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Many organizations are stuck in the cloudify or not to cloudify limbo, mainly due to concerns related to the security of enterprise sensitive data. Removing this barrier is a key pre-condition to fully unleash the tremendous potential of cloud computing. In this paper, we provide a comprehensive analysis of the main threats that hamper cloud computing adoption on a wide scale, and a right to the point review of the solutions that are currently being provided by the major vendors. The paper also presents the (near) future directions of cloud security research, by taking a snapshot of the main research trends and most accredited approaches. The study is done on a best of breed selection of proprietary and Open Source cloud offerings. The paper is thus a useful navigation tool, that can be used by the IT personnel to gain more insight into the security risks related to the use of cloud computing, as well as to quickly weigh the pros and cons of state of the art solutions. [ABSTRACT FROM AUTHOR]

Modern businesses benefit significantly from advances in computer vision technology, one of the important sectors of artificially intelligent and computer science research. Advanced computer vision issues like image processing, object recognition, and biometric authentication can benefit from using deep learning methods. As smart devices and facilities advance rapidly, current networks such as 4 G and the forthcoming 5 G networks may not adapt to the rapidly increasing demand. Classification of images, object classification, and facial recognition software are some of the most difficult computer vision problems that can be solved using deep learning methods. As a new paradigm for 6Core network design and analysis, artificial intelligence (AI) has recently been used. Therefore, in this paper, the 6 G wireless network is used along with Deep Learning to solve the above challenges by introducing a new methodology named Optimizing Computer Vision with AI-enabled technology (OCV-AI). This research uses deep learning – efficiency algorithms (DL-EA) for computer vision to address the issues mentioned and improve the system's outcome. Therefore, deep learning 6 G proposed frameworks (Dl-6 G) are suggested in this paper to recognize pattern recognition and intelligent management systems and provide driven methodology planned to be provisioned automatically. For Advanced analytics wise, 6 G networks can summarize the significant areas for future research and potential solutions, including image enhancement, machine vision, and access control. [ABSTRACT FROM AUTHOR]

This survey paper attempts to study the existence, importance and impact of the notion of context in modern humanistic computing. Given its inherent diversity, the term is nowadays widely acknowledged among computer science tasks and has become a major topic of interest in several of its sub-fields, ranging from contextual semantics to social networks, social media and recently emerged innovative applications, such as travel routing. We start by presenting a brief review of contextual semantics as nowadays they are considered suitable for most common content analysis problems. Focus is also given within the next survey sections on the impact of context within the social networks and media field that came into sight over the last years. A short, closing discussion on the identified challenges and potential future research directions concludes this survey. [ABSTRACT FROM AUTHOR]

Orthogonal frequency division multiplexing (OFDM) has been adopted in various high-speed wireless communication systems. OFDM systems using direct-conversion transceivers are gaining much interest because of low cost and power. However, such systems can suffer from carrier frequency offset (CFO) and transmitter (TX)/receiver (RX) in-phase/quadrature-phase (I/Q) imbalances. These impairments may have a tremendous impact on the performance and cannot be efficiently eliminated in the analog domain. In this paper, we propose an efficient joint estimator for CFO and TX/RX I/Q imbalances in the digital baseband domain. The proposed method designs a training sequence at the transmitter that isolates the estimation of TX I/Q imbalance from the other parameters. At the receiver, we form a sinusoidal sequence from the received signal, which is used to jointly estimate the CFO and RX I/Q imbalance iteratively through the maximum likelihood estimation (MLE). Then we estimate the TX I/Q imbalance using the ratio of the estimated sequences just before CFO and RX I/Q imbalance with previously estimated parameters. Simulation results confirm the advantages of the proposed method over existing algorithms in terms of efficiency and estimation accuracy. [ABSTRACT FROM AUTHOR]