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Modelling of a database performance depending on numerous factors is the first step towards its optimization. The linear regression model with optional parameters was created. Regression equation coefficients are optimized with the Flower Pollination metaheuristic algorithm. The algorithm is executed with numerous possible execution parameter combinations and results are discussed. Potential obstacles...
First-person videos (FPVs) in daily living help us to memorize our life experience and information systems to process daily activities. Summarizing FPVs into key frames that represent the entire data would allow us to remember our memory in the past and computers to efficiently process the data. However, most video summarization approaches only use visual information, even though our daily activities...
Clustering is a fundamental tool for data analysis. Typically, all attributes of the data are used for clustering. However, if a set of attributes can be divided into meaningful subsets, it may be effective to cluster the data for each subset. In this paper, we propose a method for dividing the set of elements of feature vectors into meaningful subsets. Considering the dependencies between the elements,...
In the last years the volume of data that was generated by the mankind has increased and the complexity of data generated has also increased. Since the computers have evolved and provide more processing power, it is possible to carry out the real-time analysis of big volumes of data. This paper suggests the architecture of a big data processing platform called BigTim, which is able to run clustering...
Multimodal biometric verification systems use information from several biometric modalities to verify an identity of a person. The false acceptance rate (FAR) and false rejection rate (FRR) are metrics generally used to measure the performance of such systems. In this paper we propose a novel approach to determine the upper and lower acceptance thresholds in sequential multimodal biometric matching,...
We introduce a general framework for end-to-end optimization of the rate-distortion performance of nonlinear transform codes assuming scalar quantization. The framework can be used to optimize any differentiable pair of analysis and synthesis transforms in combination with any differentiable perceptual metric. As an example, we consider a code built from a linear transform followed by a form of multi-dimensional...
This paper presents a Large Margin Coupled Feature Learning (LMCFL) method for cross-modal face recognition, which recognizes persons from facial images captured from different modalities. Most previous cross-modal face recognition methods utilize hand-crafted feature descriptors for face representation, which require strong prior knowledge to engineer and cannot exploit data-adaptive characteristics...
Software Defined Networking (SDN) is attracting many researchers in networking area, especially in the field of network operations and managements. A fine-grained network management that controls traffic dynamically by the unit of flows is one of the key challenges towards a resilient network for the future Internet. To this end, the network is required to re-configure timely, adaptively and dynamically...
Digital photo management is becoming indispensable for the explosively growing family photo albums due to the rapid popularization of digital cameras and mobile phone cameras. An effective photo management system could accurately and efficiently group all faces of the same person into a small number of clusters. In this paper, we present a novel photo grouping method based on spectral theory. The...
The advancement in process technology has enabled integration of different types of processing cores into a single chip towards creating heterogeneous Multiprocessor Systems-on-Chip (MPSoCs). While providing high level of computation power to support complex applications, these modern systems also introduce novel challenges for system designers, like managing a huge number of mappings (application...
This paper presents an empirical approach for the performance tuning of Java EE application servers (ASs) using a multi-objective differential evolution algorithm. It features multi-objective black-box optimization of selected AS's configuration parameters. The proposed approach is used for performance tuning of the AS GlassFish and Java EE test application DayTrader. The obtained results improve...
K-anonymization is a wide-spread technique for the de-identification of biomedical datasets. To not render the data useless for further analysis it is often important to find an optimal solution to the k-anonymity problem, i.e., a transformation with minimum information loss. As performance is often a key requirement this paper describes an efficient implementation of a k-anonymization algorithm which...
In this paper, we raise important issues on scalability and the required degree of supervision of existing Mahalanobis metric learning methods. Often rather tedious optimization procedures are applied that become computationally intractable on a large scale. Further, if one considers the constantly growing amount of data it is often infeasible to specify fully supervised labels for all data points...
Crowd-based data sourcing is a new and powerful data procurement paradigm that engages Web users to collectively contribute information. In this work, we target the problem of gathering data from the crowd in an economical and principled fashion. We present Ask It!, a system that allows interactive data sourcing applications to effectively determine which questions should be directed to which users...
As traditional and mission-critical relational database workloads migrate to the cloud in the form of Database-as-a-Service (DaaS), there is an increasing motivation to provide performance goals in Service Level Objectives (SLOs). Providing such performance goals is challenging for DaaS providers as they must balance the performance that they can deliver to tenants and the data center's operating...
In this paper, we address the problem of score fusion in biometric authentication. Single valued metrics related to the receiver operating characteristics (ROC) curve, such as Equal Error Rate (EER) and False Rejection Rate (FRR) when False Acceptance Rate equals zero, are extensively used for evaluating biometric authentication performances. Various requirements and preferences, for example, lower...
The mechanism for unearthing hidden facts in large datasets and drawing inferences on how a subset of items influences the presence of another subset is known as Association Rule Mining (ARM). There is a wide variety of rule interestingness metrics that can be applied in ARM. Due to the wide range of rule quality metrics it is hard to determine which are the most ‘interesting’ or ‘optimal’ rules in...
High availability, reliability and scalability are basic prerequisites for cloud applications. Due to dynamically varying workloads, it's necessary to provide resource guarantees to cloud applications for meeting QoS requirements. However, it's not trivial to generate a precise scalability policy for multi-tiers cloud applications to adapt to dynamically varying workload and satisfy QoS requirements...
The article analyses the software process metrics, and propose an optimized algorithm of a dynamic program of software project process, to better improve the software project process controlling ability, promote the dynamic and reasonable allocation of the resources, and improve the success rate.
This paper presents a novel approach to simultaneously compute the motion segmentation and the 3D reconstruction of a set of 2D points extracted from an image sequence. Starting from an initial segmentation, our method proposes an iterative procedure that corrects the misclassified points while reconstructing the 3D scene, which is composed of objects that move independently. This optimization procedure...
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