leverage the relation between document subsets induced by multi-dimensional context and identify phrases that truly distinguish the queried subset of documents from neighboring subsets (i.e., background). Our quality evaluation suggests the new measure involving dynamic, query-dependent background generation is more effective than previous measures using the whole corpus as a static background for finding representative phrases. Computing this measure is more expensive due to the need of access to many subsets of documents to answer one query. We develop a cube-based analytical platform that implements an efficient solution by materializing a deliberately selected part of statistics, and using these statistics to perform online query processing within a constant latency constraint. Our experiments in a large news dataset demonstrate the efficiency in both query processing time and storage cost.
Sampling-oscilloscope measurement of a microwave mixer with single-digit phase accuracy

We describe a straightforward method of separately characterizing up- and down-conversion in microwave mixers using a sampling oscilloscope. The method mismatch-corrects the results, determines both magnitude and phase, and uses a novel time-base correction scheme to improve the accuracy of the measurements. We estimate our measurement accuracy to be on the order of a tenth of a decibel in magnitude and a few degrees in phase. We use the method to characterize the magnitude and phase reciprocity of a microwave mixer.
Weakly-Supervised Neural Text Classification

Deep neural networks are gaining increasing popularity for the classic text classification task, due to their strong expressive power and less requirement for feature engineering. Despite such attractiveness, neural text classification models suffer from the lack of training data in many real-world applications. Although many semi-supervised and weakly-supervised text classification models exist, they cannot be easily applied to deep neural models and meanwhile support limited supervision types. In this paper, we propose a weakly-supervised method that addresses the lack of training data in neural text classification. Our method consists of two modules: (1) a pseudo-document generator that leverages seed information to generate pseudo-labeled documents for model pre-training, and (2) a self-training module that bootstraps on real unlabeled data for model refinement. Our method has the flexibility to handle different types of weak supervision and can be easily integrated into existing deep neural models for text classification. We have performed extensive experiments on three real-world datasets from different domains. The results demonstrate that our proposed method achieves inspiring performance without requiring excessive training data and outperforms baseline methods significantly.
Computer Networks as Social Networks: Collaborative Work, Telework, and Virtual Community

When computer networks link people as well as machines, they become social networks. Such computer-supported social networks (CSSNs) are becoming important bases of virtual communities, computer-supported cooperative work, and telework. Computer-mediated communication such as electronic mail and computerized conferencing is usually text-based and asynchronous. It has limited social presence, and on-line communications are often more uninhibited, creative, and blunt than in-person communication. Nevertheless, CSSNs sustain strong, intermediate, and weak ties that provide information and social support in both specialized and broadly based relationships. CSSNs foster virtual communities that are usually partial and narrowly focused, although some do become encompassing and broadly based. CSSNs accomplish a wide variety of cooperative work, connecting workers within and between organizations who are often physically dispersed. CSSNs also link teleworkers from their homes or remote work centers to main organizational offices. Although many relationships function off-line as well as on-line, CSSNs have developed their own norms and structures. The nature of the medium both constrains and facilitates social control. CSSNs have strong societal implications, fostering situations that combine global connectivity, the fragmentation of solidarities, the de-emphasis of local organizations (in the neighborhood and workplace), and the increased importance
A chaincode based scheme for fingerprint feature extraction

A feature extraction method using the chaincode representation of fingerprint ridge contours is presented for use by Automatic Fingerprint Identification Systems. The representation allows efficient image quality enhancement and detection of fine minutiae feature points. For image enhancement a given fingerprint image is first binarized after a quick averaging to generate its chaincode representation. The direction field is estimated from a set of selected chaincodes. The original gray scale image is then enhanced using connected component analysis and a dynamic filtering scheme that takes advantage of the knowledge gained from the estimated direction flow of the contours. For feature extraction, the enhanced fingerprint image is carefully binarized using a local-global binarization algorithm for generating the chaincode representation. The minutiae are generated using a sophisticated ridge contour following procedure. Visual inspection of the experiment images shows that the method is very effective.
A Computational Analysis of Mahabharata

Indian epics have not been analyzed computationally to the extent that Greek epics have. In this paper, we show how interesting insights can be derived from the ancient epic Mahabharata by applying a variety of analytical techniques based on a combination of natural language processing, sentiment/emotion analysis and social network analysis methods. One of our key findings is the pattern of significant changes in the overall sentiment of the epic story across its eighteen chapters and the corresponding characterization of the primary protagonists in terms of their sentiments, emotions, centrality and leadership attributes in the epic saga.
Named Entity Recognition System for Urdu

Named Entity Recognition (NER) is a task which helps in finding out Persons name, Location names, Brand names, Abbreviations, Date, Time etc and classifies the m into predefined different categories. NER plays a major role in various Natural Language Processing (NLP) fields like Information Extraction, Machine Translations and Question Answering. This paper describes the problems of NER in the context of Urdu Language and provides relevan t solutions. The system is developed to tag thirteen different Named Entities (NE), twelve NE proposed by IJCNLP-08 and Izaafats. We have used the Rule Based approach and developed the various rules to extract the Named Entities in the given Urdu text.
Gender differences in scholastic achievement: a meta-analysis.

A female advantage in school marks is a common finding in education research, and it extends to most course subjects (e.g., language, math, science), unlike what is found on achievement tests. However, questions remain concerning the quantification of these gender differences and the identification of relevant moderator variables. The present meta-analysis answered these questions by examining studies that included an evaluation of gender differences in teacher-assigned school marks in elementary, junior/middle, or high school or at the university level (both undergraduate and graduate). The final analysis was based on 502 effect sizes drawn from 369 samples. A multilevel approach to meta-analysis was used to handle the presence of nonindependent effect sizes in the overall sample. This method was complemented with an examination of results in separate subject matters with a mixed-effects meta-analytic model. A small but significant female advantage (mean d = 0.225, 95% CI [0.201, 0.249]) was demonstrated for the overall sample of effect sizes. Noteworthy findings were that the female advantage was largest for language courses (mean d = 0.374, 95% CI [0.316, 0.432]) and smallest for math courses (mean d = 0.069, 95% CI [0.014, 0.124]). Source of marks, nationality, racial composition of samples, and gender composition of samples were significant moderators of effect sizes. Finally, results showed that the magnitude of the female advantage was not affected by year of publication, thereby contradicting claims of a recent "boy crisis" in school achievement. The present meta-analysis demonstrated the presence of a stable female advantage in school marks while also identifying critical moderators. Implications for future educational and psychological research are discussed.
The analgesic efficacy of pre-operative bilateral erector spinae plane (ESP) blocks in patients having ventral hernia repair.

Laparoscopic ventral hernia repair is an operation associated with significant postoperative pain, and regional anaesthetic techniques are of potential benefit. The erector spinae plane (ESP) block performed at the level of the T5 transverse process has recently been described for thoracic surgery, and we hypothesised that performing the ESP block at a lower vertebral level would provide effective abdominal analgesia. We performed pre-operative bilateral ESP blocks with 20-30 ml ropivacaine 0.5% at the level of the T7 transverse process in four patients undergoing laparoscopic ventral hernia repair. Median (range) 24-h opioid consumption was 18.7 mg (0.0-43.0 mg) oral morphine. The highest and lowest median (range) pain scores in the first 24 h were 3.5 (3.0-5.0) and 2.5 (0.0-3.0) on an 11-point numerical rating scale. We also performed the block in a fresh cadaver and assessed the extent of injectate spread using computerised tomography. There was radiographic evidence of spread extending cranially to the upper thoracic levels and caudally as far as the L2-L3 transverse processes. We conclude that the ESP block is a promising regional anaesthetic technique for laparoscopic ventral hernia repair and other abdominal surgery when performed at the level of the T7 transverse process. Its advantages are the ability to block both supra-umbilical and infra-umbilical dermatomes with a single-level injection and its relative simplicity.
A Convolutional Autoencoder Approach to Learn Volumetric Shape Representations for Brain Structures

We propose a novel machine learning strategy for studying neuroanatomical shape variation. Our model works with volumetric binary segmentation images, and requires no preprocessing such as the extraction of surface points or a mesh. The learned shape descriptor is invariant to affine transformations, including shifts, rotations and scaling. Thanks to the adopted autoencoder framework, inter-subject differences are automatically enhanced in the learned representation, while intra-subject variances are minimized. Our experimental results on a shape retrieval task showed that the proposed representation outperforms a state-of-the-art benchmark for brain structures extracted from MRI scans.
Automated Hate Speech Detection and the Problem of Offensive Language

A key challenge for automatic hate-speech detection on social media is the separation of hate speech from other instances of offensive language. Lexical detection methods tend to have low precision because they classify all messages containing particular terms as hate speech and previous work using supervised learning has failed to distinguish between the two categories. We used a crowd-sourced hate speech lexicon to collect tweets containing hate speech keywords. We use crowd-sourcing to label a sample of these tweets into three categories: those containing hate speech, only offensive language, and those with neither. We train a multi-class classifier to distinguish between these different categories. Close analysis of the predictions and the errors shows when we can reliably separate hate speech from other offensive language and when this differentiation is more difficult. We find that racist and homophobic tweets are more likely to be classified as hate speech but that sexist tweets are generally classified as offensive. Tweets without explicit hate keywords are also more difficult to classify.
3D Deep Learning for Efficient and Robust Landmark Detection in Volumetric Data

Recently, deep learning has demonstrated great success in computer vision with the capability to learn powerful image features from a large training set. However, most of the published work has been confined to solving 2D problems, with a few limited exceptions that treated the 3D space as a composition of 2D orthogonal planes. The challenge of 3D deep learning is due to a much larger input vector, compared to 2D, which dramatically increases the computation time and the chance of over-fitting, especially when combined with limited training samples (hundreds to thousands), typical for medical imaging applications. To address this challenge, we propose an efficient and robust deep learning algorithm capable of full 3D detection in volumetric data. A two-step approach is exploited for efficient detection. A shallow network (with one hidden layer) is used for the initial testing of all voxels to obtain a small number of promising candidates, followed by more accurate classification with a deep network. In addition, we propose two approaches, i.e., separable filter decomposition and network sparsification, to speed up the evaluation of a network. To mitigate the over-fitting issue, thereby increasing detection robustness, we extract small 3D patches from a multi-resolution image pyramid. The deeply learned image features are further combined with Haar wavelet features to increase the detection accuracy. The proposed method has been quantitatively evaluated for carotid artery bifurcation detection on a head-neck CT dataset from 455 patients. Compared to the state-ofthe-art, the mean error is reduced by more than half, from 5.97 mm to 2.64 mm, with a detection speed of less than 1 s/volume.
Generating Stimuli for Neuroscience Using PsychoPy

PsychoPy is a software library written in Python, using OpenGL to generate very precise visual stimuli on standard personal computers. It is designed to allow the construction of as wide a variety of neuroscience experiments as possible, with the least effort. By writing scripts in standard Python syntax users can generate an enormous variety of visual and auditory stimuli and can interact with a wide range of external hardware (enabling its use in fMRI, EEG, MEG etc.). The structure of scripts is simple and intuitive. As a result, new experiments can be written very quickly, and trying to understand a previously written script is easy, even with minimal code comments. PsychoPy can also generate movies and image sequences to be used in demos or simulated neuroscience experiments. This paper describes the range of tools and stimuli that it provides and the environment in which experiments are conducted.
SystemT: a system for declarative information extraction

As applications within and outside the enterprise encounter increasing volumes of unstructured data, there has been renewed interest in the area of information extraction (IE) -- the discipline concerned with extracting structured information from unstructured text. Classical IE techniques developed by the NLP community were based on cascading grammars and regular expressions. However, due to the inherent limitations of grammarbased extraction, these techniques are unable to: (i) scale to large data sets, and (ii) support the expressivity requirements of complex information tasks. At the IBM Almaden Research Center, we are developing SystemT, an IE system that addresses these limitations by adopting an algebraic approach. By leveraging well-understood database concepts such as declarative queries and costbased optimization, SystemT enables scalable execution of complex information extraction tasks. In this paper, we motivate the SystemT approach to information extraction. We describe our extraction algebra and demonstrate the effectiveness of our optimization techniques in providing orders of magnitude reduction in the running time of complex extraction tasks.
A theory of self-calibration of a moving camera

There is a close connection between the calibration of a single camera and the epipolar transformation obtained when the camera undergoes a displacement. The epipolar transformation imposes two algebraic constraints on the camera calibration. If two epipolar transformations, arising from different camera displacements, are available then the compatible camera calibrations are parameterized by an algebraic curve of genus four. The curve can be represented either by a space curve of degree seven contained in the intersection of two cubic surfaces, or by a curve of degree six in the dual of the image plane. The curve in the dual plane has one singular point of order three and three singular points of order two. If three epipolar transformations are available, then two curves of degree six can be obtained in the dual plane such that one of the real intersections of the two yields the correct camera calibration. The two curves have a common singular point of order three. Experimental results are given to demonstrate the feasibility of camera calibration based on the epipolar transformation. The real intersections of the two dual curves are found by locating the zeros of a function defined on the interval [0, 2π]. The intersection yielding the correct camera calibration is picked out by referring back to the three epipolar transformations.
Adaptive Haptic Feedback Steering Wheel for Driving Simulators

Controlling a virtual vehicle is a sensory-motor activity with a specific rendering methodology that depends on the hardware technology and the software in use. We propose a method that computes haptic feedback for the steering wheel. It is best suited for low-cost, fixed-base driving simulators but can be ported to any driving simulator platform. The goal of our method is twofold. 1) It provides an efficient yet simple algorithm to model the steering mechanism using a quadri-polar representation. 2) This model is used to compute the haptic feedback on top of which a tunable haptic augmentation is adjusted to overcome the lack of presence and the unavoidable simulation loop latencies. This algorithm helps the driver to laterally control the virtual vehicle. We also discuss the experimental results that demonstrate the usefulness of our haptic feedback method.
Torque and Flux Weakening Control with MTPV for Interior Permanent Magnet Synchronous Motor

This paper proposes an improved stator flux control algorithm based on the torque feedforward control technique for interior permanent magnet synchronous motor (IPMSM). The proposed flux linkage control algorithm can prevent the d and q- axis voltages from saturation and improve the dynamic behavior. A lookup table is used in the torque feedforward control technique, which outputs the desired d and q-axis currents according to the two inputs: torque command and desired stator flux linkage magnitude. The method of constructing lookup table is mentioned. Simulations are carried out. The results prove the effectiveness of the proposed method.
Social Media Metrics — A Framework and Guidelines for Managing Social Media

Social media are becoming ubiquitous and need to be managed like all other forms of media that organizations employ to meet their goals. However, social media are fundamentally different from any traditional or other online media because of their social network structure and egalitarian nature. These differences require a distinct measurement approach as a prerequisite for proper analysis and subsequent management. To develop the right social media metrics and subsequently construct appropriate dashboards, we provide a tool kit consisting of three novel components. First, we theoretically derive and propose a holistic framework that covers the major elements of social media, drawing on theories from marketing, psychology, and sociology. We continue to support and detail these elements — namely ‘motives,’ ‘content,’ ‘network structure,’ and ‘social roles & interactions’ — with recent research studies. Second, based on our theoretical framework, the literature review, and practical experience, we suggest nine guidelines that may prove valuable for designing appropriate social media metrics and constructing a sensible social media dashboard. Third, based on the framework and the guidelines we derive managerial implications and suggest an agenda for future research.