nils-herrmann/clean_scirepeval huggingface dataset:
Consistently faster and smaller compressed bitmaps with Roaring

Compressed bitmap indexes are used in databases and search engines. Many bitmap compression techniques have been proposed, almost all relying primarily on run-length encoding (RLE). However, on unsorted data, we can get superior performance with a hybrid compression technique that uses both uncompressed bitmaps and packed arrays inside a two-level tree. An instance of this technique, Roaring, has recently been proposed. Due to its good performance, it has been adopted by several production platforms (e.g., Apache Lucene, Apache Spark, Apache Kylin and Druid). Yet there are cases where run-length encoded bitmaps are smaller than the original Roaring bitmaps— typically when the data is sorted so that the bitmaps contain long compressible runs. To better handle these cases, we build a new Roaring hybrid that combines uncompressed bitmaps, packed arrays and RLE compressed segments. The result is a new Roaring format that compresses better. Overall, our new implementation of Roaring can be several times faster (up to two orders of magnitude) than the implementations of traditional RLE-based alternatives (WAH, Concise, EWAH) while compressing better. We review the design choices and optimizations that make these good results possible.
User cooperation diversity. Part I. System description

Mobile users’ data rate and quality of service are limited by the fact that, within the duration of any given call, they experience severe variations in signal attenuation, thereby necessitating the use of some type of diversity. In this two-part paper, we propose a new form of spatial diversity, in which diversity gains are achieved via the cooperation of mobile users. Part I describes the user cooperation strategy, while Part II focuses on implementation issues and performance analysis. Results show that, even though the interuser channel is noisy, cooperation leads not only to an increase in capacity for both users but also to a more robust system, where users’ achievable rates are less susceptible to channel variations.
RMiner: a tool set for role mining

Recently, there are many approaches proposed for mining roles using automated technologies. However, it lacks a tool set that can be used to aid the application of role mining approaches and update role states. In this demonstration, we introduce a tool set, RMiner, which is based on the core of WEKA, an open source data mining tool. RMiner implements most of the classic and latest role mining algorithms and provides interactive tools for administrator to update role states. The running examples of RMiner are presented to demonstrate the effectiveness of the tool set.
A Study on the Current State of the Art in Tool-Supported UML-Based Static Reverse Engineering

Today, software-engineering research and industry alike recognize the need for practical tools to support reverseengineering activities. Most of the well-known CASE-tools nowadays support reverse engineering in some way. But although the Unified Modeling Language (UML) has emerged as the de facto standard for the abstract graphical representation of object-oriented software systems, there does not yet exist a standard scheme for representing the reverse engineered models of software systems. Due to the differences in understanding and application of the UML notation and the proprietary extensions that different tools adopt, it is often difficult to ensure that model semantics remains unambiguous when working with different tools at the same
Learning Feature Representations with K-Means

Many algorithms are available to learn deep hierarchies of features from unlabeled data, especially images. In many cases, these algorithms involve multi-layered networks of features (e.g., neural networks) that are sometimes tricky to train and tune and are difficult to scale up to many machines effectively. Recently, it has been found that K-means clustering can be used as a fast alternative training method. The main advantage of this approach is that it is very fast and easily implemented at large scale. On the other hand, employing this method in practice is not completely trivial: K-means has several limitations, and care must be taken to combine the right ingredients to get the system to work well. This chapter will summarize recent results and technical tricks that are needed to make effective use of K-means clustering for learning large-scale representations of images. We will also connect these results to other well-known algorithms to make clear when K-means can be most useful and convey intuitions about its behavior that are useful for debugging and engineering new systems.
Adversarial Feature Learning

The ability of the Generative Adversarial Networks (GANs) framework to learn generative models mapping from simple latent distributions to arbitrarily complex data distributions has been demonstrated empirically, with compelling results showing that the latent space of such generators captures semantic variation in the data distribution. Intuitively, models trained to predict these semantic latent representations given data may serve as useful feature representations for auxiliary problems where semantics are relevant. However, in their existing form, GANs have no means of learning the inverse mapping – projecting data back into the latent space. We propose Bidirectional Generative Adversarial Networks (BiGANs) as a means of learning this inverse mapping, and demonstrate that the resulting learned feature representation is useful for auxiliary supervised discrimination tasks, competitive with contemporary approaches to unsupervised and self-supervised feature learning.
Design of S-curve Direct Landing Position Control System for Elevator Using Microcontroller Tou

— This paper discusses the design of the motion pattern generator used for elevator vertical movements. A typical S-Curve like pattern generator dedicated to be used on microcontroller-based system is proposed. Pattern can be easily optimized to various needs by changing the acceleration and jerk setting. It features in direct landing of the elevator cabin and precise position correction. The performance specification, uncertainties in the realistic case and constraints of designing an elevator motion controller when using microcontroller are also introduced. An elevator motion controller using a mid-end microcontroller is implemented using the discussed new pattern generator to verify the design. Simulation and experimental results have shown that the proposed pattern generator is robust to disturbance and can meet the specification.
CANAuth - A Simple, Backward Compatible Broadcast Authentication Protocol for CAN bus

The Controller-Area Network (CAN) bus protocol  is a bus protocol invented in 1986 by Robert Bosch GmbH, originally intended for automotive use. By now, the bus can be found in devices ranging from cars and trucks, over lightning setups to industrial looms. Due to its nature, it is a system very much focused on safety, i.e., reliability. Unfortunately, there is no build-in way to enforce security, such as encryption or authentication. In this paper, we investigate the problems associated with implementing a backward compatible message authentication protocol on the CAN bus. We show which constraints such a protocol has to meet and why this eliminates, to the best of our knowledge, all the authentication protocols published so far. Furthermore, we present a message authentication protocol, CANAuth, that meets all of the requirements set forth and does not violate any constraint of the CAN bus. CAN bus, embedded networks, broadcast authentication, symmetric cryptography
Sinkhorn Distances: Lightspeed Computation of Optimal Transport

Optimal transport distances are a fundamental family of dis tances for probability measures and histograms of features. Despite their appeali ng theoretical properties, excellent performance in retrieval tasks and intuiti ve formulation, their computation involves the resolution of a linear program whose c ost an quickly become prohibitive whenever the size of the support of these me asur s or the histograms’ dimension exceeds a few hundred. We propose in this work a new family of optimal transport distances that look at transport probl ems from a maximumentropy perspective. We smooth the classic optimal transpo rt pr blem with an entropic regularization term, and show that the resulting o ptimum is also a distance which can be computed through Sinkhorn’s matrix scali ng algorithm at a speed that is several orders of magnitude faster than that of transport solvers. We also show that this regularized distance improves upon clas si optimal transport distances on the MNIST classification problem.
Do you "like" my photo? Facebook use maintains eating disorder risk.

Social media sites, such as Facebook, merge two factors that influence risk for eating disorders:media and peers. Previous work has identified cross-sectional and temporal associations between Facebook use and disordered eating. This study sought to replicate and extend these findings using an experimental design.



In Study 1, 960 women completed self-report surveys regarding Facebook use and disordered eating. In Study 2, 84 women were randomly assigned to use Facebook or to use an alternate internet site for 20 min.



More frequent Facebook use was associated with greater disordered eating in a cross-sectional survey. Facebook use was associated with the maintenance of weight/shape concerns and state anxiety compared to an alternate internet activity.



Facebook use may contribute to disordered eating by maintaining risk for eating pathology. As such, targeting Facebook use may be helpful in intervention and prevention programs.
SMIT: Stochastic Multi-Label Image-to-Image Translation

Cross-domain mapping has been a very active topic in recent years. Given one image, its main purpose is to translate it to the desired target domain, or multiple domains in the case of multiple labels. This problem is highly challenging due to three main reasons: (i) unpaired datasets, (ii) multiple attributes, and (iii) the multimodality associated with the translation. Most of the existing state-of-theart has focused only on two reasons, i.e. producing disentangled representations from unpaired datasets in a one-toone domain translation or producing multiple unimodal attributes from unpaired datasets. In this work, we propose a joint framework of diversity and multi-mapping image-toimage translations, using a single generator to conditionally produce countless and unique fake images that hold the underlying characteristics of the source image. Extensive experiments over different datasets demonstrate the effectiveness of our proposed approach with comparisons to the state-of-the-art in both multi-label and multimodal problems. Additionally, our method is able to generalize under different scenarios: continuous style interpolation, continuous label interpolation, and multi-label mapping.
Chrome Extensions: Threat Analysis and Countermeasures

The widely popular browser extensions now become one of the most commonly used malware attack vectors. The Google Chrome browser, which implements the principles of least privileges and privilege separation by design, offers a strong security mechanism to protect malicious websites from damaging the whole browser system via extensions. In this study, we however reveal that Chrome’s extension security model is not a panacea for all possible attacks with browser extensions. Through a series of practical bot-based attacks that can be performed even under typical settings, we demonstrate that malicious Chrome extensions pose serious threats, including both information dispersion and harvesting, to browsers. We further conduct an in-depth analysis of Chrome’s extension security model, and conclude that its vulnerabilities are rooted fro m the violation of the principles of least privileges and priv ilege separation. Following these principles, we propose a set of countermeasures that enforce the policies of microprivilege management and differentiating DOM elements. Using a prototype developed on the latest Chrome browser, we show that they can effectively mitigate the threats posed by malicious Chrome extensions with little effect on normal browsing experience.
Fetal facial profile markers of Down syndrome in the second and third trimesters of pregnancy.

To investigate the use of the maxilla-nasion-mandible (MNM) angle and fetal profile (FP) line to assess the degree of midfacial hypoplasia in Down-syndrome fetuses in the second and third trimesters of pregnancy.



The MNM angle and FP line were measured retrospectively in stored two-dimensional images or three-dimensional volumes of fetuses with Down syndrome. Data collected from January 2006 to July 2013 were retrieved from the digital databases of participating units. The MNM angle was expressed as a continuous variable (degrees) and the FP line as positive, negative or zero. Measurements were obtained from stored images in the midsagittal plane by two experienced examiners and compared with our previously reported normal ranges for euploid fetuses. A MNM angle below the 5(th) centile of the reference range and a positive or negative FP line were considered as abnormal.



A total of 133 fetuses with Down syndrome were available for analysis, eight of which were subsequently excluded because of inadequate images. The MNM angle was not influenced by gestational age (P = 0.48) and was significantly smaller in Down-syndrome fetuses than in euploid fetuses (mean, 12.90° vs 13.53°, respectively; P = 0.015). The MNM angle was below the 5th centile for euploid fetuses in 16.8% of fetuses with Down syndrome (P < 0.01). In the cohort of Down-syndrome fetuses, a positive FP line was present in 41.6% of cases (with a false-positive rate (FPR) of 6.3%) and was positively correlated with Down syndrome and gestational age (P < 0.01). There was no case with a negative FP line. In cases of Down syndrome, a positive FP line was correlated with a small MNM angle (P < 0.01).



A small MNM angle and a positive FP line can be regarded as novel markers for Down syndrome. The FP line is an easy marker to measure, has a low FPR, does not require knowledge of normal reference values and has the potential to differentiate between Down syndrome and trisomy 18, as, in the latter, the FP line is often negative.
Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion

Here, we demonstrate that subject motion produces substantial changes in the timecourses of resting state functional connectivity MRI (rs-fcMRI) data despite compensatory spatial registration and regression of motion estimates from the data. These changes cause systematic but spurious correlation structures throughout the brain. Specifically, many long-distance correlations are decreased by subject motion, whereas many short-distance correlations are increased. These changes in rs-fcMRI correlations do not arise from, nor are they adequately countered by, some common functional connectivity processing steps. Two indices of data quality are proposed, and a simple method to reduce motion-related effects in rs-fcMRI analyses is demonstrated that should be flexibly implementable across a variety of software platforms. We demonstrate how application of this technique impacts our own data, modifying previous conclusions about brain development. These results suggest the need for greater care in dealing with subject motion, and the need to critically revisit previous rs-fcMRI work that may not have adequately controlled for effects of transient subject movements.
Walking on Water : Biolocomotion at the Interface

We consider the hydrodynamics of creatures capable of sustaining themselves on the water surface by means other than flotation. Particular attention is given to classifying water walkers according to their principal means of weight support and lateral propulsion. The various propulsion mechanisms are rationalized through consideration of energetics, hydrodynamic forces applied, or momentum transferred by the driving stroke. We review previous research in this area and suggest directions for future work. Special attention is given to introductory discussions of problems not previously treated in the fluid mechanics literature, with hopes of attracting physicists, applied mathematicians, and engineers to this relatively unexplored area of fluid mechanics. 339 A nn u. R ev . F lu id . M ec h. 2 00 6. 38 :3 39 -3 69 . D ow nl oa de d fr om a rj ou rn al s. an nu al re vi ew s. or g by Y al e U ni ve rs ity S O C IA L S C IE N C E L IB R A R Y o n 12 /1 7/ 05 . F or p er so na l u se o nl y. AR266-FL38-13 ARI 11 November 2005 20:7
Hashtag Recommendation with Topical Attention-Based LSTM

Microblogging services allow users to create hashtags to categorize their posts. In recent years, the task of recommending hashtags for microblogs has been given increasing attention. However, most of existing methods depend on hand-crafted features. Motivated by the successful use of long short-term memory (LSTM) for many natural language processing tasks, in this paper, we adopt LSTM to learn the representation of a microblog post. Observing that hashtags indicate the primary topics of microblog posts, we propose a novel attention-based LSTM model which incorporates topic modeling into the LSTM architecture through an attention mechanism. We evaluate our model using a large real-world dataset. Experimental results show that our model significantly outperforms various competitive baseline methods. Furthermore, the incorporation of topical attention mechanism gives more than 7.4% improvement in F1 score compared with standard LSTM method.
Detecting spammers on social networks

Social networking has become a popular way for users to meet and interact online. Users spend a significant amount of time on popular social network platforms (such as Facebook, MySpace, or Twitter), storing and sharing a wealth of personal information. This information, as well as the possibility of contacting thousands of users, also attracts the interest of cybercriminals. For example, cybercriminals might exploit the implicit trust relationships between users in order to lure victims to malicious websites. As another example, cybercriminals might find personal information valuable for identity theft or to drive targeted spam campaigns.
 In this paper, we analyze to which extent spam has entered social networks. More precisely, we analyze how spammers who target social networking sites operate. To collect the data about spamming activity, we created a large and diverse set of "honey-profiles" on three large social networking sites, and logged the kind of contacts and messages that they received. We then analyzed the collected data and identified anomalous behavior of users who contacted our profiles. Based on the analysis of this behavior, we developed techniques to detect spammers in social networks, and we aggregated their messages in large spam campaigns. Our results show that it is possible to automatically identify the accounts used by spammers, and our analysis was used for take-down efforts in a real-world social network. More precisely, during this study, we collaborated with Twitter and correctly detected and deleted 15,857 spam profiles.
A Latent Variable Recurrent Neural Network for Discourse-Driven Language Models

This paper presents a novel latent variable recurrent neural network architecture for jointly modeling sequences of words and (possibly latent) discourse relations between adjacent sentences. A recurrent neural network generates individual words, thus reaping the benefits of discriminatively-trained vector representations. The discourse relations are represented with a latent variable, which can be predicted or marginalized, depending on the task. The resulting model can therefore employ a training objective that includes not only discourse relation classification, but also word prediction. As a result, it outperforms state-ofthe-art alternatives for two tasks: implicit discourse relation classification in the Penn Discourse Treebank, and dialog act classification in the Switchboard corpus. Furthermore, by marginalizing over latent discourse relations at test time, we obtain a discourse informed language model, which improves over a strong LSTM baseline.
Kinematics and the Implementation of an Elephant's Trunk Manipulator and Other Continuum Style Robots

Traditionally, robot manipulators have been a simple arrangement of a small number of serially connected links and actuated joints. Though these manipulators prove to be very effective for many tasks, they are not without their limitations, due mainly to their lack of maneuverability or total degrees of freedom. Continuum style (i.e., continuous "back-bone") robots, on the other hand, exhibit a wide range of maneuverability, and can have a large number of degrees of freedom. The motion of continuum style robots is generated through the bending of the robot over a given section; unlike traditional robots where the motion occurs in discrete locations, i.e., joints. The motion of continuum manipulators is often compared to that of biological manipulators such as trunks and tentacles. These continuum style robots can achieve motions that could only be obtainable by a conventionally designed robot with many more degrees of freedom. In this paper we present a detailed formulation and explanation of a novel kinematic model for continuum style robots. The design, construction, and implementation of our continuum style robot called the elephant trunk manipulator is presented. Experimental results are then provided to verify the legitimacy of our model when applied to our physical manipulator. We also provide a set of obstacle avoidance experiments that help to exhibit the practical implementation of both our manipulator and our kinematic model.
3D Human Pose Estimation via Deep Learning from 2D Annotations

We propose a deep convolutional neural network for 3D human pose and camera estimation from monocular images that learns from 2D joint annotations. The proposed network follows the typical architecture, but contains an additional output layer which projects predicted 3D joints onto 2D, and enforces constraints on body part lengths in 3D. We further enforce pose constraints using an independently trained network that learns a prior distribution over 3D poses. We evaluate our approach on several benchmark datasets and compare against state-of-the-art approaches for 3D human pose estimation, achieving comparable performance. Additionally, we show that our approach significantly outperforms other methods in cases where 3D ground truth data is unavailable, and that our network exhibits good generalization properties.
CzEngVallex: a Bilingual Czech-English Valency Lexicon

This paper introduces a new bilingual Czech-English verbal valency lexicon (called CzEngVallex) representing a relatively large empirical database. It includes 20,835 aligned valency frame pairs (i.e., verb senses which are translations of each other) and their aligned arguments. This new lexicon uses data from the Prague Czech-English Dependency Treebank and also takes advantage of the existing valency lexicons for both languages: the PDT-Vallex for Czech and the EngVallex for English. The CzEngVallex is available for browsing as well as for download in the LINDAT/CLARIN repository. The CzEngVallex is meant to be used not only by traditional linguists, lexicographers, translators but also by computational linguists both for the purposes of enriching theoretical linguistic accounts of verbal valency from a cross-linguistic perspective and for an innovative use in various NLP tasks.