A Transactional Approach to Enforce Resource Availabilities: Application to the Cloud

This paper looks into the availability of resources, exemplified with the cloud, in an open and dynamic environment like the Internet. A growing number of users consume resources to complete their operations requiring a better way to manage these resources in order to avoid conflicts, for example. Resource availability is defined using a set of consumption properties (limited, limited-but-renewable, and non-shareable) and is enforced at run-time using a set of transactional properties (pivot, retriable, and compensatable). In this paper, a CloudSim-based system simulates how mixing consumption and transactional properties allows to capture users’ needs and requirements in terms of what cloud resources they need, for how long, and to what extent they tolerate the unavailability of these resources

An Approach for Mitigating Disruptions on Resources’ Consumption Cycles

This paper examines the impact of disruptions on consumption cycles of resources. Such a cycle consists of states and transitions that depict how a resource is prepared, consumed, locked, unlocked, and withdrawn. It happens that events like last-minute upgrades and urgent fixes arise disrupting the resource’s ongoing consumption. Disruption leads to suspending an ongoing consumption to accommodate these events according to 3 scenarios referred to, in this paper, as co-existence, taking turns, and co-existence/taking turns. To verify the correctness of the resources’ consumption cycles with respect to each scenario, Petri Nets (PN) are developed linking this verification to properties like liveness and deadlock freeness

Influence of Injection Molding Parameters on the Mechanical Properties of Injected PC/ABS Parts

This study optimized the influence of process parameters on the mechanical properties during injection molding (IM) of PC/ABS blend. The Taguchi method of design of experiments (DOE) was employed to optimize the process parameters and to increase the tensile strength and the elasticity module. Taguchi’s L9 (34) orthogonal array design was employed for the experimental plan. Process parameters of the injection molding such as material temperature, injection pressure, holding time, and mold temperature were studied with three levels. The Signal to noise (S/N) ratio for mechanical properties of PC/ABS blend using the Taguchi method was calculated. Taguchi’s results proposed two sets of optimal injection parameters conditions to achieve the best mechanical characteristics (σ, E). The (S/N) ratio results proved that the injection pressure was the more prominent than the other IM process parameters for the tensile strength, and the material temperature was the more prominent for the elasticity module

Fast Motion Estimation’s Configuration Using Diamond Pattern and ECU, CFM, and ESD Modes for Reducing HEVC Computational Complexity

The high performance of the high efficiency video coding (HEVC) video standard makes it more suitable for high-definition resolutions. Nevertheless, this encoding performance is coupled with a tremendous encoding complexity compared to the earlier H264 video codec. The HEVC complexity is mainly a return to the motion estimation (ME) module that represents the important part of encoding time which makes several researches turn around the optimization of this module. Some works are interested in hardware solutions exploiting the parallel processing of FPGA, GPU, or other multicore architectures, and other works are focused on software optimizations by inducing fast mode decision algorithms. In this context, this article proposes a fast HEVC encoder configuration to speed up the encoding process. The fast configuration uses different options such as the early skip detection (ESD), the early CU termination (ECU), and the coded block flag (CBF) fast method (CFM) modes. Regarding the algorithm of ME, the diamond search (DS) is used in the encoding process through several video resolutions. A time saving around 46.75% is obtained with an acceptable distortion in terms of video quality and bitrate compared to the reference test model HM.16.2. Our contribution is compared to other works for better evaluation

On Modelling and Analyzing Composite Resources’ Consumption Cycles using Time Petri-Nets

ICT community cornerstones (IoT in particular) gain competitive advantage from using physical resources. This paper adopts Time Petri-Nets (TPNs) to model and analyze the consumption cycles of composite resources. These resources consist of primitive, and even other composite, resources that are associated with consumption properties and could be subject to disruptions. These properties are specialized into unlimited, shareable, limited, limited-but-renewable, and non-shareable, and could impact the availability of resources. This impact becomes a concern when disruptions suspend ongoing consumption cycles to make room for the unplanned consumptions. Resuming the suspended consumption cycles depends on the resources’ consumption properties. To ensure correct modeling and analysis of consumption cycles, whether disrupted or not, TPNs are adopted to verify that composite resources are reachable, bound, fair, and live

SV MIXTURE, CLASSIFICATION USING EM ALGORITHM

ABSTRACT The present paper presents a theoretical extension of our earlier work entitled"A comparative study of two models SV with MCMC algorithm" cited, Rev Quant Finan Acc (2012

A Guiding Framework for Vetting the Internet of Things

© 2020 Like any emerging and disruptive technology, multiple obstacles are slowing down the Internet of Things (IoT) expansion for instance, multiplicity of things’ standards, users’ reluctance and sometimes rejection due to privacy invasion, and limited IoT platform interoperability. IoT expansion is also accompanied by the widespread use of mobile apps supporting anywhere, anytime service provisioning to users. By analogy to vetting mobile apps, this paper addresses the lack of principles and techniques for vetting IoT devices (things) in preparation for their integration into mission-critical systems. Things have got vulnerabilities that should be discovered and assessed through proper device vetting. Unfortunately, this is not happening. Rather than sensing a nuclear turbines steam level, a thing could collect some sensitive data about the turbine without the knowledge of users and leak these data to third parties. This paper presents a guiding framework that defines the concepts of, principles of, and techniques for thing vetting as a pro-active response to potential things vulnerabilities

A Plug&Play approach for modeling and simulating applications in the era of internet of social things

This article presents an approach to model and simulate Plug&Play social things. Confined into silos, existing (not social) things are restricted to basic operations like sensing and actuating, which deprive them from participating in the satisfaction of complex business applications. Contrarily, social things are expected to engage in collaborative scenarios and to tap into specific relations that connect them to peers when achieving these scenarios. These relations are referred to as complimentary, antagonism, and competition, and allow to develop networks of things. To capitalize on such networks, the approach to model and simulate Plug&Play social things puts forward four stages that are referred to as connecting to demystify social relations between things, influencing to examine the impact of social relations on things, playing to make things perform while considering influence, and incentivizing to reward things based on their performance. A smart system for elderly the care centers has been developed to showcase the technical doability of Plug&Play social things. The system is an integrated development environment allowing IoT engineers to define the collaboration of social things, thanks to a set of drag&drop operations

A novel CNN gap layer for growth prediction of palm tree plantlings

Monitoring palm tree seedlings and plantlings presents a formidable challenge because of the microscopic size of these organisms and the absence of distinguishing morphological characteristics. There is a demand for technical approaches that can provide restoration specialists with palm tree seedling monitoring systems that are high-resolution, quick, and environmentally friendly. It is possible that counting plantlings and identifying them down to the genus level will be an extremely time-consuming and challenging task. It has been demonstrated that convolutional neural networks, or CNNs, are effective in many aspects of image recognition; however, the performance of CNNs differs depending on the application. The performance of the existing CNN-based models for monitoring and predicting plantlings growth could be further improved. To achieve this, a novel Gap Layer modified CNN architecture (GL-CNN) has been proposed with an IoT effective monitoring system and UAV technology. The UAV is employed for capturing plantlings images and the IoT model is utilized for obtaining the ground truth information of the plantlings health. The proposed model is trained to predict the successful and poor seedling growth for a given set of palm tree plantling images. The proposed GL-CNN architecture is novel in terms of defined convolution layers and the gap layer designed for output classification. There are two 64×3 conv layers, two 128×3 conv layers, two 256×3 conv layers and one 512×3 conv layer for processing of input image. The output obtained from the gap layer is modulated using the ReLU classifier for determining the seedling classification. To evaluate the proposed system, a new dataset of palm tree plantlings was collected in real time using UAV technology. This dataset consists of images of palm tree plantlings. The evaluation results showed that the proposed GL-CNN model performed better than the existing CNN architectures with an average accuracy of 95.96%