nils-herrmann/clean_scirepeval huggingface dataset:
Green ICT: The rationale for a focus on curbing greenhouse gas emissions

Green Information and Communications Technologies (ICT) have the potential to significantly curb greenhouse gas emissions worldwide. This paper provides a rationale for focusing ICT research and development efforts on curbing GHG emissions as opposed to only targeting energy efficiency goals. ICT's potential as a GHG emission reduction agent is described as are recent developments with respect to the creation of international standards for quantifying these ICT GHG emission reductions.
Unsupervised Ensemble of Experts (EoE) Framework for Automatic Binarization of Document Images

In recent years, a large number of binarization methods have been developed, with varying performance generalization and strength against different benchmarks. In this work, to leverage on these methods, an ensemble of experts (EoE) framework is introduced, to efficiently combine the outputs of various methods. The proposed framework offers a new selection process of the binarization methods, which are actually the experts in the ensemble, by introducing three concepts: confident ness, endorsement and schools of experts. The framework, which is highly objective, is built based on two general principles: (i) consolidation of saturated opinions and (ii) identification of schools of experts. After building the endorsement graph of the ensemble for an input document image based on the confident ness of the experts, the saturated opinions are consolidated, and then the schools of experts are identified by thresholding the consolidated endorsement graph. A variation of the framework, in which no selection is made, is also introduced that combines the outputs of all experts using endorsement-dependent weights. The EoE framework is evaluated on the set of participating methods in the H-DIBCO'12 contest and also on an ensemble generated from various instances of grid-based Sauvola method with promising performance.
Energy Efficient Resource Allocation in Cloud Computing Environments

Power consumption is one of the major concerns for the cloud providers. The issue of disorganized power consumption can be categorized into two main groups: one caused by server operations and one occurred during the network communications. In this paper, a platform for virtual machine (VM) placement/migration is proposed to minimize the total power consumption of cloud data centers (DCs). The main idea behind this paper is that with the collaboration of optimization scheduling and estimation techniques, the power consumption of DC can be optimally lessened. In the platform, an estimation module has been embedded to predict the future loads of the system, and then, two schedulers are considered to schedule the expected and unpredicted loads, respectively. The proposed scheduler applies the column generation technique to handle the integer linear/quadratic programming optimization problem. Also, the cut-and-solve-based algorithm and the call back method are proposed to reduce the complexity and computation time. Finally, numerical and experimental results are presented to validate our findings. Adaptation and scalability of the proposed platform result in a notable performance in VM placement and migration processes. We believe that our work advances the state of the art in workload estimation and dynamic power management of cloud DCs, and the results will be helpful to cloud service providers in achieving energy saving.
Help-training semi-supervised LS-SVM

Help-training for semi-supervised learning was proposed in our previous work in order to reinforce self-training strategy by using a generative classifier along with the main discriminative classifier. This paper extends the Help-training method to least squares support vector machine (LS-SVM) where labeled and unlabeled data are used for training. Experimental results on both artificial and real problems show its usefulness when comparing with other classical semisupervised methods.
Content analysis in document images: a scale space approach

With the growing interest in automatic transformation of paper document to its electronic version, geometric and logical structures have become an active research area for a decade. Nowadays, kernel scale space has been widely adopted as the most promising multi-scale image document analysis method. Yet still, traditional methods using scale space approach has its limitations: they are useful mostly on character extraction and they carry a large computational load. In view of these limitations, this paper proposes a new approach using scale space in order to analyse the composite document content. In the proposed method, scale space transform is used to decompose an image into different scaled objects where the scale value is used for detecting progressively finer objects: text, line drawing, logo, and image, with encouraging results on real-life data.
Cognitive behavior analysis framework for fault prediction in cloud computing

Complex computing systems, including clusters, grids, clouds and skies, are becoming the fundamental tools of green and sustainable ecosystems of future. However, they can also pose critical bottlenecks and ignite disasters. The complexity and high number of variables could easily go beyond the capacity of any analyst or traditional operational research paradigm. In this work, we introduce a multi-paradigm, multi-layer and multi-level behavior analysis framework which can adapt to the behavior of a target complex system. It not only learns and detects normal and abnormal behaviors, it could also suggest cognitive responses in order to increase the system resilience and its grade. The multi-paradigm nature of the framework provides a robust redundancy in order to cross-cover possible hidden aspects of each paradigm. After providing the high-level design of the framework, three different paradigms are discussed. We consider the following three paradigms: Probabilistic Behavior Analysis, Simulated Probabilistic Behavior Analysis, and Behavior-Time Profile Modeling and Analysis. To be more precise and because of paper limitations, we focus on the fault prediction in the paper as a specific event-based abnormal behavior. We consider both spontaneous and gradual failure events. The promising potential of the framework has been demonstrated using simple examples and topologies. The framework can provide an intelligent approach to balance between green and high probability of completion (or high probability of availability) aspects in computing systems.
Carbon Metering and Effective Tax Cost Modeling for Virtual Machines

With raising concerns about global warming and environmental impacts of Greenhouse Gases (GhGs) emissions, energy efficiency and carbon footprint reduction attracted many researchers to provide efficient models and tools for energy, carbon, and cost estimation and management. In this paper, a model for measuring the energy consumption and carbon footprint of an individual virtual machine is presented based on resource usage and performance monitoring counters. A simple cost model is represented in order to evaluate the energy consumption and carbon footprint models. The model evaluated on a simulated virtual private cloud with different methodologies such as server consolidation and multi-level grouping heuristic algorithms. The results show that such heuristic algorithms are able to significantly reduce the cost of energy and carbon footprint of an individual virtual machine in comparison with other methodologies such as server consolidation. The results also show that this cost reduction efficiency is positively correlated to the increase in carbon footprint tax rates.
Mean Deviation Similarity Index: Efficient and Reliable Full-Reference Image Quality Evaluator

Applications of perceptual image quality assessment (IQA) in image and video processing, such as image acquisition, image compression, image restoration, and multimedia communication, have led to the development of many IQA metrics. In this paper, a reliable full reference IQA model is proposed that utilize gradient similarity (GS), chromaticity similarity (CS), and deviation pooling (DP). By considering the shortcomings of the commonly used GS to model the human visual system (HVS), a new GS is proposed through a fusion technique that is more likely to follow HVS. We propose an efficient and effective formulation to calculate the joint similarity map of two chromatic channels for the purpose of measuring color changes. In comparison with a commonly used formulation in the literature, the proposed CS map is shown to be more efficient and provide comparable or better quality predictions. Motivated by a recent work that utilizes the standard DP, a general formulation of the DP is presented in this paper and used to compute a final score from the proposed GS and CS maps. This proposed formulation of DP benefits from the Minkowski pooling and a proposed power pooling as well. The experimental results on six data sets of natural images, a synthetic data set, and a digitally retouched dataset show that the proposed index provides comparable or better quality predictions than the most recent and competing state-of-the-art IQA metrics in the literature, it is reliable and has low complexity. The MATLAB source code of the proposed metric is available at https://dl.dropboxusercontent.com/u/74505502/MDSI.m.
Forensic evidence reporting using GMM-UBM, JFA and I-vector methods: Application to Algerian Arabic dialect

Nowadays, under controlled conditions the speaker verification systems based on the GMM-UBM paradigm show very good performance. However, in forensic investigation activities the conditions; in which recordings are acquired; are uncontrollable, a naive use of the baseline GMM-UBM system without feature normalization, model transformation and score normalization techniques yields to unreliable forensic reporting. In this paper, we investigate forensic reporting using corpus-based likelihood ratio evaluation; which gained popularity in recent years; using two state-of-the-art speaker recognition systems: The JFA system which models explicitly the speaker and session variability during training stage and the I-vector paradigm which models the total variability and use compensation techniques to handle session mismatch. The GMM-UBM, Joint Factor Analysis and I-vector systems are compared in verification performance using Half Total Error Rates (HTER) and in forensic reporting using TIPPET plots. Experimental results on an Algerian Arabic dialect under different telephonic recording conditions confirm the robustness of I-vector and JFA systems in handling cross-channel mismatch and highlight clearly the drastic deterioration of the performance of the GMM-UBM system.
Face recognition based on 2DPCA, DIAPCA and DIA2DPCA in DCT domain

In this paper, we introduce 2DPCA, DiaPCA and DiaPCA+2DPCA in DCT domain for the aim of face recognition. The 2D DCT transform has been used as a preprocessing step, then 2DPCA, DiaPCA and DiaPCA+2DPCA are applied to a wtimesw upper left block of the global 2D DCT transform matrix of the original images. The experiments which are performed on the ORL face database show that: in addition to the expected considerable gain in both the training and testing time, the recognition rate is higher using 2DPCA, DiaPCA and DiaPCA+2DPCA in DCT domain than applying these three methods directly on the raw pixel images.
A new non-linear exponential 2-D adaptive filter and its application in texture characterization

We propose, in this paper, a new non-linear exponential adaptive bi-dimensional (2D) filter for image modeling. The filter coefficients are updated with the least mean square (LMS) algorithm. Furthermore, the proposed nonlinear model is used for texture modeling with a 2D auto-regressive (AR) adaptive model. The characterization efficiency of the proposed exponential model is compared with the 2D linear AR model updated with the LMS algorithm. The comparison criteria is based on the computation of a characterization rate using the ratio of "between-class" variances with respect to "within-class" variances of the estimated coefficients. Extensive experiments show that the exponential model coefficients give better results in texture discrimination than those of the linear model, even in a noisy context.
A threshlod selection method based on multiscale and graylevel co-occurrence matrix analysis

Noise and complex backgrounds often make the thresholding of degraded document images difficult. In this paper, we propose a new threshold selection method to handle severely degraded document images. First, multiscale image description is adopted to analyze the image edge. From this, edge pixel pair information is derived and recorded by a graylevel co-occurrence matrix. An appropriate threshold value is chosen by measuring the edge pixel pair co-occurrence matrix. The new method is tested with degraded document images. The experimental results show it is resistant to noise and complex backgrounds.
Ancient degraded document image binarization based on texture features

In this paper, we present a promising method for binarization of historical and degraded document images, based on texture features. The proposed method is an adaptive threshold-based. This latter is computed by using a descriptor based on a co-occurrence matrix. The proposed method is tested objectively, using DIBCO dataset degraded documents and subjectively, using a set of ancient degraded documents provided by a national library. The results are satisfactory and promising, and present an improvement to classical methods.
Classification of time-series data using a generative/discriminative hybrid

Classification of time-series data using discriminative models such as SVMs is very hard due to the variable length of this type of data. On the other hand generative models such as HMMs have become the standard tool for modeling time-series data due to their efficiency. This paper proposes a general generative/discriminative hybrid that uses HMMs to map the variable length time-series data into a fixed p-dimensional vector that can be easily classified using any discriminative model. The hybrid system was tested on the MNIST database for unconstrained handwritten numerals and has achieved an improvement of 1.23% (on the test set) over traditional 2D discrete HMMs.
Evolving Fuzzy Classifiers: Application to Incremental Learning of Handwritten Gesture Recognition Systems

In this paper, we present a new method to design customizable self-evolving fuzzy rule-based classifiers. The presented approach combines an incremental clustering algorithm with a fuzzy adaptation method in order to learn and maintain the model. We use this method to build an evolving handwritten gesture recognition system. The self-adaptive nature of this system allows it to start its learning process with few learning data, to continuously adapt and evolve according to any new data, and to remain robust when introducing a new unseen class at any moment in the life-long learning process.
KMOD - a new support vector machine kernel with moderate decreasing for pattern recognition. Application to digit image recognition

A new direction in machine learning area has emerged from Vapnik's theory in support vectors machine (SVM) and its applications on pattern recognition. In this paper we propose a new SVM kernel family, called KMOD (kernel with moderate decreasing) with distinctive properties that allow better discrimination in the feature space. The experiments that we carry out show its effectiveness on synthetic and large-scale data. We found KMOD performs better than RBF and exponential RBF kernels on the two-spiral problem. In addition, a digit recognition task was processed using the proposed kernel. The results show, at least, comparable performances to state of the art kernels.
Smart Packet: Re-distributing the Routing Intelligence among Network Components in SDNs

In this work, a new region-based, multipath-enabled packet routing is presented and called Smart Packet Routing. The proposed approach provides several opportunities to re-distribute the smartness and decision making among various elements of a network including the packets themselves toward providing a decentralized solution for SDNs. This would bring efficiency and scalability, and therefore also lower environmental footprint for the ever-growing networks. In particular, a region-based representation of the network topology is proposed which is then used to describe the routing actions along the possible paths for a packet flow. In addition to a region stack that expresses a partial or full regional path of a packet, QoS requirements of the packet (or its associated flow) is considered in the packet header in order to enable possible QoS-aware routing at region level without requiring a centralized controller.
Handwritten characters recognition based on SKCS-polyline and hidden Markov model (HMM)

In this paper, we present a new handwritten character recognition algorithm. The proposed algorithm is based on three main steps. In the first one the original characters are segmented using separable kernel compact support (SKCS) method. In the second step a preprocessing phases: skeleton, separation, resizing, and a polyline approximation processes are then applied to the SKCS segmented characters. In the last step a handwritten hidden Markov model (HMM) is used to recognize the characters from the Cartesian coordinates of their main strokes. Simulation results are presented showing the usefulness of this new recognition method.
Model-based character extraction from complex backgrounds

This paper proposes a model-based character extraction method. We model a pixel belonging to a character as a double-edge, whose range is defined by the stroke width, and whose intensity is proportional to the local contrast. By extracting such double-edge feature at a predefined stroke width, sharply changing as well as slowly varying backgrounds can be eliminated. A set of morphological operators is designed to extract the double-edge feature at each pixel of the raw image, and the characters are extracted by thresholding these features. A goal directed evaluation of extraction of the courtesy amount from bank cheques reveals the advantages of the proposed method over other existing methods. Visual inspection of legal amount extraction shows further promise in extracting characters from complex backgrounds.
Constrained Energy Maximization and Self-Referencing Method for Invisible Ink Detection from Multispectral Historical Document Images

This article deals with a serious form of degradation that often affects the readability of historical document images: the invisibility of text or ink. Due to wear over long periods of storage, the ink may become invisible to the human eye, an undesirable situation for scholars (i.e. Indian Ocean World project (IOW1, with whom we are working closely). Because only the class of ink is known a priori (reference), it can be considered as a target to be detected. This can be achieved by designing a linear filter that maximizes an energy function while minimizing the false detection of document image background elements. For each document image in which the ink is targeted, an internal reference is defined by a new self-referencing strategy. The proposed method is compared with a state-of-the-art methods, and validated on samples of real historical document images.
Optimized IoT service orchestration

Although Internet of Things (IoT) has been growing rapidly in recent years, the deployment of large-scale IoT solutions are facing several challenges, in particular resource optimization. This is due to high requirements of IoT applications which are usually composed of numerous services each of which could be provided by multiple providers with various specifications. Since an IoT service is often micro-services, dynamically opting appropriate micro-services to guarantee the quality of delivered services to end-users is challenging because conventional network paradigm was not designed to support removal or insertion of modular services on the fly. In this paper, we investigate a cloud-based IoT service orchestration architecture to facilitate the IoT service deployment and propose algorithms to optimize service chaining process. Experimental results show our algorithms effectively orchestrating microservices by reducing total service cost up to 36% while maintaining real-time requirement.
A new weighted ℓp-norm for sparse hyperspectral unmixing

In this paper, we propose a new approach to approximate the ℓ<inf>0</inf>-norm for the sparse hyperspectral unmixing. In our method, we first approximate the ℓ<inf>0</inf>-norm with the ℓ<inf>p</inf>-norm and reduce p iteratively. It is motivated by our theorem that the set of minima for the ℓ<inf>p</inf>-norm problem is continuous in terms of p, which implies that smooth iterative reduction of p results in an enhanced solution. We introduce a weighted ℓ<inf>1</inf>-norm approximation of the ℓ<inf>p</inf>-norm problem involving a parameter e to deal with the fact that the ℓ<inf>p</inf>-norm problem is not Lipschitz continuous for p < 1 and propose an updating rule for the pair (p, ∊). To solve this approximated problem using the previous solution, we employ the alternating direction method of multipliers (ADMM) approach to derive the remaining updating steps. Experimental results show that our proposed method outperforms several state-of-the-art methods in terms of the reconstruction errors and their probability of success.
Spiral recognition methodology and its application for recognition of Chinese bank checks

This paper presents the spiral recognition methodology with its application in unconstrained handwritten Chinese legal amount recognition in a practical environment of a CheckReader/spl trade/. This paper first describes the failed application of neural network - hidden Markov model hybrid recognizer on Chinese bank check legal amount recognition, and explains the reasons for the failure: the neural network - hidden Markov model hybrid recognizer cannot handle the complexity in the training for Chinese legal amounts. Then a spiral recognition methodology is presented. This methodology enables the system to increase its recognition power (both the recognition rate and the number of recognized characters) during the training iterations. Some experiments were done to show that the spiral recognition methodology has a high performance in the recognition of unconstrained handwritten Chinese legal amounts. The recognition rate at the character level is 93.5%, and the recognition rate at the legal amount level is 60%. Combined with the recognition of courtesy amount, the overall error rate is less than 1%.
A new framework based on signature patches, micro registration, and sparse representation for optical text recognition

A framework for development of segmentation-free optical recognizers of ancient manuscripts, which work free from line, word, and character segmentation, is proposed. The framework introduces a new representation of visual text using the concept of signature patches. These patches which are free from traditional guidelines of text, such as the baseline, are registered to each other using a microscale registration method based on the estimation of the active regions using a multilevel classifier, the directional map. Then, an one-dimensional feature vector is extracted from the registered signature patches, named spiral features. The incremental learning process is performed using a sparse representation using a dictionary of spiral feature atoms. The framework is applied to the George Washington database with promising results.