401 research outputs found
The Effects of Video Games on Human Intelligence
With the help of rapidly growing electronics industry offering more affordable electronic gaming devices, an increasing number of people have stepped into the realm of video games and as a result, playing video games has become part of life for many to some extent. While the majority of people are embracing the fun and the thrill that video games have brought about, a handful of people are still holding relatively negative opinions on video games, thinking that playing video game is just a waste of time and money. In fact, the truth is quite the opposite. It has proved that video game is actually playing a multifaceted positive role in improving people’s intelligence, or making people smarter on the physiological aspect, the psychological aspect as well as the sociological aspect
Image In-painting Based FMM Algorithm by Edge Prediction Using Gradient Matrix
In this paper, we propose an improved image in-painting method based on Fast Matching Method (FMM) algorithm. The traditional approach speeds less time but it cannot contribute an optimal edge result. To overcome this disadvantage and improve the edge effect. First we use gradient matrix to select less but more significant pixels to join into the gray value calculation. Secondly we use an edge prediction method to predict the edge in the in-painting region and reset the in-painting sequence. Furthermore, this procedure also had an advantage in in-painting the image which had a large destroyed region. Therefore, our improved method contributes an obvious edge for in-painting procedure than the traditional method.The 2nd International Conference on Intelligent Systems and Image Processing 2014 (ICISIP2014), September 26-29, 2014, Nishinippon Institute of Technology, Kitakyushu, Japa
Edge and Central Cloud Computing: A Perfect Pairing for High Energy Efficiency and Low-latency
In this paper, we study the coexistence and synergy between edge and central
cloud computing in a heterogeneous cellular network (HetNet), which contains a
multi-antenna macro base station (MBS), multiple multi-antenna small base
stations (SBSs) and multiple single-antenna user equipment (UEs). The SBSs are
empowered by edge clouds offering limited computing services for UEs, whereas
the MBS provides high-performance central cloud computing services to UEs via a
restricted multiple-input multiple-output (MIMO) backhaul to their associated
SBSs. With processing latency constraints at the central and edge networks, we
aim to minimize the system energy consumption used for task offloading and
computation. The problem is formulated by jointly optimizing the cloud
selection, the UEs' transmit powers, the SBSs' receive beamformers, and the
SBSs' transmit covariance matrices, which is {a mixed-integer and non-convex
optimization problem}. Based on methods such as decomposition approach and
successive pseudoconvex approach, a tractable solution is proposed via an
iterative algorithm. The simulation results show that our proposed solution can
achieve great performance gain over conventional schemes using edge or central
cloud alone. Also, with large-scale antennas at the MBS, the massive MIMO
backhaul can significantly reduce the complexity of the proposed algorithm and
obtain even better performance.Comment: Accepted in IEEE Transactions on Wireless Communication
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Properties of Machine Learning Applications for Use in Metamorphic Testing
It is challenging to test machine learning (ML) applications, which are intended to learn properties of data sets where the correct answers are not already known. In the absence of a test oracle, one approach to testing these applications is to use metamorphic testing, in which properties of the application are exploited to define transformation functions on the input, such that the new output will be unchanged or can easily be predicted based on the original output; if the output is not as expected, then a defect must exist in the application. Here, we seek to enumerate and classify the metamorphic properties of some machine learning algorithms, and demonstrate how these can be applied to reveal defects in the applications of interest. In addition to the results of our testing, we present a set of properties that can be used to define these metamorphic relationships so that metamorphic testing can be used as a general approach to testing machine learning applications
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Properties of Machine Learning Applications for Use in Metamorphic Testing
It is challenging to test machine learning (ML) applications, which are intended to learn properties of data sets where the correct answers are not already known. In the absence of a test oracle, one approach to testing these applications is to use metamorphic testing, in which properties of the application are exploited to define transformation functions on the input, such that the new output will be unchanged or can easily be predicted based on the original output; if the output is not as expected, then a defect must exist in the application. Here, we seek to enumerate and classify the metamorphic properties of some machine learning algorithms, and demonstrate how these can be applied to reveal defects in the applications of interest. In addition to the results of our testing, we present a set of properties that can be used to define these metamorphic relationships so that metamorphic testing can be used as a general approach to testing machine learning applications
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