Adaptive moving shadow detection and removal by new semi-supervised learning technique

The efficient application of current methods of shadow detection in video is hindered by the difficulty in defining their parameters or models and/or their application domain dependence. This paper presents a new shadow detection and removal method that aims to overcome these inefficiencies. It proposes a semi-supervised learning rule using a new variant of co-training technique for shadow detection and removal in uncontrolled scenes. The new variant both reduces the run-time through a periodical execution of a co-training process according to a novel temporal framework, and generates a more generic prediction model for an accurate classification. The efficiency of the proposed method is shown experimentally on a testbed of videos that were recorded by a static camera and that included several constraints, e.g., dynamic changes in the natural scene and various visual shadow features. The conducted experimental study produced quantitative and qualitative results that highlighted the robustness of our shadow detection method and its accuracy in removing cast shadows. In addition, the practical usefulness of the proposed method was evaluated by integrating it in a Highway Control and Management System software called RoadGuard.     

On line background modeling for moving object segmentation in dynamic scenes

Fast and accurate moving object segmentation in dynamic scenes is the first step in many computer vision applications. In this paper, we propose a new background modeling method for moving object segmentation based on dynamic matrix and spatio-temporal analyses of scenes. Our method copes with some challenges related to this field. A new algorithm is proposed to detect and remove cast shadow. A comparative study by quantitative evaluations shows that the proposed approach can detect foreground robustly and accurately from videos recorded by a static camera and which include several constraints. A Highway Control and Management System called RoadGuard is proposed to show the robustness of our method. In fact, our system has the ability to control highway by detecting strange events that can happen like vehicles suddenly stopped in roads, parked vehicles in emergency zones or even illegal conduct such as going out from the road. Moreover, RoadGuard is capable of managing highways by saving information about the date and time of overloaded roads.     

Synergistic verification and validation of systems and software engineering models

In this paper, we present a unified approach for the verification and validation of software and systems engineering design models expressed in UML 2.0 and SysML 1.0. The approach is based on three well-established techniques, namely formal analysis, programme analysis and software engineering (SwE) techniques. More precisely, our contribution consists of the synergistic combination of model checking, static analysis and SwE metrics that enables the automatic and efficient assessment of design models from static and dynamic perspectives. Additionally, we present the design and implementation of an automated computer-aided assessing framework integrating the proposed approach. Moreover, we discuss the related technical details and the underlying synergism. Finally, we illustrate the proposed approach by assessing a design case study that is composed of state machine and sequence diagrams.     

Experimental Investigation of the Interface Behavior of Balanced and Unbalanced E-Glass/Polyester Woven Fabric Composite Laminates

The aim of this work is to study the influence of weave structure on the crack growth behavior of thick E-glass/polyester woven fabric composites laminates. Two different types of laminates were fabricated: (i) balanced: plain weave (taffetas T)/chopped strand mat weave (M) [T/M]₆ and (ii) unbalanced: 4-hardness satin weave (S)/chopped strand mat weave [S/M]₇. In order to accurately predict damage criticality in such structures, mixed mode fracture toughness data is required. So, the experiments were conducted using standards delamination tests under mixed mode loading and pure mode loading. These tests were carried out in mode II using End Load Split (ELS) tests and in mixed-mode I+II by Mixed Mode Flexure (MMF) tests under static conditions. The test methodology used for the experiments will be presented. The experimental results have been expressed in terms of total strain energy release rate and R-curves. The fracture toughness results show that the T/M interface is more resistant to delamination than the S/M interface.     

The instructional benefits of dynamic visualizations in the acquisition of basketball tactical actions

Recent studies exploring the effects of dynamic visualizations on learning compared with static visualizations have yielded mixed results. Procedural motor learning is one of the few fields in which dynamic representations have shown to be effective. Many of the studies have suggested that this advantage is mainly due to the activation of the “mirror‐neuron system.” This study explores this explanation in physical education domain and analysed the effects of instructional media (video vs. photographs), showing tactical actions in basketball, on learning outcomes (i.e., game understanding and game performance), cognitive load (i.e., mental effort invested and estimated difficulty), and attitudes (i.e., attention, enjoyment, engagement, and challenge) in secondary school students. For all of the indicators, the results show that learning from video was more effective than learning from photographs. These findings have implications for the effective design of instructional media and provide confirmation of the superiority of video for teaching tactical actions involving the entire body.     

Chemical composition and antimicrobial activity of the essential oils from four Ruta species growing in Algeria

Antimicrobial properties of plants essential oils have been investigated in order to suggest them as potential tools to overcome the microbial drug resistance and the increasing incidence of food borne diseases problems. The aim of this research is to study the antibacterial and antifungal effects of four traditional plants essential oils, Ruta angustifolia, Ruta chalepensis, Ruta graveolens and Ruta tuberculata, against standard bacterial and fungal strains. The chemical compounds of the oils were examined by GC/MS. Results revealed a powerful antifungal activity against filamentous fungi. Aspergillus fumigatus and Cladosporium herbarum are the most sensitive strains to these oils with MIC values less than 3.5 μg ml⁻¹ for certain oils, reaching 7.8 μg ml⁻¹ for other. GC/MS essay exhibited ketones as the most abundant constituent of these oils except for R. tuberculata essential oil which has a completely different composition, monoterpenes alcohols being the most abundant. These compositions explain their potential antifungal activity.     

Engagement Detection Based on Analyzing Micro Body Gestures Using 3D CNN

This paper proposes a novel, efficient and affordable approach to detect the students’ engagement levels in an e-learning environment by using webcams. Our method analyzes spatiotemporal features of e-learners’ micro body gestures, which will be mapped to emotions and appropriate engagement states. The proposed engagement detection model uses a three-dimensional convolutional neural network to analyze both temporal and spatial information across video frames. We follow a transfer learning approach by using the C3D model that was trained on the Sports-1M dataset. The adopted C3D model was used based on two different approaches; as a feature extractor with linear classifiers and a classifier after applying fine-tuning to the pre-trained model. Our model was tested and its performance was evaluated and compared to the existing models. It proved its effectiveness and superiority over the other existing methods with an accuracy of 94%. The results of this work will contribute to the development of smart and interactive e-learning systems with adaptive responses based on users’ engagement levels.     

Fractional derivative and hereditary combined model for memory effects on flexible polyurethane foam

In a quasi-static regime with cyclic loading, the force–displacement curve of flexible polyurethane exhibits complicated behavior: nonlinearity, visco-elasticity, hysteresis, residual force, etc. Beside nonlinearity and visco-elasticity, this material displays high dependence on the displacement rate and past loading history. Its dependence on compression rate helps to appropriately identify the force–displacement curve. Based on the new curve identification, the overall foam response is assumed to be a composite of a nonlinear elastic component and a visco-elastic component. The elastic component is expressed as a polynomial function in displacement, while the visco-elastic one is formulated according to the hereditary approach to represent the foam visco-elastic damping force during the loading phase and according to the fractional derivative approach during unloading to represent the visco-elastic residual force in the material. The focus of this study was to develop mathematical formulations and identification parameters to faithfully characterize the visco-elastic behavior of flexible polyurethane foam under multi-cycle compressive tests. A parameter calibration methodology based on the separation of the measurement data of each component force was established. This optimization process helps to avoid the parameter values admixture problem during the phase of numeric calculations of the same component force. The validity of the model results is checked according to the simulation accuracy, the physical significance of results and their agreement with the obtained force–displacement curve identification.     

Quantitative and qualitative analysis of SysML activity diagrams

Model-driven engineering refers to a range of approaches that uses models throughout systems and software development life cycle. Towards sustaining such a successful approach in practice, we present a model-based verification framework that supports the quantitative and qualitative analysis of SysML activity diagrams. To this end, we propose an algorithm that maps SysML activity diagrams into Markov decision processes expressed using the language of the probabilistic symbolic model checker PRISM. Furthermore, we elaborate on the correctness of our translation algorithm by proving its soundness with respect to a SysML activity diagrams operational semantics that we also present in this work. The generated models can be verified against a set of properties expressed in the probabilistic computation tree logic. To automate our approach, we developed a prototype tool that interfaces a modeling environment and the probabilistic model checker. We also show how to leverage adversary generation to provide the developer with a useful counterexample/witness as a feedback on the verified properties. Finally, the established theoretical foundations are complemented with an illustrative case study that demonstrates the usability and benefit of such a framework.