Reviewerly/exHarmony huggingface dataset:
Characterization of adhesive bonded CFRP laminates using full-field digital image stereo-correlation and finite element analysis

In this paper, the full field experimental analysis, and numerical and morphological investigation on two groups of adhesive bonded CFRP laminates are presented. The Digital Image Stereo Correlation (Stereo DIC) technique is used to characterize the out of plane deformation of the specimens under loading. Despite the simplicity of the adopted approach, the analysis and the comparison between the load-displacement curve and the stereo DIC results show that a significant damage in the specimens can be found during the early stages of the test. This first significant failure is the result of out of plane deformation due to the adhesive opening, which plays a crucial role in the ultimate failure of the specimen. The stereo DIC and mechanical results prove the phenomena; while the localization of the first significant failure has also been verified using a numerical model. The morphological studies of the adhesive coated specimen after failure shows the nature of the fracture and the load carrying behaviour of the adhesive. It has been quantitatively proved that the thickness of the adherend has a very less substantial role in the failure of specimens in comparison with the thickness of the adhesive.
A Multiobjective Optimization Approach for Selecting Risk Response Strategies of Software Project: From the Perspective of Risk Correlations

Studies on software risk response theories and methods play an important role in improving the success rate of software project. With the requirement of software risk management, the existing single-objective risk response strategies section model is difficult to manage software risks effectively. This paper regards software risk response cost and software risk exposure as optimization objectives and proposes a multiobjective risk response strategies optimization model for software project. Furthermore, it analyzes the risk correlation from the perspective of risk probability dependence and risk loss interaction and puts forward a multiobjective risk response strategies optimization model for software project from the perspective of risk correlation. Empirical analysis results show that there is a trade-off relationship between the software risk exposure and software risk response cost. The software manager can identify the corresponding optimal risk response strategies according to the actual risk response budget. The results also indicate that the consequence of the multiobjective risk response strategies optimization model for software project considering risk correlation can better describe the actual situation of risk management.
Experimental demonstration of imaging hidden objects in opaque liquid-based media by fusion of single-shot multiview polarized and unpolarized speckle images

Turbid water-based liquids are common scattering media within which imaging objects is challenging, since scattering particles of various origins and sizes may cause severe image degradation, resulting in poor object recovery and resolution. Therefore, counteracting the nontrivial effects of light scattering is a primary challenge, limiting the utility of optical imaging within liquid media. In this work, we sought to circumvent the optical degradation in turbid liquid media and to recover high-quality images of hidden objects by averaging polarized speckle images projected from several directions through a lens array. Inspired by astronomy imaging techniques, the averaging process utilized a Shift-and-Add (SAA) algorithm developed to reconstruct atmospherically-degraded solar images. Both linear and circular polarization strategies were then applied to further improve image reconstruction. Experiments were carried out using two targets, each having a distinct geometrical shape, embedded within turbid liquid media comprising a range of dilutions of commercially available cow's milk in seawater. The media were irradiated with a polarized laser beam, after which multiple polarized speckled images were captured with a CCD camera. Offline, images were first shifted to a common center and then fused, using the SAA algorithm, to yield reconstructed images of each hidden object. Quantitative image quality metrics, including SNR, entropy, and sharpness were applied to evaluate quality of the reconstructed images. The reconstructed images obtained demonstrate successful adaptation of our methodology for the identification of hidden objects within turbid media. To the best of our knowledge, this is the first report to demonstrate successful imaging of hidden object images within turbid liquid media by averaging multiple polarized speckle projections.
D-SRTF: Distributed Shortest Remaining Time First Scheduling for Data Center Networks

Many recent works utilize scheduling to minimize the Flow Completion Time (FCT) in Data Center Networks (DCN), like PIAS using Shortest Job First (SJF) scheduling and pFabric using Shortest Remaining Size First (SRSF) scheduling. However, they only consider the flow size information, without consideration of available bandwidth of the network, leading to inferior performance when the network is congested. Besides, information on flow size is hard to obtain in practice. Moreover, although a centralized scheduler may have optimal scheduling decisions, it suffers from high system overhead. Therefore, a new DCN scheme is expected which is deployment-friendly and implements SRTF scheduling in a distributed manner. In this paper, we propose D-SRTF, a light-weight yet effective DCN scheme to implement SRTF scheduling. D-SRTF determines the remaining time of each flow according to the estimated remaining flow size and the available bandwidth, in order to determine the priority of each flow. Switches perform Strict Priority (SP) scheduling according to the priority of each flow, in order to realize SRTF scheduling. Experiments show that D-SRTF performs better than the currently best implementable scheme, PIAS, and could perform better than pFabric if information on flow size is available.
The minimum level for soil allocation using topsoil reflectance spectra: Genus or species?

Spectroscopy has been used for rapid determination of soil physicochemical parameters and soil classification or allocation, usually at the great group level of soil genetic classification. Soil genetic classification is still the main system of classification used in China, on which the Chinese Soil Classification System is based, and includes order, suborder, great group, subgroup, genus, species, and subspecies. In this paper, we introduce a soil allocation model that uses topsoil spectral characteristics and determined the minimum level for soil allocation. The topsoil spectra of four typical soils from the Songnen Plain in Northeast China were used to extract spectral feature parameters (SFPs) with clear physical meaning for soil allocation at the great group, genus, and species levels, and principal components of reflectance and first-derivative reflectance were used for allocation to determine the optimal input. Multinomial logistic regression MLR, multi-layer perception neural network, MLPNN, and decision trees (DT) were used to build allocation models. The results show the following: 1) the first and second absorption valleys of the topsoil spectral curves are substantially different between soils at the great group and genus levels but similar at the species level; 2) the topsoil samples can be allocated with SFPs, for which genus is the minimum level within the Chinese Soil Classification System for soil allocation with topsoil hyperspectral reflectance; and 3) the DT model with SFPs as input is the most accurate at the genus level; the accuracy of allocation is 83.3% and the Kappa coefficient, an evaluation index, is 0.8. Our results suggest that soil allocations using topsoil hyperspectral reflectance can be performed accurately at genus level of classification, which can be of considerable help in research on the effects of soil minerals on soil spectral characteristics and in detailed soil mapping.
Designing Plateaued Boolean Functions in Spectral Domain and Their Classification

The design of plateaued functions over GF(2) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> , also known as 3-valued Walsh spectra functions (taking the values from the set {0, ±2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Γ(n+s/2)1</sup> }), has been commonly approached by specifying a suitable algebraic normal form which then induces this particular Walsh spectral characterization. In this paper, we consider the reversed design method which specifies these functions in the spectral domain by specifying a suitable allocation of the nonzero spectral values and their signs. We analyze the properties of trivial and nontrivial plateaued functions (as affine inequivalent distinct subclasses), which are distinguished by their Walsh support Sf (the subset of GF(2)n having the nonzero spectral values) in terms of whether it is an affine subspace or not. The former class exactly corresponds to partially bent functions and admits linear structures, whereas the latter class may contain functions without linear structures. A simple sufficient condition on Sf , which ensures the nonexistence of linear structures, is derived and some generic design methods of nontrivial plateaued functions without linear structures are given. The extended affine equivalence of plateaued functions is also addressed using the concept of dual of plateaued functions. Furthermore, we solve the problem of specifying disjoint spectra (non)trivial plateaued functions of maximal cardinality whose concatenation can be used to construct bent functions in a generic manner. This approach may lead to new classes of bent functions due to large variety of possibilities to select underlying duals that define these disjoint spectra plateaued functions. An additional method of specifying affine in equivalent plateaued functions, obtained by applying a nonlinear transform to their input domain, is also given.
A Novel Polynomial-Chaos-Based Kalman Filter

This letter proposes a new polynomial-chaos-based Kalman filter (PCKF) that is able to track the dynamics of nonlinear dynamical systems subject to strong nonlinearities. Specifically, by resorting to the polynomial chaos theory, the uncertainties of the model and the measurements can be effectively propagated through a set of collocation points. However, this polynomial-chaos-based algorithm suffers from the curse of dimensionality. To overcome this weakness, a dimension reduction strategy is proposed based on variance analysis. This allows us to construct more effective collocations points and to significantly improve the computational efficiency of the PCKF without any loss of estimation accuracy. Simulations carried out on various IEEE systems validate the effectiveness of the proposed method.
Functional networks and applications: A survey

Functional networks (FNs) are extensions of neural networks (NNs). Unlike NNs, FNs considers general functional models instead of sigmoid-like models. Additionally, in FNs, there are no weights associated with the links that connect neurons. In this paper, we review the research progress and applications of FNs models in recent years. First, we introduce FNs architecture, three typical functional models and the learning process, and we explain the differences between NNs and FNs. Second, we discuss recent applications of FNs that have been introduced in many fields, such as time series prediction, differential and functional equations, pattern classification, detection and prediction, approximation computation, complex system modeling, computer-aided design (CAD), and linear and nonlinear regression. Finally, we present some remarks on future research directions for FNs.
Retraining-free methods for fast on-the-fly pruning of convolutional neural networks

Modern Convolutional Neural Networks (CNNs) are complex, encompassing millions of parameters. Their deployment exerts computational, storage and energy demands, particularly on embedded platforms. Existing approaches to prune or sparsify CNNs require retraining to maintain inference accuracy. Such retraining is not feasible in some contexts. In this paper, we explore the sparsification of CNNs by proposing three model-independent methods. Our methods are applied on-the-fly and require no retraining. We show that the state-of-the-art models' weights can be reduced by up to 73% (compression factor of 3.7x) without incurring more than 5% loss in Top-5 accuracy. Additional fine-tuning gains only 8% in sparsity, which indicates that our fast on-the-fly methods are effective.
Seeing All From a Few: $\ell_{1}$ -Norm-Induced Discriminative Prototype Selection

Prototype selection aims to remove redundancy and irrelevance from large-scale data by selecting an informative subset, which makes it possible to see all data from a few prototypes. However, due to the outliers and uncertain distribution of the data, the selected prototypes are generally less representative and diversified. To alleviate this issue, we develop, in this paper, a l1 -norm-induced discriminative prototype selection model ( l1 -ProSe). Inspired by the good performance of sparse representation, the sparsity property of data is rationally exploited in the formulated model. Meanwhile, to characterize the pairwise similarity in the learned sparse representation space, a more promising l1 -norm metric is applied for robust selection instead of the popularly used Euclidean metric in previous works. Considering the convexity of the model to be solved, a composite block coordinate descent solver is presented with rigorous theoretical analysis on its convergence. Furthermore, we extend our model to support online prototype selection by using already obtained prototypes and newly arrived data. Experimental results on synthetic data sets and some applications such as video summarization, motion segmentation, and scene categorization demonstrate that the proposed method is considerably superior to the state-of-the-art methods in the prototype selection.
DCAT-AP representation of Czech National Open Data Catalog and its impact

Open data is now a heavily discussed topic around the world and in the European Union. In the Czech Republic, open data is a term anchored in legislation, which includes the requirement of registration of all open data in the Czech National Open Data Portal (NODC). In this paper we describe the NODC, its architecture, dataset registration processes including the harvesting of Local Open Data Catalogs (LODCs), proprietary XML API and its obsolete dataset viewer. Next we describe the process of transformation of the NODC metadata to the DCAT-AP v1.1 RDF representation from the data model point of view and from the technical environment point of view. We describe the dataset quality measurements computed using the new data representation and its further impact on the Linked Open Data (LOD) environment including the harvesting of the metadata by the European Data Portal (EDP). Finally, we evaluate the data transformation and publishing environment from the usability, portability, availability and performance perspectives.
Fast and Efficient Multiview Access Control Mechanism for Cloud Based Agriculture Storage Management System

Agriculture and its related industries are the backbone of many countries' economic growth. To achieve an efficient agricultural management system, remote sensing forecasting and GIS technology are providing information to users/stakeholders of various agricultural application uses. This information is huge in size and is stored in the cloud computing storage environment. Minimizing data access and storage costs on such an environment is desired. For achieving fine-grained role-based access control mechanisms, researchers are now focusing on ensuring such roles are enforced correctly. Existing models, though they are using role-based access control at various levels, are facing challenges like high computation rates and storage overhead. Currently, existing systems are using XML and UML for role and user creation. To address these research challenges, this article presents a model Fast and Efficient Multi View Access Control (FEMVAC) using the Amazon S3 public cloud environment for agriculture. The model minimizes storage overhead by adopting a banarization method over UML/XML method. The experimental outcome shows that the FEMVAC method is efficient compared with existing models.
Block sparse representation for pattern classification: Theory, extensions and applications

By exploiting the low-dimensional structure of high-dimensional data, sparse representation based classifiers (SRC) has recently attracted massive attention in pattern recognition. In this paper, we study a natural generalization of SRC, i.e., block sparse representation based classifiers (BSRC), which takes into account the block structure of the dictionary. Our contributions are two-fold: (1) we provide theoretical guarantees for BSRC and theoretically show that BSRC performs perfect classification for any test sample under both cases of independent subspaces and arbitrary subspaces settings; (2) we extend BSRC and propose three robust BSRC methods based on M-estimators originating in robust statistics. This is motivated by the observation that many previous representation based classifiers utilize the mean square error (MSE) criterion as the loss function, which is sensitive to outliers and complicated noises in reality. In contrast, M-estimators has shown much stronger robustness than MSE against gross corruptions. We demonstrate the efficacy of the proposed methods through experiments on both synthetic and real-world databases for block sparse recovery, handwritten digit recognition and robust face recognition.
Concept learning through deep reinforcement learning with memory-augmented neural networks

Deep neural networks have shown superior performance in many regimes to remember familiar patterns with large amounts of data. However, the standard supervised deep learning paradigm is still limited when facing the need to learn new concepts efficiently from scarce data. In this paper, we present a memory-augmented neural network which is motivated by the process of human concept learning. The training procedure, imitating the concept formation course of human, learns how to distinguish samples from different classes and aggregate samples of the same kind. In order to better utilize the advantages originated from the human behavior, we propose a sequential process, during which the network should decide how to remember each sample at every step. In this sequential process, a stable and interactive memory serves as an important module. We validate our model in some typical one-shot learning tasks and also an exploratory outlier detection problem. In all the experiments, our model gets highly competitive to reach or outperform those strong baselines.
Grey Relational Analysis based Keypoints Selection in Bag-of-Features for Histopathological Image Classification

Background: With the expeditious development of current medical imaging technology, the availability of histopathological images has been increased in a large number. Hence, histopathological image classification and annotation have emerged as the prime research fields in the pathological diagnosis and clinical practices. Several methods are available for the automation of image classification. Methods: Recently, the bag-of-features appeared as a successful histopathological image classification method. However, all the extracted keypoints in bag-of-features are not relevant and generally have very high dimensions, which degrade the performance of a classifier. Therefore, this paper introduces a new Grey relational analysis-based bag-of-features method to search the relevant keypoints. Results: The efficacy of the proposed method has been analyzed on animal diagnostics lab histopathological image datasets having healthy and inflamed images of three organs. The average accuracy of the proposed method is 88.3%, which is the highest among other state-of-the-art methods. Conclusion: This paper introduced a new Grey relational analysis-based bag-of-features which improves the efficiency of vector quantization step of the standard bag-of-features method. The method used Grey relational analysis for similarity measure in vector quantization method of bag-offeatures. The proposed method has been validated in terms of precision, recall, G-mean, F1 score, and radar charts on three datasets, Kidney, Lung, and Spleen of ADL histopathological images.
Optimal Privacy Preserving Technique Over Big Data Analytics Using Oppositional Fruit Fly Algorithm

Background: Big data analytics is the process of utilizing a collection of data accompanied on the internet to store and retrieve anywhere and at any time. Big data is not simply a data but it involves the data generated by variety of gadgets or devices or applications. Objective: When massive volume of data is stored, there is a possibility for malevolent attacks on the searching data are stored in the server because of under privileged privacy preserving approaches. These traditional methods result in many drawbacks due to various attacks on sensitive information. Hence, to enhance the privacy preserving for sensitive information stored in the database, the proposed method makes use of efficient methods. Methods: In this manuscript, an optimal privacy preserving over big data using Hadoop and mapreduce framework is proposed. Initially, the input data is grouped by modified fuzzy c means clustering algorithm. Then we are performing a map reduce framework. And then the clustered data is fed to the mapper; in mapper the privacy of input data is done by convolution process. To validate the privacy of input data the recommended technique utilizes the optimal artificial neural network. Here, oppositional fruit fly algorithm is used to enhancing the neural networks. Results: The routine of the suggested system is assessed by means of clustering accuracy, error value, memory, and time. The experimentation is performed by KDD dataset. Conclusion: A result shows that our proposed system has maximum accuracy and attains the effective convolution process to improve privacy preserving.
A collaboration-based particle swarm optimizer with history-guided estimation for optimization in dynamic environments

Optimization problems widely exist in many expert and intelligent systems, e.g., greenhouse intelligent control systems in agriculture, energy management systems for hybrid electric vehicle, and job shop scheduling systems in manufacture. For the optimization problems in these systems, the objective functions may change over time. This kind of problem is usually called as dynamic optimization problems (DOPs) or optimizing in dynamic environments. The optimization algorithm plays an important role in designing an expert and intelligent system. In this paper, we present a novel particle swarm optimizer for optimization in dynamic environments. We introduce two schemes to improve performance of particle swarm optimization in dynamic environments. Firstly, the classical particle swarm optimization is enhanced by a collaborative mechanism, in which a target particle learns from another randomly selected particle and the global best one in the swarm. Instead of moving to the new position directly, a worst replacement operator is used to update the swarm, whereby the worst particle in the swarm moves to the better newly generated position. During optimizing, the best solution in each generation is stored. When an environmental change is detected, the historical solutions are retrieved to collaborate with some newly generated solutions to adapt to the new environment. The performance of the proposed algorithm is compared with several reported algorithms over the benchmark problems. Experimental results indicate that the proposed algorithm offers superior performance compared with the competitors.