nils-herrmann/clean_scirepeval huggingface dataset:
Altmetric: enriching scholarly content with article-level discussion and metrics

Scholarly content is increasingly being discussed, shared and bookmarked online by researchers. Altmetric is a start-­up that focuses on tracking, collecting and measuring this activity on behalf of publishers and here we describe our approach and general philosophy. Over the past year we've seen sharing and discussion activity around approximately 750k articles. The average number of articles shared each day grows by 5-­ 10% a month. We look at examples of how people are interacting with papers online and at how publishers can collect and present the resulting data to deliver real value to their authors and readers.  Scholars are increasingly visible on the web and social media 1. While the majority of their online activities may not be directly related to their research they are nevertheless discussing, sharing and bookmarking scholarly articles online in large numbers. We know this because our job at Altmetric is to track the attention paid to papers online. Founded in January 2011 and with investment from Digital Science we're a London based start-­‐up that identifies, tracks and collects article level metrics on behalf of publishers. Article level metrics are quantitative or qualitative indicators of the impact that a single article has had. Examples of the former would be a count of the number of times the article has been downloaded, or shared on Twitter. Examples of the latter would be media coverage or a blog post from somebody well respected in the field. Tracking the conversations around papers Encouraging audiences to engage with articles online isn't anything new for many publishers. The Public Library of Science (PLoS), BioMed Central,  Publishing Group have all tried encouraging users to leave comments on papers with varying degrees of success but the response from users has generally been poor, with only a small fraction of papers ever receiving notable attention 2. A larger proportion of papers are discussed in some depth on academic blogs and a larger still proportion shared on social networks like Twitter, Facebook and Google+. Scholars seem to feel more comfortable sharing or discussing content in more informal environments tied to their personal identity and where
Light Field Camera Design for Integral View Photography

This paper introduces the matrix formalism of optics as a useful approach to the area of “light fields”. It is capable of reproducing old results in Integral Photography, as well as generating new ones. Furthermore, we point out the equivalence between radiance density in optical phase space and the light field. We also show that linear transforms in matrix optics are applicable to light field rendering, and we extend them to affine transforms, which are of special importance to designing integral view cameras. Our main goal is to provide solutions to the problem of capturing the 4D light field with a 2D image sensor. From this perspective we present a unified affine optics view on all existing integral / light field cameras. Using this framework, different camera designs can be produced. Three new cameras are proposed.  Integral view of a seagull
Uncertainty Quantification - Theory, Implementation, and Applications

Give us 5 minutes and we will show you the best book to read today. This is it, the uncertainty quantification theory implementation and applications that will be your best choice for better reading book. Your five times will not spend wasted by reading this website. You can take the book as a source to make better concept. Referring the books that can be situated with your needs is sometime difficult. But here, this is so easy. You can find the best thing of book that you can read.
Heavy-duty omni-directional Mecanum-wheeled robot for autonomous navigation: System development and simulation realization

A Mecanum-wheeled robot benefits from great omni-direction maneuverability. However it suffers from random slippage and high-speed vibration, which creates electric power safety, uncertain position errors and energy waste problems for heavy-duty tasks. A lack of Mecanum research on heavy-duty autonomous navigation demands a robot platform to conduct experiments in the future. This paper introduces AuckBot, a heavy-duty omni-directional Mecanum robot platform developed at the University of Auckland, including its hardware overview, the control system architecture and the simulation design. In particular the control system, synergistically combining the Beckhoff system as the Controller-PC to serve low-level motion execution and ROS as the Navigation-PC to accomplish highlevel intelligent navigation tasks, is developed. In addition, a computer virtual simulation based on ISG-virtuos for virtual AuckBot has been validated. The present status and future work of AuckBot are described at the end.
VERTEXICA: Your Relational Friend for Graph Analytics!

In this paper, we present Vertexica, a graph analytics tools on top of a relational database, which is user friendly and yet highly ecient. Instead of constraining programmers to SQL, Vertexica offers a popular vertex-centric query interface, which is more natural for analysts to express many graph queries. e programmers simply provide their vertex-compute functions and Vertexica takes care of eciently executing them in the standard SQL engine. e advantage of using Vertexica is its ability to leverage the relational features and enable much more sophisticated graph analysis. ese include expressing graph algorithms which are dicult in vertexcentric but straightforward in SQL and the ability to compose endto-end data processing pipelines, including preand postprocessing of graphs as well as combining multiple algorithms for deeper insights. Vertexica has a graphical user interface and we outline several demonstration scenarios including, interactive graph analysis, complex graph analysis, and continuous and time series analysis.
Semi-Autonomous Vehicle Control for Road Departure and Obstacle Avoidance

This paper presents a semi-autonomous controller for integrated design of an active safety system. A model of the driver’s nominal behavior is estimated based on observed behavior. A nonlinear model of the vehicle is developed that utilizes a coordinate transformation which allows for obstacles and road bounds to be modeled as constraints while still allowing the controller full steering and braking authority. A Nonlinear Model Predictive Controller (NMPC) is designed that utilizes the vehicle and driver models to predict a future threat of collision or roadway departure. Simulations are presented which demonstrate the ability of the suggested approach to successfully avoid multiple obstacles while staying safely within the road bounds.
Connecting the Dots Between MLE and RL for Sequence Generation

Sequence generation models such as recurrent networks can be trained with a diverse set of learning algorithms. For example, maximum likelihood learning is simple and efficient, yet suffers from the exposure bias problem. Reinforcement learning like policy gradient addresses the problem but can have prohibitively poor exploration efficiency. A variety of other algorithms such as RAML, SPG, and data noising, have also been developed from different perspectives. This paper establishes a formal connection between these algorithms. We present a generalized entropy regularized policy optimization formulation, and show that the apparently divergent algorithms can all be reformulated as special instances of the framework, with the only difference being the configurations of reward function and a couple of hyperparameters. The unified interpretation offers a systematic view of the varying properties of exploration and learning efficiency. Besides, based on the framework, we present a new algorithm that dynamically interpolates among the existing algorithms for improved learning. Experiments on machine translation and text summarization demonstrate the superiority of the proposed algorithm.
1 First Order Methods for Nonsmooth Convex Large-Scale Optimization , I : General Purpose Methods

We discuss several state-of-the-art computationally cheap, as opposed to the polynomial time Interior Point algorithms, first order methods for minimizing convex objectives over “simple” large-scale feasible sets. Our emphasis is on the general situation of a nonsmooth convex objective represented by deterministic/stochastic First Order oracle and on the methods which, under favorable circumstances, exhibit (nearly) dimension-independent convergence rate.
A Semi-Automatic Method for Road Centerline Extraction From VHR Images

This letter presents a semi-automatic approach to delineating road networks from very high resolution satellite images. The proposed method consists of three main steps. First, the geodesic method is used to extract the initial road segments that link the road seed points prescribed in advance by users. Next, a road probability map is produced based on these coarse road segments and a further direct thresholding operation separates the image into two classes of surfaces: the road and nonroad classes. Using the road class image, a kernel density estimation map is generated, upon which the geodesic method is used once again to link the foregoing road seed points. Experiments demonstrate that this proposed method can extract smooth correct road centerlines.
Imaging brain development: The adolescent brain

The past 15 years have seen a rapid expansion in the number of studies using neuroimaging techniques to investigate maturational changes in the human brain. In this paper, I review MRI studies on structural changes in the developing brain, and fMRI studies on functional changes in the social brain during adolescence. Both MRI and fMRI studies point to adolescence as a period of continued neural development. In the final section, I discuss a number of areas of research that are just beginning and may be the subject of developmental neuroimaging in the next twenty years. Future studies might focus on complex questions including the development of functional connectivity; how gender and puberty influence adolescent brain development; the effects of genes, environment and culture on the adolescent brain; development of the atypical adolescent brain; and implications for policy of the study of the adolescent brain.
Understanding contention-based channels and using them for defense

Microarchitectural resources such as caches and predictors can be used to leak information across security domains. Significant prior work has demonstrated attacks and defenses for specific types of such microarchitectural side and covert channels. In this paper, we introduce a general mathematical study of microarchitectural channels using information theory. Our conceptual contribution is a simple mathematical abstraction that captures the common characteristics of all microarchitectural channels. We call this the Bucket model and it reveals that microarchitectural channels are fundamentally different from side and covert channels in networking. We then quantify the communication capacity of several microarchitectural covert channels (including channels that rely on performance counters, AES hardware and memory buses) and measure bandwidths across both KVM based heavy-weight virtualization and light-weight operating-system level isolation. We demonstrate channel capacities that are orders of magnitude higher compared to what was previously considered possible. Finally, we introduce a novel way of detecting intelligent adversaries that try to hide while running covert channel eavesdropping attacks. Our method generalizes a prior detection scheme (that modeled static adversaries) by introducing noise that hides the detection process from an intelligent eavesdropper.
THE GOAL QUESTION METRIC APPROACH

As with any engineering discipline, software development requires a measurement mechanism for feedback and evaluation. Measurement is a mechanism for creating a corporate memory and an aid in answering a variety of questions associated with the enactment of any software process. It helps support project planning (e.g., How much will a new project cost?); it allows us to determine the strengths and weaknesses of the current processes and products (e.g., What is the frequency of certain types of errors?); it provides a rationale for adopting/refining techniques (e.g., What is the impact of the technique XX on the productivity of the projects?); it allows us to evaluate the quality of specific processes and products (e.g., What is the defect density in a specific system after deployment?). Measurement also helps, during the course of a project, to assess its progress, to take corrective action based on this assessment, and to evaluate the impact of such action.
A Novel Broadband Circularly Polarized Antenna Based on Off-Center-Fed Dipoles

A novel broadband circularly polarized (CP) antenna is proposed. The operating principle of this CP antenna is different from those of conventional CP antennas. An off-center-fed dipole is introduced to achieve the 90° phase difference required for circular polarization. The new CP antenna consists of two off-center-fed dipoles. Combining such two new CP antennas leads to a bandwidth enhancement for circular polarization. A T-shaped microstrip probe is used to excite the broadband CP antenna, featuring a simple planar configuration. It is shown that the new broadband CP antenna achieves an axial ratio (AR) bandwidth of 55% (1.69-3.0 GHz) for AR <; 3 dB, an impedance bandwidth of 60% (1.7-3.14 GHz) for return loss (RL) > 15 dB, and an antenna gain of 6-9 dBi. The new mechanism for circular polarization is described and an experimental verification is presented.
Fish'n'Steps: Encouraging Physical Activity with an Interactive Computer Game

A sedentary lifestyle is a contributing factor to chronic diseases, and it is often correlated with obesity. To promote an increase in physical activity, we created a social computer game, Fish'n'Steps, which links a player’s daily foot step count to the growth and activity of an animated virtual character, a fish in a fish tank. As further encouragement, some of the players’ fish tanks included other players’ fish, thereby creating an environment of both cooperation and competition. In a fourteen-week study with nineteen participants, the game served as a catalyst for promoting exercise and for improving game players’ attitudes towards physical activity. Furthermore, although most player’s enthusiasm in the game decreased after the game’s first two weeks, analyzing the results using Prochaska's Transtheoretical Model of Behavioral Change suggests that individuals had, by that time, established new routines that led to healthier patterns of physical activity in their daily lives. Lessons learned from this study underscore the value of such games to encourage rather than provide negative reinforcement, especially when individuals are not meeting their own expectations, to foster long-term behavioral change.
Effectiveness of PSO Based Neural Network for Seasonal Time Series Forecasting

Recently, the Particle Swarm Optimization (PSO) technique has gained much attention in the field of time series forecasting. Although PSO trained Artificial Neural Networks (ANNs) performed reasonably well in stationary time series forecasting, their effectiveness in tracking the structure of non-stationary data (especially those which contain trends or seasonal patterns) is yet to be justified. In this paper, we have trained neural networks with two types of PSO (Trelea1 and Trelea2) for forecasting seasonal time series data. To assess their performances, experiments are conducted on three well-known real world seasonal time series. Obtained forecast errors in terms of three common performance measures, viz. MSE, MAE and MAPE for each dataset are compared with those obtained by the Seasonal ANN (SANN) model, trained with a standard backpropagation algorithm. Comparisons demonstrate that training with PSO-Trelea1 and PSO-Trelea2 produced significantly better results than the standard backpropagation rule.
Gender and Competition

Laboratory studies have documented that women often respond less favorably to competition than men. Conditional on performance, men are often more eager to compete, and the performance of men tends to respond more positively to an increase in competition. This means that few women enter and win competitions. We review studies that examine the robustness of these differences as well the factors that may give rise to them. Both laboratory and field studies largely confirm these initial findings, showing that gender differences in competitiveness tend to result from differences in overconfidence and in attitudes toward competition. Gender differences in risk aversion, however, seem to play a smaller and less robust role. We conclude by asking what could and should be done to encourage qualified males and females to compete. 601 A nn u. R ev . E co n. 2 01 1. 3: 60 163 0. D ow nl oa de d fr om w w w .a nn ua lre vi ew s.o rg by $ {i nd iv id ua lU se r.d is pl ay N am e} o n 08 /1 6/ 11 . F or p er so na l u se o nl y.
Interactive collision detection

Collision detection and response can make a virtual-reality application seem more believable. Unfortunately , existing collision-detection algorithms are too slow for interactive use. We present a new algorithm that is not only fast but also interruptible, allowing an application to trade quality for more speed. Our algorithm uses simple four-dimensional geometry to approximate motion, and sets of spheres to approximate three-dimensional surfaces. The algorithm allows a sample application to run 5 to 7 times faster than it runs with existing algorithms.
Coordination of Directional Overcurrent Relays Using Seeker Algorithm

Coordination of directional overcurrent relays in a multiloop subtransmission or distribution network is formulated as an optimization problem. In this paper, the coordination of directional overcurrent relays is formulated as a mixed-integer nonlinear programming problem and is then solved by a new seeker optimization technique. Based on the act of human searching, in the proposed seeker technique, the search direction and step length are determined in an adaptive way. The proposed method is implemented in three different test cases. The results are compared with previously proposed analytic and evolutionary approaches.
Multimedia Content Identification Through Smart Meter Power Usage Profiles

Advanced metering devices (smart meters) are being installed throughout electric networks in Germany (as well as in other parts of Europe and in the United States). Unfortunately, smart meters are able to become surveillance devices that monitor the behavior of the customers. This leads to unprecedented invasions of consumer privacy. The high-resolution energy consumption data which are transmitted to the utility company allow intrusive identification and monitoring of equipment within consumers’ homes (e. g., TV set, refrigerator, toaster, and oven). Our research shows that the analysis of the household’s electricity usage profile at a 0.5s−1 sample rate does reveal what channel the TV set in the household was displaying. It is also possible to identify (copyright-protected) audiovisual content in the power profile that is displayed on a CRT, a Plasma display TV or a LCD television set with dynamic backlighting. Our test results indicate that a 5 minutes-chunk of consecutive viewing without major interference by other appliances is sufficient to identify the content. Our investigation also reveals that the data transmitted via the Internet by the smart meter are unsigned and unencrypted. Our tests were performed on a sealed, operational smart meter used for electricity metering in a private home in North RhineWestphalia, Germany. Parameters for other television sets were obtained with an identical smart meter deployed in a university lab.
IoT-Based Big Data: From Smart City towards Next Generation Super City Planning

Recently, a rapid growth in the population in urban regions demands the provision of services and infrastructure. These needs can be come up wit the use of Internet of Things (IoT) devices, such as sensors, actuators, smartphones and smart systems. This leans to building Smart City towards the next generation Super City planning. However, as thousands of IoT devices are interconnecting and communicating with each other over the Internet to establish smart systems, a huge amount of data, termed as Big Data, is being generated. It is a challenging task to integrate IoT services and to process Big Data in an efficient way when aimed at decision making for future Super City. Therefore, to meet such requirements, this paper presents an IoT-based system for next generation Super City planning using Big Data Analytics. Authors have proposed a complete system that includes various types of IoT-based smart systems like smart home, vehicular networking, weather and water system, smart parking, and surveillance objects, etc., for dada generation. An architecture is proposed that includes four tiers/layers i.e., 1) Bottom Tier-1, 2) Intermediate Tier-1, 3) Intermediate Tier 2, and 4) Top Tier that handle data generation and collections, communication, data administration and processing, and data interpretation, respectively. The system implementation model is presented from the generation and collection of data to the decision making. The proposed system is implemented using Hadoop ecosystem with MapReduce programming. The throughput and processing time results show that the proposed Super City planning system is more efficient and scalable.  Big Data, Hadoop, IoT, Smart City, Super City
MR imaging of the lymphatic system in patients with lipedema and lipo-lymphedema.

To assess for the first time the morphology of the lymphatic system in patients with lipedema and lipo-lymphedema of the lower extremities by MR lymphangiography.



26 lower extremities in 13 consecutive patients (5 lipedema, 8 lipo-lymphedema) were examined by MR lymphangiography. 18 mL of gadoteridol and 1 mL of mepivacainhydrochloride 1% were subdivided into 10 portions and injected intracutaneously in the forefoot. MR imaging was performed with a 1.5-T system equipped with high-performance gradients. For MR lymphangiography, a 3D-spoiled gradient-echo sequence was used. For evaluation of the lymphedema a heavily T2-weighted 3D-TSE sequence was performed.



In all 16 lower extremities (100%) with lipo-lymphedema, high signal intensity areas in the epifascial region could be detected on the 3D-TSE sequence. In the 16 examined lower extremities with lipo-lymphedema, 8 lower legs and 3 upper legs demonstrated enlarged lymphatic vessels up to a diameter of 3 mm. In two lower legs with lipo-lymphedema, an area of dermal back-flow was seen, indicating lymphatic outflow obstruction. In the 10 examined lower extremities with clinically pure lipedema, 4 lower legs and 2 upper legs demonstrated enlarged lymphatic vessels up to a diameter of 2 mm, indicating a subclinical status of lymphedema. In all examined extremities, the inguinal lymph nodes demonstrated a contrast material enhancement in the first image acquisition 15 min after injection.



MR lymphangiography is a safe and accurate minimal-invasive imaging modality for the evaluation of the lymphatic circulation in patients with lipedema and lipo-lymphedema of the lower extremities. If the extent of lymphatic involvement is unclear at the initial clinical examination or requires a better definition for optimal therapeutic planning, MR lymphangiography is able to identify the anatomic and physiological derangements and to establish an objective baseline.