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In this paper, we propose a method for generating guide data, and investigate its efficiency and efficacy for on-line learning with guide data. On-line learning in this research updates a learning model initialized by the decision boundary making algorithm proposed by us in our earlier study. The problem is that, if the guide data are not properly generated, on-line learning may require high computational...
Online Analytics Processing (OLAP) is utilised to develop multidimensional operations enabling queries and visualisation for Business Intelligence (BI). Most of the OLAP systems come with a tightly integrated user interface for querying and visualisation of data without the core OLAP operations exposed as an API. Advanced BI applications can be developed and composed to create complex workflows if...
In response to increasing threats of malicious activity and data loss on servers, we propose a different and practical strategy for access control modeled after flight plans for pilots, which mixes existing role-based, object-based, and intention-based access models; it supports much finer grained, real-time, sequence-oriented anomaly detection. Users are required to declare their intended “flight...
Cloud computing is a model that offers on-demand computational resources to clients as services. In this concept, users are charged based on a pay-per-use business model. Therefore, it dramatically reduces operating costs associated with the maintenance of the local data center. Recently, database is provided as a service to fulfill clients' demand. Following this, companies can rely on a remote database...
The modernization of the European air transportation system will increase the need for data communication. This creates the need for large-scale simulations to ascertain that new aeronautical communication systems fulfil the requirements of future air traffic management communication. This paper presents a framework for such simulations based on service-oriented software architecture. The Framework...
The map-reduce parallel programming model has become extremely popular in the big data community. Many big data workloads can benefit from the enhanced performance offered by supercomputers. LLMapReduce provides the familiar map-reduce parallel programming model to big data users running on a supercomputer. LLMapReduce dramatically simplifies map-reduce programming by providing simple parallel programming...
Predicting memory occupancy during the execution of large-scale analytical workloads becomes critical for in-memory databases. In particular, probabilistic performance measures for such systems are of interest, but difficult to model with analytical methods due to the highly variable threading levels in corresponding workloads. Since literature with queueing theoretic background largely ignores the...
Manually developing analytic performance models of operational systems can be challenging, time consuming, and costly. This paper describes a method that uses system logs and configuration files to automatically derive analytic performance models of operational systems. The method described here automatically determines: (1) the system software servers, (2) the system devices, (3) the deployment of...
We present a progressive method of applying easy-to-hard grouping technique that applies increasingly sophisticated feature descriptors and classifiers on reducing number of image samples from each of the iteratively generated clusters. The primary goal of the proposed approach is to design a cost effective face clustering method to deploy on low-power devices like Mobile phones, while handling various...
A signcryption scheme can realize the security objectives of confidentiality and integrity simultaneously, which has lower computational cost and communication overhead than the sign-then-encrypt approach. To adapt multi-user settings and solve key escrow problem of ID-based multi-signcryption schemes, we defined the formal model of certificatless multi-signcryption scheme and proposed a certificateless...
It is well known that wireless telecommunication systems operating at frequencies higher than approximately 10 GHz are strongly affected by atmospheric components, among which rain always plays the prevailing role. An accurate description of the atmospheric impairments, especially those caused by precipitation, is then very useful to select the best mitigation technique to counteract deep fades. In...
Sensor Networks have great potential for many applications. In time-critical and security applications, it is important to have on time delivery, minimum access time and maximum data rate. Most of sensor networks have a large number of sensor nodes which considering the memory and processor of each of them all together they provide a huge data storage space and also high processing capability. In...
Cloud computing is an emerging trend that provides on-demand computational resources to the end users as services. Hence, consumers are charged based on their usage and consumption of services delivered through the Internet. This paradigm aims at outsourcing and delegating computations to an external third-party and reducing operating costs. As a result, public sector and private enterprises are interested...
Face detection has been a hotspot either in research and in commercial application. In this paper, Locally Assembled Binary (LAB) feature and Adaboost algorithm are combined to recognize human face in images. On the basis of ensuring the detection speed, the detection accuracy is improved. Integral image technology is also conducted in consideration of detection speed. The proposed method is tested...
In knowledge processing systems, data is gathered from several sources, in the system, some calculating and processing steps are taken, and finally a result is computed and may be used for further steps or other systems. Most of the time the origin of input data is not verified. Using unverified data may cause inconsistencies in processing and generating output, and could lead to corrupting threats...
The main purpose of transfer learning is to resolve the problem of different data distribution, generally, when the training samples of source domain are different from the training samples of the target domain. Prediction of salient areas in natural video suffers from the lack of large video benchmarks with human gaze fixations. Different databases only provide dozens up to one or two hundred of...
In distributed sensing systems that use compressed videos for video analysis tasks, the lossy compression of videos can damage the accuracy of object detection, which is an essential step for various vision applications. This paper aims at constructing a new quality model to predict the performance of object detection. To achieve this goal, a distorted video database is constructed by applying object...
This paper proposes a novel and efficient shape-based approach for hand dorsal vein recognition. A coarse-to-fine segmentation method is first introduced to precisely detect the boundaries of the vein areas. A generalized graph model, namely Width Skeleton Model (WSM), is built then, which takes both the topology of the vein network and the width of the vessel into account, thereby achieving more...
We address the problem of single-image geo-calibration, in which an estimate of the geographic location, viewing direction and field of view is sought for the camera that captured an image. The dominant approach to this problem is to match features of the query image, using color and texture, against a reference database of nearby ground imagery. However, this fails when such imagery is not available...
Given a child's and a couple's facial photos, tri-subject kinship verification aims to determine the existence of blood relation between the child and the couple. Different from existing methods which model the kinship inheritance process among three persons in separate stages and only use simple features, this work establishes a simple model inspired by genetics to measure tri-subject kinship similarity...
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