Secure Distributed Mobile Volunteer Computing with Android

Volunteer Computing provision of seamless connectivity that enables convenient and rapid deployment of greener and cheaper computing infrastructure is extremely promising to complement next-generation distributed computing systems. Undoubtedly, without tactile Internet and secure VC ecosystems, harnessing its full potentials and making it an alternative viable and reliable computing infrastructure is next to impossible. Android-enabled smart devices, applications, and services are inevitable for Volunteer computing. Contrarily, the progressive developments of sophisticated Android malware may reduce its exponential growth. Besides, Android malwares are considered the most potential and persistent cyber threat to mobile VC systems. To secure Android-based mobile volunteer computing, the authors proposed MulDroid, an efficient and self-learning autonomous hybrid (Long-Short-Term Memory, Convolutional Neural Network, Deep Neural Network) multi-vector Android malware threat detection framework. The proposed mechanism is highly scalable with well-coordinated infrastructure and self-optimizing capabilities to proficiently tackle fast-growing dynamic variants of sophisticated malware threats and attacks with 99.01% detection accuracy. For a comprehensive evaluation, the authors employed current state-of-the-art malware datasets (Android Malware Dataset, Androzoo) with standard performance evaluation metrics. Moreover, MulDroid is compared with our constructed contemporary hybrid DL-driven architectures and benchmark algorithms. Our proposed mechanism outperforms in terms of detection accuracy with a trivial tradeoff speed efficiency. Additionally, a 10-fold cross-validation is performed to explicitly show unbiased results.

Screen Content Quality Assessment: Overview, Benchmark, and Beyond

Screen content, which is often computer-generated, has many characteristics distinctly different from conventional camera-captured natural scene content. Such characteristic differences impose major challenges to the corresponding content quality assessment, which plays a critical role to ensure and improve the final user-perceived quality of experience (QoE) in various screen content communication and networking systems. Quality assessment of such screen content has attracted much attention recently, primarily because the screen content grows explosively due to the prevalence of cloud and remote computing applications in recent years, and due to the fact that conventional quality assessment methods can not handle such content effectively. As the most technology-oriented part of QoE modeling, image/video content/media quality assessment has drawn wide attention from researchers, and a large amount of work has been carried out to tackle the problem of screen content quality assessment. This article is intended to provide a systematic and timely review on this emerging research field, including (1) background of natural scene vs. screen content quality assessment; (2) characteristics of natural scene vs. screen content; (3) overview of screen content quality assessment methodologies and measures; (4) relevant benchmarks and comprehensive evaluation of the state-of-the-art; (5) discussions on generalizations from screen content quality assessment to QoE assessment, and other techniques beyond QoE assessment; and (6) unresolved challenges and promising future research directions. Throughout this article, we focus on the differences and similarities between screen content and conventional natural scene content. We expect that this review article shall provide readers with an overview of the background, history, recent progress, and future of the emerging screen content quality assessment research.