nils-herrmann/clean_scirepeval huggingface dataset:
Learning to simplify: fully convolutional networks for rough sketch cleanup

In this paper, we present a novel technique to simplify sketch drawings based on learning a series of convolution operators. In contrast to existing approaches that require vector images as input, we allow the more general and challenging input of rough raster sketches such as those obtained from scanning pencil sketches. We convert the rough sketch into a simplified version which is then amendable for vectorization. This is all done in a fully automatic way without user intervention. Our model consists of a fully convolutional neural network which, unlike most existing convolutional neural networks, is able to process images of any dimensions and aspect ratio as input, and outputs a simplified sketch which has the same dimensions as the input image. In order to teach our model to simplify, we present a new dataset of pairs of rough and simplified sketch drawings. By leveraging convolution operators in combination with efficient use of our proposed dataset, we are able to train our sketch simplification model. Our approach naturally overcomes the limitations of existing methods, e.g., vector images as input and long computation time; and we show that meaningful simplifications can be obtained for many different test cases. Finally, we validate our results with a user study in which we greatly outperform similar approaches and establish the state of the art in sketch simplification of raster images.
Principles of Guidance, Navigation and Control of UAVs

Two complete system architectures for a guidance, navigation and control solution of small UAVs is presented. These systems (developed at the University of California Santa Cruz and the University of Minnesota) are easily reconfigurable and are intended to support testbeds used in navigation, guidance and control research. The systems described both integrate a low cost inertial measurement unit, a GPS receiver, a triad of magnetometers to generate a navigation solution (position, velocity and attitude estimation) which, in turn, is used in the guidance and control algorithms. The navigation solution described is a 15 state Extended Kalman Filter which integrates the inertial sensor and GPS measurement to generate a high-bandwidth estimate of a UAV’s state. Guidance algorithms for generating a flight trajectory based on waypoint definitions are also described. A PID controller which uses the navigation filter estimate and guidance algorithm to track a flight trajectory is detailed. The architecture: the hardware, software and algorithms is included for completeness. Hardware in the loop simulation and flight test results documenting the performance of these two systems is given.
Characterization and Modeling of the Capacitive HBC Channel

The increasing interest in wireless body area networks has created the need for alternative communication schemes. One example of such schemes is the use of the human body as a communication medium. This technology is called human body communication (HBC), and it offers advantages over the most common radiation-based methods, which makes it an interesting alternative to implement body area networks. The aim of this paper is to identify the influence of a fixture on the HBC channel characterization, and an extended model that includes the test fixtures to explain the measured channel response is proposed. The model was tested against the channel measurement results, and a good experiment-model correlation was obtained. The results show that the test fixture has a nonnegligible influence and that an extended model, based on the physical meaning of the phenomena involved, helps to explain the channel frequency profile results and behavior.
Weakly Supervised Semantic Image Segmentation with Self-correcting Networks

Building a large image dataset with high-quality object masks for semantic segmentation is costly and time consuming. In this paper, we reduce the data preparation cost by leveraging weak supervision in the form of object bounding boxes. To accomplish this, we propose a principled framework that trains a deep convolutional segmentation model that combines a large set of weakly supervised images (having only object bounding box labels) with a small set of fully supervised images (having semantic segmentation labels and box labels). Our framework trains the primary segmentation model with the aid of an ancillary model that generates initial segmentation labels for the weakly supervised instances and a self-correction module that improves the generated labels during training using the increasingly accurate primary model. We introduce two variants of the self-correction module using either linear or convolutional functions. Experiments on the PASCAL VOC 2012 and Cityscape datasets show that our models trained with a small fully supervised set perform similar to, or better than, models trained with a large fully supervised set while requiring ∼7x less annotation effort.
How Learning Works: Seven Research-Based Principles for Smart Teaching

In this volume, the authors introduce seven general principles of learning, distilled from the research literature as well as from twenty-seven years of experience working one-on-one with college faculty. They have drawn on research from a breadth of perspectives (cognitive, developmental, and social psychology; educational research; anthropology; demographics; and organizational behavior) to identify a set of key principles underlying learningfrom how effective organization enhances retrieval and use of information to what impacts motivation. These principles provide instructors with an understanding of student learning that can help them see why certain teaching approaches are or are not supporting student learning, generate or refine teaching approaches and strategies that more effectively foster student learning in specific contexts, and transfer and apply these principles to new courses.
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

Accurate detection of objects in 3D point clouds is a central problem in many applications, such as autonomous navigation, housekeeping robots, and augmented/virtual reality. To interface a highly sparse LiDAR point cloud with a region proposal network (RPN), most existing efforts have focused on hand-crafted feature representations, for example, a bird's eye view projection. In this work, we remove the need of manual feature engineering for 3D point clouds and propose VoxelNet, a generic 3D detection network that unifies feature extraction and bounding box prediction into a single stage, end-to-end trainable deep network. Specifically, VoxelNet divides a point cloud into equally spaced 3D voxels and transforms a group of points within each voxel into a unified feature representation through the newly introduced voxel feature encoding (VFE) layer. In this way, the point cloud is encoded as a descriptive volumetric representation, which is then connected to a RPN to generate detections. Experiments on the KITTI car detection benchmark show that VoxelNet outperforms the state-of-the-art LiDAR based 3D detection methods by a large margin. Furthermore, our network learns an effective discriminative representation of objects with various geometries, leading to encouraging results in 3D detection of pedestrians and cyclists, based on only LiDAR.
Blockchain-Based Decentralized Applications for Multiple Administrative Domain Networking

Evolving networking scenarios include multi-administrative domain network services as drivers of novel business opportunities along with emerging operational challenges. As a potential approach to tackle upcoming requirements providing basic primitives to encompass analytics, automation, and distributed orchestration, we investigate blockchain-based decentralized applications (DApps) in the context of operational phases in support of multi-administrative domain networking. We present and discuss a generalized framework for multi-domain service orchestration using blockchain-based DApps and then showcase proof-of-concept prototype experiments based on best of breed open source components that demonstrate DApp functionalities as candidate enablers of multi-domain network services. We then analyze three use case scenarios pursued by ongoing work at standards development organizations, namely MEF, 3GPP, and ETSI NFV, discussing standardization opportunities around blockchain-based DApps.
Fleet-sizing for multi-depot and periodic vehicle routing problems using a modular heuristic algorithm

In this paper, we address the problem of determining the optimal fleet size for three vehicle routing problems, i.e., multi-depot VRP, periodic VRP and multidepot periodic VRP. In each of these problems, we consider three kinds of constraints that are often found in reality, i.e., vehicle capacity, route duration and budget constraints. To tackle the problems, we propose a new Modular Heuristic Algorithm (MHA) whose exploration and exploitation strategies enable the algorithm to produce promising results. Extensive computational experiments show that MHA performs impressively well, in terms of solution quality and computational time, for the three problem classes.
Gender Stereotypes and Discrimination: How Sexism Impacts Development.

In this chapter, we summarize and integrate some of the latest developmental science research on gender stereotypes and discrimination in childhood and adolescence. We focus on five forms of sexism: (a) stereotypes and discrimination against boys regarding their school behaviors and disciplinary actions; (b) stereotypes and discrimination against girls in science, technology, engineering, and mathematics (STEM) domains; (c) stereotypes and discrimination in sports; (d) peer gendered harassment, including sexual harassment and teasing because of gender atypicality or nonconformity; and (e) sexualized gender stereotypes that sexually objectify girls and assume boys are sexually voracious. First, we document each type of sexism and examine children's awareness and perceptions of that bias, including their own self-reports and attributions. We examine the implications of this sexism for children and adolescents' developmental health (i.e., social, academic, and psychological well-being). We then draw connections between these various areas of research, focusing on how these different forms of sexism interact to reduce equity and justice among children and negatively impact positive developmental outcomes. The chapter concludes with suggestions for future research.
Semantic text similarity using corpus-based word similarity and string similarity

We present a method for measuring the semantic similarity of texts using a corpus-based measure of semantic word similarity and a normalized and modified version of the Longest Common Subsequence (LCS) string matching algorithm. Existing methods for computing text similarity have focused mainly on either large documents or individual words. We focus on computing the similarity between two sentences or two short paragraphs. The proposed method can be exploited in a variety of applications involving textual knowledge representation and knowledge discovery. Evaluation results on two different data sets show that our method outperforms several competing methods.
Learning Distributed Representations of Texts and Entities from Knowledge Base

We describe a neural network model that jointly learns distributed representations of texts and knowledge base (KB) entities. Given a text in the KB, we train our proposed model to predict entities that are relevant to the text. Our model is designed to be generic with the ability to address various NLP tasks with ease. We train the model using a large corpus of texts and their entity annotations extracted from Wikipedia. We evaluated the model on three important NLP tasks (i.e., sentence textual similarity, entity linking, and factoid question answering) involving both unsupervised and supervised settings. As a result, we achieved state-of-the-art results on all three of these tasks. Our code and trained models are publicly available for further academic research.
What makes Web sites credible?: a report on a large quantitative study

The credibility of web sites is becoming an increasingly important area to understand. To expand knowledge in this domain, we conducted an online study that investigated how different elements of Web sites affect people's perception of credibility. Over 1400 people participated in this study, both from the U.S. and Europe, evaluating 51 different Web site elements. The data showed which elements boost and which elements hurt perceptions of Web credibility. Through analysis we found these elements fell into one of seven factors. In order of impact, the five types of elements that increased credibility perceptions were “real-world feel”, “ease of use”, “expertise”, “trustworthiness”, and “tailoring”. The two types of elements that hurt credibility were “commercial implications&rdquo ;and “amateurism”. This large-scale study lays the groundwork for further research into the elements that affect Web credibility. The results also suggest implications for designing credible Web sites.
Patterns of Internet and Traditional News Media Use in a Networked Community

This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution , reselling , loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. The growing popularity of the World Wide Web as a source of news raises questions about the future of traditional news media. Is the Web likely to become a supplement to newspapers and television news, or a substitute for these media? Among people who have access to newspapers, television, and the World Wide Web, why do some prefer to use the Web as a source of news, while others prefer traditional news media? Drawing from a survey of 520 undergraduate students at a large public university where Internet use is woven into the fabric of daily life, this study suggests that use of the Web as a news source is positively related with reading newspapers but has no relationship with viewing television news. Members of this community use the Web mainly as a source of entertainment. Patterns of Web and traditional media exposure are examined in light of computer anxiety, desire for control, and political knowledge. This study suggests that even when computer skills and Internet access become more widespread in the general population, use of the World Wide Web as a news source seems unlikely to diminish substantially use of traditional news media. Will the World Wide Web become a supplement or substitute for traditional news media? As the World Wide Web became popularly accessible only with the advent of Mosaic browser software in 1993, it is still unclear at this early stage of the Web's development how dominant it is likely to become as a provider of daily news to the general public. Yet the rapid spread of on-line news outlets raises profound questions about the future of traditional newspapers and television news programs. The number of newspapers around the world being published …
Cheek defects.

The challenge surgeons face when reconstructing cheek defects varies significantly depending on the location and depth of the defect and the distensibility of the surrounding tissues. The cheek is a large aesthetic unit characterized in most areas with a convex surface and inherent transitions in color and texture. These characteristics demand the surgeon's attention to achieve superior results during reconstruction. Surgeons must also recognize the free margins of the adjacent structures, including the lower eyelid, nasal ala, and lip, to minimize distortion of these areas during healing. With these challenges in mind, this article discusses several approaches to reconstruction of various types of cheek defects.
Neuroanatomical correlates of visually evoked sexual arousal in human males.

Brain areas activated in human male sexual behavior have not been characterized precisely. For the first time, positron emission tomography (PET) was used to identify the brain areas activated in healthy males experiencing visually evoked sexual arousal. Eight male subjects underwent six measurements of regional brain activity following the administration of [15O]H2O as they viewed three categories of film clips: sexually explicit clips, emotionally neutral control clips, and humorous control clips inducing positive but nonsexual emotions. Statistical Parametric Mapping was used to identify brain regions demonstrating an increased activity associated with the sexual response to the visual stimulus. Visually evoked sexual arousal was characterized by a threefold pattern of activation: the bilateral activation of the inferior temporal cortex, a visual association area; the activation of the right insula and right inferior frontal cortex, which are two paralimbic areas relating highly processed sensory information with motivational states; and the activation of the left anterior cingulate cortex, another paralimbic area known to control autonomic and neuroendocrine functions. Activation of some of these areas was positively correlated with plasma testosterone levels. Although this study should be considered preliminary, it identified brain regions whose activation was correlated with visually evoked sexual arousal in males.
Congenital symmastia revisited.

Symmastia is defined as medial confluence of the breast. The term 'symmastia' is modified from Greek (syn meaning 'together', and mastos meaning 'breast') and was first presented by Spence et al. in 1983. Two forms of symmastia exist: an iatrogenic and a congenital version. Congenital symmastia is a rare condition in which web-like soft tissue traverses the sternum to connect the breasts medially. The literature on congenital symmastia is limited, few cases have been published, and knowledge about ideal treatment is still insufficient.



Congenital symmastia was identified as a distinct deformation using a review of the literature and a theoretical model. We analysed the malady using a three-step principle, formulated by Blondeel, which describes the breast as a 'footprint', 'conus' and 'skin-envelope'. To date, few papers on congenital symmastia have been published, most of which focus on the application of various surgical approaches. We examined the literature and evaluated the procedures used, and are presenting two recent cases of congenital symmastia as examples. By combining review and analysis we offer a rational treatment practice.



The analysis showed that the optimal treatment begins by correcting the 'footprint', removing the excess 'conus' over the sternum, and finally reattaching the 'skin-envelope' to the sternum to recreate the normal medial border of the 'footprint'. Thus far, the two most common approaches used to treat congenital symmastia are: reduction mammaplasty and liposuction.



By combining the Blondeel analysis with a procedural review, we developed a flow chart to offer a possible treatment practice.
Book recommendation using Neo4j graph database in BibTeX book metadata

In digital era, book has an important role in life. There are not only a lot of books for different purpose. But also, there are many book metadata which can use for another reason, such as book recommendation. By processing the book metadata, an information can be given to user that needs book recommendation. By combining BibTeX book metadata and Graph Database from Neo4j, data from metadata can be processed. Then, with cypher query by inputting author's parameter or book type's parameter, user can get book recommendation based on their input's criteria. The result is exactly the same with process the metadata manually in relational database. Neo4j, from this paper, takes 180 milliseconds to execute cypher query with author's criteria and takes 184 milliseconds to execute cypher query with book type's criteria.
Addressing supply chain risks of microelectronic devices through computer vision

Microelectronics are at the heart of nearly all modern devices, ranging from small embedded integrated circuits (ICs) inside household products to complex microprocessors that power critical infrastructure systems. Devices often consist of numerous ICs from a variety of different manufacturers and procured through different vendors, all of whom may be trusted to varying degrees. Ensuring the quality, safety, and security of these components is a critical challenge. One possible solution is to use automated imaging techniques to check devices' physical appearance against known reference models in order to detect counterfeit or malicious components. This analysis can be performed at both a macro level (i.e., ensuring that the packaging of the IC appears legitimate and undamaged) and a micro level (i.e., comparing microscopic, transistor-level imagery of the circuit itself to detect suspicious deviations from a reference model). This latter analysis in particular is very challenging, considering that modern devices can contain billions of transistors. In this paper, we review the problem of microelectronics counterfeiting, discuss the potential application of computer vision to microelectronics inspection, present initial results, and recommend directions for future work.
A Novel Spectrum-Scheduling Scheme for Multichannel Cognitive Radio Network and Performance Analysis

In this paper, we develop an opportunistic spectrum-scheduling scheme for multichannel cognitive radio networks. In the proposed scheme, the primary user activity and channel quality (signal-to-noise ratio (SNR) at the receiving secondary user) vary on a slot-by-slot basis. The scheduling is performed at the beginning of the frame, which consists of multiple slots. The scheduling algorithm estimates the expected number of packets that can be transmitted over the frame by each secondary user for each licensed channel. We present a Markov chain formulation to calculate the expected number of packets that can be transmitted over the frame for a secondary user that corresponds to each licensed channel. Based on these expected packet transmissions, a central scheduler allocates the licensed channels to the secondary users. The objective of the scheduling algorithm is to allocate the licensed channels to maximize the aggregate throughput of the secondary users. Compared with the existing dynamic spectrum access schemes, the proposed scheduling scheme incurs smaller scheduling overhead and achieves higher throughput. We also present an analytical framework that models the proposed algorithm using the Markov chain analysis. The performance of the proposed scheduling scheme is evaluated in terms of the average user throughput and blocking probability.