nils-herrmann/clean_scirepeval huggingface dataset:
Chinese Text in the Wild

We introduce Chinese Text in the Wild, a very large dataset of Chinese text in street view images. While optical character recognition (OCR) in document images is well studied and many commercial tools are available, detection and recognition of text in natural images is still a challenging problem, especially for more complicated character sets such as Chinese text. Lack of training data has always been a problem, especially for deep learning methods which require massive training data. In this paper we provide details of a newly created dataset of Chinese text with about 1 million Chinese characters annotated by experts in over 30 thousand street view images. This is a challenging dataset with good diversity. It contains planar text, raised text, text in cities, text in rural areas, text under poor illumination, distant text, partially occluded text, etc. For each character in the dataset, the annotation includes its underlying character, its bounding box, and 6 attributes. The attributes indicate whether it has complex background, whether it is raised, whether it is handwritten or printed, etc. The large size and diversity of this dataset make it suitable for training robust neural networks for various tasks, particularly detection and recognition. We give baseline results using several state-ofthe-art networks, including AlexNet, OverFeat, Google Inception and ResNet for character recognition, and YOLOv2 for character detection in images. Overall Google Inception has the best performance on recognition with 80.5% top-1 accuracy, while YOLOv2 achieves an mAP of 71.0% on detection. Dataset, source code and trained models will all be publicly available on the website1.  High intra-class variance versus low inter-class variance. Each row shows instances of a Chinese character. The first character differs from the second character by a single stroke, and the second character differs from the third character by another stroke. While the three characters are very similar in shape, the instances of the same character have very different appearance, due to color, font, occlusion, and background differences, etc. The most right column shows the corresponding Chinese character.
A framework for detection and measurement of phishing attacks

Phishing is form of identity theft that combines social engineering techniques and sophisticated attack vectors to harvest financial information from unsuspecting consumers. Often a phisher tries to lure her victim into clicking a URL pointing to a rogue page. In this paper, we focus on studying the structure of URLs employed in various phishing attacks. We find that it is often possible to tell whether or not a URL belongs to a phishing attack without requiring any knowledge of the corresponding page data. We describe several features that can be used to distinguish a phishing URL from a benign one. These features are used to model a logistic regression filter that is efficient and has a high accuracy. We use this filter to perform thorough measurements on several million URLs and quantify the prevalence of phishing on the Internet today
Incremental Dual-memory LSTM in Land Cover Prediction

Land cover prediction is essential for monitoring global environmental change. Unfortunately, traditional classification models are plagued by temporal variation and emergence of novel/unseen land cover classes in the prediction process. In this paper, we propose an LSTM-based spatio-temporal learning framework with a dual-memory structure. The dual-memory structure captures both long-term and short-term temporal variation patterns, and is updated incrementally to adapt the model to the ever-changing environment. Moreover, we integrate zero-shot learning to identify unseen classes even without labelled samples. Experiments on both synthetic and real-world datasets demonstrate the superiority of the proposed framework over multiple baselines in land cover prediction.
Towards a Linked-Data based Visualization Wizard

Datasets published in the LOD cloud are recommended to follow some best practice in order to be 4-5 stars Linked Data compliant. They can often be consumed and accessed by different means such as API access, bulk download or as linked data fragments, but most of the time, a SPARQL endpoint is also provided. While the LOD cloud keeps growing, having a quick glimpse of those datasets is getting harder and there is a need to develop new methods enabling to detect automatically what an arbitrary dataset is about and to recommend visualizations for data samples. We consider that “a visualization is worth a million triples”, and in this paper, we propose a novel approach that mines the content of datasets and automatically generates visualizations. Our approach is directly based on the usage of SPARQL queries that will detect the important categories of a dataset and that will specifically consider the properties used by the objects which have been interlinked via owl:sameAs links. We then propose to associate type of visualization for those categories. We have implemented this approach into a so-called Linked Data Vizualization Wizard (LDVizWiz).
Statistical Analysis of a Telephone Call Center : A Queueing-Science Perspective

Statistical Analysis of a Telephone Call Center Lawrence Brown, Noah Gans, Avishai Mandelbaum, Anat Sakov, Haipeng Shen, Sergey Zeltyn and Linda Zhao Lawrence Brown is Professor, Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104 . Noah Gans is Associate Professor, Department of Operations and Information Management, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104 . Avishai Mandelbaum is Professor, Faculty of Industrial Engineering and Management, Technion, Haifa, Israel . Anat Sakov is Postdoctoral Fellow, Tel-Aviv University, Tel-Aviv, Israel . Haipeng Shen is Assistant Professor, Department of Statistics, University of North Carolina, Durham, NC 27599 . Sergey Zeltyn is Ph.D. Candidate, Faculty of Industrial Engineering and Management, Technion, Haifa, Israel . Linda Zhao is Associate Professor, Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104 . This work was supported by National Science Foundation DMS-99-71751 and DMS-99-71848, the Sloane Foundation, Israeli Science Foundation grants 388/99 and 126/02, the Wharton Financial Institutions Center, and Technion funds for the promotion of research and sponsored research. Version of record first published: 31 Dec 2011.
Objects as Attributes for Scene Classification

Robust low-level image features have proven to be effective representations for a variety of high-level visual recognition tasks, such as object recognition and scene classification. But as the visual recognition tasks become more challenging, the semantic gap between low-level feature representation and the meaning of the scenes increases. In this paper, we propose to use objects as attributes of scenes for scene classification. We represent images by collecting their responses to a large number of object detectors, or “object filters”. Such representation carries high-level semantic information rather than low-level image feature information, making it more suitable for high-level visual recognition tasks. Using very simple, off-the-shelf classifiers such as SVM, we show that this object-level image representation can be used effectively for high-level visual tasks such as scene classification. Our results are superior to reported state-of-the-art performance on a number of standard datasets.
Plasmodium falciparum: multifaceted resistance to artemisinins

Plasmodium falciparum resistance to artemisinins, the most potent and fastest acting anti-malarials, threatens malaria elimination strategies. Artemisinin resistance is due to mutation of the PfK13 propeller domain and involves an unconventional mechanism based on a quiescence state leading to parasite recrudescence as soon as drug pressure is removed. The enhanced P. falciparum quiescence capacity of artemisinin-resistant parasites results from an increased ability to manage oxidative damage and an altered cell cycle gene regulation within a complex network involving the unfolded protein response, the PI3K/PI3P/AKT pathway, the PfPK4/eIF2α cascade and yet unidentified transcription factor(s), with minimal energetic requirements and fatty acid metabolism maintained in the mitochondrion and apicoplast. The detailed study of these mechanisms offers a way forward for identifying future intervention targets to fend off established artemisinin resistance.
INRIASAC: Simple Hypernym Extraction Methods

For information retrieval, it is useful to classify documents using a hierarchy of terms from a domain. One problem is that, for many domains, hierarchies of terms are not available. The task 17 of SemEval 2015 addresses the problem of structuring a set of terms from a given domain into a taxonomy without manual intervention. Here we present some simple taxonomy structuring techniques, such as term overlap and document and sentence cooccurrence in large quantities of text (English Wikipedia) to produce hypernym pairs for the eight domain lists supplied by the task organizers. Our submission ranked first in this 2015 benchmark, which suggests that overly complicated methods might need to be adapted to individual domains. We describe our generic techniques and present an initial evaluation of results.
Parameter Sharing Deep Deterministic Policy Gradient for Cooperative Multi-agent Reinforcement Learning

Deep reinforcement learning for multi-agent cooperation and competition has been a hot topic recently. This paper focuses on cooperative multi-agent problem based on actor-critic methods under local observations settings. Multi agent deep deterministic policy gradient obtained state of art results for some multi-agent games, whereas, it cannot scale well with growing amount of agents. In order to boost scalability, we propose a parameter sharing deterministic policy gradient method with three variants based on neural networks, including actor-critic sharing, actor sharing and actor sharing with partially shared critic. Benchmarks from rllab show that the proposed method has advantages in learning speed and memory efficiency, well scales with growing amount of agents, and moreover, it can make full use of reward sharing and exchangeability if possible.
On the control of the KNTU CDRPM: A cable driven redundant parallel manipulator

This paper is devoted to the control of a cable driven redundant parallel manipulator, which is a challenging problem due the optimal resolution of its inherent redundancy. Additionally to complicated forward kinematics, having a wide workspace makes it difficult to directly measure the pose of the end-effector. The goal of the controller is trajectory tracking in a large and singular free workspace, and to guarantee that the cables are always under tension. A control topology is proposed in this paper which is capable to fulfill the stringent positioning requirements for these type of manipulators. Closed-loop performance of various control topologies are compared by simulation of the closed-loop dynamics of the KNTU CDRPM, while the equations of parallel manipulator dynamics are implicit in structure and only special integration routines can be used for their integration. It is shown that the proposed joint space controller is capable to satisfy the required tracking performance, despite the inherent limitation of task space pose measurement.
Real-time captioning by groups of non-experts

Real-time captioning provides deaf and hard of hearing people immediate access to spoken language and enables participation in dialogue with others. Low latency is critical because it allows speech to be paired with relevant visual cues. Currently, the only reliable source of real-time captions are expensive stenographers who must be recruited in advance and who are trained to use specialized keyboards. Automatic speech recognition (ASR) is less expensive and available on-demand, but its low accuracy, high noise sensitivity, and need for training beforehand render it unusable in real-world situations. In this paper, we introduce a new approach in which groups of non-expert captionists (people who can hear and type) collectively caption speech in real-time on-demand. We present Legion:Scribe, an end-to-end system that allows deaf people to request captions at any time. We introduce an algorithm for merging partial captions into a single output stream in real-time, and a captioning interface designed to encourage coverage of the entire audio stream. Evaluation with 20 local participants and 18 crowd workers shows that non-experts can provide an effective solution for captioning, accurately covering an average of 93.2% of an audio stream with only 10 workers and an average per-word latency of 2.9 seconds. More generally, our model in which multiple workers contribute partial inputs that are automatically merged in real-time may be extended to allow dynamic groups to surpass constituent individuals (even experts) on a variety of human performance tasks.
A risk management framework for cloud computing

Although cloud computing has the advantages of cost-saving, efficiency and scalability, it also brings about many security issues. Because almost all software, hardware, and application data are deployed and stored in the cloud platforms, there is often the distrust between users and cloud suppliers. To resolve the problem, this paper proposes a risk management framework on the basis of the previous work. The framework consists of five components: user requirement self-assessment, cloud service providers desktop assessment, risk assessment, third-party agencies review, and continuous monitoring. By means of the framework, the cloud service suppliers can better understand the user's requirements, and the trust between the users and the suppliers is more easily acquired.
Database-friendly random projections: Johnson-Lindenstrauss with binary coins

A classic result of Johnson and Lindenstrauss asserts that any set of n points in d-dimensional Euclidean space can be embedded into k-dimensional Euclidean space—where k is logarithmic in n and independent of d—so that all pairwise distances are maintained within an arbitrarily small factor. All known constructions of such embeddings involve projecting the n points onto a spherically random k-dimensional hyperplane through the origin. We give two constructions of such embeddings with the property that all elements of the projection matrix belong in f 1;  Such constructions are particularly well suited for database environments, as the computation of the embedding reduces to evaluating a single aggregate over k random partitions of the attributes. r 2003 Elsevier Science (USA). All rights reserved.
Discriminative Mixture-of-Templates for Viewpoint Classification

Object viewpoint classification aims at predicting an approximate 3D pose of objects in a scene and is receiving increasing attention. State-of-the-art approaches to viewpoint classification use generative models to capture relations between object parts. In this work we propose to use a mixture of holistic templates (e.g. HOG) and discriminative learning for joint viewpoint classification and category detection. Inspired by the work of Felzenszwalb et al 2009, we discriminatively train multiple components simultaneously for each object category. A large number of components are learned in the mixture and they are associated with canonical viewpoints of the object through different levels of supervision, being fully supervised, semi-supervised, or unsupervised. We show that discriminative learning is capable of producing mixture components that directly provide robust viewpoint classification, significantly outperforming the state of the art: we improve the viewpoint accuracy on the Savarese et al 3D Object database from 57% to 74%, and that on the VOC 2006 car database from 73% to 86%. In addition, the mixture-of-templates approach to object viewpoint/pose has a natural extension to the continuous case by discriminatively learning a linear appearance model locally at each discrete view. We evaluate continuous viewpoint estimation on a dataset of everyday objects collected using IMUs for groundtruth annotation: our mixture model shows great promise comparing to a number of baselines including discrete nearest neighbor and linear regression.
Visual Query Language: Finding patterns in and relationships among time series data

Many scientific datasets archive a large number of variables over time. These timeseries data streams typically track many variables over relatively long periods of time, and therefore are often both wide and deep. In this paper, we describe the Visual Query Language (VQL) , a technology for locating time series patterns in historical or real time data. The user interactively specifies a search pattern, VQL finds similar shapes, and returns a ranked list of matches. VQL supports both univariate and multivariate queries, and allows the user to interactively specify the the quality of the match, including temporal warping, amplitude warping, and temporal constraints between features.
HoloMed: A Low-Cost Gesture-Based Holographic

During medicine studies, visualization of certain elements is common and indispensable in order to get more information about the way they work. Currently, we resort to the use of photographs -which are insufficient due to being staticor tests in patients, which can be invasive or even risky. Therefore, a low-cost approach is proposed by using a 3D visualization. This paper presents a holographic system built with low-cost materials for teaching obstetrics, where student interaction is performed by using voice and gestures. Our solution, which we called HoloMed, is focused on the projection of a euthocic normal delivery under a web-based infrastructure which also employs a Kinect. HoloMed is divided in three (3) essential modules: a gesture analyzer, a data server, and a holographic projection architecture, which can be executed in several interconnected computers using different network protocols. Tests used for determining the user’s position, illumination factors, and response times, demonstrate HoloMed’s effectiveness as a low-cost system for teaching, using a natural user interface and 3D images.
Building Classifiers with Independency Constraints

In this paper we study the problem of classifier learning where the input data contains unjustified dependencies between some data attributes and the class label. Such cases arise for example when the training data is collected from different sources with different labeling criteria or when the data is generated by a biased decision process. When a classifier is trained directly on such data, these undesirable dependencies will carry over to the classifier’s predictions. In order to tackle this problem, we study the classification with independency constraints problem: find an accurate model for which the predictions are independent from a given binary attribute. We propose two solutions for this problem and present an empirical validation.
XtratuM: a Hypervisor for Safety Critical Embedded Systems

XtratuM is an hypervisor designed to meet safety critical requirements. Initially designed for x86 architectures (version 2.0), it has been strongly redesigned for SPARC v8 arquitecture and specially for the to the LEON2 processor. Current version 2.2, includes all the functionalities required to build safety critical systems based on ARINC 653, AUTOSTAR and other standards. Although XtratuMdoes not provides a compliant API with these standards, partitions can offer easily the appropriated API to the applications. XtratuM is being used by the aerospace sector to build software building blocks of future generic on board software dedicated to payloads management units in aerospace.  provides ARINC 653 scheduling policy, partition management, inter-partition communications, health monitoring, logbooks, traces, and other services to easily been adapted to the ARINC standard. The configuration of the system is specified in a configuration file (XML format) and it is compiled to achieve a static configuration of the final container (XtratuM and the partition’s code) to be deployed to the hardware board. As far as we know, XtratuM is the first hypervisor for the SPARC v8 arquitecture. In this paper, the main design aspects are discussed and the internal architecture described. An evaluation of the most significant metrics is also provided. This evaluation permits to affirm that the overhead of a hypervisor is lower than 3% if the slot duration is higher than 1 millisecond.
Random Walks for Interactive Alpha-matting

Interactive, efficient, methods of foreground extraction a nd alpha-matting are of increasing practical importance for digital image editing. Although several new approaches to this problem have recently been developed, many challenges remain. We propose a new technique based on random walks that has the following advantages: First, by leveraging a recent technique from manifold learning theory, we effectively use RGB values to set boundaries for the random walker, even in fuzzy or low-contrast images. Second, the algorithm is straightforward to implement, requires specification of only a single free parameter (set the same for all images), and performs the segmentation and alpha-matting in a single step. Third, the user may locally fine tune the results by interactively manipulating the fore ground/background maps. Finally, the algorithm has an inherit parallelism that leads to a particularly efficient implementation via the graphics processing unit (GPU). Our method processes a 1024 × 1024 image at the interactive speed of 0.5 seconds and, most importantly, produces highquality results. We show that our algorithm can generate good segmentation and matting results at an interactive rat e with minimal user interaction.
Discrete fixed-resolution representations in visual working memory

Limits on the storage capacity of working memory significantly affect cognitive abilities in a wide range of domains, but the nature of these capacity limits has been elusive. Some researchers have proposed that working memory stores a limited set of discrete, fixed-resolution representations, whereas others have proposed that working memory consists of a pool of resources that can be allocated flexibly to provide either a small number of high-resolution representations or a large number of low-resolution representations. Here we resolve this controversy by providing independent measures of capacity and resolution. We show that, when presented with more than a few simple objects, human observers store a high-resolution representation of a subset of the objects and retain no information about the others. Memory resolution varied over a narrow range that cannot be explained in terms of a general resource pool but can be well explained by a small set of discrete, fixed-resolution representations.