nils-herrmann/clean_scirepeval huggingface dataset:
Exploring Personalized Command Recommendations based on Information Found in Web Documentation

Prior work on command recommendations for feature-rich software has relied on data supplied by a large community of users to generate personalized recommendations. In this work, we explore the feasibility of using an alternative data source: web documentation. Specifically, our approach uses QF-Graphs, a previously proposed technique that maps higher-level tasks (i.e., search queries) to commands referenced in online documentation. Our approach uses these command-to-task mappings as an automatically generated plan library, enabling our prototype system to make personalized recommendations for task-relevant commands. Through both offline and online evaluations, we explore potential benefits and drawbacks of this approach.
The Adaptive Nature of Human Categorization

A rational model of human categorization behavior is presented that assumes that categorization reflects the derivation of optimal estimates of the probability of unseen features of objects. A Bayesian analysis is performed of what optimal estimations would be if categories formed a disjoint partitioning of the object space and if features were independently displayed within a category. This Bayesian analysis is placed within an incremental categorization algorithm. The resulting rational model accounts for effects of central tendency of categories, effects of specific instances, learning of linearly nonseparable categories, effects of category labels, extraction of basic level categories, base-rate effects, probability matching in categorization, and trial-by-trial learning functions. Although the rational model considers just I level of categorization, it is shown how predictions can be enhanced by considering higher and lower levels. Considering prediction at the lower, individual level allows integration of this rational analysis of categorization with the earlier rational analysis of memory (Anderson & Milson, 1989).
Design of Matching Circuitry for 100-kHz MOSFET Induction Heating Power Supply

This paper is concerned with the design of matching circuitry for a full-bridge current-fed MOSFET inverter feeding a parallel resonant induction heating load. Circuit operation is explained and analyzed and a means of suppressing ringing between parasitic lead inductance and drain-source capacitance using a filter is described. Guidelines for the selection of filter components are outlined.
Signal Processing for FMCW SAR

The combination of frequency-modulated continuous-wave (FMCW) technology and synthetic aperture radar (SAR) techniques leads to lightweight cost-effective imaging sensors of high resolution. One limiting factor to the use of FMCW sensors is the well-known presence of nonlinearities in the transmitted signal. This results in contrast- and range-resolution degradation, particularly when the system is intended for high-resolution long-range applications, as it is the case for SAR. This paper presents a novel processing solution, which solves the nonlinearity problem for the whole range profile. Additionally, the conventional stop-and-go approximation used in pulse-radar algorithms is not valid in FMCW SAR applications under certain circumstances. Therefore, the motion within the sweep needs to be taken into account. Analytical development of the FMCW SAR signal model, starting from the deramped signal and without using the stop-and-go approximation, is presented in this paper. The model is then applied to stripmap, spotlight, and single-transmitter/multiple-receiver digital-beamforming SAR operational mode. The proposed algorithms are verified by processing real FMCW SAR data collected with the demonstrator system built at the Delft University of Technology.
Computed tomography--an increasing source of radiation exposure.

The advent of computed tomography (ct) has revolutionized diagnostic radiology. Since the inception of CT in the 1970s, its use has increased rapidly. It is estimated that more than 62 million CT scans per year are currently obtained in the United States, including at least 4 million for children.1 By its nature, CT involves larger radiation doses than the more common, conventional x-ray imaging procedures (Table 1). We briefly review the nature of CT scanning and its main clinical applications, both in symptomatic patients and, in a more recent development, in the screening of asymptomatic patients. We focus on the increasing number of CT scans being obtained, the associated radiation doses, and the consequent cancer risks in adults and particularly in children. Although the risks for any one person are not large, the increasing exposure to radiation in the population may be a public health issue in the future.
An automatic vision-based malaria diagnosis system.

Malaria is a worldwide health problem with 225 million infections each year. A fast and easy-to-use method, with high performance is required to differentiate malaria from non-malarial fevers. Manual examination of blood smears is currently the gold standard, but it is time-consuming, labour-intensive, requires skilled microscopists and the sensitivity of the method depends heavily on the skills of the microscopist. We propose an easy-to-use, quantitative cartridge-scanner system for vision-based malaria diagnosis, focusing on low malaria parasite densities. We have used special finger-prick cartridges filled with acridine orange to obtain a thin blood film and a dedicated scanner to image the cartridge. Using supervised learning, we have built a Plasmodium falciparum detector. A two-step approach was used to first segment potentially interesting areas, which are then analysed in more detail. The performance of the detector was validated using 5,420 manually annotated parasite images from malaria parasite culture in medium, as well as using 40 cartridges of 11,780 images containing healthy blood. From finger prick to result, the prototype cartridge-scanner system gave a quantitative diagnosis in 16 min, of which only 1 min required manual interaction of basic operations. It does not require a wet lab or a skilled operator and provides parasite images for manual review and quality control. In healthy samples, the image analysis part of the system achieved an overall specificity of 99.999978% at the level of (infected) red blood cells, resulting in at most seven false positives per microlitre. Furthermore, the system showed a sensitivity of 75% at the cell level, enabling the detection of low parasite densities in a fast and easy-to-use manner. A field trial in Chittagong (Bangladesh) indicated that future work should primarily focus on improving the filling process of the cartridge and the focus control part of the scanner.
3D printed RF passive components by liquid metal filling

We present three-dimensional (3D) micro-scale electrical components and systems by means of 3D printing and a liquid-metal-filling technique. The 3D supporting polymer structures with hollow channels and cavities are fabricated from inkjet printing. Liquid metals made of silver particles suspension in this demonstration are then injected into the hollow paths and solidified to form metallic elements and interconnects with high electrical conductivity. In the proof-of-concept demonstrations, various radio-frequency (RF) passive components, including 3D-shaped inductors, capacitors and resistors are fabricated and characterized. High-Q inductors and capacitors up to 1 GHz have been demonstrated. This work establishes an innovative way to construct arbitrary 3D electrical systems with efficient and labor-saving processes.
Opinion Mining and Sentiment Analysis : A Survey

Mining and Sentiment Analysis is the field of study that analyzes customer opinions, feedback, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in Natural Language Processing (NLP) and is also widely studied in data mining, Web mining, and text mining. The growing importance of sentiment analysis coincides with the growth of social media and Web 2.0 technologies such as reviews, discussion forums, blogs, micro-blogs, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. In this paper we do a survey of papers on Opinion Mining and Sentiment Analysis and detail the techniques used.
Hybrid encryption/decryption technique using new public key and symmetric key algorithm

This research study proposes Hybrid Encryption System using new public key algorithm and private key algorithm. A hybrid cryptosystem is one which combines the convenience of a public-key cryptosystem with the efficiency of a symmetrickey cryptosystem. Here, we propose a provably two way secured data encryption system, which addresses the concerns of user’s privacy, authentication and accuracy. This system has two different encryption algorithms have been used both in the Encryption and decryption sequence. One is public key cryptography based on linear block cipher another one is private key cryptography based on simple symmetric algorithm. This cryptography algorithm provides more security as well as authentication comparing to other existing hybrid algorithm.
A new approach for Kinematics-based design of 3-RRR delta robots with a specified workspace

One of the most significant issues in design of the parallel robots is to determine proper kinematic parameters leading to a desired workspace. The aim of this research is to propose a new approach for kinematics-based design of 3-RRR Delta robots considering a specified workspace. To this end, a new concept, called the Maximum Surrounded Workspace (MSW),will be presented, which is the basis of the new design methodology utilized in this paper. Additionally, the kinematic parameters of some of the prominent industrial 3-RRR Delta robots and their relationships have carefully been examined which are then will be employed in the proposed design procedure. The obtained real-world results of using the proposed approach for designing a 3-RRR Delta robot sample, reveal not only the proper performance of the suggested method, but also its simplicity and efficiency.
Utilizing dependency relationships between math expressions in math IR

Current mathematical search systems allow math expressions within a document to be queried using math expressions and keywords. To accept such queries, math search systems must index both math expressions and textual information in documents. Each indexed math expression is usually associated with all the words in its surrounding context within a given window size. However, we found that this context is often ineffective for explaining math expressions in scientific papers. The meaning of an expression is usually defined in the early part of a document, and the meaning of each symbol contained in the expression can be useful for explaining the entire expression. This explanation may not be captured within the context of a math expression, unless we set the context to have a very wide window size. However, widening the window size also increases the proportion of words that are unrelated to the expression. This paper proposes the use of dependency relationships between math expressions to enrich the textual information of each expression. We examine the influence of this enrichment in a math search system. The experimental results show that significantly better precision can be obtained using the enriched textual information rather than the math expressions’ own textual information. This indicates that the enrichment of textual information for each math expression using dependency relationships enhances the math search system.
On the Parameter Extraction of a Five-Parameter Double-Diode Model of Photovoltaic Cells and Modules

The main contribution of this paper is to present a new set of approximate analytical solutions for the parameters of a photovoltaic (PV) five-parameter double-diode model that can be used as initial values for the numerical solutions based on the Newton-Raphson method. The proposed formulations are developed based on only the limited information given by the PV manufacturers, i.e., the open-circuit voltage ( Voc), the short circuit current ( Isc), and the current and voltage at the maximum power point (Im and Vm). Compared with the existing techniques that require the entire experimental I-V curve or additional information such as the slope of the I-V curves of the open circuit and the short circuit points, the proposed technique is quite independent of these additional data, and, it is therefore, a low cost and fast parameter extraction method. The accuracy of the theoretical I-V curves is evaluated through the comparison of the simulation results and experimental data. The results of the application of the proposed technique to different PV modules show the accuracy and validity of the proposed analytical-numerical method.
The school-level factors associated with internet addiction among adolescents: A Cross-Sectional study in Bangladesh

The adolescents have increased the use of the Internet not ably over the last few years. Here we investigate the prevalence of Internet addiction among adolescents and identify the school-level factors on Internet addiction.  This was a school-based cross-sectional study with 279 students aged 14 17years in the education year of 2016. The students Internet addiction was determined through the Ormans test, which consists of nine questions.  The prevalence of severe internet addiction among the surveyed adolescents was 2.51%, and moderate internet addiction was 64.87%. The study revealed from the multivariatelogisticmodel that the male students are 31% (OR = 0.69, confidence interval: 0.36 1.34) less likely to be moderate/severely internet addicted compared to female students. Similarly, considering the number of siblings, lonely child in a house is 3.12 times (OR = 3.12, CI: 1.14 9.29) more likely to have moderate/severe internet addiction compared to who has 3ormore siblings. The students with English medium studies (OR = 0.58) and the students who play out door games (OR = 0.56) are also negatively associated with severe internet addiction.  The results indicate that the adolescents of grade nine students, Bangla medium studies and the students who don’t play outside games are more prone to be the Internet addicted. Besides, female students are more in risk to be Internet addicted than male students. The findings will help us for strategies to be put in the school to reduce the Internet addiction among adolescents.  Ahmed Hossain, Department of Public Health, North South University, Canada, E-mail: ahmed.hossain@utoronto.ca Received Date: September 13, 2017  2017
Resilient Cell-Based Architecture for Time-to-Digital Converter

This paper proposes a resilient Time-to-Digital Converter (TDC) that lends itself to cell-based design automation. We adopt a shrinking-based architecture with a number of distinctive techniques. First of all, a specialized on-chip re-calibration scheme is developed so that the real-time transfer function of the TDC in silicon (which maps an input pulse-width to its corresponding output code) can be derived on the chip and thereby the absolute value (instead of just a relative code) of an input pulse-width under measurement can be reported. Secondly, the sampling errors stemming from the jitters of training clocks used in the calibration scheme are mitigated by the principle of multi sampling. Thirdly, a flexible coarse-shrinking block is adopted and an automatic adjustment scheme is employed so that the coarse-shrinking block can adjust itself when operated under different input pulse-width ranges.
Dihedral angle prediction using generative adversarial networks

Several dihedral angles prediction methods were developed for protein structure prediction and their other applications. However, distribution of predicted angles would not be similar to that of real angles. To address this we employed generative adversarial networks (GAN) which showed promising results in image generation tasks. Generative adversarial networks are composed of two adversarially trained networks: a discriminator and a generator. A discriminator is trained to distinguish between samples from a dataset and generated samples while a generator is trained to generate realistic samples. Although the discriminator of GANs is trained to estimate density, the explicit density of the model is not tractable. On the other hand, noise-contrastive estimation (NCE) was introduced to estimate a normalization constant of an unnormalized statistical model and thus the density function. In this thesis, we introduce noise-contrastive estimation generative adversarial networks (NCE-GAN) which enables explicit density estimation of the generative adversarial networks by feeding noise samples from a known distribution, like noise-contrastive estimation, and adding a corresponding class for the discriminator. We analyzed the minibatch discrimination and new loss for the generator is proposed. We also propose residue-wise variants of auxiliary classifier GAN (AC-GAN) and Semi-supervised GAN to handle sequence information in a window. In our experiment, the conditional generative adversarial network (C-GAN), AC-GAN and Semi-supervised GAN were compared. And experiments done with improved conditions were invested. We identified a phenomenon of AC-GAN that distribution of its predicted angles is composed of unusual clusters. The distribution of the predicted angles of Semi-supervised GAN was most similar to the Ramachandran plot. We found that adding the output of the NCE as an additional input of the discriminator is helpful to stabilize the training of the GANs and to capture the detailed structures. However, using the newly proposed loss in the generator were only helpful in C-GAN and AC-GAN. Adding regression loss to the object of the generator and using predicted angles by regression loss only model as an additional input of the generator could improve the conditional generation performance of the C-GAN and AC-GAN.
Footwear bio-modelling: An industrial approach

There is a growing need within the footwear sector to customise the design of the last from which a specific footwear style is to be produced. This customisation is necessary for user comfort and health reasons, as the user needs to wear a suitable shoe. For this purpose, a relationship must be established between the user foot and the last with which the style will be made; up until now, no model has existed that integrates both elements. On the one hand, traditional customised footwear manufacturing techniques are based on purely artisanal procedures which make the process arduous and complex; on the other hand, geometric models proposed by different authors present the impossibility of implementing them in an industrial environment with limited resources for the acquisition of morphometric and structural data for the foot, apart from the fact that they do not prove to be sufficiently accurate given the non similarity of the foot and last. In this paper, two interrelated geometric models are defined, the first, a bio-deformable foot model and the second, a deformable last model. The experiments completed show the goodness of the model, with it obtaining satisfactory results in terms of comfort, efficiency and precision, which make it viable for use in the sector.
Sensor Data Fusion for Full Arm Tracking Using Myo Armband and Leap Motion

The Myo Armband (Myo) is a sensor which can be attached to the forearm, and allows to send to devices equipped with Bluetooth muscular patterns of arm movements triggered by some gestures made by the hand. Also, it produces rotation data based on a gyroscope, accelerometer and magnetometer embedded on it. The Leap Motion (LM) is a sensor-based cameras and infrared lights, which allows accurately to track the hands motion, including fingers. Therefore, this work describes the use of sensor data fusion techniques to combine the data from both sensors in order to create a 3D virtual simulation in real-time of an arm motion, including the forearm, hand and fingers. The purpose of this combination is also improve the performance, aiming to overcome the main sensors' limitations when they are used individually, as occurs with Myo, which is limited to produce only forearm motion data and the LM, which has a limited field of view and range tracking. The experiment results shows that the studied algorithm is able to combines the data from both sensors achieving a considerable gain in the precision and tracking of the arm.
Fusion of facial expressions and EEG for implicit affective tagging

The explosion of user-generated, untagged multimedia data in recent years, generates a strong need for efficient search and retrieval of this data. The predominant method for content-based tagging is through slow, labour-intensive manual annotation. Consequently, automatic tagging is currently a subject of intensive research. However, it is clear that the process will not be fully automated in the foreseeable future. We propose to involve the user and investigate methods for implicit tagging, wherein users’ responses to the interaction with the multimedia content are analysed in order to generate descriptive tags. Here, we present a multi-modal approach that analyses both facial expressions and Electroencephalography(EEG) signals for the generation of affective tags. We perform classification and regression in the valence-arousal space and present results for both feature-level and decision-level fusion. We demonstrate improvement in the results when using both modalities, suggesting the modalities contain complementary information.
Automatically Identifying Fake News in Popular Twitter Threads

Information quality in social media is an increasingly important issue, but web-scale data hinders experts' ability to assess and correct much of the inaccurate content, or "fake news," present in these platforms. This paper develops a method for automating fake news detection on Twitter by learning to predict accuracy assessments in two credibility-focused Twitter datasets: CREDBANK, a crowdsourced dataset of accuracy assessments for events in Twitter, and PHEME, a dataset of potential rumors in Twitter and journalistic assessments of their accuracies. We apply this method to Twitter content sourced from BuzzFeed's fake news dataset and show models trained against crowdsourced workers outperform models based on journalists' assessment and models trained on a pooled dataset of both crowdsourced workers and journalists. All three datasets, aligned into a uniform format, are also publicly available. A feature analysis then identifies features that are most predictive for crowdsourced and journalistic accuracy assessments, results of which are consistent with prior work. We close with a discussion contrasting accuracy and credibility and why models of non-experts outperform models of journalists for fake news detection in Twitter.
Fraudulent ID using face morphs: Experiments on human and automatic recognition

Matching unfamiliar faces is known to be difficult, and this can give an opportunity to those engaged in identity fraud. Here we examine a relatively new form of fraud, the use of photo-ID containing a graphical morph between two faces. Such a document may look sufficiently like two people to serve as ID for both. We present two experiments with human viewers, and a third with a smartphone face recognition system. In Experiment 1, viewers were asked to match pairs of faces, without being warned that one of the pair could be a morph. They very commonly accepted a morphed face as a match. However, in Experiment 2, following very short training on morph detection, their acceptance rate fell considerably. Nevertheless, there remained large individual differences in people's ability to detect a morph. In Experiment 3 we show that a smartphone makes errors at a similar rate to 'trained' human viewers-i.e. accepting a small number of morphs as genuine ID. We discuss these results in reference to the use of face photos for security.