No abstract

No abstract

No abstract

Abstract:Quantifying visually annoying blocking artifacts is essential for image and video quality assessment. This paper presents a no-reference technique that uses the multi neural channels aspect of human visual system (HVS) to quantify visual impairment by altering the outputs of these sensory channels independently using statistical "standard score" formula in the Fourier domain. It also uses the bit patterns of the least significant bits (LSB) to extract blocking artifacts. Simulation results show that the blocking artifact extracted using this approach follows subjective visual interpretation of blocking artifacts. This paper also presents a visually significant blocking artifact metric (VSBAM) along with some experimental results. Keywords: Natural scene images, JPEG compression, Neural channels, Blocking artifacts Article:

This paper focuses on the specific problem of Big Data classification of network intrusion traffic. It discusses the system challenges presented by the Big Data problems associated with network intrusion prediction. The prediction of a possible intrusion attack in a network requires continuous collection of traffic data and learning of their characteristics on the fly. The continuous collection of traffic data by the network leads to Big Data problems that are caused by the volume, variety and velocity properties of Big Data. The learning of the network characteristics require machine learning techniques that capture global knowledge of the traffic patterns. The Big Data properties will lead to significant system challenges to implement machine learning frameworks. This paper discusses the problems and challenges in handling Big Data classification using geometric representation-learning techniques and the modern Big Data networking technologies. In particular this paper discusses the issues related to combining supervised learning techniques, representation-learning techniques, machine lifelong learning techniques and Big Data technologies (e.g. Hadoop, Hive and Cloud) for solving network traffic classification problems.