nils-herrmann/clean_scirepeval huggingface dataset:
Application of Phase-Based Features and Denoising in Postprocessing and Binarization of Historical Document Images

Preprocessing and post processing steps significantly improve the performance of binarization methods, especially in the case of severely-degraded historical documents. In this paper, an unsupervised post processing method is introduced based on the phase-preserved denoised image and also phase congruency features extracted from the input image. The core of the method consists of two robust mask images that can be used to cross out false positive pixels on the output of the binarization method. First, a mask with a high recall value is obtained from the denoised image using morphological operations. In parallel, a second mask is obtained based on phase congruency features. Then, a median filter is used to remove noise on these two masks, which then are used to correct the output of any binarization method. This approach was tested along with several state-of the-art binarization methods on the DIBCO'09, H-DIBCO'10, DIBCO'11 and H-DIBCO'12 datasets with promising and robust results. Furthermore, the high performance of the proposed masks shows their potential use as unsupervised semi-ground truth generator for learning-based binarization methods.
A multi-resolution based approach for handwriting segmentation in gray-scale images

We present a new method to segment visual handwritten data in gray-scale images. In handwriting recognition, visual shapes are very important in improving the system's performance. We introduce a robust method for extracting visual shapes of handwritten data from a noisy background. We adopted a multi-resolution Marr-Hildreth (1980) based approach to correctly segment visual data in variable contrasted images. Encouraging results have been obtained on real data, from the CEDAR database.<<ETX>>
Feature Set Evaluation for Offline Handwriting Recognition Systems: Application to the Recurrent Neural Network Model

The performance of handwriting recognition systems is dependent on the features extracted from the word image. A large body of features exists in the literature, but no method has yet been proposed to identify the most promising of these, other than a straightforward comparison based on the recognition rate. In this paper, we propose a framework for feature set evaluation based on a collaborative setting. We use a weighted vote combination of recurrent neural network (RNN) classifiers, each trained with a particular feature set. This combination is modeled in a probabilistic framework as a mixture model and two methods for weight estimation are described. The main contribution of this paper is to quantify the importance of feature sets through the combination weights, which reflect their strength and complementarity. We chose the RNN classifier because of its state-of-the-art performance. Also, we provide the first feature set benchmark for this classifier. We evaluated several feature sets on the IFN/ENIT and RIMES databases of Arabic and Latin script, respectively. The resulting combination model is competitive with state-of-the-art systems.
Shape-Based Alphabet for Off-line Arabic Handwriting Recognition

This article describes an off-line handwritten Arabic words recognition system. Both explicit grapheme segmentation and feature extraction are originally designed for Latin cursive handwriting. The recognizer itself is a hybrid HMM/NN. We introduce a new shape-based alphabet for handwriting Arabic recognition which is intended to benefit from some specificities of Arabic writing. We performed several experiments using IFN/ENIT benchmark database to validate our approach. Our recognizer performs as close as the state of the art recognition rate with 87%. The latter results are indeed very encouraging as many perspectives and improvements may be considered. Especially, the explicit processing of dots and diacritics, therefore making use of more prior knowledge of Arabic writing specificities.
Hyperspectral band selection based on graph clustering

In this paper we present a new method for hyperspectral band selection problem. The principle is to create a band adjacency graph (BAG) where the nodes represent the bands and the edges represent the similarity weights between the bands. The Markov Clustering Process (abbreviated MCL process) defines a sequence of stochastic matrices by alternation of two operators on the associated affinity matrix to form distinct clusters of high correlated bands. Each cluster is represented by one band and the representative bands will form the new data cube to be used in subsequent processing. The proposed algorithm is tested on a real dataset and compared against state-of-art. The results are promising.
Dynamic Optimal Countermeasure Selection for Intrusion Response System

Designing an efficient defense framework is challenging with respect to a network's complexity, widespread sophisticated attacks, attackers’ ability, and the diversity of security appliances. The Intrusion Response System (IRS) is intended to respond automatically to incidents by attuning the attack damage and countermeasure costs. The existing approaches inherit some limitations, such as using static countermeasure effectiveness, static countermeasure deployment cost, or neglecting the countermeasures’ negative impact on service quality (QoS). These limitations may lead the IRS to select inappropriate countermeasures and deployment locations, which in turn may reduce network performance and disconnect legitimate users. In this paper, we propose a dynamic defense framework that selects an optimal countermeasure against different attack damage costs. To measure the attack damage cost, we propose a novel defense-centric model based on a service dependency graph. To select the optimal countermeasure dynamically, we formulate the problem at hand using a multi-objective optimization concept that maximizes the security benefit, minimizes the negative impact on users and services, and minimizes the security deployment cost with respect to the attack damage cost.
Towards flexible, scalable and autonomic virtual tenant slices

Multi-tenant flexible, scalable and autonomic virtual networks isolation has long been a goal of the network research and industrial community. For today's Software-Defined Networking (SDN) platforms, providing cloud tenants requirements for scalability, elasticity, and transparency is far from straightforward. SDN programmers typically enforce strict and inflexible traffic isolation resorting to low-level encapsulations mechanisms which help and facilitate network programmer reasoning about their complex slices behavior. In this paper, we propose SD-NMS, a novel software-defined architecture overcoming SDN and encapsulation techniques limitations. SD-NMS lifts several network virtualization roadblocks by combining these two separate approaches into an unified design. SD-NMS design leverages the benefits of SDN to provide Layer 2 (L2) isolation coupled with network overlay protocols with simple and flexible virtual tenant slices abstractions. This yields a network virtualization architecture that is both flexible, scalable and secure on one side, and self-manageable on the other. The experiment results showed that the proposed design offers negligible overhead and guarantees the network performance while achieving the desired isolation goals.
Side-Information based Exponential Discriminant Analysis for face verification in the wild

Recently, there is an extensive research efforts devoted to the challenging problem of face verification in unconstrained settings and weakly labeled data, where the task is to determine whether pairs of images are from the same person or not. In this paper, we propose a novel discriminative dimensionality reduction technique called Side-Information Exponential Discriminant Analysis (SIEDA) which inherits the advantages of both Side-Information Linear Discriminant (SILD) and Exponential Discriminant Analysis (EDA). SIEDA transforms the problem of face verification under weakly labeled data into a generalized eigenvalue problem while alleviating the preprocessing step of PCA dimensionality reduction. To further boost the performance, the multi-scale variant of the binarized statistical image features histograms are adopted for efficient and rich facial texture representation. Extensive experimental evaluation on the challenging Labeled Faces in the Wild LFW benchmark database demonstrates the superiority of SIEDA over SILD. Moreover, the obtained verification accuracy is impressive and compares favorably against the state-of-the-art.
Enabling infrastructure as a service (IaaS) on IP networks: from distributed to virtualized control plane

Infrastructure as a Service (IaaS) is considered a prominent model for IP based service delivery. As grid and cloud computing have become a stringent demand for today's Internet services, IaaS is required for providing services, particularly "private cloud," regardless of physical infrastructure locations. However, enabling IaaS on traditional Internet Service Provider (ISP) network infrastructures is challenging because IaaS requires a high abstraction level of network architectures, protocols, and devices. Network control plane architecture plays therefore an essential role in this transition, particularly with respect to new requirements of scalability, reliability, and flexibility. In this article we review the evolutionary trend of network element control planes from monolithic to distributed architectures according to network growth, and then present a new virtualization oriented architecture that allows infrastructure providers and service providers to achieve service delivery independently and transparently to end users based on virtualized network control planes. As a result, current ISP infrastructures will be able to support new services, such as heavy resource consuming data center applications. We also show how to use network virtualization for providing cloud computing and data center services in a flexible manner on the nationwide CANARIE network infrastructure.
Low Carbon Virtual Private Clouds

Data center energy efficiency and carbon footprint reduction have attracted a great deal of attention across the world for some years now, and recently more than ever. Live Virtual Machine (VM) migration is a prominent solution for achieving server consolidation in Local Area Network (LAN) environments. With the introduction of live Wide Area Network (WAN) VM migration, however, the challenge of energy efficiency extends from a single data center to a network of data centers. In this paper, intelligent live migration of VMs within a WAN is used as a reallocation tool to minimize the overall carbon footprint of the network. We provide a formulation to calculate carbon footprint and energy consumption for the whole network and its components, which will be helpful for customers of a provider of cleaner energy cloud services. Simulation results show that using the proposed Genetic Algorithm (GA)-based method for live VM migration can significantly reduce the carbon footprint of a cloud network compared to the consolidation of individual data center servers. In addition, the WAN data center consolidation results show that an optimum solution for carbon reduction is not necessarily optimal for energy consumption, and vice versa. Also, the simulation platform was tested under heavy and light VM loads, the results showing the levels of improvement in carbon reduction under different loads.
A recursive thresholding technique for image segmentation

In this correspondence, we present a general recursive approach for image segmentation by extending Otsu's (1978) method. The new approach has been implemented in the scope of document images, specifically real-life bank checks. This approach segments the brightest homogeneous object from a given image at each recursion, leaving only the darkest homogeneous object after the last recursion. The major steps of the new technique and the experimental results that illustrate the importance and the usefulness of the new approach for the specified class of document images of bank checks will be presented.
Green process offloading in smart home

Smart home applications are becoming increasingly popular due to the explosive growth of intelligent devices in recent years. However, such applications require extra computing resources. In this paper, the task offloading management in the smart home scenarios are investigated in micro and macro scales. First, the optimal process of different applications is determined to be offloaded from the smart home controller to the cloud system. Second, a policy in the home gateway is proposed to select the best server in the cloud to dynamically offloading the computing tasks considering energy saving while satisfying the applications QoS metrics. Using queuing theoretical approach, we model the smart home application execution time at macro level considering the applications QoS constraints. Then, we apply duality theory to find the optimal offloading ratio. Moreover, an optimal interface/server selection is investigated to minimize the energy consumption. On the other hand, Lyapunov drift optimization is applied at micro level to find the optimal performance point of the smart home applications in real time. The effectiveness and optimality of our proposed algorithms are evaluated by experiments over realistic smart home scenarios.
Dynamical morphological processing: a fast method for base line extraction

In most document analysis and recognition systems, straight lines are considered as one of the basic elements that should be located and eliminated to simplify the process of document analysis and recognition. The superposition or the intersection of different objects of interest found in the same area makes the process of detecting and extracting these line segments a non-trivial task to pursue, especially, if the method should preserve the valuable objects of interest that intersect with these lines. In this paper, we present a new and effective approach that detects the existence of line segments and eliminates them with the challenge of preserving the valuable information that intersects these line segments. The new approach makes use of the well-known morphological processing technique of the closing operation, that uses a fixed structuring element, towards the use of a dynamic structuring element. The purpose of the new dynamic structuring element is to detect and preserve the valuable objects intersecting the line segments that should be eliminated regardless of the different orientations with which the objects intersect these line segments.
Evaluation of techniques for signature classification from accelerometer and gyroscope data

In this paper, we present an exhaustive comparison of techniques for classification of signature data extracted from gyroscope and accelerometer devices. Since there exists large pool of classifiers and features for this kind of data, in order to provide a guide in choosing a particular setup, we decided to explore performance of these methods in a comparative study, which is a missing factor of current works on the topic. Also, we propose a framework for the combination of evaluated techniques in order to achieve a higher precision of the final classifier. The evaluated factors are: transformation of the time-series data into a fixed-size vector, classification methods and the performance of generative techniques without fixed-size input.
Sparse Hyperspectral Unmixing via Heuristic $\ell _p$ -Norm Approach

In this paper, we propose a new approach to approximate the <inline-formula><tex-math notation="LaTeX">$\ell _0$ </tex-math></inline-formula>-norm for linear sparse hyperspectral unmixing of images. We approximate the <inline-formula><tex-math notation="LaTeX">$\ell _0$</tex-math></inline-formula>-norm with the <inline-formula> <tex-math notation="LaTeX">$\ell _p$</tex-math></inline-formula>-norm and iteratively reduce <inline-formula> <tex-math notation="LaTeX">$p$</tex-math></inline-formula> to enhance the results. The <inline-formula> <tex-math notation="LaTeX">$p$</tex-math></inline-formula>-changing heuristic scheme that reduces the value of <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula> smoothly and iteratively, results in an enhanced sparse solution. We introduce an iteratively reweighted <inline-formula><tex-math notation="LaTeX">$\ell _2$ </tex-math></inline-formula>-norm to approximate the <inline-formula><tex-math notation="LaTeX">$\ell _p$</tex-math> </inline-formula>-norm. In this approach, a parameter <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math> </inline-formula> is involved to deal with the fact that <inline-formula><tex-math notation="LaTeX">$\ell _p$</tex-math> </inline-formula>-norm problem is not Lipschitz continuous for the region of <inline-formula><tex-math notation="LaTeX"> $p<1$</tex-math></inline-formula>. We propose two different methods to update the pair <inline-formula> <tex-math notation="LaTeX">$(p,\epsilon)$</tex-math></inline-formula>. Finally, we evaluate our proposed heuristic <inline-formula><tex-math notation="LaTeX">$\ell _p$</tex-math></inline-formula>-norm method over the synthetic data as well as real hyperspectral dataset. Experimental results show that our algorithm outperforms several state-of-the-art algorithms in terms of the reconstruction errors and their probability of success.
Perceptual video quality evaluation using fuzzy inference system

In this paper, we present a perceptual quality metric for evaluating image and video quality. The metric is based on a fuzzy inference system with Takagi-Sugeno's inference engine. Three visually important factors including visual masking error, blurring distortion and contrast distortion are used as the inputs to the inference system. Through learning algorithm with the subjective test data, the inference system can predict video quality with high accuracy and monotonicity.
Modelling visual attention and motion effect for visual quality evaluation

The perceptual visual quality evaluation of Human Visual System (HVS) is very complex. It concerns almost all aspects of visual processing in vision path, from low-level neuron activities to high-level visual perception. Existing perceptual Visual Quality Metrics (VQMs) only considered several of the mechanisms of HVS and many others are ignored. In this paper, two global modulatory factors, visual attention and motion suppression, are modelled and combined to form a mathematic expression - Perceptual Quality Significant Level (PQSL). To a certain extent, it is believed that PQSL value reflect the processing ability of human brain on local visual contents. To evaluate their effects on visual quality evaluation, two VQMs are proposed. One is a MSE-like VQM based on PQSL-modulated JND profile, which was proposed in Z.K. Lu et al., (2004); the other VQM is based on Wang's visual quality assessment Zhou Wang et al., (2004) PQSL values are used to adjust the weights of his structural similarity index. Experimental results show that introducing of the global modulatory factors can improve the performance of current visual quality metrics.
Video coding based on true motion estimation

A new approach for MC-DCT (motion compensated-discrete cosine transform) hybrid video coding based on true motion estimation is proposed. The true motion estimation technique employs the least-median-squares (LMedS) matching criterion in block matching process for motion estimation. The rationale for using such true motion estimator is that at low bit-rates, very few bits are available for coding the motion-compensated DCT errors, and thus the detection of the true underlying motion of the picture block becomes much more important than just trying to find a block-match that satisfies the traditional least-mean-squares (LMS) matching criterion. Without detecting the true motion of the picture block, the errors due to insufficient bits for coding the motion-compensated DCT errors will be propagated to succeeding inter-frames, resulting in a progressive degeneration on perceptual quality of the pictures. An important advantage of using true motion estimation is that it results in better perceptual video quality compared to the conventional block-matching algorithm that utilizes least-mean-squares matching criterion. Experimental results obtained had supported our proposition on using the LMedS-based matching criterion in block-matching motion estimation for low bit rate video coding.
Modeling visual attention's modulatory aftereffects on visual sensitivity and quality evaluation

With the fast development of visual noise-shaping related applications (visual compression, error resilience, watermarking, encryption, and display), there is an increasingly significant demand on incorporating perceptual characteristics into these applications for improved performance. In this paper, a very important mechanism of the human brain, visual attention, is introduced for visual sensitivity and visual quality evaluation. Based upon the analysis, a new numerical measure for visual attention's modulatory aftereffects, perceptual quality significance map (PQSM), is proposed. To a certain extent, the PQSM reflects the processing ability of the human brain on local visual contents statistically. The PQSM is generated with the integration of local perceptual stimuli from color contrast, texture contrast, motion, as well as cognitive features (skin color and face in this study). Experimental results with subjective viewing demonstrate the performance improvement on two PQSM-modulated visual sensitivity models and two PQSM-based visual quality metrics.
Perceptual Quality Metric for H.264 Low Bit Rate Videos

This paper proposed an objective video quality metric designed for automatically assessing the perceived quality of digitally compressed multimedia videos using H.264 video compression. The rationale in proposing perceptual-based metric is because traditional measure, peak signal-to-noise ratio (PSNR), has been found to correlate poorly with subjective quality ratings, particularly at much lower bit rates. In this paper, computational models have been applied to emulate human visual perception based on a combination of local and global modulating factors. The proposed video quality metric has been tested on GIF and QCIF video sequences compressed using H.264 video compression technique at various bit rates (24-384 Kbps) and frame rates (7.5-30 Hz). Performance of the proposed metric with respect to subjective scores will be reported and a comparison with PSNR and also the video structural similarity method (being one of the best video quality metric for high bit rate videos recently reported in the literature) will also be provided in this paper
A Wavelet-Based Visible Distortion Measure for Video Quality Evaluation

In this paper, we propose a new video quality evaluation method, which measures the perceived error between original and distorted images using wavelet just-noticeable-distortion (JND) profile. The error of the subband coefficient that is below the visual detection threshold is ignored while some errors that take place in the content-activity regions are masked. Luminance masking and temporal contrast masking are integrated to adjust the perceived error. Experiments on VQEG (video quality expert group) test sequences show that the proposed method can achieve good correlation with subjective evaluation.