nils-herrmann/clean_scirepeval huggingface dataset:
MUG: A Parameterless No-Reference JPEG Quality Evaluator Robust to Block Size and Misalignment

In this letter, a very simple no-reference image quality assessment (NR-IQA) model for JPEG compressed images is proposed. The proposed metric called median of unique gradients (MUG) is based on the very simple facts of unique gradient magnitudes of JPEG compressed images. MUG is a parameterless metric and does not need training. Unlike other NR-IQAs, MUG is independent to block size and cropping. A more stable index called MUG<inline-formula> <tex-math notation="LaTeX">$^+$</tex-math></inline-formula> is also introduced. The experimental results on six benchmark datasets of natural images and a benchmark dataset of synthetic images show that MUG is comparable to the state-of-the-art indices in the literature. In addition, its performance remains unchanged for the case of the cropped images in which block boundaries are not known. The MATLAB source code of the proposed metrics is available at <uri xlink:type="simple"> https://dl.dropboxusercontent.com/u/74505502/MUG.m</uri> and <uri xlink:type="simple"> https://dl.dropboxusercontent.com/u/74505502/MUGplus.m</uri>.
Leveraging green communications for carbon emission reductions: Techniques, testbeds, and emerging carbon footprint standards

Green communication systems and, in broader terms, green information and communications technologies have the potential to significantly reduce greenhouse gas emissions worldwide. This article provides an overview of two issues related to achieving the full carbon abatement potential of ICT. First, green communications research challenges are discussed, notably as they pertain to networking issues. Various initiatives regarding green ICT testbeds are presented in the same realm in order to validate the "green performance" and functionality of such greener cyber-infrastructure. Second, this article offers a description of ongoing international efforts to standardize methodologies that accurately quantify the carbon abatement potential of ICTs, an essential tool to ensure the economic viability of green ICT in the low carbon economy and carbon credit marketplace of the 21st century.
Validation of Hybrid MinMax FuzzyNeuro Systems

The validation and verification (V&V) of hybrid fuzzyneuro (HFN) or hybrid neurofuzzy (HNF) systems becomes of increasing concern as these systems are fielded and embedded in the every day operations of medical diagnosis, pattern recognition, fuzzy control and other industries particularly so when life-critical and environment-critical aspects are involved. We provide in this paper a V&V perspective on the nature of HFN components, an appropriate life-cycle, and applicable systematic formal testing approaches. We consider why HFN V&V may be both easier and harder than traditional means, and we conclude with a series of practical V&V guidelines. Validation of HFN systems brings us to a systematic study of value approximation performed during the inference phase. It is accepted that generalization capability is proportional to value approximation
PKCS: A Polynomial Kernel Family With Compact Support for Scale- Space Image Processing

In a scale-space framework, the Gaussian kernel has some properties that make it unique. However, because of its infinite support, exact implementation of this kernel is not possible. To avoid this drawback, there exist two different approaches: approximating the Gaussian kernel by a finite support kernel, or defining new kernels with properties closed to the Gaussian. In this paper, we propose a polynomial kernel family with compact support which overcomes the Gaussian practical drawbacks while preserving a large number of the useful Gaussian properties. The new kernels are not obtained by approximating the Gaussian, though they are derived from it. We show that, for a suitable choice of kernel parameters, this family provides an approximated solution of the diffusion equation and satisfies some other basic constraints of the linear scale-space theory. The construction and properties of the proposed kernel are described, and an application in which handwritten data are extracted from noisy document images is presented.
An extended-shadow-code based approach for off-line signature verification

Evaluates the extended shadow code used as a global feature vector for the signature verification problem. The latter as a shape factor is very easy to implement. Numerical experiments have been made with a signature database of 800 images (20 writers /spl times/ 40 signatures per writer) in the context of random forgeries. In the first experiment, a kNN classifier with voting shows a mean total error rate of 0.01% with k=1. In the second experiment, a minimum distance classifier has been used. The thresholds were evaluated for each of the 20 writers, and the number of reference signatures was varied in the range 1 /spl les/ N/sub ref/ /spl les/ 10. The mean total error rate was below 1.00% with N/sub ref/ = 4 genuine signatures.<<ETX>>
BackHauling-as-a-Service (BHaaS) for 5G Optical Sliced Networks: An Optimized TCO Approach

Due to their initial overestimation of demand, many network operators are overprovisioning their infrastructure. Overdesigned networks vastly increase operational costs without generating expected revenues. In particular, high-density cell architecture in future 5G networks will face big technical and financial challenges due to avalanche of traffic volume and massive growth in connected devices. Planning scalable 5G mobile backhaul (MBH) transport networks becomes one of the most challenging issues. However, existing planning solutions are no longer appropriate for coming 5G requirements. New 5G MBH architecture emphasizes on multitenancy and network slicing, which requires new methods to optimize MBH planning resource utilization. In this paper, we introduce an algorithm based on a stochastic geometry model (Voronoi Tessellation) to define backhauling zones within a geographical area and optimize their estimated traffic demands and MBH resources. Then, we propose a novel method called backhauling-as-a-service (BHaaS) for network planning and total cost of ownership (TCO) analysis based on “you-pay-only-for-what-you-use” approach. Finally, we enhanced the BHaaS performance by introducing a more service-aware method called traffic-profile-as-a-service (TPaaS) to further drive down the costs based on yearly activated traffic profiles.  BHaaS and TPaaS may control and enhance 22${\%}$  of the project benefit compared to traditional TCO model.
Let's adapt to network change: Towards energy saving with rate adaptation in SDN

The exponential growth of network users and their communication demands have led to a tangible increment of energy consumption in network infrastructures. A new networking paradigm called Software-Defined Networking (SDN) recently emerged which simplifies network management by offering programmability of network devices. SDN assists to lower link data rates via rate-adaptation technique which reduces power consumption of the network. The main idea behind this paper is to find a distribution of traffic flows over pre-calculated paths which allow adapting the transmission rate of maximum links into lower states. We first formulate the problem as a Mixed Integer Linear Program ( We then present four different computationally efficient algorithms namely greedy first fit, greedy best fit, greedy worst fit and a meta-heuristic genetic algorithm to solve the problem for a realistic network topology. Simulation results show that the genetic algorithm consistently outperforms the three greedy algorithms.
Automatic filter selection using image quality assessment

We present a method for automatically selecting the best filter to treat poor quality printed documents using image quality assessment. We introduce five quality measures to obtain information about the quality of the images, and morphological filters to improve their quality. A training set of 370 images was used to develop the system. Experimental results on the test set show a significant improvement in the recognition rate from 73.24% using no filter at all to 93.09% after applying a filter that was automatically selected.
Learning Semi-supervised SVM with Genetic Algorithm

Support vector machine (SVM) is an interesting classifier that has an excellent power of generalization. In this paper, we consider SVM in semi-supervised learning. We propose to use an additional criterion with the standard formulation of the transductive SVM for reinforcing the classifier regularization. Also, we use a genetic algorithm for optimizing the objective function, since the transductive SVM yields a non-convex problem. We tested our algorithm on artificial and real data, which gives promising results in comparison with Joachims' algorithm known as SVMlight TSVM.
Relational Fisher Analysis: A general framework for dimensionality reduction

In this paper, we propose a novel and general framework for dimensionality reduction, called Relational Fisher Analysis (RFA). Unlike traditional dimensionality reduction methods, such as linear discriminant analysis (LDA) and marginal Fisher analysis (MFA), RFA seamlessly integrates relational information among data into the representation learning framework, which in general provides strong evidence for related data to belong to the same class. To address nonlinear dimensionality reduction problems, we extend RFA to its kernel version. Furthermore, the convergence of RFA is also proved in this paper. Extensive experiments on documents understanding and recognition, face recognition and other applications from the UCI machine learning repository demonstrate the effectiveness and efficiency of RFA.
Bayes Classification of Online Arabic Characters by Gibbs Modeling of Class Conditional Densities

This study investigates Bayes classification of online Arabic characters using histograms of tangent differences and Gibbs modeling of the class-conditional probability density functions. The parameters of these Gibbs density functions are estimated following the Zhu et al. constrained maximum entropy formalism, originally introduced for image and shape synthesis. We investigate two partition function estimation methods: one uses the training sample, and the other draws from a reference distribution. The efficiency of the corresponding Bayes decision methods, and of a combination of these, is shown in experiments using a database of 9,504 freely written samples by 22 writers. Comparisons to the nearest neighbor rule method and a Kohonen neural network method are provided.
Efficient No-Reference Quality Assessment and Classification Model for Contrast Distorted Images

In this paper, an efficient Minkowski distance-based metric for no-reference (NR) quality assessment of contrast distorted images is proposed. It is shown that higher orders of Minkowski distance and entropy provide accurate quality prediction for the contrast distorted images. The proposed metric performs predictions by extracting only three features from the distorted images followed by a regression analysis. Furthermore, the proposed features are able to classify type of the contrast distorted images with a high accuracy. Experimental results on four datasets CSIQ, TID2013, CCID2014, and SIQAD show that the proposed metric with a very low complexity provides better quality predictions than the state-of-the-art NR metrics. The MATLAB source code of the proposed metric is available to public at http://www.synchromedia.ca/system/files/MDM.zip.
Statistical-based method to determine the best hour of the day regarding GHG emissions for a smart home appliance

Smart homes play a critical role in supporting energy management and environmental emissions reduction. Different types of residential loads allow flexible scheduling, thus bringing optimization opportunities regarding pricing and Greenhouse Gases (GHG) emissions reduction. In this work, we present an approach to provide GHG emissions information to the electricity users, based on data available online and using prediction tools. Our aim is to predict which is the best time of the day from the GHG emissions point of view. In the first step, information to calculate emission factors was collected near real time for different Canadian provinces comprising local generation and imports/exports from neighbors. With the data structured, we used LASSO regression analysis and feature selection tools to predict and analyze the data. After determining the time of the day with the smallest emission factor (for a given location, season, if the day is during the week or not, and hour), we applied this information to decide the best time to turn on an appliance, achieving 25% fewer emissions for our case study.
Introducing termination probabilities to HMM

HMM is very well suited to model sequential patterns. This paper introduces a new parameter, called the termination probability, to a hidden Markov model (HMM). The new parameter provides a better initialization for the backward variable during the training and evaluation phases. This improves the discriminatory power of HMM by allowing the system to judge the input observation sequence based on where it is completed. Experimental results show the improvement was achieved by this parameter.
Multistage OCDO: Scalable Security Provisioning Optimization in SDN-Based Cloud

Cloud computing is increasingly changing the landscape of computing, however, one of the main issues that is refraining potential customers from adopting the cloud is the security. Network functions virtualization together with software-defined networking can be used to efficiently coordinate different network security functionality in the network. To squeeze the best out of network capabilities, there is need for algorithms for optimal placement of the security functionality in the cloud infrastructure. However, due to the large number of flows to be considered and complexity of interactions in these networks, the classical placement algorithms are not scalable. To address this issue, we elaborate an optimization framework, namely OCDO, that provides adequate and scalable network security provisioning and deployment in the cloud. Our approach is based on an innovative multistage approach that combines together decomposition and segmentation techniques to the problem of security functions placement while coping with the complexity and the scalability of such an optimization problem. We present the results of multiple scenarios to assess the efficiency and the adequacy of our framework. We also describe our prototype implementation of the framework integrated into an open source cloud framework, i.e. Open stack.
A new framework for online sketch-based image retrieval in web environment

We present a novel framework for retrieval of images, based on the user's sketches performed in web environment. Unlike previous approaches, our method is capable of online retrieval and provides balanced trade-off between computational cost and robustness, while still preserving local properties. In our approach novel combination of features is introduced, which describes properties of desired results and query images. Based on the extracted features we use nearest neighbor recognizer, trained on dynamic neighborhoods, in combination with k-means and k-D tree technique for further speedup. A novel technique for online retrieval, based on sequential input processing and partial hierarchical score evaluation, is introduced, allowing us to suggest on the fly entries based on in-progress sketch. We tested our method on small and large scale databases and achieved promising results. The solution itself is implemented in collaborative environment, what allows users to produce queries cooperatively.
Network Function Virtualization as a Service for multi-tenant software defined networks

In this paper, we assess the performance of a Network Function Virtualization as a Service (NFVaaS) platform when deployed using a unikernel minimal operating system and running on top of a network hypervisor for software defined networks (SDNs). Our NFVaaS infrastructure is built on top of the unikernel minimal operating system ClickOS and the OpenVirteX network hypervisor, which enforces network isolation in multi-tenant SDN networks. Our experimental results demonstrate the effectiveness of using ClickOS when deploying and moving individual virtual network functions (VNFs) in multi-tenant SDN networks. We also identified the main limitations in terms of number of active VNFs that can be deployed at the same time, as well as the maximum memory size that can be allocated to each VNF in respect to the underlying available hardware resources. Moreover, we performed a thorough analysis and assessment of the impact of VNFs' placement on network performance, especially when multiple VNFs are organized as a service chain to create more sophisticated network functionalities.
Footnote-based document image classification using 1D convolutional neural networks and histograms

Classifying historical document images is a challenging task due to the high variability of their content and the common presence of degradation in these documents. For scholars, footnotes are essential to analyze and investigate historical documents. In this work, a novel classification method is proposed for detecting and segmenting footnotes from document images. Our proposed method utilizes horizontal histograms of text lines as inputs to a 1D Convolutional Neural Network (CNN). Experiments on a dataset of historical documents show the proposed method to be effective in dealing with the high variability of footnotes, even while using a small training set. Our method yielded an overall F-measure of 56.36% and a precision of 89.76%, outperforming significantly existing approaches for this task.
Ground-Truth Estimation in Multispectral Representation Space: Application to Degraded Document Image Binarization

Human ground-truthing is the manual labelling of samples (pixels for example) to generate reference data without any automatic algorithm help. Although a manual ground-truth is more accurate than a machine ground-truth, it still suffers from mislabeling and/or judgement errors. In this paper we propose a new method of ground-truth estimation using multispectral (MS) imaging representation space for the sake of document image binarization. Starting from the initial manual ground-truth, the proposed classification method aims to select automatically some samples with correct labels (well-labeled pixels) from each class for the training phase, then reassign new labels to the document image pixels. The classification scheme is based on the cooperation of multiple classifiers under some constraints. A real data set of MS historical document images and their ground-truth is created to demonstrate the effectiveness of the proposed method of ground-truth estimation.
Speeding up the decision making of support vector classifiers

In this paper, we propose a new approach for speeding up the decision making of support vector classifiers (SVC) in the context of multi-class classification. A two-stage system embedded within a probabilistic framework is presented. In the first stage we pre-estimate the posterior probabilities with a model-based approach and we re-estimate only the highest probabilities with appropriate SVCs in the second stage. We have tested our system on the benchmark database MNIST and the results show that our dynamic classification process allows to speedup the full "pairwise coupling" SVCs by a factor of 7.7 while preserving the accuracy. In addition, although the "one against all" strategy estimate slightly betters probabilities, our modular architecture seems more adapted to large multi-class problems.
Novel Data Representation for Text Extraction from Multispectral Historical Document Images

The extraction and analysis of useful information from old document images is very important into cultural heritage preservation. In advanced research, where the goal is to separate the foreground (in general, text) from the background, image restoration and pattern classification techniques are used. Most of these methods consist of classifying the pixels based on their gray-scale value. In this paper, we propose to perform foreground pattern extraction using regions-of-interest (ROI) analysis and a maximum likelihood classifier designed for multispectral document images. As contribution, a new feature vector is proposed to improve discrimination between patterns that is embedded in a simple statistical classification method. The results, which are promising, are compared to the state-of-the-art.
Virtual Slice Assignment in Large-Scale Cloud Interconnects

Software-defined networking is an emerging method for providing flexible and scalable network connectivity in both intra- and inter-datacenter interconnects with regard to various requirements, including traffic-awareness, quality of service, energy efficiency, and renewable-power intermittency. This article investigates issues and solutions for the software-defined planning of a virtual slice that involves multiple virtual machines with interdependent constraints spanning a network of distributed data-centers. A flexible and optimized virtual-slice assignment that considers server consolidation and multipath forwarding can address large-scale cloud computing services. Simulations on large-scale testbeds, such as the GreenStar Network and the Green Telco Cloud, showed that the proposed model achieves good performance in terms of flexibility and energy efficiency.
A prototype system for handwritten sub-word recognition: Toward Arabic-manuscript transliteration

A prototype system for the transliteration of diacriticsless Arabic manuscripts at the sub-word or part of Arabic word (PAW) level is developed. The system is able to read sub-words of the input manuscript using a set of skeleton-based features. A variation of the system is also developed which reads archigraphemic Arabic manuscripts, which are dot-less, into archigraphemes transliteration. In order to reduce the complexity of the original highly multiclass problem of sub-word recognition, it is redefined into a set of binary descriptor classifiers. The outputs of trained binary classifiers are combined to generate the sequence of sub-word letters. SVMs are used to learn the binary classifiers. Two specific Arabic databases have been developed to train and test the system. One of them is a database of the Naskh style. The initial results are promising. The systems could be trained on other scripts found in Arabic manuscripts.
Sketch-Based Retrieval of Document Illustrations and Regions of Interest

In this paper we present a novel approach towards retrieval of documents with pictorial data. Many prior works focused on word-spotting and text-based retrieval, but none of these techniques handled the retrieval of pictorial part of documents. In this paper, we present a new method that allows users to retrieve any visual data from documents, based on a sketch example. It mainly emphasizes all three main aspects of a visual retrieval system: feature representation, indexing and retrieval. Especially we focus on the design of salient descriptors, capable of capturing unique mapping from sketched images to document illustrations. We evaluate several approaches towards feature representation and indexing, with the aim to maximize the performance of our method. Our proposed technique is highly useful to complement word-spotting technique, when the indexed documents are composed of mixture of visual and textual data. This technique has shown promising results, both on pictorial data automatically extracted from documents as well for those selected by users as regions of interest.