Unsupervised and semi-supervised fuzzy clustering with multiple kernels.

For real-world clustering tasks, the input data is typically not easily separable due to the highly complex data structure or when clusters vary in size, density and shape. Recently, kernel-based clustering has been proposed to perform clustering in a higher-dimensional feature space spanned by embedding maps and corresponding kernel functions. Although good results were obtained using the Gaussian kernel function, its performance depends on the selection of the scaling parameter among an extensive range of possibilities. This step is often heavily influenced by prior knowledge about the data and by the patterns we expect to discover. Unfortunately, it is often unclear which kernels are more suitable for a particular task. The problem is aggravated for many real-world clustering applications, in which the distributions of the different clusters in the feature space exhibit large variations. Thus, in the absence of a priori knowledge, a single kernel selected from a predefined group is sometimes insufficient to represent the data. One way to learn optimal scaling parameters is through an exhaustive search of one optimal scaling parameter for each cluster. However, this approach is not practical since it is computationally expensive, especially when the data includes a large number of clusters and when the dynamic range of possible values of the scaling parameters is large. Moreover, the evaluation of the resulting partition in order to select the optimal parameters is not an easy task. To overcome the above drawbacks, we introduce two novel fuzzy clustering techniques that use Multiple Kernel Learning to provide an elegant solution for parameter selection. The Fuzzy C-Means with Multiple Kernels algorithm (FCMK) simultaneously finds the optimal partition and the cluster-dependent kernel combination weights that reflect the intrinsic structure of the data. The Relational Fuzzy Clustering with Multiple Kernels (RFCMK) learns the kernel combination weights by optimizing the relational dissimilarities. Consequently, the learned kernel combination weights reflect the relative density, size, and position of each cluster with respect to the other clusters. We also extended FCMK and RFCMK to the semi-supervised paradigms. We show that the incorporation of prior knowledge in the unsupervised clustering task in the form of a small set of constraints on which instances should or should not reside in the same cluster, guides the unsupervised approaches to a better partitioning of the data and avoid local minima, especially for high dimensional real world data. All of the proposed algorithms are optimized iteratively by dynamically updating the partition and the kernel combination weights in each iteration. This makes these algorithms simple and fast. Moreover, our algorithms are formulated to work on both vector and relational data. This makes them applicable to data where objects cannot be represented by vectors or when clusters of similar objects cannot be represented efficiently by a single prototype. We also introduced two relational fuzzy clustering with multiple kernel algorithms for large data to deal with the scalability issue of RFCMK. The random sample and extend RFCMK (rseRFCMK) computes cluster prototypes from a smaller sample of randomly selected objects, and then extends the partition to the remainder of the data. The single pass RFCMK (spRFCMK) sequentially loads manageable sized chunks, clustering the chunks in a single pass, and then combining the results from each chunk. Our extensive experiments show that RFCMK and SS-RFCMK outperform existing algorithms. In particular, we show that when data include clusters with various intrinsic structures and densities, learning kernel weights that vary over clusters is crucial in obtaining a good partition

Evaluation of Kermeta for Solving Graph-based Problems

Kermeta is a meta-language for specifying the structure and behavior of graphs of interconnected objects called models. In this paper,\ud we show that Kermeta is relatively suitable for solving three graph-based\ud problems. First, Kermeta allows the specification of generic model\ud transformations such as refactorings that we apply to different metamodels\ud including Ecore, Java, and Uml. Second, we demonstrate the extensibility\ud of Kermeta to the formal language Alloy using an inter-language model\ud transformation. Kermeta uses Alloy to generate recommendations for\ud completing partially specified models. Third, we show that the Kermeta\ud compiler achieves better execution time and memory performance compared\ud to similar graph-based approaches using a common case study. The\ud three solutions proposed for those graph-based problems and their\ud evaluation with Kermeta according to the criteria of genericity,\ud extensibility, and performance are the main contribution of the paper.\ud Another contribution is the comparison of these solutions with those\ud proposed by other graph-based tools

Interacting Tsallis holographic dark energy in qq-modified DGP braneworld

We explore the cosmological aspects of interacting Tsallis holographic dark energy (THDE) in a $q$-modified DGP braneworld setup emerging from non-Gaussian statistical mechanics.To this end, three classes of superstatistics, that is, log-normal, inverse $\chi^2$ and $\chi^2$ superstatistics were incorporated into the model. We examined the implication of the three superstatistics on different cosmological parameters, namely, the dimensionless energy density and the equation-of-state (EoS) of THDE, along with the deceleration parameter and the squared speed of sound. As a result, we noted that the cosmological parameters stemming from the $\chi^2$ superstatistics, with a parameter $q > 1$, represent the highest deviation from those ascribed to the standard DGP model. While the system parameters show appropriate behavior in all three cases, the model cannot achieve stability throughout the history of the Universe. It is probably the outcome of setting the Hubble horizon as the infrared cutoff. Furthermore, the behavior of EoS was found to be governed by the value of the THDE parameter ${\delta}$. That is to say, for ${\delta} > 2$ THDE exhibits a phantom-like behavior while for ${\delta} < 2$ it displays a quintessence behavior. Constrained by the dominant energy condition, an upper bound on ${\delta}$ $({\delta} < 2)$ has been imposed

Deep HMResNet Model for Human Activity-Aware Robotic Systems

Endowing the robotic systems with cognitive capabilities for recognizing daily activities of humans is an important challenge, which requires sophisticated and novel approaches. Most of the proposed approaches explore pattern recognition techniques which are generally based on hand-crafted features or learned features. In this paper, a novel Hierarchal Multichannel Deep Residual Network (HMResNet) model is proposed for robotic systems to recognize daily human activities in the ambient environments. The introduced model is comprised of multilevel fusion layers. The proposed Multichannel 1D Deep Residual Network model is, at the features level, combined with a Bottleneck MLP neural network to automatically extract robust features regardless of the hardware configuration and, at the decision level, is fully connected with an MLP neural network to recognize daily human activities. Empirical experiments on real-world datasets and an online demonstration are used for validating the proposed model. Results demonstrated that the proposed model outperforms the baseline models in daily human activity recognition.Comment: Presented at AI-HRI AAAI-FSS, 2018 (arXiv:1809.06606

A Domain Analysis to Specify Design Defects and Generate Detection Algorithms

Quality experts often need to identify in software systems design defects, which are recurring design problems, that hinder development\ud and maintenance. Consequently, several defect detection approaches\ud and tools have been proposed in the literature. However, we are not\ud aware of any approach that defines and reifies the process of generating\ud detection algorithms from the existing textual descriptions of defects.\ud In this paper, we introduce an approach to automate the generation\ud of detection algorithms from specifications written using a domain-specific\ud language. The domain-specific is defined from a thorough domain analysis.\ud We specify several design defects, generate automatically detection\ud algorithms using templates, and validate the generated detection\ud algorithms in terms of precision and recall on Xerces v2.7.0, an\ud open-source object-oriented system

Chemical Analysis, Antioxidant and Antibacterial Activities of Aniseeds Essential Oil

This study was conducted to evaluate the chemical composition, antioxidant and antibacterial activities of the essential oil of Pimpinella anisum L. seeds. The main constituents of the essential oil obtained by hydrodistillation using a Clevenger-type apparatus were identified by GC, GC/MS-EI, GC/MS-CI, and NMR spectroscopy. The antioxidant potential was assessed by using the DPPH method and the ferric reducing power. The antibacterial activity was determined by using the disk diffusion and the micro dilution methods against some Gram-positive and negative pathogenic bacteria. Results showed that aniseeds essential oil was characterized by a higher yield 2.6 ± 0.02% and good physic-chemical characteristics. Chemical analysis showed that the major components of the essential oil identified were anethole and estragole with percentages of 94.82 and 1.69%. Aniseeds essential oil showed a higher percentage of inhibition of DPPH 88.3 ± 0.5% and a lower value of IC50 118 ± 1.5 µg mL-1 determined at concentration of 1000 µg mL-1. This oil displayed a good ability to reduce Fe+3 to Fe+2 and provided an optical density of 1.78 ± 0.3 and IC50 of 60 ± 0.2 µg mL-1. Tested oil showed bactericidal activity against Pseudomonas aeruginosa and Escherichia coli with report MBC/MIC of 2 and antibacterial effect against Staphylococcus aureus with MBC/MIC of 32. It can be concluded that aniseeds essential oil contains substances with significant biological potential such as anethole and estragole that can be exploited in different pharmaceutical and therapeutic fields

Antioxidant Properties of the Aerial Part of Celery and Flaxseeds

The aim of the present study was to evaluate the antioxidant activity of essential oil and extracts obtained from the aerial parts of celery (Apium graveolens L.) and from flaxseeds (Linum usitatissimum (Linn.)). In vitro antioxidant activity was determined by 2,2-diphenyl-2-picryl-hydrazyl (DPPH) and ferric reducing power assay. Results showed that the essential oil of celery and methanol extracts of celery and flaxseeds exhibited a good scavenging activity of DPPH radical respectively 84 &plusmn; 0.4%, 79 &plusmn; 0.5%, and 78 &plusmn; 0.3% at concentration of 1000 &micro;g mL-1 comapred to the queous extracts. These results were lower than those found with ascorbic acid 96.23 &plusmn; 1.2% and catechin 94.50 &plusmn; 1.4% at the same concentration. IC50 is defined as concentration of substrate that inhibits 50% of the DPPH radicals present in the reaction medium. The positive control catechin and ascorbic acid displayed lower values of IC50 (7.81 &plusmn; 0.1, 31.5 &plusmn; 0.3 &micro;g mL-1), followed by methanol extract of celery and flaxseeds (130 &plusmn; 0.2, 150 &plusmn; 0.4 &micro;g mL-1), essential oil of celery (180 &plusmn; 0.2 &micro;g mL-1), then aqueous extracts of flaxseeds and celery (950 &plusmn; 0.5, 980 &plusmn; 0.4 &micro;g mL-1). For aerial part of celery, significant activities for reducing iron were obtained, values observed by optical density (OD) of 1.8 &plusmn; 0.2 for essential oil and 1.7 &plusmn; 0.1 for methanol extract, while ascorbic acid and catechin provided an OD of 2.069 &plusmn; 0.03 and 2.66 &plusmn; 0.016 in the same concentration 1000 &micro;g mL-1. The results of the current study showed that flaxseeds and celery exhibited a higher antioxidant activities that could be exploited in food and pharmaceutical industries.&nbs

Using FCA to Suggest Refactorings to Correct Design Defects

Design defects are poor design choices resulting in a hard-to- maintain software, hence their detection and correction are key steps of a\ud disciplined software process aimed at yielding high-quality software\ud artifacts. While modern structure- and metric-based techniques enable\ud precise detection of design defects, the correction of the discovered\ud defects, e.g., by means of refactorings, remains a manual, hence\ud error-prone, activity. As many of the refactorings amount to re-distributing\ud class members over a (possibly extended) set of classes, formal concept\ud analysis (FCA) has been successfully applied in the past as a formal\ud framework for refactoring exploration. Here we propose a novel approach\ud for defect removal in object-oriented programs that combines the\ud effectiveness of metrics with the theoretical strength of FCA. A\ud case study of a specific defect, the Blob, drawn from the\ud Azureus project illustrates our approach