Toward analyzing impact of disjoint axioms for merging heterogeneous ontologies

In recent years, the question on Automatic Ontology Merging (AOM) become challenging to address for the researchers. Our research and development for the Disjoint Knowledge Perservation based Automatic Ontology Merging (DKP-AOM) is a milestone in the same direction. This paper provides a more specific discussion about disjoint knowledge axioms in DKP-AOM and makes an assessment of our merge algorithm that looks-up within disjoint partitions of concept hierarchies of ontologies. The significant use of disjoint knowledge is corroborated by testing conference and vertebrate ontologies. The results reveal that disjoint knowledge axioms help identifying initial inaccurate mappings and remove ambiguity when the concept with same symbolic identifier has a different meaning in different local ontologies in the process of ontology merging. Disjoint axioms separate the knowledge in distinct chunks and enable concept matching within the boundaries of sub-hierarchies of the entire ontology concept hierarchy. While finding matches between concepts of ontologies, disjoint partitions with the disjoint knowledge about concepts in source ontologies minimize the search space and reduce the runtime complexity of ontology merging. We also discuss encouraging results obtained by our DKP-AOM system within the OAEI 2015 campaign.     

Scale coding bag of deep features for human attribute and action recognition

Most approaches to human attribute and action recognition in still images are based on image representation in which multi-scale local features are pooled across scale into a single, scale-invariant encoding. Both in bag-of-words and the recently popular representations based on convolutional neural networks, local features are computed at multiple scales. However, these multi-scale convolutional features are pooled into a single scale-invariant representation. We argue that entirely scale-invariant image representations are sub-optimal and investigate approaches to scale coding within a bag of deep features framework. Our approach encodes multi-scale information explicitly during the image encoding stage. We propose two strategies to encode multi-scale information explicitly in the final image representation. We validate our two scale coding techniques on five datasets: Willow, PASCAL VOC 2010, PASCAL VOC 2012, Stanford-40 and Human Attributes (HAT-27). On all datasets, the proposed scale coding approaches outperform both the scale-invariant method and the standard deep features of the same network. Further, combining our scale coding approaches with standard deep features leads to consistent improvement over the state of the art.     

Optimization of parallel firewalls filtering rules

As filtering policies are getting larger and more complex, packet filtering at firewalls needs to keep low delays. New firewall architectures are needed to enforce security and meet the increasing demand for high-speed networks. Two main architectures exist for parallelization, data-parallel and function-parallel firewalls. In the first, packets are distributed across a set of identical firewalls that implement the entire policy. In the second, each firewall implements a subset of the policy with a fewer number of rules, but the packets have to be duplicated and processed by all the firewalls. This paper proposes a new architecture function-parallel with pre-processing that combines the advantages of both architectures. The proposed architecture has the advantage of not duplicating the data, so that the processing time can be significantly reduced. Moreover, our architecture enables stateful inspection of packets, which is necessary to prevent multiple types of attacks. The performances of this architecture have been proven to be scalable for large security policies.

     

Speech emotion recognition using optimized genetic algorithm-extreme learning machine

Multimedia Tools and Applications - Automatic Emotion Speech Recognition (ESR) is considered as an active research field in the Human-Computer Interface (HCI). Typically, the ESR system is...     

Hilbert Transform and Statistical Analysis for Channel Selection and Epileptic Seizure Prediction

This paper is concerned with Electroencephalography (EEG) seizure prediction, which means the detection of the pre-ictal state prior to ictal activity occurrence. The basic idea of the proposed approach for EEG seizure prediction is to work on the signals in the Hilbert domain. The operation in the Hilbert domain guarantees working on the low-pass spectra of EEG signal segments to avoid artifacts. Signal attributes in the Hilbert domain including amplitude, derivative, local mean, local variance, and median are analyzed statistically to perform the channel selection and seizure prediction tasks. Pre-defined prediction and false-alarm probabilities are set to select the channels, the attributes, and bins of probability density functions (PDFs) that can be useful for seizure prediction. Due to the multi-channel nature of this process, there is a need for a majority voting strategy to take a decision for each signal segment. Simulation results reveal an average prediction rate of 96.46%, an average false-alarm rate of 0.028077/h and an average prediction time of 60.1595 min for a 90-min prediction horizon.

     

Realtime multi-aircraft tracking in aerial scene with deep orientation network

Tracking the aircrafts from an aerial view is very challenging due to large appearance, perspective angle, and orientation variations. The deep-patch orientation network (DON) method was proposed for the multi-ground target tracking system, which is general and can learn the target’s orientation based on the structure information in the training samples. Such approach leverages the performance of tracking-by-detection framework into two aspects: one is to improve the detectability of the targets by using the patch-based model for the target localization in the detection component and the other is to enhance motion characteristics of the individual tracks by incorporating the orientation information as an association metric in the tracking component. Based on the DON structure, you only look once (YOLO) and faster region convolutional neural network (FrRCNN) detection frameworks with simple online and realtime tracking (SORT) tracker are utilized as a case study. The Comparative experiments demonstrate that the overall detection accuracy is improved at the same processing speed of both detection frameworks. Furthermore, the number of Identity switches (IDsw) has reduced about 67% without affecting the computational complexity of the tracking component. Consequently, the presented method is efficient for realtime ground target-tracking scenarios.     

Pole assignment of multivariable systems using proportional-derivative state feedback

In this article, the parametric solution to the pole assignment problem for multivariable linear time-invariant systems controlled by proportional-derivative (PD) state feedback is developed. The new expressions for the PD gain controllers are derived which describe the available degrees of freedom offered by PD state feedback. The freedom provided by PD state gain matrices is utilised to obtain closed-loop systems with robust and small gain matrices. Two computational algorithms are introduced, and their effectiveness is demonstrated by two simulation examples.     

Mechanical Alloying and Sintering of a Ni/10wt.%Al<Subscript>2</Subscript>O<Subscript>3</Subscript> Nanocomposite and its Characterization

A Ni matrix nanocomposite reinforced by 10 wt.% Al₂O₃ was fabricated by mechanical alloying. The powders mixture was milled up to 24 h in a ball mill. Phase composition and morphology of prepared powders were investigated by X-ray diffraction, scanning electron microscopy and transmission electron microscopy. To obtain compact bodies, pressing was applied on the milled powders; then sintered at different temperatures for 1 h in argon atmosphere. Furthermore, the effect of milling time and sintering temperature on microstructure. Physical, mechanical and electrical properties of the sintered nanocomposite specimens were evaluated. The results show decrease of particle size of milled powders (69 nm) as the time increased up to 24 h of milling with a noticeable presence of agglomerates. On the other hand, relative density, microhardness, compressive strength, elastic modulus and electrical conductivity of the sintered samples were found to progressively increase with the increasing of milling time and sintering temperature. Their maximum values were 97.36%, 1137 MPa, 633 MPa, 21.6 GPa and 9.71 × 10⁵ S/m, respectively, for the sample that was milled for 24 h and sintered at 1200 °C. On the other hand, the increasing milling time tended to decrease the fracture strain while it increased with increase of the sintering temperature.     

Stability limits and consolute critical conditions for liquid mixtures

This work addresses the determination of stability limits and consolute critical conditions for multicomponent liquid mixtures. Using the NRTL model, thermodynamic stability and criticality criteria are implemented for ternary liquid mixtures to predict stability limit loci and critical composition at its specified temperature and pressure. The method is general and applicable for the prediction of spinodal curves and critical points for liquid–liquid-phase transitions in multicomponent liquid mixture using liquid–liquid equilibrium data even over a limited range of composition. Stability limits’ loci for 18 aqueous and 3 nonaqueous ternary liquid mixtures were used in validating the method. For ternary systems that were studied over the whole composition range including the critical zone, where experimental compositions at the critical point were available, the predicted results agree with the experimental measurements within 0.7?mol%.     

Enrichment,in vitro,and quantification study of antidiabetic compounds from neglected weed Mimosa pudica using supercritical CO2 and CO2-Soxhlet

Supercritical fluid extraction (SFE) using carbon dioxide (CO₂) and liquid CO₂ using Soxhlet (CO₂-Soxhlet) extraction were employed to extract three (3) antidiabetic compounds viz. stigmasterol, quercetin, and avicularin from Mimosa pudica. Various extraction parameters were studied. Extracts were analyzed pharmacologically, qualitatively and quantitatively to ascertain enrichment levels. All three antidiabetic compounds were effectively enriched under optimized conditions of temperature 60°C, pressure 40 MPa, co-solvent ratio 30%, and CO₂ flow rate of 5 ml min^?1. SFE was found to be the better method for enrichment of the antidiabetic compounds than the CO₂-Soxhlet method. Extraction conditions were seen to affect the enrichment of desired compounds.