nils-herrmann/clean_scirepeval huggingface dataset:
A framework of combining numeric string recognizers

Although promising results on the combination of character recognizers have been reported recently, the combination strategies can not be readily applied to the recognition of character strings due to m-n correspondence problems caused by segmentation errors. In this paper, we propose a new paradigm of combining multiple string recognizers and contribute a generic framework for off-line combination. We designed and implemented a graph based off-line combination system, StrCombo, which has achieved a substantial improvement over any one of the individual recognizers in a real-life application. This open combination system provides the possibility of further improving the performance of string recognizers when new recognizers and combination rules are available.
Automated intrusion attack with permanent control: Analysis and countermeasures

We investigate an intrusion attack called automated intrusion which persists the control of victim system for long time. This attack combines intrusion techniques, encodings schemes and social engineering methods to enhance hacking capability. Experiments in local and wide-area networks show how this type of attack can penetrate a computer system and take control over multiple OSes. We then propose few countermeasures in order to protect networks and keep users informed of the attacks.
Recognition of unconstrained legal amounts handwritten on Chinese bank checks

This work presents a novel research investigation on legal amount recognition of unconstrained cursive handwritten Chinese character in the environment of A2iA CheckReader - a commercial bank check recognition system. The following problems and their solutions are described: character set of Chinese legal amounts, preprocessing (slant detection and correction), segmentation, feature extraction, grammar, automatic annotation of Chinese characters before and during training, and neural network/hidden Markov model training and recognition. The system is trained with 47.8 thousand real bank checks, and validated with 12 thousand real bank checks. The recognition rate at the character level is 93.5%, and the recognition rate at the legal amount level is 60%. This is the first successful commercial product in this domain.
On-line recognition of handwritten Arabic characters using a Kohonen neural network

Neural networks have been applied to various pattern classification and recognition problems for their learning ability, discrimination power and generalization ability The neural network most referenced in the pattern recognition literature are the multi-layer perceptron, the Kohonen associative memory and the Capenter-Grossberg ART network. The Kohonen memory runs an unsupervised clustering algorithm. It is easily trained and has attractive properties such as topological ordering and good generalization. In this study an on-line system for the recognition of handwriting Arabic characters using a Kohonen network is investigated. The input of the neural network is a feature vector of elliptic Fourier coefficients extracted from the handwritten dynamic representation. Experimental results show that the network successfully recognizes both clearly and roughly written characters with good performance.
Feature Design for Offline Arabic Handwriting Recognition: Handcrafted vs Automated?

In handwriting recognition, design of relevant feature is a very important but daunting task. On one hand, handcraft design of features is difficult, depending on expert knowledge and on heuristics. On the other hand, biologically inspired neural networks are able to learn automatically features from the input image, but requires a good underlying model. The goal of this paper is to evaluate the performance of automatically learned features compared to handcrafted features, as they provide a promising alternative to the difficult task of features handcrafting. In this work, the recognition model is based on the long short-term memory (LSTM) and connectionist temporal classification (CTC) neural networks. This model has been shown to outperform the well-known HMM model for various handwriting tasks, thanks to its reliable probabilistic modeling. In its multidimensional form, called MDLSTM, this network is able to automatically learn features from the input image. For evaluation, we compare the MDLSTM learned features and four state-of-the-art handcrafted features. The IFN/ENIT database has been used as benchmark for Arabic word recognition, where the results are promising.
FSITM: A Feature Similarity Index For Tone-Mapped Images

In this work, based on the local phase information of images, an objective index, called the feature similarity index for tone-mapped images (FSITM), is proposed. To evaluate a tone mapping operator (TMO), the proposed index compares the locally weighted mean phase angle map of an original high dynamic range (HDR) to that of its associated tone-mapped image calculated using the output of the TMO method. In experiments on two standard databases, it is shown that the proposed FSITM method outperforms the state-of-the-art index, the tone mapped quality index (TMQI). In addition, a higher performance is obtained by combining the FSITM and TMQI indices.
Payless Monitoring Service for Tenants in Cloud with Traffic and Energy-Aware Function Deployment

Many applications are distributed in cloud as they consist of different modules and tiers. Cloud tenant must be able to monitor the deployed applications to ensure that it is operating correctly, meeting its SLAs, and fulfilling business requirements. Activating all type of monitoring functions for all tenant's applications at the same time is costly. The more required monitoring functions the more allocated cloud resources. Moreover, it incurs monetary cost for tenants. In this paper, the problem of virtual monitoring function (vMF) placement for service chains is modeled by maximizing both the network and computing resource utilization. Beside that, we propose a set of tenant policies, which are either monetary-driven or performance-driven, translated as constrains in optimal placement algorithm. Simulation results show that the proposed model can reduce the total incurred cost by up to 10% compared to the best non-optimal heuristic. Moreover, the proposed model can decrease the execution time about 84% compared to the basic solution.
Toward a Zero-Carbon Network: Converging Cloud Computing and Network Virtualization

Reducing greenhouse gas (GHG) emissions is one of the most challenging research topics in ICT because of people's overwhelming use of electronic devices. Current solutions focus mainly on efficient power consumption at the micro level; few consider large-scale energy-management strategies. The low-carbon, nationwide GreenStar Network in Canada uses network and server virtualization techniques to migrate data center services among network nodes according to renewable energy availability. The network deploys a "follow the sun, follow the wind" optimization policy as a virtual infrastructure-management technique.
Optimal placement of sequentially ordered virtual security appliances in the cloud

Traditional enterprise network security is based on the deployment of security appliances placed on some specific locations filtering, monitoring the traffic going through them. In this perspective, security appliances are chained in specific order to perform different security functions on the traffic. In the cloud, the same approach is often adopted using virtual security appliances to protect traffic for different virtual applications with the challenge of dealing with the flexible and elastic nature of the cloud. In this paper, we investigate the problem of placing virtual security appliances within the data center in order to minimize network latency and computing costs for security functions while maintaining the required sequential order of traversing virtual security appliances. We propose a new algorithm computing the best place to deploy these virtual security appliances in the data center. We further integrated our placement algorithm in an open source cloud framework, i.e. Openstack, in our test laboratory. The preliminary results show that we are placing the virtual security appliances in the required sequential order while improving the efficiency compared to the current default placement algorithm in Openstack.
On the use of a new compact support kernel in time frequency analysis

This article introduces a new time frequency distribution based on a kernel with compact support. The properties of the new distribution are emphasized. Through a parameter that controls the kernel width, the new representation allows a tradeoff between a good autoterm resolution and a high crossterm rejection. A signal representation example is provided and compared with respect to the Wigner distribution, the Choi-Williams (1989) distribution, the spectrograrn and the Born-Jordan distribution as a member of the reduced interference distribution class.
Ontology-Based Resource Description and Discovery Framework for Low Carbon Grid Networks

Using smart grids to build low carbon networks is one of the most challenging topics in ICT (Information and Communication Technologies) industry. One of the first worldwide initiatives is the GreenStar Network, completely powered by renewable energy sources such as solar, wind and hydroelectricity across Canada. Smart grid techniques are deployed to migrate data centers among network nodes according to energy source availabilities, thus CO2 emissions are reduced to minimal. Such flexibility requires a scalable resource management support, which is achieved by virtualization technique. It enables the sharing, aggregation, and dynamic configuration of a large variety of resources. A key challenge in developing such a virtualized management is an efficient resource description and discovery framework, due to a large number of elements and the diversity of architectures and protocols. In addition, dynamic characteristics and different resource description methods must be addressed. In this paper, we present an ontology-based resource description framework, developed particularly for ICT energy management purpose, where the focus is on energy-related semantic of resources and their properties. We propose then a scalable resource discovery method in large and dynamic collections of ICT resources, based on semantics similarity inside a federated index using a Bayesian belief network. The proposed framework allows users to identify the cleanest resource deployments in order to achieve a given task, taking into account the energy source availabilities. Experimental results are shown to compare the proposed framework with a traditional one in terms of GHG emission reductions.
A generative-discriminative hybrid for sequential data classification [image classification example]

Classification of sequential data using discriminative models such as support vector machines is very hard due to the variable length of this type of data. On the other hand, generative models such as HMMs have become the standard tool for representing sequential data due to their efficiency. This paper proposes a general generative-discriminative framework that uses HMMs to map the variable length sequential data into a fixed size P-dimensional vector (likelihood score) that can be easily classified using any discriminative model. The preliminary experiments of the framework on the MNIST database for handwritten digits have achieved a better recognition rate of 98.02% than that of standard HMMs (94.19%).
An Efficient Ground Truthing Tool for Binarization of Historical Manuscripts

For the purpose of facilitating benchmark contributions for binarization methods, a new fast ground truthing approach, called the PhaseGT, is proposed. This approach is used for building the 1st groundtruthed Persian Heritage Image Binarization Dataset (PHIBD 2012). The PhaseGT is a semiautomatic approach to ground truthing of images of any language, especially designed for historical document images. The main goal of the PhaseGT is to accelerate the ground truthing process and reduce the manual ground truthing effort. It uses the phase congruency features to preprocess the input image and to provide a more accurate initial binarization to the human expert who performs the manual part. This preprocessing is in turn based on a priori knowledge that is provided by human user. The PHIBD 2012 dataset contains 15 historical document images with their corresponding ground truth binary images. The historical images in the dataset suffer from various types of degradation. It has been also divided into two subsets of training and testing images for those binarization methods that use learning approaches.
3-D Curvilinear Structure Detection Filter Via Structure-Ball Analysis

Curvilinear structure detection filters are crucial building blocks in many medical image processing applications, where they are used to detect important structures, such as blood vessels, airways, and other similar fibrous tissues. Unfortunately, most of these filters are plagued by an implicit single structure direction assumption, which results in a loss of signal around bifurcations. This peculiarity limits the performance of all subsequent processes, such as understanding angiography acquisitions, computing an accurate segmentation or tractography, or automatically classifying image voxels. This paper presents a new 3-D curvilinear structure detection filter based on the analysis of the structure ball, a geometric construction representing second order differences sampled in many directions. The structure ball is defined formally, and its computation on a discreet image is discussed. A contrast invariant diffusion index easing voxel analysis and visualization is also introduced, and different structure ball shape descriptors are proposed. A new curvilinear structure detection filter is defined based on the shape descriptors that best characterize curvilinear structures. The new filter produces a vesselness measure that is robust to the presence of X- and Y-junctions along the structure by going beyond the single direction assumption. At the same time, it stays conceptually simple and deterministic, and allows for an intuitive representation of the structure's principal directions. Sample results are provided for synthetic images and for two medical imaging modalities.
A multiple-expert binarization framework for multispectral images

In this work, a multiple-expert binarization framework for multispectral images is proposed. The framework is based on a constrained subspace selection limited to the spectral bands combined with state-of-the-art gray-level binarization methods. The framework uses a binarization wrapper to enhance the performance of the gray-level binarization. Nonlinear preprocessing of the individual spectral bands is used to enhance the textual information. An evolutionary optimizer is considered to obtain the optimal and some suboptimal 3-band subspaces from which an ensemble of experts is then formed. The framework is applied to a ground truth multispectral dataset with promising results. In addition, a generalization to the cross-validation approach is developed that not only evaluates generalizability of the framework, it also provides a practical instance of the selected experts that could be then applied to unseen inputs despite the small size of the given ground truth dataset.
Analysis of manuscript characters by the moments of wavelet packets

The extraction of characteristics is one of the principal components of all form recognition systems. This work relates to the use of wavelet moments as characteristics for the recognition of manuscript characters. We propose here new families and new methods of treatment of this type of characteristic. Encouraging experimental results were obtained: the most discriminating 39 characteristics among 168 others were extracted and selected. These were validated using two classifiers, 1-NN and SVM. The first gives an error rate of 4.56% (leave-one-out), and the second an error rate of 2.99% (hold-out).
Deep-Belief-Network Based Rescoring Approach for Handwritten Word Recognition

This paper presents a novel verification approach towards improvement of handwriting recognition systems using a word hypotheses rescoring scheme by Deep Belief Networks (DBNs). A recurrent neural network based sequential text recognition system is used at first to provide the N-best recognition hypotheses of word images. Word hypotheses are aligned with the word image to obtain the character boundaries. Then, a verification approach using a DBN classifier is performed for each character segments. DBNs are recently proved to be very effective for a variety of machine learning problems. The character probabilities obtained from DBNs are next combined with the base recognition system. Finally, the N-best recognition hypotheses list is reranked according to the new score. We have compared our proposed approach with an MLP based rescoring approach on the Rimes dataset. The results obtained show that the verification approach using DBNs outperforms that of MLP systems.
Influence of Color-to-Gray Conversion on the Performance of Document Image Binarization: Toward a Novel Optimization Problem

This paper presents a novel preprocessing method of color-to-gray document image conversion. In contrast to the conventional methods designed for natural images that aim to preserve the contrast between different classes in the converted gray image, the proposed conversion method reduces as much as possible the contrast (i.e., intensity variance) within the text class. It is based on learning a linear filter from a predefined data set of text and background pixels that: 1) when applied to background pixels, minimizes the output response and 2) when applied to text pixels, maximizes the output response, while minimizing the intensity variance within the text class. Our proposed method (called learning-based color-to-gray) is conceived to be used as preprocessing for document image binarization. A data set of 46 historical document images is created and used to evaluate subjectively and objectively the proposed method. The method demonstrates drastically its effectiveness and impact on the performance of state-of-the-art binarization methods. Four other Web-based image data sets are created to evaluate the scalability of the proposed method.
Automatic segmentation and reconstruction of historical manuscripts in gradient domain

Separating content from noise in historical manuscripts is a fundamental task in digital palaeography. This study presents a fully automated segmentation approach based on the response of Harris corner detectors. The strength and clustering efficiency of the detected corners in the manuscripts are evaluated and used to segment the content from the background and noise. In addition, a manuscript reconstruction technique is proposed from the gradient field using the Poisson method to guide the interpolation. This reconstruction is able to remove noise significantly and hence enhances the contrast of the content thus making it easier for users to read and process these documents. The proposed approaches are evaluated using various standard databases to highlight their effectiveness and robustness to a multitude of noise and writing styles. Subjective and objective evaluations of the experimental results show that these techniques are able to successfully segment and reconstruct a very diverse set of scanned documents. An analysis of the results has also shown that the proposed technique compares favourably against similar counterparts.
A formal model for document processing of business forms

We present a formal model for processing gray-scale images of business forms such as bank cheques. The formal model is based on a new hybrid-based approach namely the base lines. In fact, to segment handwritten and hand-printed data from bank cheques, knowledge rules and base lines will have important roles to segment and extract the information from bank cheques. The architectural design as well as the major components of the system is discussed in full detail. Moreover, the significant use of the morphological followed by the topological processing on gray-scale images is used as a major aspect to restore the lost information after the elimination of the background and the base lines from the gray-scale cheques.
Stroke-model-based character extraction from gray-level document images

Global gray-level thresholding techniques such as Otsu's method, and local gray-level thresholding techniques such as edge-based segmentation or the adaptive thresholding method are powerful in extracting character objects from simple or slowly varying backgrounds. However, they are found to be insufficient when the backgrounds include sharply varying contours or fonts in different sizes. A stroke-model is proposed to depict the local features of character objects as double-edges in a predefined size. This model enables us to detect thin connected components selectively, while ignoring relatively large backgrounds that appear complex. Meanwhile, since the stroke width restriction is fully factored in, the proposed technique can be used to extract characters in predefined font sizes. To process large volumes of documents efficiently, a hybrid method is proposed for character extraction from various backgrounds. Using the measurement of class separability to differentiate images with simple backgrounds from those with complex backgrounds, the hybrid method can process documents with different backgrounds by applying the appropriate methods. Experiments on extracting handwriting from a check image, as well as machine-printed characters from scene images demonstrate the effectiveness of the proposed model.
Help-training for semi-supervised discriminative classifiers. Application to SVM

In this paper, we propose to reinforce self-training strategy by using a generative classifier that may help the main discriminative classifier training in semi-supervised mode to label the unlabeled data. We called this semi-supervised strategy: help-training. We apply this method for training support vector machine with labeled and unlabeled data. Experimental results on both artificial and real problems show its usefulness comparing with other classical semi-supervised methods.
Length Increasing Active Contour for the Segmentation of Small Blood Vessels

A new level-set based active contour method for the segmentation of small blood vessels and other elongated structures is presented. Its main particularity is the presence of a length increasing force in the contour driving equation. The effect of this force is to push the active contour in the direction of thin elongated shapes. Although the proposed force is not stable in general, our experiments show that with few precautions it can successfully be integrated in a practical segmentation scheme and that it helps to segment a longer part of the structures of interest. For the segmentation of blood vessels, this may reduce the amount of user interactivity needed: only a small region inside the structure of interest need to be specified.