nils-herrmann/clean_scirepeval huggingface dataset:
Federal Smart House Regulator (FSHR): A Self-Managing and Ecosystemic Approach to Resource Management, Automation, and Sustainability in Smart Houses

In this work, a decentralized, cloud-supported management of smart house is proposed using autonomous ICT agents we call Federal Smart House Regulators (FSHRs). The proposed management solution is hybrid in that sense that it takes full control of 'resource access' while at the same time give freedom to other entities presented in the house ecosystem, i.e., the appliances, to operate at their best capabilities. To enforce the resource-related decisions made by the FSHR, a series of resource gateways are considered in the solution. A greedy but fair and smart algorithm is also proposed as part of scheduling module of the FSHR in order to enable almost real-time operation of the solution which is a critical requirement for highly heterogeneous and dynamic house ecosystems. The proposed FSHR solution is by definition vendor agnostic because it allows entrance of any appliance in the ecosystem as long as they are authenticated by the house owner or the FSHR authority and also if they follow the basic FSHR requirements for resource request and utilization. Each FSHR agent is linked using the Telco connections to the FSHR support-cloud that ensures reliable state of the deployed intelligence and code in the FSHR modules and also provides trusted profiles of external variables such as resource price. The solution is highly localized within the premises of the house and therefore it could achieve high level of trust and adoption of house owners at scales. In addition, an object-oriented simulator is developed to evaluate the scalability and performance of the solution at various scales from a single house to a province.
Markovian clustering for the non-local means image denoising

The non-local means filter is one of powerful denoising methods which allows participation of far, but proper pixels in the denoising process. Although the weights of non-similar pixels are very small, high number of these pixels results in introduction of blur. In this work, we introduce an automatic and robust method to select the best candidate pixels based on their similarity to the target pixel. This method is based on graphs partitioning and uses Markovian clustering on the pixel adjacency graph (PAG). In this way, a set of relevant pixels is obtained that is used in weighted averaging for denoising each pixel. To evaluate the method, denoising of the natural images is conducted, and the results are compared to the standard NLM filter and the SVD-based method. The results are promising.
Combination of pruned Kohonen maps for on-line arabic characters recognition

The purpose of this study is to investigate a methodfor high performance on-line Arabic characters recognition.This method is based on the use of Kohonen mapsand their corresponding confusion matrices which serve toprune them of error-causing nodes, and to combine themconsequently. We use two Kohonen maps obtained usingtwo distinct character representations, namely, Fourier descriptorsand tangents extracted along the characters on-linesignals. The two Kohonen maps are then combined usinga majority vote decision rule that, for each character,favors the most reliable map. This combination, withoutadding in any significant way to the process complexity, affordsa much better recognition rate.
ICDAR 2015 contest on MultiSpectral Text Extraction (MS-TEx 2015)

The first competition on the MultiSpectral Text Extraction (MS-TEx) from historical document images has been organized in conjunction with the ICDAR 2015 conference. The goal of this contest is evaluation of the most recent advances in text extraction from historical document images captured by a multispectral imaging system. The MS-TEx 2015 dataset contains 10 handwritten and machine-printed historical document images along with eight spectral images for each image. This paper provides a report on the methodology and performance of the five submitted algorithms by various research groups across the world. The objective evaluation and ranking was performed by using well-known evaluation metrics of binarization and classification.
KMOD - a two-parameter SVM kernel for pattern recognition

It has been shown that the support vector machine (SVM) theory optimizes a smoothness functional hypothesis through kernel applications. We present KMOD, a two-parameter SVM kernel with distinctive properties of good discrimination between patterns while preserving the data neighborhood information. In classification problems, the experiments we carried out on the breast cancer benchmark produced better performance than the RBF kernel and some state of the art classifiers. It also generated favorable results when subjected to a 10-class problem of recognizing handwritten digits in the NIST database.
Shock filters for character image enhancement and peeling

In this paper, we propose a generalized shock modelfor the enhancement and restoration of degradedgray-level character images. This model is a quasi-linearhyperbolic partial differential equation withdiscontinuous coefficients, based on a recently-publishedwork. The model was extended to peeling charactershapes with a uniform and centered width of value K.These processing tools may significantly improve thesubsequent stages of the character image recognitiontask. Both models have been tested on a set of gray-levelcharacter images of varying quality from the CEDARdatabase, and experimental results have proved theefficiency and effectiveness of such modeling.
Compact support kernels based time-frequency distributions: Performance evaluation

This paper presents two new time-frequency distributions based on kernels with compact support (KCS) namely the separable CB (SCB) and the polynomial CB (PCB) TFDs. The implementation of these distributions follows the method developed for the Cheriet-Belouchrani CB TFD. The performance of this family of TFDs is compared to the most known quadratic distributions through tests on multi-component signals with linear and nonlinear frequency modulations (FMs) considering the noise effects as well. Comparisons are based on the evaluation of an objective criterion namely the Boashash-Sucic's normalized instantaneous resolution performance measure that allows to provide the optimized TFD using a specific methodology. In all presented examples, the KCS TFDs have been shown to have a significant interference mitigation, with the component energy concentration around their respective instantaneous frequency laws being well preserved giving high resolution measure values.
Environment-Aware Virtual Slice Provisioning in Green Cloud Environment

Environmental footprint resulting from datacenters activities can be reduced by both energy efficiency and renewable energy in a complementary fashion thanks to cloud computing paradigms. In a cloud hosting multi-tenant applications, virtual service providers can be provided with real-time recommendation techniques to allocate their virtual resources in edge, core, or access layers in an optimal way to minimize costs and footprint. Such a dynamic technique requires a flexible and optimized networking scheme to enable elastic virtual tenants spanning multiple physical nodes. In this paper, we investigate an environment-aware paradigm for virtual slices that allows improving energy efficiency and dealing with intermittent renewable power sources. A virtual slice consists of optimal flows assigned to virtual machines (VMs) in a virtual data center taking into account traffic requirements, VM locations, physical network capacity, and renewable energy availability. Considering various cloud consolidation schemes, we formulate and then propose an optimal solution for virtual slice assignment problem. Simulations on the GSN showed that the proposed model achieves better performance than the existing methods with respect to network footprint reductions.
Estimating accurate multi-class probabilities with support vector machines

In this paper, we propose a comparison of several post-processing methods for estimating multi-class probabilities with standard support vector machines. The different approaches have been tested on a real pattern recognition problem with a large number of training samples. The best results have been obtained by using a "one against air coupling strategy along with a softmax function optimized by minimizing the negative log-likelihood of the training data. Finally, the analysis of the error-reject tradeoff have shown that SVM allows to estimate probabilities more accurate than a classical MLP, which is indeed promising in the view of incorporated within pattern recognition system using probabilistic framework.
Incorporating user specific normalization in multimodal biometric fusion system

The aim of this paper is to investigate the user-specific two-level fusion strategy in the context of multimodal biometrics. In this strategy, a client-specific score normalization procedure is applied firstly to each of the system outputs to be fused. Then, the resulting normalized outputs are fed into a common classifier. The logistic regression, non-confidence weighted sum and the likelihood ratio based on Gaussian mixture model are used as back-end classifiers. Three client-specific score normalization procedures are considered in this paper, i.e. Z-norm, F-norm and the Model-Specific Log-Likelihood Ratio MSLLR-norm. Our first findings based on 15 fusion experiments on the XM2VTS score database show that when the previous two-level fusion strategy is applied, the resulting fusion classifier outperforms the baseline classifiers significantly and a relative reduction of more than 50% in the equal error rate can be achieved. The second finding is that when using this two-level user-specific fusion strategy, the design of the final classifier is simplified and performance generalization of baseline classifiers is not straightforward. A great attention must be given to the choice of the combination normalization-back-end classifier.
Semisupervised Learning Using Bayesian Interpretation: Application to LS-SVM

Bayesian reasoning provides an ideal basis for representing and manipulating uncertain knowledge, with the result that many interesting algorithms in machine learning are based on Bayesian inference. In this paper, we use the Bayesian approach with one and two levels of inference to model the semisupervised learning problem and give its application to the successful kernel classifier support vector machine (SVM) and its variant least-squares SVM (LS-SVM). Taking advantage of Bayesian interpretation of LS-SVM, we develop a semisupervised learning algorithm for Bayesian LS-SVM using our approach based on two levels of inference. Experimental results on both artificial and real pattern recognition problems show the utility of our method.
Effective document image deblurring via gradient histogram preservation

Traditional deblurring algorithms are often focused on natural-scaled images, which are not adapted for document texts and images without having some negative impacts on the accuracy of the OCR and the visual quality. In this paper, we propose a gradient histogram preservation method. An effective optimization method was developed and achieves satisfying results for kernel estimation. By combining the gradient histogram preservation prior with conventional image deblurring methods, it significantly improves the simulations and experimental results on document images and a high SSIM is achieved with the proposed method.
Iterative Classifiers Combination Model for Change Detection in Remote Sensing Imagery

In this paper, we propose a new unsupervised change detection method designed to analyze multispectral remotely sensed image pairs. It is formulated as a segmentation problem to discriminate the changed class from the unchanged class in the difference images. The proposed method is in the category of the committee machine learning model that utilizes an ensemble of classifiers (i.e., the set of segmentation results obtained by several thresholding methods) with a dynamic structure type. More specifically, in order to obtain the final “change/no-change” output, the responses of several classifiers are combined by means of a mechanism that involves the input data (the difference image) under an iterative Bayesian-Markovian framework. The proposed method is evaluated and compared to previously published results using satellite imagery.
Dynamic resource allocation of smart home workloads in the cloud

Cloud computing offers provision for elastic and scalable infrastructure resource allocation across the network that allows deployment of services for controlling home devices and appliances. Data generated from heterogeneous smart home devices are processed in different application services deployed in the cloud data center. The primary challenge of smart home service provider's is to optimize the cloud resource allocation while satisfying the Quality of Service(QoS) constraints of the application services. Service execution time is one of the most vital QoS parameters. In this paper, a queuing theoretic approach is proposed to model the smart home workload. First, M/M/c queue model is applied to find the response time of smart home tasks with light variation over the arrival rate. Then, Markovian Modulated Poisson Process (MMPP) is used to extend the model to a more advanced type of smart home processing workloads. Next, the optimal number of Virtual Machines(VMs) required deploying the application servers that can satisfy the execution time constraint of incoming workloads is calculated. Finally, total service time of a smart home application is calculated considering into account the possible level of concurrency and dependency among tasks of an application service. In the end, some numerical and simulation examples are provided to validate our findings.
Large Margin Low Rank Tensor Analysis

We present a supervised model for tensor dimensionality reduction, which is called large margin low rank tensor analysis (LMLRTA). In contrast to traditional vector representation-based dimensionality reduction methods, LMLRTA can take any order of tensors as input. And unlike previous tensor dimensionality reduction methods, which can learn only the low-dimensional embeddings with a priori specified dimensionality, LMLRTA can automatically and jointly learn the dimensionality and the low-dimensional representations from data. Moreover, LMLRTA delivers low rank projection matrices, while it encourages data of the same class to be close and of different classes to be separated by a large margin of distance in the low-dimensional tensor space. LMLRTA can be optimized using an iterative fixed-point continuation algorithm, which is guaranteed to converge to a local optimal solution of the optimization problem. We evaluate LMLRTA on an object recognition application, where the data are represented as 2D tensors, and a face recognition application, where the data are represented as 3D tensors. Experimental results show the superiority of LMLRTA over state-of-the-art approaches.
Semi-supervised learning for weighted LS-SVM

The least squares support vector machine (LS-SVM) is an interesting variant of the SVM. It performs structural risk through margin-maximization and has excellent power of generalization. For some applications, it is more interesting to use the weighted LS-SVM where the impact of each training sample is controlled by weighting factors. In this paper, we consider the use of the weighted LS-SVM in semi-supervised learning. We propose an algorithm to perform this type of learning by extending the transductive SVM idea. We tested our algorithm on both artificial and real problems and demonstrate its usefulness comparing with other semi-supervised learning methods.
Phase-Based Binarization of Ancient Document Images: Model and Applications

In this paper, a phase-based binarization model for ancient document images is proposed, as well as a postprocessing method that can improve any binarization method and a ground truth generation tool. Three feature maps derived from the phase information of an input document image constitute the core of this binarization model. These features are the maximum moment of phase congruency covariance, a locally weighted mean phase angle, and a phase preserved denoised image. The proposed model consists of three standard steps: 1) preprocessing; 2) main binarization; and 3) postprocessing. In the preprocessing and main binarization steps, the features used are mainly phase derived, while in the postprocessing step, specialized adaptive Gaussian and median filters are considered. One of the outputs of the binarization step, which shows high recall performance, is used in a proposed postprocessing method to improve the performance of other binarization methodologies. Finally, we develop a ground truth generation tool, called PhaseGT, to simplify and speed up the ground truth generation process for ancient document images. The comprehensive experimental results on the DIBCO'09, H-DIBCO'10, DIBCO'11, H-DIBCO'12, DIBCO'13, PHIBD'12, and BICKLEY DIARY data sets show the robustness of the proposed binarization method on various types of degradation and document images.
Empirical error based optimization of SVM kernels: application to digit image recognition

We address the problem of optimizing kernel parameters in support vector machine modeling, especially when the number of parameters is greater than one as in polynomial kernels and KMOD, our newly introduced kernel. The present work is an extended experimental study of the framework proposed by Chapelle et al. (2001) for optimizing SVM kernels using an analytic upper bound of the error. However our optimization scheme minimizes an empirical error estimate using a quasi-Newton optimization method. To assess our method, the approach is further used for adapting KMOD, RBF and polynomial kernels on synthetic data and NIST database. The method shows a much faster convergence with satisfactory results in comparison with the simple gradient descent method.
Optimized resource allocation in edge-cloud environment

Edge computing has recently emerged as a promising solution that provides services with stringent requirements, like extremely low latency and bandwidth costs, for a class of new 5G applications. However, due to their limited computing capacity, edge data centers are not able to afford a large number of user requests. A combined scheme of edge and cloud is therefore required to provide extended cloud computing service at the last-mile close to end-users. Key challenges faced by this complex system which includes edge, core, and public cloud is optimizing resource allocation to provide desired QoS levels and meet data center capacity constraints. In this paper, we present a completed formulation of the optimized resource allocation problem in the edge-cloud environment, and then propose an algorithmic solution to the problem. Experimental simulations show that our solution is cost-effective with an improvement in terms of efficiency compared to prior work
Indexing On-line Handwritten Texts Using Word Confusion Networks

In the context of handwriting recognition, word confusion networks (WCN) are convenient representations of alternative recognition candidates. They provide alignment for mutually exclusive words along with the posterior probability of each word. In this paper, we present a method for indexing on-line handwriting based on WCN. The proposed method exploits the information provided by WCN in order to enhance relevant keyword extraction. In addition, querying the index for a given keyword has worst case complexity O(log n), as compared to usual keyword spotting algorithms which run in O(n). Experiments show promising results in keyword retrieval effectiveness by using WCN when compared to keyword search over 1-best recognition results.
Handling Out-of-Vocabulary Words and Recognition Errors Based on Word Linguistic Context for Handwritten Sentence Recognition

In this paper we investigate the use of linguistic information given by language models to deal with word recognition errors on handwritten sentences. We focus especially on errors due to out-of-vocabulary (OOV) words. First, word posterior probabilities are computed and used to detect error hypotheses on output sentences. An SVM classifier allows these errors to be categorized according to defined types. Then, a post-processing step is performed using a language model based on Part-of-Speech (POS) tags which is combined to the n-gram model previously used. Thus, error hypotheses can be further recognized and POS tags can be assigned to the OOV words. Experiments on on-line handwritten sentences show that the proposed approach allows a significant reduction of the word error rate.
Virtual Edge-Based Smart Community Network Management

Providing multitenant, multiaccess, and multiservice solutions in communities is a key enabler to sustain innovations and economic development in society. This article investigates a solution for rearchitecting a telecommunications company's (telco's) central office to offer services in a smart community, enabled by virtual network function elements running on a smart edge. At the same time, these elements manage a multiaccess underlying infrastructure and deploy telco services with minimal resource consumption. This solution has successfully been implemented in a living lab model the authors built within a Canadian university, providing various Internet of Things (IoT) services such as smart grid monitoring and public safety. The virtual smart edge flexibly routes traffic, aggregates, and splits flows across a heterogeneous network to achieve network operational efficiency, as well as optimizes computing resource allocation for last-mile IoT services hosted close to the end user. Instead of reducing resource consumption in a single home as in the traditional approach, this solution minimizes a community's global consumption.
A new representation of character shape and its use in on-line character recognition by a self organizing map

The purpose of this study is to investigate a new representation of shape and its use in handwritten on-line character recognition by a Kohonen associative memory. This representation is based on the empirical distribution of features such as tangents, and tangent differences at regularly spaced points along the character signal. Recognition is carried out by a Kohonen neural network trained using the representation. In addition to the traditional Hudidean distance, functions such as the Kullback Leibler divergence and the Hellinger distance are investigated to evaluate similarity of feature vectors because these functions provide measures of distance between distributions. We report on extensive experiments using a database of on-line Arabic characters produced without constraints by a large number of writers. Comparative results show the representation relevance and the superior performance of the scheme.
An active contour based method for image binarization: Application to degraded historical document images

Old document images often suffer from degradations that render their binarization a challenging task. In this paper, a new binarization method for degraded document images is presented. The method is based on active contours evolving according to intrinsic geometric measures of the document image; Niblack's thresholding is also used to control the active contour propagation. The validity of the proposed method is demonstrated on both recent and historical document images including different types of degradations, the results are compared with a number of known techniques in the literature.