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This paper proposes a new 3D video quality assessment based on 3D visual perception for texture and depth image for measuring the quality of stereoscopic 3D videos by detecting 3D distortions. The effectiveness of the proposed metrics is verified by conducting subjective evaluations on publicly available stereoscopic image databases. Experimental results show that significant consistency can be reached...
This paper presents a method for assess the risk index in a power transformers park, risk index is a metric that allows park administrator to ensure an optimal physical asset management, allocating properly financial and human resources in operation and maintenance actions. Assessing risk index requires calculating two secondary sub-index termed failure probability and consequence factor. Those indexes...
We focus on developer code review performance, and analyze whether the age of a subject affects the efficiency and preciseness of their code. Generally, older coders have more experience. Therefore, the age is considered to positively affect code review. However, in our past study, code understanding speed was relatively slow for older subjects, and memory is needed to understand programs. Similarly,...
Network Function Virtualization is an emerging paradigm to allow the creation, at software level, of complex network services by composing simpler ones. However, this paradigm shift exposes network services to faults and bottlenecks in the complex software virtualization infrastructure they rely on. Thus, NFV services require effective anomaly detection systems to detect the occurrence of network...
The incorrect use of cryptography is a common source of critical software vulnerabilities. As developers lack knowledge in applied cryptography and support from experts is scarce, this situation is frequently addressed by adopting static code analysis tools to automatically detect cryptography misuse during coding and reviews, even if the effectiveness of such tools is far from being well understood...
Distributed computing platforms provide a robust mechanism to perform large-scale computations by splitting the task and data among multiple locations, possibly located thousands of miles apart geographically. Although such distribution of resources can lead to benefits, it also comes with its associated problems such as rampant duplication of file transfers increasing congestion, long job completion...
Resource selection and task placement for distributed execution poses conceptual and implementation difficulties. Although resource selection and task placement are at the core of many tools and workflow systems, the methods are ad hoc rather than being based on models. Consequently, partial and non-interoperable implementations proliferate. We address both the conceptual and implementation difficulties...
Monte Carlo Tree Search (MCTS) is frequently used for online planning and decision making in large space problems, where the move maximizing a reward score is chosen as the optimal solution. As many problems have more than one objective, this paper presents a multi-objective version of MCTS. The algorithm employs a non-linear scalarization function, the Chebyshev metric based function, as a basis...
In this paper, we propose PreDA-a preferencebased truthful double auction for dynamic spectrum access (DSA) networks where multiple heterogeneous spectrum bands are sold by the primary users and bought by the secondary users. We consider channels' heterogeneity and multi-bids from buyers, and also consider buyers' preferences for the channels. We use the signal to interference and noise ratio (SINR)...
Social network analysis can be used to investigate collaboration in learning networks, which can be modeled as social graphs. We have already proposed a conceptual framework for knowledge extraction and visualization from a social media-based learning environment, starting from specific educational needs identified by the instructors. In the current paper, we experimentally validate this framework...
Following the trend of privacy preserving online social network publishing, various anonymization mechanisms have been designed and employed. Many differential privacybased mechanisms claim that they can preserve the utility as well as guarantee the privacy. Their utility analysis are always based on some specifically chosen metrics.This paper aims to find a novel angle that describing the network...
We show that approximate similarity (near neighbour) search can be solved in high dimensions with performance matching state of the art (data independent) Locality Sensitive Hashing, but with a guarantee of no false negatives. Specifically we give two data structures for common problems. For c-approximate near neighbour in Hamming space, for which we get query time dn^{1/c+o(1)} and space dn^{1+1/c+o(1)}...
Clustering is a classic topic in optimization with k-means being one of the most fundamental such problems. In the absence of any restrictions on the input, the best known algorithm for k-means with a provable guarantee is a simple local search heuristic yielding an approximation guarantee of 9+≥ilon, a ratio that is known to be tight with respect to such methods.We overcome this barrier...
This paper presents a design methodology with uncertainty quantification to estimate the required margin for two main design variables in a modular multilevel converter (MMC). In this methodology, the minimum required design margins are calculated by quantifying all sources of uncertainty in the modeling and simulation of MMCs. To this end, an enhanced modeling framework is presented to take into...
Wide Band Gap power semiconductor switching devices offer superior performance compared to an equivalent Si power device. SiC MOSFETs can be competitive when compared to Si IGBTs of the same voltage class, whilst offering greater benefits at high switching frequencies. Operating SiC MOSFETs at high switching frequencies imposes significant challenges, including: Ringing and voltage overshoot issues,...
In this empirical study we develop forecasting models for electricity demand using publicly available data and three models based on machine learning algorithms. It compares accuracy of these models using different evaluation metrics. The data consist of several measurements and observations related to the electricity market in Turkey from 2011 to 2016. It is available in different time granularities...
The solution of difficult problems can be realized in shorter time with heuristic algorithms. There are many heuristic algorithms. In this study, artificial bee colony (ABC), biogeography based optimization (BBO), cuckoo bird search algorithm (CSO), differential evolution (DE), imperialist competitive algorithm (ICA) and particle swarm algorithm (PSO) have been chosen due to reasons such as the widespread...
Failure modes and effects analysis (FMEA) is a powerful and proactive quality tool for defining, detecting, and identifying potential failure modes and their effects. However, conventional FMEA process is sometimes difficult to implement due to workload required and subjectivity of the evaluations performed. Hence, automation of this tool can be useful for some application domains to objectively evaluate...
In this study, we apply machine learning algorithms to predict technical failures that can be encountered in Oracle databases and related services. In order to train machine learning algorithms, data from log files are collected hourly from Oracle database systems and labeled with two classes; normal or abnormal. We use several data science approaches to preprocess and transform the input data from...
Direct method for visual odometry has gained popularity, it needs not to compute feature descriptor and uses the actual values of camera sensors directly. Hence, it is very fast. However, its accuracy and consistency are not satisfactory. Based on these considerations, we propose a tightly-coupled, optimization-based method to fuse inertial measurement unit (IMU) and visual measurement, in which uses...
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