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Cloud Computing has been widely used by Customers to outsource IT services and business applications managed by a third-party called Cloud Provider in order to reduce their equipment management costs. The academic sector have also taken notice and are investigating how Cloud technology can provide results more quickly and at lower cost in order to deploy Grid services which benefit already from distributed...
To reduce the chance of traffic crashes, many driver monitoring systems (DMSs) have been developed. A DMS warns the driver under abnormal driving conditions. However, traditional approaches require enumerating abnormal driving conditions. In this paper, we propose a novel DMS, which models the driver's normal driving statuses based on sparse reconstruction. The proposed DMS compares the driver's statuses...
Process monitoring has a central role in the process industry to enhance productivity, efficiency, and safety, and to avoid expensive maintenance. In this paper, a statistical approach that exploit the advantages of multiscale PLS models (MSPLS) and those of a generalized likelihood ratio (GLR) test to better detect anomalies is proposed. Specifically, to consider the multivariate and multi-scale...
We introduce a run-time security monitor for embedded system applications that detects both known and unknown computational cyber-attacks. Our security monitor is rigorous (i.e. sound and complete), eliminating false alarms, as well as efficient, supporting real-time detection. In contrast, conventional run-time security monitors for application software either produce (high rates of) false alarms...
Although it is possible to design and manufacture MPSoCs with hundreds of processors, there is still a gap in the ability to debug hardware, software, and applications for such chips. Current state-of-the-art works related to MPSoC debugging suffer from poor integration, scalability in data storage, and simple graphical data representation. This work proposes a modular debugging framework to aid the...
One of the most important machine learning techniques include clustering of data into different clusters or categories. There are several decent algorithms and techniques that exist to perform clustering on small to medium scale data. In the era of Big Data and with applications being large-scale and data-intensive in nature, there is a significant increment in volume, variety and velocity of data...
Providing security for interacting cloud services requires more than user authentication with passwords or digital certificates and confidentiality in data transmission. Existing data protection mechanisms have previously failed in preventing data theft attacks perpetrated by an insider to the cloud provider or impersonators. In this paper, we focus on the service cloud model, which facilitates the...
Many researchers have been applying computer technology, finite element analysis combined with Orthopedic manipulation of TCM research Finite element analysis method to study spinal manipulation has some advantages which can not be compared with traditional methods. This paper analyzes the application of finite element in this field from the aspects of mechanics of traction, rationality and safety...
Paroxysmal sympathetic hyperactivity (PSH) is an important clinical problem of severe traumatic brain injury (TBI) which incurs approximately 90% of all TBI-related costs. However, current detection approach is hampered by no consensus clinical diagnostic criteria, paroxysmal episode feature with complex manifestations, and already overloaded clinical activities. These limitations cause delayed recognitions...
In this paper, a shadow detection and removal framework for a video-based traffic monitoring system is proposed. The proposed framework is able to correctly detect if a video sequence has cast shadows that needs to be removed. Shadow detection results in 90.39% shadow detection rate and 88.71% shadow discrimination rate for different daytime traffic scenes with varying shadow strength. A traffic monitoring...
A methodology to ascertain the impact of disruption downstream of the point of disruption is proposed. Two parameters were defined to quantify the extent of disruption in any node in the Supply Chain (SC), including the end customer. A complex SC network consisting of two sub-nets is discussed as an illustration of the methodology. Two experiments were then conducted: (i) disruptions at a single,...
To obtain an automatic and intelligent monitoring system, RFID is used to aid with CCTV system which is an innovative method. Therefore, some problems exist as integrating these two technologies, and signal loss is one of the problems as RFID signal is vital to the final result. Since the targets which are necessary to be monitored through the CCTV system are dynamic, the experiments in this research...
In statistical process control, Gamma control charts are often designed for time-between-events (TBEs) monitoring. For Gamma distribution, the traditional equal-tail probability limits lead to the problem that some out-of-control events may occur in higher probability than in-control events. To overcome this problem, Gamma control limits with known parameters are proposed based on the narrowest confidence...
The modeling of machines and their operation modes is a key approach for optimization of their performance as well as for avoiding unwanted operational states which may lead to the occurrence of faults, and finally, to the breakdown. The developed model of a machine should be always parametrized, i.e. the certain number of parameters should be selected in the certain ranges. The most of the parameters...
We present Asterism, an open source data-intensive framework, which combines the strengths of traditional workflow management systems with new parallel stream-based dataflow systems to run data-intensive applications across multiple heterogeneous resources, without users having to: re-formulate their methods according to different enactment engines; manage the data distribution across systems; parallelize...
Most of recommender systems build their predictions by analysing the preferences of users. However, there are many situations, such as in intelligent tutoring systems, where recommendations of pedagogical resources should rather be based on their memory. So as to infer in real time and with low involvement what has been memorized by users, we highlight in the paper the link between gaze features and...
HPC systems run applications that can take several hours to executeand have to deal with the occurrence of a potentially large numberof faults. Most of the existing fault-tolerance strategies for thesesystems assume crash faults that are permanent events easily detected. This is not the case in several real systems, in particular in shared clusters, in which even the load variation may cause performance...
Wearable health-monitoring systems must achieve a balance between the often opposing goals of hardware overhead and classification accuracy. Prior works have presented various approaches to dynamically scale the accuracy of these systems as a function of available resources. In this paper, we present a framework which retroactively improves the accuracy of prior estimates when resources become available,...
Teaching Computer has led to the design of an educational model that is increasingly making use of market practices linked to business corporations. Within this scenario, a practical and dynamic learning system is being fostered that allows simulations to be carried out in real contexts through problem resolution. Based on constructivist theories, PBL (Problem-Based Learning) is a teaching method...
Ensuring satisfactory results by using problem-based learning in education in the Computing area is challenging. Faithfully maintaining the philosophy of PBL requires not only full compliance with its principles but also that its processes are managed efficiently. To facilitate the adoption of PBL, especially as to managing its processes, this article puts forward a framework based on Demig's PDCA...
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