nils-herrmann/clean_scirepeval huggingface dataset:
Has the Internet become indispensable?

As was empirically demonstrated in , “the adoption rate of the Internet has exceeded that of earlier mass communication technologies by several magnitudes,” making it an “irreversible” innovation. Studies have also shown that for the generation of U.S.-based youth that grew up with the Internet, it is gradually displacing television as their main source of entertainment, communication, and education . Here, we explore how the Internet has become indispensable to people in their daily lives and develop a conceptual model allowing us to address the associated research questions. The idea is that the Internet has become so embedded in the daily fabric of people’s lives that they simply cannot live without it. How is the Internet indispensable and in what ways? For which groups of people is it indispensable, for what tasks, and how has BY DONNA L. HOFFMAN, THOMAS P. NOVAK, AND ALLADI VENKATESH
An image synthesizer

We introduce the concept of a Pixel Stream Editor. This forms the basis for an interactive synthesizer for designing highly realistic Computer Generated Imagery. The designer works in an interactive Very High Level programming environment which provides a very fast concept/implement/view iteration cycle.Naturalistic visual complexity is built up by composition of non-linear functions, as opposed to the more conventional texture mapping or growth model algorithms. Powerful primitives are included for creating controlled stochastic effects. We introduce the concept of "solid texture" to the field of CGI.We have used this system to create very convincing representations of clouds, fire, water, stars, marble, wood, rock, soap films and crystal. The algorithms created with this paradigm are generally extremely fast, highly realistic, and asynchronously parallelizable at the pixel level.
Using Clustering Algorithms in Legacy Systems Remodularization

Incited by the observation that cluster analysis and the remodularization of software systems solve similar problems, we have done research in both these areas in order to provide theoretical background for the application of cluster analysis in systems remodularization. In this article we present an overview of cluster analysis and of systems remodularization. It appears that system remodularization techniques often either reinvent clustering techniques or could be augmented by them. We also give directions for further research.  The author was sponsored by bank ABN AMRO, software house DPFinance, and the Dutch Ministry of Economical Affairs via the Senter Project #ITU95017 “SOS Re-
Cellular and molecular mechanisms of hepatocellular carcinoma: an update

Hepatocellular carcinoma (HCC) is the most common primary malignant tumor that accounts for ~80 % of all liver cancer cases worldwide. It is a multifactorial disease caused by a variety of risk factors and often develops in the background of underlying cirrhosis. A number of cellular phenomena, such as tumor microenvironment, inflammation, oxidative stress, and hypoxia act in concert with various molecular events to facilitate tumor initiation, progression, and metastasis. The emergence of microRNAs and molecular-targeted therapies adds a new dimension in our efforts to combat this deadly disease. Intense research in this multitude of areas has led to significant progress in our understanding of cellular processes and molecular mechanisms that occur during multistage events that lead to hepatocarcinogenesis. In this review, we discuss the current knowledge of HCC, focusing mainly on advances that have occurred during the past 5 years and on the development of novel therapeutics for liver cancer.
Physiological-emotional reactivity to nightmare-related imagery in trauma-exposed persons with chronic nightmares.

Script-driven imagery was used to assess nightmare imagery-evoked physiological-emotional reactivity (heart rate, skin conductance, facial electromyogram, subjective ratings) in trauma-exposed persons suffering from chronic nightmares. Goals were to determine the efficacy of nightmare imagery to evoke physiological-emotional reactivity, correlates (mental health, nightmare characteristics) of reactivity, and consequences (sleep and health problems) of reactivity. Nightmare imagery resulted in significant reactivity relative to control imagery. No mental health variable (posttraumatic stress disorder status, depressive symptoms, dissociation) or nightmare characteristic (months experienced, frequency, similarity to trauma) was associated with reactivity level. However, nightmare imagery-evoked autonomic responses were associated with greater sleep disturbance and reported health symptoms, even when nightmare frequency was controlled. These results suggest nightmare-related autonomic reactions may contribute to sleep and health disturbance.
Towards Text Generation with Adversarially Learned Neural Outlines

Recent progress in deep generative models has been fueled by two paradigms – autoregressive and adversarial models. We propose a combination of both approaches with the goal of learning generative models of text. Our method first produces a high-level sentence outline and then generates words sequentially, conditioning on both the outline and the previous outputs. We generate outlines with an adversarial model trained to approximate the distribution of sentences in a latent space induced by general-purpose sentence encoders. This provides strong, informative conditioning for the autoregressive stage. Our quantitative evaluations suggests that conditioning information from generated outlines is able to guide the autoregressive model to produce realistic samples, comparable to maximum-likelihood trained language models, even at high temperatures with multinomial sampling. Qualitative results also demonstrate that this generative procedure yields natural-looking sentences and interpolations.
Investigation on perceptual and robustness of LSB digital watermarking scheme on Halal Logo authentication

In Malaysia, Halal Certificate can only be issued by Jakim. For the time being, there is no specific method to verify authentication of Halal Certificate displayed at food or service premises. Watermarking technique however, offered a solution for authenticity of the data and copyright protection. In this paper, an investigation on perceptual and robustness of least significant bit (LSB) digital watermarking scheme on Halal Logo authentication is implemented using MATLAB software. LSB digital watermarking scheme allows pixel value modification by dividing its entire bit leaving most significant bit (contains most information) and least significant bit (contains less information). These small modifications offer a high perceptual transparency to the watermarked image. A Quick response (QR) code with message is generated as an embedded watermark image for Halal Logo. The watermarked image quality is measured based on Peak signal-to-noise (PSNR), mean square error (MSE), and Normalized Cross-Correlation (NC). The investigation shows the scheme provided high PSNR performance which is between 12-22 dB with Gaussian noise added. The scheme successfully shows the ability to retrieve the embed watermark even though the cover image visual quality is degraded with 50 % Gaussian noise variance. The reliability of the scheme is proven when it successfully to produce an acceptable 0.8442 NC value. The observation on perceptibility shows 51 dB of PSNR with 0.4714 MSE.
Faces engage us: photos with faces attract more likes and comments on Instagram

Photos are becoming prominent means of communication online. Despite photos' pervasive presence in social media and online world, we know little about how people interact and engage with their content. Understanding how photo content might signify engagement, can impact both science and design, influencing production and distribution. One common type of photo content that is shared on social media, is the photos of people. From studies of offline behavior, we know that human faces are powerful channels of non-verbal communication. In this paper, we study this behavioral phenomena online. We ask how presence of a face, it's age and gender might impact social engagement on the photo. We use a corpus of 1 million Instagram images and organize our study around two social engagement feedback factors, likes and comments. Our results show that photos with faces are 38% more likely to receive likes and 32% more likely to receive comments, even after controlling for social network reach and activity. We find, however, that the number of faces, their age and gender do not have an effect. This work presents the first results on how photos with human faces relate to engagement on large scale image sharing communities. In addition to contributing to the research around online user behavior, our findings offer a new line of future work using visual analysis.
New types of acetate-oxidizing, sulfate-reducing Desulfobacter species, D. hydrogenophilus sp. nov., D. latus sp. nov., and D. curvatus sp. nov.

Sulfate-reducing bacteria with oval to rod-shaped cells (strains AcRS1, AcRS2) and vibrio-shaped cells (strains AcRM3, AcRM4, AcRM5) differing by size were isolated from anaerobic marine sediment with acetate as the only electron donor. A vibrio-shaped type (strain AcKo) was also isolated from freshwater sediment. Two strains (AcRS1, AcRM3) used ethanol and pyruvate in addition to acetate, and one strain (AcRS1) grew autotrophically with H2, sulfate and CO2. Higher fatty acids or lactate were never utilized. All isolates were able to grow in ammonia-free medium in the presence of N2. Nitrogenase activity under such conditions was demonstrated by the acetylene reduction test. The facultatively lithoautotrophic strain (AcRS1), a strain (AcRS2) with unusually large cells (2×5 μm), and a vibrio-shaped strain (AcRM3) are described as new Desulfobacter species, D. hydrogenophilus, D. latus, and D. curvatus, respectively.
Three-way decisions with probabilistic rough sets

The rough set theory approximates a concept by three regions, namely, the positive, boundary and negative regions. Rules constructed from the three regions are associated with different actions and decisions, which immediately leads to the notion of three-way decision rules. A positive rule makes a decision of acceptance, a negative rule makes a decision of rejection, and a boundary rule makes a decision of abstaining. This paper provides an analysis of three-way decision rules in the classical rough set model and the decision-theoretic rough set model. The results enrich the rough set theory by ideas from Bayesian decision theory and hypothesis testing in statistics. The connections established between the levels of tolerance for errors and costs of incorrect decisions make the rough set theory practical in applications.
Empirical extensions of the lasso penalty to reduce the false discovery rate in high-dimensional Cox regression models.

Correct selection of prognostic biomarkers among multiple candidates is becoming increasingly challenging as the dimensionality of biological data becomes higher. Therefore, minimizing the false discovery rate (FDR) is of primary importance, while a low false negative rate (FNR) is a complementary measure. The lasso is a popular selection method in Cox regression, but its results depend heavily on the penalty parameter λ. Usually, λ is chosen using maximum cross-validated log-likelihood (max-cvl). However, this method has often a very high FDR. We review methods for a more conservative choice of λ. We propose an empirical extension of the cvl by adding a penalization term, which trades off between the goodness-of-fit and the parsimony of the model, leading to the selection of fewer biomarkers and, as we show, to the reduction of the FDR without large increase in FNR. We conducted a simulation study considering null and moderately sparse alternative scenarios and compared our approach with the standard lasso and 10 other competitors: Akaike information criterion (AIC), corrected AIC, Bayesian information criterion (BIC), extended BIC, Hannan and Quinn information criterion (HQIC), risk information criterion (RIC), one-standard-error rule, adaptive lasso, stability selection, and percentile lasso. Our extension achieved the best compromise across all the scenarios between a reduction of the FDR and a limited raise of the FNR, followed by the AIC, the RIC, and the adaptive lasso, which performed well in some settings. We illustrate the methods using gene expression data of 523 breast cancer patients. In conclusion, we propose to apply our extension to the lasso whenever a stringent FDR with a limited FNR is targeted.
Mapping high dimensional features onto Hilbert curve: Applying to fast image retrieval

The use of high-dimension features is unavoidable in many applications of image retrieval and techniques of dimension reductions are not always efficient. The space-filling curve reduces the number of dimensions to one while preserving the neighborhood relation. In this paper, Hilbert curve, the most neighborhood preserving space-filling curve, is used in shape retrieval. The retrieving is accelerated by ordering the shapes in 1-D dynamical data structure, which enables rapid insertions of new images without changing existing data. A proposal of fast mapping facilitate the computing of 1-D Hilbert indexes from high dimensional features.
Entropy analysis of the EEG background activity in Alzheimer's disease patients.

Alzheimer's disease (AD) is the most common neurodegenerative disorder. Although a definite diagnosis is only possible by necropsy, a differential diagnosis with other types of dementia and with major depression should be attempted. The aim of this study was to analyse the electroencephalogram (EEG) background activity of AD patients to test the hypothesis that the regularity of the AD patients' EEG is higher than that of age-matched controls. We recorded the EEG from 19 scalp electrodes in 11 AD patients and 11 age-matched controls. Two different methods were used to estimate the regularity of the EEG background activity: spectral entropy (SpecEn) and sample entropy (SampEn). We did not find significant differences between AD patients and control subjects' EEGs with SpecEn. On the other hand, AD patients had significantly lower SampEn values than control subjects (p < 0.01) at electrodes P3, P4, O1 and O2. Our results show an increase of EEG regularity in AD patients. These findings suggest that nonlinear analysis of the EEG with SampEn could yield essential information and may contribute to increasing the insight into brain dysfunction in AD in ways which are not possible with more classical and conventional statistical methods.
SIGN LANGUAGE RECOGNITION: STATE OF THE ART

Sign language is used by deaf and hard hearing people to exchange information between their own community and with other people. Computer recognition of sign language deals from sign gesture acquisition and continues till text/speech generation. Sign gestures can be classified as static and dynamic. However static gesture recognition is simpler than dynamic gesture recognition but both recognition systems are important to the human community. The sign language recognition steps are described in this survey. The data acquisition, data preprocessing and transformation, feature extraction, classification and results obtained are examined. Some future directions for research in this area also suggested.
Augmented Reality in Astrophysics

Augmented Reality consists of merging live images with virtual layers of information. The rapid growth in the popularity of smartphones and tablets over recent years has provided a large base of potential users of Augmented Reality technology, and virtual layers of information can now be attached to a wide variety of physical objects. In this article, we explore the potential of Augmented Reality for astrophysical research with two distinct experiments: (1) Augmented Posters and (2) Augmented Articles. We demonstrate that the emerging technology of Augmented Reality can already be used and implemented without expert knowledge using currently available apps. Our experiments highlight the potential of Augmented Reality to improve the communication of scientific results in the field of astrophysics. We also present feedback gathered from the Australian astrophysics community that reveals evidence of some interest in this technology by astronomers who experimented with Augmented Posters. In addition, we discuss possible future trends for Augmented Reality applications in astrophysics, and explore the current limitations associated with the technology. This Augmented Article, the first of its kind, is designed to allow the reader to directly experiment with this technology.
Index prediction with neuro-genetic hybrid network: A comparative analysis of performance

Forecasting stock exchange rates is an important financial problem that is receiving increasing attention. During the last few years, a number of neural network models and hybrid models have been proposed for obtaining accurate prediction results, in an attempt to outperform the traditional linear and nonlinear approaches. This paper evaluates the effectiveness of neural network models which are known to be dynamic and effective in stock-market predictions. The models analyzed are artificial neural network (ANN) trained with gradient descent (GD) technique, ANN trained with genetic algorithm (GA) and functional link neural network (FLANN) trained with GA. The stock price index of Bombay stock exchange data has been considered to train these models and to compare their relative performance. Experimental results and analysis has been presented to show the performance of different models.
I feel how you feel but not always: the empathic brain and its modulation

The ability to share the other's feelings, known as empathy, has recently become the focus of social neuroscience studies. We review converging evidence that empathy with, for example, the pain of another person, activates part of the neural pain network of the empathizer, without first hand pain stimulation to the empathizer's body. The amplitude of empathic brain responses is modulated by the intensity of the displayed emotion, the appraisal of the situation, characteristics of the suffering person such as perceived fairness, and features of the empathizer such as gender or previous experience with pain-inflicting situations. Future studies in the field should address inter-individual differences in empathy, development and plasticity of the empathic brain over the life span, and the link between empathy, compassionate motivation, and prosocial behavior.
The monolithic op amp: a tutorial study

A study is made of the integrated circuit operational amplifier (IC op amp) to explain details of its behavior in a simplified and understandable manner. Included are analyses of thermal feedback effects on gain, basic relationships for bandwidth and slew rate, and a discussion of pole-splitting frequency compensation. Sources of second-order bandlimiting in the amplifier are also identified and som(s approaches to speed and bandwidth improvement are developed. Brief sections are included on new JFETbipolar circuitry and die area reduction techniques using transconductance reduction,
Efficient Large-Scale Domain Classification with Personalized Attention

In this paper, we explore the task of mapping spoken language utterances to one of thousands of natural language understanding domains in intelligent personal digital assistants (IPDAs). This scenario is observed for many mainstream IPDAs in industry that allow third parties to develop thousands of new domains to augment built-in ones to rapidly increase domain coverage and overall IPDA capabilities. We propose a scalable neural model architecture with a shared encoder, a novel attention mechanism that incorporates personalization information and domain-specific classifiers that solves the problem efficiently. Our architecture is designed to efficiently accommodate new domains that appear in-between full model retraining cycles with a rapid bootstrapping mechanism two orders of magnitude faster than retraining. We account for practical constraints in real-time production systems, and design to minimize memory footprint and runtime latency. We demonstrate that incorporating personalization results in significantly more accurate domain classification in the setting with thousands of overlapping domains.
PCM and Memristor based nanocrossbars

This paper presents performance comparison between two emerging resistive Non-Volatile Memory (NVM) technologies; namely Memristors and Phase Change Memory (PCM); using nanocrossbar architecture. A comparison in terms of leakage current, reading and writing delay, and energy consumption between both non-volatile memory devices, with SRAM based nanocrossbar as benchmark was carried. It was found that Memristive crossbars offer 3 orders of magnitude improvement in the average read cycle, compared to SRAM based crossbars. On the other hand; both PCM based and Memristor based crossbars offered more than 2 orders of magnitude improvement in leakage energy compared to SRAM based crossbars. The aim of this comparison is to provide a fair simulation platform to study and compare PCM crossbar and Memristive crossbar.
Modeling and Verification of a Six-Phase Interior Permanent Magnet Synchronous Motor

In this paper, a new mathematical modeling for a six-phase interior permanent magnet synchronous motor (IPMSM) is presented. The proposed model utilizes two synchronous reference frames. First, the flux model in the <inline-formula> <tex-math notation="LaTeX">$abcxyz$</tex-math></inline-formula> frame is mapped into the stationary <inline-formula> <tex-math notation="LaTeX">$dq$</tex-math></inline-formula> frames and then to two synchronous rotating frames. Then, differentiating the flux models, voltage equations are derived in rotating frames. Through this analysis, the interaction between the <inline-formula><tex-math notation="LaTeX">$abc$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$xyz$</tex-math></inline-formula> subsystems is properly described by a coupling matrix. The torque equation is also derived using the two reference current variables. Flux model was verified through FEM analysis. Experiments were done using a 100 kW six-phase IPMSM in a dynamo system. The validity of the torque equation was checked with some experimental results under a shorted condition on an <inline-formula><tex-math notation="LaTeX">$xyz$</tex-math></inline-formula> subsystem.