nils-herrmann/clean_scirepeval huggingface dataset:
An English Grammar Checker as a Writing Aid for Students of English as a Second Language

This paper describes an implemented on-line English grammar checker for students of English as a second language. This system focuses on a limited category of frequently occurring grammatical mistakes in essays written by students in the English Language Programs at the University of X. The grammar checker exploits the syntactic domain of locality from a Combina-tory Categorial Grammar for the purpose of identifying speciic types of grammatical mistakes as well as accepting grammatical expressions. It includes grammatical mistakes as ungrammatical variations of the constituents that can be related to given lexical entries in a categorial lexicon. The grammar checker is developed using one set of essays and tested against another set of essays. Unpredicted grammatical mistakes are either incorporated into the next revision or left as they are, depending on the diiculty and desirability of detecting them. The system also provides an interactive Internet interface for convenience of use. Finally, we discuss issues of constructing a full-scale English lexicon for an educational domain.
Dynamic Whitening Saliency

General dynamic scenes involve multiple rigid and flexible objects, with relative and common motion, camera induced or not. The complexity of the motion events together with their strong spatio-temporal correlations make the estimation of dynamic visual saliency a big computational challenge. In this work, we propose a computational model of saliency based on the assumption that perceptual relevant information is carried by high-order statistical structures. Through whitening, we completely remove the second-order information (correlations and variances) of the data, gaining access to the relevant information. The proposed approach is an analytically tractable and computationally simple framework which we call Dynamic Adaptive Whitening Saliency (AWS-D). For model assessment, the provided saliency maps were used to predict the fixations of human observers over six public video datasets, and also to reproduce the human behavior under certain psychophysical experiments (dynamic pop-out). The results demonstrate that AWS-D beats state-of-the-art dynamic saliency models, and suggest that the model might contain the basis to understand the key mechanisms of visual saliency. Experimental evaluation was performed using an extension to video of the well-known methodology for static images, together with a bootstrap permutation test (random label hypothesis) which yields additional information about temporal evolution of the metrics statistical significance.
Social Interactions and Well-Being: The Surprising Power of Weak Ties.

Although we interact with a wide network of people on a daily basis, the social psychology literature has primarily focused on interactions with close friends and family. The present research tested whether subjective well-being is related not only to interactions with these strong ties but also to interactions with weak social ties (i.e., acquaintances). In Study 1, students experienced greater happiness and greater feelings of belonging on days when they interacted with more classmates than usual. Broadening the scope in Studies 2A and 2B to include all daily interactions (with both strong and weak ties), we again found that weak ties are related to social and emotional well-being. The current results highlight the power of weak ties, suggesting that even social interactions with the more peripheral members of our social networks contribute to our well-being.
Recent trends in the U.S. Behavioral and Social Sciences Research (BSSR) workforce

While behavioral and social sciences occupations comprise one of the largest portions of the "STEM" workforce, most studies of diversity in STEM overlook this population, focusing instead on fields such as biomedical or physical sciences. This study evaluates major demographic trends and productivity in the behavioral and social sciences research (BSSR) workforce in the United States during the past decade. Our analysis shows that the demographic trends for different BSSR fields vary. In terms of gender balance, there is no single trend across all BSSR fields; rather, the problems are field-specific, and disciplines such as economics and political science continue to have more men than women. We also show that all BSSR fields suffer from a lack of racial and ethnic diversity. The BSSR workforce is, in fact, less representative of racial and ethnic minorities than are biomedical sciences or engineering. Moreover, in many BSSR subfields, minorities are less likely to receive funding. We point to various funding distribution patterns across different demographic groups of BSSR scientists, and discuss several policy implications.
Complementary Aspect-Based Opinion Mining

Aspect-based opinion mining is finding elaborate opinions towards a subject such as a product or an event. With explosive growth of opinionated texts on the Web, mining aspect-level opinions has become a promising means for online public opinion analysis. In particular, the boom of various types of online media provides diverse yet complementary information, bringing unprecedented opportunities for cross media aspect-opinion mining. Along this line, we propose CAMEL, a novel topic model for complementary aspect-based opinion mining across asymmetric collections. CAMEL gains information complementarity by modeling both common and specific aspects across collections, while keeping all the corresponding opinions for contrastive study. An auto-labeling scheme called AME is also proposed to help discriminate between aspect and opinion words without elaborative human labeling, which is further enhanced by adding word embedding-based similarity as a new feature. Moreover, CAMEL-DP, a nonparametric alternative to CAMEL is also proposed based on coupled Dirichlet Processes. Extensive experiments on real-world multi-collection reviews data demonstrate the superiority of our methods to competitive baselines. This is particularly true when the information shared by different collections becomes seriously fragmented. Finally, a case study on the public event “2014 Shanghai Stampede” demonstrates the practical value of CAMEL for real-world applications.
Corrosive acid ingestion in man - a clinical and endoscopic study.

Sixteen patients with corrosive acid ingestion were studied. The majority of patients (n = 10) had ingested sulphuric acid, and three other patients had ingested hydrochloric acid. The extent and severity of upper gastrointestinal tract injury was determined by fibreoptic endoscopy and necropsy. All the patients had oesophageal and gastric involvement but the duodenum was spared in the majority. The injury was not considered as mild (grade I) in any of these patients; five patients having moderate (grade II) and 10 patients having severe (grade III) injury. Complications and mortality occurred only in patients with grade III injury. Feeding jejunostomy for nutritional support was used in five patients (all grade III) with good results.
Peginterferon alfa-2b plus ribavirin compared with interferon alfa-2b plus ribavirin for initial treatment of chronic hepatitis C: a randomised trial

A sustained virological response (SVR) rate of 41% has been achieved with interferon alfa-2b plus ribavirin therapy of chronic hepatitis C. In this randomised trial, peginterferon alfa-2b plus ribavirin was compared with interferon alfa-2b plus ribavirin.



1530 patients with chronic hepatitis C were assigned interferon alfa-2b (3 MU subcutaneously three times per week) plus ribavirin 1000-1200 mg/day orally, peginterferon alfa-2b 1.5 microg/kg each week plus 800 mg/day ribavirin, or peginterferon alfa-2b 1.5 microg/kg per week for 4 weeks then 0.5 microg/kg per week plus ribavirin 1000-1200 mg/day for 48 weeks. The primary endpoint was the SVR rate (undetectable hepatitis C virus [HCV] RNA in serum at 24-week follow-up). Analyses were based on patients who received at least one dose of study medication.



The SVR rate was significantly higher (p=0.01 for both comparisons) in the higher-dose peginterferon group (274/511 [54%]) than in the lower-dose peginterferon (244/514 [47%]) or interferon (235/505 [47%]) groups. Among patients with HCV genotype 1 infection, the corresponding SVR rates were 42% (145/348), 34% (118/349), and 33% (114/343). The rate for patients with genotype 2 and 3 infections was about 80% for all treatment groups. Secondary analyses identified bodyweight as an important predictor of SVR, prompting comparison of the interferon regimens after adjusting ribavirin for bodyweight (mg/kg). Side-effect profiles were similar between the treatment groups.



In patients with chronic hepatitis C, the most effective therapy is the combination of peginterferon alfa-2b 1.5 microg/kg per week plus ribavirin. The benefit is mostly achieved in patients with HCV genotype 1 infections.
Estimating Outlier Score Probabilities

Outlier detection is a critical function across a diverse range of tasks and domains. There are numerous outlier detection methods, the majority of which produce scores to indicate an outlier versus inlier. An issue with these scores is that they can be difficult to interpret and do not allow for comparisons between different methods. One solution is to convert the outlier score to probabilities. These probability estimates can provide understandable and meaningful results for assessing outlying values. Moreover, the probabilities can be combined to produce an ensemble of outlier detection methods, further enhancing the detection of outliers. In this paper, we propose a unique approach leveraging probabilistic programming to fit the original outlier score distributions to a 3-parameter Lognormal distribution. We provide empirical evidence for the use of this distribution, compare the probability estimates with the outlier scores, discuss confidence in these estimates, evaluate detection performance via the probabilities, and provide an ensemble detection example. Our research indicates this approach reasonably models the original outlier scores, resulting in meaningful outlier probability estimates.
Model Identification and Control Design for a Humanoid Robot

In this paper, model identification and adaptive control design are performed on Devanit-Hartenberg model of a humanoid robot. We focus on the modeling of the 6 degree-of-freedom upper limb of the robot using recursive Newton-Euler (RNE) formula for the coordinate frame of each joint. To obtain sufficient excitation for modeling of the robot, the particle swarm optimization method has been employed to optimize the trajectory of each joint, such that satisfied parameter estimation can be obtained. In addition, the estimated inertia parameters are taken as the initial values for the RNE-based adaptive control design to achieve improved tracking performance. Simulation studies have been carried out to verify the result of the identification algorithm and to illustrate the effectiveness of the control design.
Coming of Age (Digitally): An Ecological View of Social Media Use among College Students

We take an ecological approach to studying social media use and its relation to mood among college students. We conducted a mixed-methods study of computer and phone logging with daily surveys and interviews to track college students' use of social media during all waking hours over seven days. Continual and infrequent checkers show different preferences of social media sites. Age differences also were found. Lower classmen tend to be heavier users and to primarily use Facebook, while upper classmen use social media less frequently and utilize sites other than Facebook more often. Factor analysis reveals that social media use clusters into patterns of content-sharing, text-based entertainment/discussion, relationships, and video consumption. The more constantly one checks social media daily, the less positive is one's mood. Our results suggest that students construct their own patterns of social media usage to meet their changing needs in their environment. The findings can inform further investigation into social media use as a benefit and/or distraction for students.
An Adaptive Parameter Estimation for Guided Filter based Image Deconvolution

Image deconvolution is still to be a challenging illposed problem for recovering a clear image from a given blurry image, when the point spread function is known. Although competitive deconvolution methods are numerically impressive and approach theoretical limits, they are becoming more complex, making analysis, and implementation difficult. Furthermore, accurate estimation of the regularization parameter is not easy for successfully solving image deconvolution problems. In this paper, we develop an effective approach for image restoration based on one explicit image filter guided filter. By applying the decouple of denoising and deblurring techniques to the deconvolution model, we reduce the optimization complexity and achieve a simple but effective algorithm to automatically compute the parameter in each iteration, which is based on Morozov’s discrepancy principle. Experimental results demonstrate that the proposed algorithm outperforms many state-of-the-art deconvolution methods in terms of both ISNR and visual quality. Image deconvolution, guided filter, edge-preserving, adaptive parameter estimation.
Stochastic Synchronization of Complex Networks With Mixed Impulses

In this paper, stochastic synchronization is studied for complex networks with delayed coupling and mixed impulses. Mixed impulses are composed of desynchronizing and synchronizing impulses. The delayed coupling term involves transmission delay and self-feedback delay. By using the average impulsive interval approach and the comparison principle, several conditions are derived to guarantee that exponential synchronization of complex networks is achieved in the mean square. The derived conditions are closely related to the impulsive strengths, the frequency of impulse occurrence, and the coupling structure of complex networks. Numerical simulations are presented to further demonstrate the effectiveness of the proposed approach.
The Role of Capsule on the Performance of a Dry Powder Inhaler Using Computational and Experimental Analyses

To study the fundamental effects of the spinning capsule on the overall performance of a dry powder inhaler (Aerolizer®). The capsule motion was visualized using high-speed photography. Computational fluid dynamics (CFD) analysis was performed to determine the flowfield generated in the device with and without the presence of different sized capsules at 60 l min−1. The inhaler dispersion performance was measured with mannitol powder using a multistage liquid impinger at the same flowrate. The capsule size (3, 4, and 5) was found to make no significant difference to the device flowfield, the particle-device impaction frequency, or the dispersion performance of the inhaler. Reducing the capsule size reduced only the capsule retention by 4%. In contrast, without the presence of the spinning capsule, turbulence levels were increased by 65%, FPFEm (wt% particles ≤6.8 μm in the aerosol referenced against the amount of powder emitted from the device) increased from 59% to 65%, while particle-mouthpiece impaction decreased by 2.5 times. When the powder was dispersed from within compared to from outside the spinning capsule containing four 0.6 mm holes at each end, the FPFEm was increased significantly from 59% to 76%, and the throat retention was dropped from 14% to 6%. The presence, but not the size, of a capsule has significant effects on the inhaler performance. The results suggested that impaction between the particles and the spinning capsule does not play a major role in powder dispersion. However, the capsule can provide additional strong mechanisms of deagglomeration dependent on the size of the capsule hole.
Energy-Aware Web Browsing on Smartphones

Smartphone based web browsing wastes a lot of power when downloading webpages due to the special characteristics of the wireless radio interface. In this paper, we identify these special characteristics, and address power consumption issues through two novel techniques. First, we reorganize the computation sequence of the web browser when loading a webpage, so that the web browser can first run the computations that will generate new data transmissions and retrieve these data from the web server. Then, the web browser can put the wireless radio interface into low power state, release the radio resource, and then run the remaining computations. Second, we introduce a practical data mining based method to predict the user reading time of webpages, based on which the smartphone can switch to low power state when the reading time is longer than a threshold. To demonstrate the effectiveness of our energy-aware approaches, we develop a testbed with Android phones on T-Mobile UMTS network. Experimental results show that our approach can reduce the power consumption of smartphone by more than 30 percent during web browsing. Moreover, our solution can further reduce the webpage loading time and increase the network capacity.
Divorce and children's adjustment problems at home and school: the role of depressive/withdrawn parenting.

This study examined linkages between divorce, depressive/withdrawn parenting, and child adjustment problems at home and school. Middle class divorced single mother families (n = 35) and 2-parent families (n = 174) with a child in the fourth grade participated. Mothers and teachers completed yearly questionnaires and children were interviewed when they were in the fourth, fifth, and sixth grades. Structural equation modeling suggested that the association between divorce and child externalizing and internalizing behavior was partially mediated by depressive/withdrawn parenting when the children were in the fourth and fifth grades.
Defending against VM rollback attack

Recently it became a hot topic to protect VMs from a compromised or even malicious hypervisor. However, most previous systems are vulnerable to rollback attack, since it is hard to distinguish from normal suspend/resume and migration operations that an IaaS platform usually offers. Some of the previous systems simply disable these features to defend rollback attack, while others heavily need user involvement. In this paper, we propose a new solution to make a balance between security and functionality. By securely logging all the suspend/resume and migration operation inside a small trusted computing base, a user can audit the log to check malicious rollback and constrain the operations on the VMs. The solution considers several practical issues including hardware limitations and minimizing user's interaction, and has been implemented on a recent VM protection system.
IcoRating: A Deep-Learning System for Scam ICO Identification

Cryptocurrencies (or digital tokens, digital currencies, e.g., BTC, ETH, XRP, NEO) have been rapidly gaining ground in use, value, and understanding among the public, bringing astonishing profits to investors. Unlike other money and banking systems, most digital tokens do not require central authorities. Being decentralized poses significant challenges for credit rating. Most ICOs are currently not subject to government regulations, which makes a reliable credit rating system for ICO projects necessary and urgent. In this paper, we introduce ICORATING, the first learning–based cryptocurrency rating system. We exploit natural-language processing techniques to analyze various aspects of 2,251 digital currencies to date, such as white paper content, founding teams, Github repositories, websites, etc. Supervised learning models are used to correlate the life span and the price change of cryptocurrencies with these features. For the best setting, the proposed system is able to identify scam ICO projects with 0.83 precision. We hope this work will help investors identify scam ICOs and attract more efforts in automatically evaluating and analyzing ICO projects. 1 2  J. Li designed research; Z. Sun, Z. Deng, F. Li and P. Shi prepared the data; S. Bian and A. Yuan contributed analytic tools; P. Shi and Z. Deng labeled the dataset; J. Li, W. Monroe and W. Wang designed the experiments; J. Li, W. Wu, Z. Deng and T. Zhang performed the experiments; J. Li and T. Zhang wrote the paper; W. Monroe and A. Yuan proofread the paper.  Market capitalization v.s. time.  The number of new ICO projects v.s. time.
Review of the three-field techniques in breast cancer radiotherapy.

Breast cancer is often treated with radiotherapy (RT), with two opposing tangential fields. When indicated, supraclavicular lymph nodes have to be irradiated, and a third anterior field is applied. The junction region has the potential to be over or underdosed. To overcome this problem, many techniques have been proposed. A literature review of 3 Dimensional Conformal RT (3D CRT) and older 3-field techniques was carried out. Intensity Modulated RT (IMRT) techniques are also briefly discussed. Techniques are categorized, few characteristic examples are presented and a comparison is attempted. Three-field techniques can be divided in monoisocentric and two-isocentric. Two-isocentric techniques can be further divided in full field and half field techniques. Monoisocentric techniques show certain great advantages over two-isocentric techniques. However, they are not always applicable and they require extra caution as they are characterized by high dose gradient in the junction region. IMRT has been proved to give better dosimetric results. Three-field matching is a complicated procedure, with potential of over or undredosage in the junction region. Many techniques have been proposed, each with advantages and disadvantages. Among them, monoisocentric techniques, when carefully applied, are the ideal choice, provided IMRT facility is not available. Otherwise, a two-isocentric half beam technique is recommended.
Cloud based automatic street light monitoring system

The proposed system for the cloud based automatic system involves the automatic updating of the data to the lighting system. It also reads the data from the base station in case of emergencies. Zigbee devices are used for wireless transmission of the data from the base station to the light system thus enabling an efficient street lamp control system. Infrared sensor and dimming control circuit is used to track the movement of human in a specific range and dims/bright the street lights accordingly hence saving a large amount of power. In case of emergencies data is sent from the particular light or light system and effective measures are taken accordingly.
Region-based shape control for a swarm of robots

This paper presents a region-based shape controller for a swarm of robots. In this control method, the robotsmove as a group inside a desired regionwhilemaintaining aminimumdistance among themselves. Various shapes of the desired region can be formed by choosing the appropriate objective functions. The robots in the group only need to communicate with their neighbors and not the entire community. The robots do not have specific identities or roles within the group. Therefore, the proposed method does not require specific orders or positions of the robots inside the region and yet different formations can be formed for a swarm of robots. A Lyapunov-like function is presented for convergence analysis of the multi-robot systems. Simulation results illustrate the performance of the proposed controller.