nils-herrmann/clean_scirepeval huggingface dataset:
Multi scale multi descriptor local binary features and exponential discriminant analysis for robust face authentication

In this paper we present an efficient face verification system based on the fusion of multi-scale multi-descriptor local binary features. First, the face is divided into regions and each region is divided into several patches. For each patch and at every specific scale, the statistics of the baseline Local Binary Pattern (LBP), the Local Phase Quantization (LPQ) and the recently proposed Binarized Statistical Image Feature (BSIF) are summarized by histograms. The histograms of different patches belonging to the same region are concatenated to form a highly dimensional feature vector representing a specific descriptor at a specific scale. Second, we propose an efficient dimensionality reduction technique based on Exponential Linear Discriminant Analysis EDA coupled with Within-Class Covariance Normalization (WCCN) to downgrade the effect of the directions of high intravariability and to enhance the discrimination power of the EDA. The projected histograms for each region are scored using the cosine similarity metric. Lastly, the different region scores corresponding to different descriptors at different scales are fused using support vector machine classifier (SVM). Experimental verification results demonstrate that the proposed authentication pipeline outperforms all the existing systems on the XM2VTS controlled database and interestingly compete with the top performing systems on the challenging LFW database.
Time-Frequency Distributions Based on Compact Support Kernels: Properties and Performance Evaluation

This paper presents two new time-frequency distributions (TFDs) based on kernels with compact support (KCS) namely the separable (CB) (SCB) and the polynomial CB (PCB) TFDs. The implementation of this family of TFDs follows the method developed for the Cheriet-Belouchrani (CB) TFD. The mathematical properties of these three TFDs are analyzed and their performance is compared to the best classical quadratic TFDs using several tests on multicomponent signals with linear and nonlinear frequency modulation (FM) components including the noise effects. Instead of relying solely on visual inspection of the time-frequency domain plots, comparisons include the time slices' plots and the evaluation of the Boashash-Sucic's normalized instantaneous resolution performance measure that permits to provide the optimized TFD using a specific methodology. In all presented examples, the KCS-TFDs show a significant interference rejection, with the component energy concentration around their respective instantaneous frequency laws yielding high resolution measure values.
A new feedforward neural network pruning algorithm: SSM-iterative pruning (SSMIP)

We present a new pruning algorithm based on a combination of the Statistical Stepwise Method (SSM) (Cottrell et al., 1995) and the Iterative Pruning (IP) algorithms (Castellano and Fanelli, 1997). The proposed algorithm (SSMIP) is used to prune feedforward neural networks (NN) and combines the advantages of the algorithms. The new proposed pruning algorithm works as follows: i) The Iterative Pruning Algorithm of Castellano et al. is applied in order to cancel no significant units; ii) The Statistical Stepwise Method of Cottrell et al. is then used to remove unnecessary links between units. These steps are repeated layer by layer in the forward direction with holding the previous optimised structure in each layer. Simulation results are given on standard benchmarks that highlight the effectiveness of the proposed algorithm. A comparative study is given with respect to Castelanno and Cottrell algorithms used separately.
Real-time optimized NFV architecture for internetworking WebRTC and IMS

Network Function Virtualization (NFV) technology has emerged as a promising solution to optimize the deployment of network elements in cloud computing environment, both in terms of user Quality of Service (QoS) and resource allocation. To deliver IP Multimedia Subsystem (IMS) advanced services across multiple access networks, a cloud-based model likely improves not only flexibility in network management but also in service invocation. This model is particularly suitable for the inter-network between the IMS and Web Real-Time Communication (WebRTC) domain, which is a natural combination to significantly expand potential end-points of multimedia sessions. However, optimally allocating resource for each component in the system to provide QoS is challenging, and has not been fully considered in prior work. In this paper, we investigate an NFV architecture to enable effective communication between IMS and WebRTC users supporting smart community services, and propose an optimization model to design and allocate resources for such system while ensuring desired QoS level. Experimental results show our algorithms effectively allocate required amounts of resources for virtual components according to real-time increase or decrease in user demand.
Decreasing live virtual machine migration down-time using a memory page selection based on memory change PDF

Seamless migration of Virtual Machines (VMs) will guarantee the Service Level Agreements during the live data center migration which is an important step for allocation and management of Information and Communication Technology (ICT) infrastructures and resources across the globe. At our knowledge, only simplistic models have been used to describe the governing process of memory migration. In this paper, first, we formulate a mathematical model for virtual machine memory transfer. Then, we show the limitations of such a transfer. Finally, we introduce and evaluate a more efficient method for memory transfer.
A new adaptive framework for tubular structures segmentation in X-ray angiography

Segmentation of tubular structures in X-ray angiographies, such as the aorta or coronary arteries, is a critical task for the guidance of the heart surgeon during percutaneous cardiac interventions. Extracting these structures is challenging due to the quality of X-ray angiographies (presence of noise, non-homogeneous regions) and to the characteristics of the structure of interest (tubular and fine structures ). To overcome the shortcomings of the conventional local and global methods, we propose in this paper, a coarse to fine segmentation framework to extract tubular structures from x-ray angiographies. The framework first enhances tubular structures by a vesselness filter. Then, the structures are segmented by the proposed adaptive active contour method which combines a local and global fitting energy. Those two forces are weighted according to the image's homogeneity value. Experiments have been conducted on different angiograms acquired on children. The results have shown that the proposed approach gives promising results in the segmentation of tubular structures and outperforms other active contour methods. Thanks to the combination of local and global forces, the proposed system is robust to noise and intensity inhomogeneity.
Inclusion of security requirements in SLA lifecycle management for cloud computing

Service Level Agreement (SLA) is an essential tool for managing cloud computing services. The support of the security requirements through SLA is fundamental to achieve the full potential of the cloud computing paradigm. In this paper we present how security requirements are addressed in a cloud computing SLA. Furthermore, a unified SLA lifecycle for cloud computing services is proposed. The relationship between security requirements and the lifecycle proposed is analyzed. Through the analysis, the current cloud computing security requirements scenario for the SLA context is identified in addition to its research opportunities.
Image patches analysis for text block identification

In this paper, we propose a novel text block identification method for ancient document understanding. Unlike traditional top-down and bottom-up approaches, our method is based on supervised learning on the patches of document images, which can be considered as an intermediate level method but integrates essential advantages of both the top-down and the bottom-up strategies. In our method, the document images are firstly partitioned into small patches, and then positive and negative patches are selected to form an active training set. Gabor features are extracted on each patch, while multi-linear discriminant analysis (MDA) is employed to reduce the dimensionality of the data. To deal with unseen documents, a random forest classifier is learned on the new representations of the patches. Compared to traditional approaches, our method can not only capture local texture features of each patch, but also preserve the global information of the training images. Furthermore, MDA is guaranteed to learn a low dimensional tensor subspace, which significantly avoids the curse of dimensionality dilemma. Moreover, the random forest classifier can automatically select useful features and deliver satisfactory identification results. Extensive experiments on some scripts of ancient document images demonstrated the effectiveness of our method.
Powering a Data Center Network via Renewable Energy: A Green Testbed

Today's information and communications technology (ICT) services emit an increasing amount of greenhouse gases. Carbon footprint models can enable research into ICT energy efficiency and carbon reduction. The GreenStar Network (GSN) testbed is a prototype wide-area network of data centers powered by renewable energy sources. Through their work developing the GSN, the authors have researched fundamental aspects of green ICT such as virtual infrastructure, unified management of compute, network, power, and climate resources, smart power control, and a carbon assessment protocol.
KCS-new kernel family with compact support in scale space: formulation and impact

Multiscale representation is a methodology that is being used more and more when describing real-world structures. Scale-space representation is one formulation of multiscale representation that has received considerable interest in the literature because of its efficiency in several practical applications and the distinct properties of the Gaussian kernel that generate the scale space. Together, some of these properties make the Gaussian unique. Unfortunately, the Gaussian kernel has two practical limitations: information loss caused by the unavoidable Gaussian truncation and the prohibitive processing time due to the mask size. We propose a new kernel family derived from the Gaussian with compact supports that are able to recover the information loss while drastically reducing processing time. This family preserves a great part of the useful Gaussian properties without contradicting the uniqueness of the Gaussian kernel. The construction and analysis of the properties of the proposed kernels are presented in this paper. To assess the developed theory, an application of extracting handwritten data from noisy document images is presented, including a qualitative comparison between the results obtained by the Gaussian and the proposed kernels.
Popularity based file categorization and coded caching in 5G networks

In this paper, we will study the capacity of 5G cellular networks with caching. We first investigate the capacity using uncoded caching and then we study the capacity of such networks using a novel technique based on file categorization and coded cache placement. In our cache placement strategy, we propose to create file groups with approximately similar popularity levels (within a factor of two) and then we randomly code the files in each file group together and place them in the User Terminal (UT) caches. We prove that this technique can significantly increase the network capacity by reducing the average traffic. Our proposed coded cache placement strategy is decentralized and performs close to optimal. Simulation results also confirm our theoretical results.
Efficient Provisioning of Security Service Function Chaining Using Network Security Defense Patterns

Network functions virtualization intertwined with software-defined networking opens up great opportunities for flexible provisioning and composition of network functions, known as network service chaining. In the cloud, this allows providers to create service chains tuned to each application type while optimizing resources’ utilization. This is particularly useful to accommodate different tenants’ applications with different security needs. However, considering security provisioning from the single perspective of resources optimization may lead to deployment solutions that do not comply with well-known security-related best practices and recommendations. In this paper, we propose network security defense patterns (NSDP) aimed at leveraging the best practice and know-how from the security experts and at capturing various security constraints to efficiently select compliant security functions’ deployment options. The placement problem being a NP-Hard problem to solve, we also propose a scalable networking and computing resources aware optimization framework to efficiently provision different NSDPs. We further show the feasibility of implementing NSDPs in the cloud infrastructure through the integration of our approach into an open source cloud framework, namely OpenStack, in our test laboratory. The simulation results show the effectiveness of our approach in selecting an optimal placement of the security functions for large data centers with hundreds of thousands of computing nodes, while complying with the predefined security constraints and improving the scalability compared to the current placement algorithms.
Automatic segmentation of cells from microscopic imagery using ellipse detection

Cell image segmentation is a necessary first step of many automated biomedical image-processing procedures. There certainly has been much research in the area. To this, a new method has been added, which automatically extracts cells from microscopic imagery, and does so in two phases. Phase 1 uses iterated thresholding to identify and mark foreground objects or `blobs' with an overall accuracy of >97%. Phase 2 of the method uses a novel genetic algorithms-based ellipse detection algorithm to identify cells, quickly and reliably. The mechanism, as a whole, has an accuracy rate >96% and takes <1 min (given our specific hardware configuration) to operate on a microscopic image
Skew angle estimation of printed document using linear regression, wavelet transform and anisotropic diffusion

In this paper, we present a method for skew angle detection of printed documents which is based on the linear regression analysis and wavelet transform, with skew angle toleration between 0deg and 180deg. In this work, the anisotropic diffusion was introduced as a step of pre-processing in order to reinforce the difference between areas and to eliminate the noise. The linear regression formula is applied on the black pixels which belong to the text line segment of the band of horizontal details (LH) in order to estimate their skew angle, then each skew angle is represented in a histogram of angle and its peak corresponds to the document skew angle. The text line segments are obtained by the binarization of the band LH. Our method is tested on about seventy five printed and varied documents, it provides good and accurate results.
Application of Multi-Level Classifiers and Clustering for Automatic Word Spotting in Historical Document Images

A complete system for preprocessing and word spotting of very old historical document images is presented. Document images are processed for extraction of salient information using a word spotting technique which does not need line and word segmentation and is language independent.A multi-class library of connected components of document text is created based on six features. The spotting is performed using Euclidean distance measure enhanced by rotation and dynamic time wrapping transforms. The method is applied to a dataset from Juma Al Majid Center (Dubai)with promising results. A promising performance of the word spotting technique is obtained using an automatic preprocessing stage. In this stage, using content-level classifiers, accurate stroke pixels are extracted in a robust way. The preprocessed document images are also more legible to the end user and are less costly to archive and transfer.
Gaussian Process Regression Based Traffic Modeling and Prediction in High-Speed Networks

Evolving nature of network traffic challenges existing models to fit and predict its behavior. In particular, real traffic modeling requires more flexible design that can adapt to long-range and short-range dependent traffic with dynamic patterns. Unfortunately, existing models cannot handle such requirements because various traffic behaviors such as periodic and self-similar are not taken into account. In this paper, Gaussian process regression (GPR) is adapted for traffic modeling and prediction. The connection between self-similarity as a traffic characteristic and GPR parameters has been driven and exerted to build of a new Hurst estimation method based on machine learning techniques. This led to propose self-similar covariance functions for enhancing prediction accuracy of GPR. The proposed GPR model has been applied for Hurst estimation as well as for traffic prediction on real traffic traces at different time-scales. The experimental results show the employment of self-similar covariance functions increases generalization ability of GPR for traffic modeling and prediction.
Robust authentication using Likelihood Ratio and GMM for the fusion of voice and face

With the increased use of biometrics for identity verification, there have been a similar increase in the use of multimodal fusion to overcome the limitations of unimodal biometric systems. While there are several types of fusion (e.g. decision level, score level, feature level, sensor level), research has shown that score level fusion is the most effective in delivering increased accuracy. Recently a promising framework for optimal combination of match scores based on the Likelihood Ratio-LR-test is proposed where the distributions of genuine and impostor match scores are modelled as finite Gaussian mixture model. In this paper, we examine the performance of combining face and voice biometrics at the score level using the LR classifier. Our experiments on the publicly available scores of the XM2VTS Benchmark database show a consistent improvement in performance compared to the famous efficient sum rule preceded by Min-Max, z-score and tanh score normalization techniques.
On-line character recognition using histograms of features and an associative memory

The paper investigates a new representation of shape and its use in handwritten on-line character recognition. This representation is based on the empirical distribution of features such as tangents, and tangent differences at distant points along the character signal. Recognition is carried out by a Kohonen associative memory (also called Kohonen self organizing feature map), trained using this representation, and the Hellinger distance, which measures the distance between distributions. We report on extensive experiments that show the pertinence of the representation and the superior performance of the scheme.
A Defense-Centric Model for Multi-step Attack Damage Cost Evaluation

Measuring the attack damage cost and monitoring the sequence of privilege escalations play a critical role in choosing the right countermeasure by Intrusion Response System (IRS). The existing attack damage cost evaluation approaches inherit some limitations, such as neglecting the dependencies between system assets, ignoring the backward damage of exploited non-goal services, or omitting the potential damage toward the goal service. In this paper, we propose a defense-centric model to calculate the damage cost of a multi-step attack. The main advantage of this model is providing an accurate damage cost by considering not only the damaged services (non-goal services) but also the potential damage toward the attacker target (goal service). To track the attacker's progress and find the attack path, an Attack-Defense Tree (ADT) is used. The model has been implemented in, but is not limited to, the cloud environment and tested with a multi-step attack scenario.
A Unified Framework Based on the Level Set Approach for Segmentation of Unconstrained Double-Sided Document Images Suffering from Bleed-Through

A novel method for the segmentation of double-sided ancient document images suffering from bleed-through effect is presented. It takes advantage of the level set framework to provide a completely integrated process for the segmentation of the text along with the removal of the bleed-through interfering patterns. This process is driven by three forces: 1) a binarization force based on an adaptive global threshold is used to identify region of low intensity, 2) a reverse diffusion force allows for the separation of interfering patterns from the true text, and 3) a small regularization force favors smooth boundaries. This integrated method achieves high quality results at reasonable computational cost, and can easily host other concepts to enhance its performance. The method is successfully applied to real and synthesized degraded document images. Also, the registration problem of the double-sided document images is addressed by introducing a level set method; the results are promising.
A New Approach for Skew Correction of Documents Based on Particle Swarm Optimization

This paper presents a novel approach for skew correction of documents. Skew correction is modeled as an optimization problem, and for the first time, Particle Swarm Optimization (PSO) is used to solve skew optimization. Anew objective function based on local minima and maxima of projection profiles is defined, and PSO is utilized to find the best angle that maximizes differences between values of local minima and maxima. In our approach, local minima and maxima converge to the locations of lines and spaces between lines. Results of our skew correction algorithm are shown on documents written in different scripts such as Latin and Arabic related scripts (e.g. Arabic, Farsi,Urdu,...). Experiments show that our algorithm can handle a wide range of skew angles, also it is robust to gray level and binary images of different scripts.
Strategies for visual arabic handwriting recognition: Issues and case study

Automatic recognition of Arabic handwritten text is a problem worth solving; it gained more and more interest in recent years, for various reasons. In this paper we address the most frequently encountered problems when dealing with Arabic handwriting recognition and we briefly present lessons learnt from several serious attempts that have been undertaken in this regard. We give a summary of techniques concerning Arabic handwriting recognition research. We will end by a case study, recognition of Tunisian city names, emphasizing on visual-based strategies for Arabic handwriting recognition (AHR).