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In order to realize the reliable and safe operation of the smart railways, and provide high quality information transmission service for passengers, the railway system needs to develop innovative communication network and advanced communication technology to meet the gradually increasing service demand of multi-dimensional comprehensive information resources. The Space-Air-Ground Integrated Network (SAGIN) can provide seamless information services for land, sea, air and space users, and is an effective solution to the challenge posed by the future smart railways to the all-time, all-domain, all-air, high-reliability and high-throughput communication. This paper aims to comprehensively discuss the technical development and application examples of High-Speed Railways (HSRs) based on SAGIN. Firstly, we analysis the development of the SAGIN and the mobile communication network of the HSRs, and comprehensively discuss the single network architecture of the space-based, air-based and ground-based networks, as well as the integrated network, and discuss the application scenario and network structure of the combination of the integrated networks. At the same time, the communication services, existing problems and key technologies of the space-based, air-based and ground-based networks are discussed, and the application trend of the SAGIN in HSRs is presented. Furthermore, the application scenarios of Artificial Intelligence (AI) technologies in solving the efficient resource utilization of smart railways communication and the SAGIN are studied. Based on these technologies, we point out the research direction for the future development of AI technologies in SAGIN in HSRs communications. [ABSTRACT FROM AUTHOR]

Ocean fronts have an important influence on global ocean–atmosphere interactions and marine fishery. Hence, it is of great significance to obtain the positions of the ocean fronts. However, current ocean front detection research confronts two challenges: scarcity of labeled data and limitations of ocean front detection algorithms. To address these two problems, we have collected and labeled an ocean front data set and proposed a new deep learning model for ocean front detection. For concreteness, due to the weak edge property of the ocean fronts, we formulate ocean front detection as a weak edge identification problem and propose the weak edge identification network (WEIN) for ocean front detection. WEIN consists of four convolutional blocks. Each block has a side output layer used to detect front edges at a specific image representation level. The side outputs are then fused to predict (detect) the locations of the ocean fronts. In this work, we adopt two metrics to measure the experimental results, i.e., the $F_{1}$ -score and intersection over union (IoU). The experimental results with comparison to traditional and deep learning approaches demonstrate the superiority of WEIN for ocean front detection. [ABSTRACT FROM AUTHOR]

An effective presymptomatic diagnosis and treatment of Alzheimer’s disease (AD) would have enormous public health benefits. Sparse coding (SC) has shown strong potential for longitudinal brain image analysis in preclinical AD research. However, the traditional SC computation is time-consuming and does not explore the feature correlations that are consistent over the time. In addition, longitudinal brain image cohorts usually contain incomplete image data and clinical labels. To address these challenges, we propose a novel two-stage Multi-Resemblance Multi-Target Low-Rank Coding (MMLC) method, which encourages that sparse codes of neighboring longitudinal time points are resemblant to each other, favors sparse code low-rankness to reduce the computational cost and is resilient to both source and target data incompleteness. In stage one, we propose an online multi-resemblant low-rank SC method to utilize the common and task-specific dictionaries in different time points to immune to incomplete source data and capture the longitudinal correlation. In stage two, supported by a rigorous theoretical analysis, we develop a multi-target learning method to address the missing clinical label issue. To solve such a multi-task low-rank sparse optimization problem, we propose multi-task stochastic coordinate coding with a sequence of closed-form update steps which reduces the computational costs guaranteed by a theoretical convergence proof. We apply MMLC on a publicly available neuroimaging cohort to predict two clinical measures and compare it with six other methods. Our experimental results show our proposed method achieves superior results on both computational efficiency and predictive accuracy and has great potential to assist the AD prevention. [ABSTRACT FROM AUTHOR]

While wide-area communication networks facilitate the exchange of measurements and control signals for wide-area damping controllers, they bring in new threats of cyberattacks. This article formulates the dynamic interaction between an intelligent spoof attacker and the wide-area damping control (WADC) center as a zero-sum two-player Markov game. In this Markov game, the attacker intelligently spoofs the communication links sending wide-area control signals to selected generators while the WADC selectively encrypts transmitted control signals with limited cyberresources. The successful spoof of specific communication links leads to the degradation of the WADC performance and even to system instability indicated by the real part of the targeted interarea oscillation mode. The potential performance degradation in the physical layer is uniquely included in the cost function of this game while the other two parts of the cost function are the cybersecurity investments of both the attacker and the WADC (defender). The minimax Q-learning approach is used to obtain the optimal defense and attack strategies. Simulation results show that the strategies for the attacker and the WADC converge within 3000 iterations by using the minimax Q-learning approach. The proposed Markov game provides a cyberphysical framework to make the optimal defense strategy for the WADC. [ABSTRACT FROM AUTHOR]

The 6th generation mobile network (6G) is expected to achieve a fully connected world. As a key enabling technology in 6G, edge intelligence (EI) combines artificial intelligence (AI) with mobile edge computing (MEC), further releasing the potential of edge-side data. However, edge resources from roadside units (RSUs) and base stations are insufficient to match the wide variety of services in future vehicular networks. Extensive EI with auxiliary edge resources from parked vehicles is therefore introduced in this article. To achieve the coordination between extensive edge resources and EI-assisted services for vehicular networks in 6G, we propose an intelligent service-ori-ented edge resource management architecture, which makes full use of knowledge extracted from a large amount of sensory data in vehicular networks. As a case study, a prediction-based edge resource activation method under this architecture is proposed, where resources of parked vehicles are activated based on the predicted user density. Simulation results demonstrate that our prediction model has high accuracy and the pre-diction-based resource activation approach can reduce deployment cost. [ABSTRACT FROM AUTHOR]

Presents a listing of reviewers who contributed to IEEE Transactions on Knowledge and Data Engineering in 2017. [ABSTRACT FROM PUBLISHER]

The increasing popularity of clouds drives researchers to find answers to a large variety of new and challenging questions. Through extensive experimental measurements, we show variance in performance and scalability of clouds for two non-trivial scenarios. In the first scenario, we target the public Infrastructure as a Service (IaaS) clouds, and study the case when a multi-tier application is migrated from a traditional datacenter to one of the three IaaS clouds. To validate our findings in the first scenario, we conduct similar study with three private clouds built using three mainstream hypervisors. We used the RUBBoS benchmark application and compared its performance and scalability when hosted in Amazon EC2, Open Cirrus, and Emulab. Our results show that a best-performing configuration in one cloud can become the worst-performing configuration in another cloud. Subsequently, we identified several system level bottlenecks such as high context switching and network driver processing overheads that degraded the performance. We experimentally evaluate concrete alternative approaches as practical solutions to address these problems. We then built the three private clouds using a commercial hypervisor (CVM), Xen, and KVM respectively and evaluated performance characteristics using both RUBBoS and Cloudstone benchmark applications. The three clouds show significant performance variations; for instance, Xen outperforms CVM by 75 percent on the read-write RUBBoS workload and CVM outperforms Xen by over 10 percent on the Cloudstone workload. These observed problems were confirmed at a finer granularity through micro-benchmark experiments that measure component performance directly. [ABSTRACT FROM PUBLISHER]