About 70% of the earth's surface is covered with water, and providing safe transportation in this vast area is the responsibility of the maritime transportation system. The maritime transportation system provides safety to the vessel's passengers, manages the route of the vessels to avoid collision, and monitors the vessel's condition during the journey. All tasks performed by the maritime transport system require continuous monitoring of the vessel performance, and for this, different IoT devices are installed on the vessel board. The IoT devices collect data related to the physical parameters of the vessel and share it with the coast-side servers. As the shared data contains confidential information about the vessel, there is a need for secure data sharing techniques, allowing only the authenticated personals to access the data received from the maritime IoT devices. In this context, we developed an identity-based secure information sharing scheme for the maritime transport system that maintains proper authentication measures. Our proposed approach uses the concept of identity-based encryption for authentication management. Our proposed approach is IND-sID-CCA secure and works efficiently with maritime IoT devices.

Graph sampling frequently compresses a large graph into a limited screen space. This paper proposes a hierarchical structure model that partitions scale-free graphs into three blocks: the core, which captures the underlying community structure, the vertical graph, which represents minority structures that are important in visual analysis, and the periphery, which describes the connection structure between low-degree nodes. A new algorithm named hierarchical structure sampling (HSS) was then designed to preserve the characteristics of the three blocks, including complete replication of the connection relationship between high-degree nodes in the core, joint node/degree distribution between high- and low-degree nodes in the vertical graph, and proportional replication of the connection relationship between low-degree nodes in the periphery. Finally, the importance of some global statistical properties in visualization was analyzed. Both the global statistical properties and local visual features were used to evaluate the proposed algorithm, which verify that the algorithm can be applied to sample scale-free graphs with hundreds to one million nodes from a visualization perspective.

One of the key challenges for question answering is to bridge the lexical gap between questions and answers because there may not be any matching word between them. Machine translation models have been shown to boost the performance of solving the lexical gap problem between question-answer pairs. In this paper, we introduce an attention-based deep learning model to address the answer selection task for question answering. The proposed model employs a bidirectional long short-term memory (LSTM) encoder-decoder, which has been demonstrated to be effective on machine translation tasks to bridge the lexical gap between questions and answers. Our model also uses a step attention mechanism which allows the question to focus on a certain part of the candidate answer. Finally, we evaluate our model using a benchmark dataset and the results show that our approach outperforms the existing approaches. Integrating our model significantly improves the performance of our question answering system in the TREC 2015 LiveQA task.

Recent researches proposed that the weighted spectral distribution is a robust spectral metric independent of the network size (node number) and can be quickly calculated in large-scale networks using the graph structure of 4-cycles. In this paper, we design an algorithm for calculating the spectral metric within a more complex graph structure (i.e., 5-cycles) and two theorems are proposed to verify the accuracy of the algorithm. Finally, the theoretical and experimental analyses exhibit the high time efficiency of our algorithm when applied to large-scale networks.

In terms of spectral graph theory, the graph spectrums represent more accurate structural properties and robust characteristics of the network topologies. In this study, the authors study the stability characteristics of the normalised Laplacian spectrum associated with the interactive growth mechanism for the increasing of nodes in the generation of large‐scale autonomous systems (AS) graphs. With consecutive snapshots of the AS‐level Internet topologies, the authors obtain the stable models of the spectrum. In addition, the authors investigate the stability conditions of the spectrum based on the interactive growth mechanism and the comparisons with other metrics and study how to influence the spectrum by the inputs of the interactive growth mechanism.