nils-herrmann/clean_scirepeval huggingface dataset:
Smart cities of the future

Here we sketch the rudiments of what constitutes a smart city which we define as a city in which ICT is merged with traditional infrastructures, coordinated and integrated using new digital technologies. We first sketch our vision defining seven goals which concern: developing a new understanding of urban problems; effective and feasible ways to coordinate urban technologies; models and methods for using urban data across spatial and temporal scales; developing new technologies for communication and dissemination; developing new forms of urban governance and organisation; defining critical problems relating to cities, transport, and energy; and identifying risk, uncertainty, and hazards in the smart city. To this, we add six research challenges: to relate the infrastructure of smart cities to their operational functioning and planning through management, control and optimisation; to explore the notion of the city as a laboratory for innovation; to provide portfolios of urban simulation which inform future designs; to develop technologies that ensure equity, fairness and realise a better quality of city life; to develop technologies that ensure informed participation and create shared knowledge for democratic city governance; and to ensure greater and more effective mobility and access to opportunities for a e-mail: m.batty@ucl.ac.uk 482 The European Physical Journal Special Topics urban populations. We begin by defining the state of the art, explaining the science of smart cities. We define six scenarios based on new cities badging themselves as smart, older cities regenerating themselves as smart, the development of science parks, tech cities, and technopoles focused on high technologies, the development of urban services using contemporary ICT, the use of ICT to develop new urban intelligence functions, and the development of online and mobile forms of participation. Seven project areas are then proposed: Integrated Databases for the Smart City, Sensing, Networking and the Impact of New Social Media, Modelling Network Performance, Mobility and Travel Behaviour, Modelling Urban Land Use, Transport and Economic Interactions, Modelling Urban Transactional Activities in Labour and Housing Markets, Decision Support as Urban Intelligence, Participatory Governance and Planning Structures for the Smart City. Finally we anticipate the paradigm shifts that will occur in this research and define a series of key demonstrators which we believe are important to progressing a science
Garbage-first garbage collection

<i>Garbage-First</i> is a server-style garbage collector, targeted for multi-processors with large memories, that meets a soft real-time goal with high probability, while achieving high throughput. Whole-heap operations, such as global marking, are performed concurrently with mutation, to prevent interruptions proportional to heap or live-data size. Concurrent marking both provides collection "completeness" and identifies regions ripe for reclamation via compacting evacuation. This evacuation is performed in parallel on multiprocessors, to increase throughput.
Practicing Differential Privacy in Health Care: A Review

Differential privacy has gained a lot of attention in recent years as a general model for the protection of personal information when used and dis‐ closed for secondary purposes. It has also been proposed as an appropriate model for protecting health data. In this paper we review the current literature on differential privacy and highlight important general limitations to the model and the proposed mechanisms. We then examine some practical challenges to the application of differential privacy to health data. The most severe limitation is the theoretical nature of the privacy parameter . It has implications on our ability to quantify the level of anonymization that would be guaranteed to pa‐ tients, as well as assessing responsibilities when a privacy breach occurs. The review concludes by identifying the areas that researchers and practitioners need to address to increase the adoption of differential privacy for health data.  We consider private data analysis in the setting of a trusted data custodian that has a database consisting of rows of data (records). Each row represents infor‐ mation about an individual. The custodian needs to publish an anonymized ver‐ sion of the data, a version that is useful to data analysts and that protects the privacy of the individuals in the database. The problem is known as privacy‐ preserving data publishing. Various approaches/models have been proposed in different research commu‐ nities over the past few years . These models depend on the background knowledge of adversaries; as these adversaries can infer sensitive information from the dataset by exploiting background information. Because of that de‐
Human-level CMR image analysis with deep fully convolutional networks

Cardiovascular magnetic resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accurate quantification of the cardiac chamber volume, ejection fraction and myocardial mass, providing a wealth of information for sensitive and specific diagnosis and monitoring of CVDs. However, for years, clinicians have been relying on manual approaches for CMR image analysis, which is time consuming and prone to subjective errors. It is a major clinical challenge to automatically derive quantitative and clinically relevant information from CMR images. Deep neural networks have shown a great potential in image pattern recognition and segmentation for a variety of tasks. Here we demonstrate an automated analysis method for CMR images, which is based on a fully convolutional network (FCN). The network is trained and evaluated on a dataset of unprecedented size, consisting of 4,875 subjects with 93,500 pixelwise annotated images, which is by far the largest annotated CMR dataset. By combining FCN with a large-scale annotated dataset, we show for the first time that an automated method achieves a performance on par with human experts in analysing CMR images and deriving clinical measures. We anticipate this to be a starting point for automated and comprehensive CMR analysis with human-level performance, facilitated by machine learning. It is an important advance on the pathway towards computer-assisted CVD assessment. An estimated 17.7 million people died from cardiovascular diseases (CVDs) in 2015, representing 31% of all global deaths . More people die annually from CVDs than any other cause. Technological advances in medical imaging have led to a number of options for non-invasive investigation of CVDs, including echocardiography, computed tomography (CT), cardiovascular magnetic resonance (CMR) etc., each having its own advantages and disadvantages. Due to its good image quality, excellent soft tissue contrast and absence of ionising radiation, CMR has established itself as the gold standard for assessing cardiac chamber volume and mass for a wide range of CVDs [2–4]. To derive quantitative measures such as volume and mass, clinicians have been relying on manual approaches to trace the cardiac chamber contours. It typically takes a trained
Real-Time Video-Based Modeling and Rendering of 3D Scenes

virtual reality, developing techniques for synthesizing arbitrary views has become an important technical issue. Given an object’s structural model (such as a polygon or volume model), it’s relatively easy to synthesize arbitrary views. Generating a structural model of an object, however, isn’t necessarily easy. For this reason, research has been progressing on a technique called image-based modeling and rendering (IBMR) that avoids this problem. To date, researchers have performed studies on various IBMR techniques. (See the “Related Work” sidebar for more specific information.) Our work targets 3D scenes in motion. In this article, we propose a method for view-dependent layered representation of 3D dynamic scenes. Using densely arranged cameras, we’ve developed a system that can perform processing in real time from image pickup to interactive display, using video sequences instead of static images, at 10 frames per second (frames/sec). In our system, images on layers are view dependent, and we update both the shape and image of each layer in real time. This lets us use the dynamic layers as the coarse structure of the dynamic 3D scenes, which improves the quality of the synthesized images. In this sense, our prototype system may be one of the first full real-time IBMR systems. Our experimental results show that this method is useful for interactive 3D rendering of real scenes.
Lexical Categories: Verbs, Nouns and Adjectives

published by the press syndicate of the university of cambridge A catalogue record for this book is available from the British Library Library of Congress Cataloguing in Publication data Baker, Mark C. Lexical categories: verbs, nouns, and adjectives / Mark C. Baker. p. cm. – (Cambridge studies in linguistics) Includes bibliographical references and index. isbn 0 521 80638 0 (hardback) – isbn 0 521 00110 2 (paperback)
Arcing the edge

Recent work has shown that adaptively reweighting the training set, growing a classifier using the new weights, and combining the classifiers constructed to date can significantly decrease generalization error. Procedures of this type were called arcing by Breiman[1996]. The first successful arcing procedure was introduced by Freund and Schapire[1995,1996] and called Adaboost. In an effort to explain why Adaboost works, Schapire et.al. [1997] derived a bound on the generalization error of a convex combination of classifiers in terms of the margin. We introduce a function called the edge, which differs from the margin only if there are more than two classes. A framework for understanding arcing algorithms is defined. In this framework, we see that the arcing algorithms currently in the literature are optimization algorithms which minimize some function of the edge. A relation is derived between the optimal reduction in the maximum value of the edge and the PAC concept of weak learner. Two algorithms are described which achieve the optimal reduction. Tests on both synthetic and real data cast doubt on the Schapire et.al. explanation.
Survey of real-time processing systems for big data

In recent years, real-time processing and analytics systems for big data--in the context of Business Intelligence (BI)--have received a growing attention. The traditional BI platforms that perform regular updates on daily, weekly or monthly basis are no longer adequate to satisfy the fast-changing business environments. However, due to the nature of big data, it has become a challenge to achieve the real-time capability using the traditional technologies. The recent distributed computing technology, MapReduce, provides off-the-shelf high scalability that can significantly shorten the processing time for big data; Its open-source implementation such as Hadoop has become the de-facto standard for processing big data, however, Hadoop has the limitation of supporting real-time updates. The improvements in Hadoop for the real-time capability, and the other alternative real-time frameworks have been emerging in recent years. This paper presents a survey of the open source technologies that support big data processing in a real-time/near real-time fashion, including their system architectures and platforms.
Title Generation for User Generated Videos

A great video title describes the most salient event compactly and captures the viewer’s attention. In contrast, video captioning tends to generate sentences that describe the video as a whole. Although generating a video title automatically is a very useful task, it is much less addressed than video captioning. We address video title generation for the first time by proposing two methods that extend state-of-the-art video captioners to this new task. First, we make video captioners highlight sensitive by priming them with a highlight detector. Our framework allows for jointly training a model for title generation and video highlight localization. Second, we induce high sentence diversity in video captioners, so that the generated titles are also diverse and catchy. This means that a large number of sentences might be required to learn the sentence structure of titles. Hence, we propose a novel sentence augmentation method to train a captioner with additional sentence-only examples that come without corresponding videos. We collected a large-scale Video Titles in the Wild (VTW) dataset of 18100 automatically crawled user-generated videos and titles. On VTW, our methods consistently improve title prediction accuracy, and achieve the best performance in both automatic and human evaluation. Finally, our sentence augmentation method also outperforms the baselines on the M-VAD dataset.
Characterization and Theoretical Comparison of Branch-and-Bound Algorithms for Permutation Problems

Branch-and-bound implicit enumeration algorithms for permutation problems (discrete optimization problems where the set of feasible solutions is the permutation group <italic>S<subscrpt>n</subscrpt></italic>) are characterized in terms of a sextuple (<italic>B<subscrpt>p</subscrpt> S,E,D,L,U</italic>), where (1) <italic>B<subscrpt>p</subscrpt></italic> is the branching rule for permutation problems, (2) <italic>S</italic> is the next node selection rule, (3) <italic>E</italic> is the set of node elimination rules, (4) <italic>D</italic> is the node dominance function, (5) <italic>L</italic> is the node lower-bound cost function, and (6) <italic>U</italic> is an upper-bound solution cost. A general algorithm based on this characterization is presented and the dependence of the computational requirements on the choice of algorithm parameters, <italic>S, E, D, L,</italic> and <italic>U</italic> is investigated theoretically. The results verify some intuitive notions but disprove others.
Learning Long-Term Dependencies with

Recurrent neural networks can be used to map input sequences to output sequences , such as for recognition, production or prediction problems. However, practical diiculties have been reported in training recurrent neural networks to perform tasks in which the temporal contingencies present in the input/output sequences span long intervals. We show why gradient based learning algorithms face an increasingly diicult problem as the duration of the dependencies to be captured increases. These results expose a trade-oo between eecient learning by gradient descent and latching on information for long periods. Based on an understanding of this problem, alternatives to standard gradient descent are considered.
Point Set Registration: Coherent Point Drift

Point set registration is a key component in many computer vision tasks. The goal of point set registration is to assign correspondences between two sets of points and to recover the transformation that maps one point set to the other. Multiple factors, including an unknown nonrigid spatial transformation, large dimensionality of point set, noise, and outliers, make the point set registration a challenging problem. We introduce a probabilistic method, called the Coherent Point Drift (CPD) algorithm, for both rigid and nonrigid point set registration. We consider the alignment of two point sets as a probability density estimation problem. We fit the Gaussian mixture model (GMM) centroids (representing the first point set) to the data (the second point set) by maximizing the likelihood. We force the GMM centroids to move coherently as a group to preserve the topological structure of the point sets. In the rigid case, we impose the coherence constraint by reparameterization of GMM centroid locations with rigid parameters and derive a closed form solution of the maximization step of the EM algorithm in arbitrary dimensions. In the nonrigid case, we impose the coherence constraint by regularizing the displacement field and using the variational calculus to derive the optimal transformation. We also introduce a fast algorithm that reduces the method computation complexity to linear. We test the CPD algorithm for both rigid and nonrigid transformations in the presence of noise, outliers, and missing points, where CPD shows accurate results and outperforms current state-of-the-art methods.
Feature Specific Sentiment Analysis for Product Reviews

In this paper, we present a novel approach to ide ntify feature specific expressions of opinion in product reviews with different features and mixed emotions . The objective is realized by identifying a set of potential features in the review and extract ing opinion expressions about those features by exploiting their associatio ns. Capitalizing on the view that more closely associated words come togeth er to express an opinion about a certain feature, dependency parsing i used to identify relations between the opinion expressions. The syst em learns the set of significant relations to be used by dependency parsing and a threshold parameter which allows us to merge closely associated opinio n expressions. The data requirement is minimal as thi is a one time learning of the domain independent parameters . The associations are represented in the form of a graph which is partiti oned to finally retrieve the opinion expression describing the user specified feature. We show that the system achieves a high accuracy across all domains and performs at par with state-of-the-art systems despi t its data limitations.
An evaluation of a multiple interface design solution for bloated software

This study examines a novel interface design for heavily-featured productivity software. The design includes two interfaces between which the user can easily toggle: (1) an interface personalized by the user containing desired features only, and (2) the default interface with all the standard features. This design was prototyped as a front-end to a commercial word processor and evaluated in a comprehensive field study. The study tested the effects of different interface designs on users' satisfaction and their perceived ability to navigate, control, and learn the software. There were two conditions: a commercial word processor with adaptive menus and our two-interface prototype with adaptable menus for the same word processor. Results showed that participants were better able to navigate through the menus and toolbars and were better able to learn with our prototype. There were also significant differences in satisfaction and control with our design
Recognition and classification of animals based on texture features through parallel computing

In this work, we proposed an efficient system for animal recognition and classification based on texture features which are obtained from the local appearance and texture of animals. The classification of animals are done by training and subsequently testing two different machine learning techniques, namely k-Nearest Neighbors (k-NN) and Support Vector Machines (SVM). Computer-assisted technique when applied through parallel computing makes the work efficient by reducing the time taken for the task of animal recognition and classification. Here we propose a parallel algorithm for the same. Experimentation is done for about 30 different classes of animals containing more than 3000 images. Among the different classifiers, k-Nearest Neighbor classifiers have achieved a better accuracy.
Defect detection in textile fabric images using wavelet transforms and independent component analysis

In this paper, a new method based on the use of wavelet transformation prior to independent component analysis for solving the problem of defect detection in textile fabric images is presented. Different subbands of the wavelet packet tree scheme of the defect-free subwindows are obtained and independent components of these subbands are calculated as basis vectors. The true feature vectors corresponding to these basis vectors are computed. The test subwindow is labeled as defective, or not according to the Euclidean distance between the true feature vector representing the non-defective regions and the feature vector of the subwindow under test. The advantage of adding wavelet analysis prior to the independent component analysis is presented.
Agentic and communal values: their scope and measurement.

Agency is the meta-concept associated with self-advancement in social hierarchies; communion is the partner concept associated with maintenance of positive relationships. Despite the wealth of data documenting the conceptual utility of agency and communion (A & C) as superordinate metaconcepts, no direct measures of global A & C value dimensions are currently available. The first part of this article presents structural analyses of data from 4 diverse data sets (3 archival and 1 new): Each included a broad inventory of values or life goals. All 4 data sets revealed higher order A & C dimensions that were either apparent or implicit. The second part details the development of the ACV, a 24-item questionnaire measuring global A and C values, and documents its psychometric properties. Four studies support their joint construct validity by positioning the value measures within a nomological network of interpersonal traits, self-favorability biases, ideology dimensions, gender, socio-sexuality, and religious attitudes. Potential applications of the new instrument are discussed.
A full-3D voxel-based dynamic obstacle detection for urban scenario using stereo vision

Autonomous Ground Vehicles designed for dynamic environments require a reliable perception of the real world, in terms of obstacle presence, position and speed. In this paper we present a flexible technique to build, in real time, a dense voxel-based map from a 3D point cloud, able to: (1) discriminate between stationary and moving obstacles; (2) provide an approximation of the detected obstacle's absolute speed using the information of the vehicle's egomotion computed through a visual odometry approach. The point cloud is first sampled into a full 3D map based on voxels to preserve the tridimensional information; egomotion information allows computational efficiency in voxels creation; then voxels are processed using a flood fill approach to segment them into a clusters structure; finally, with the egomotion information, the obtained clusters are labeled as stationary or moving obstacles, and an estimation of their speed is provided. The algorithm runs in real time; it has been tested on one of VisLab's AGVs using a modified SGM-based stereo system as 3D data source.
Perceiving personal discrimination: the role of group status and legitimizing ideology.

It was hypothesized that relative group status and endorsement of ideologies that legitimize group status differences moderate attributions to discrimination in intergroup encounters. According to the status-legitimacy hypothesis, the more members of low-status groups endorse the ideology of individual mobility, the less likely they are to attribute negative outcomes from higher status group members to discrimination. In contrast, the more members of high-status groups endorse individual mobility, the more likely they are to attribute negative outcomes from lower status group members to discrimination. Results from 3 studies using 2 different methodologies provide support for this hypothesis among members of different high-status (European Americans and men) and low-status (African Americans, Latino Americans, and women) groups.