Semantic Textual Similarity (STS) is important for many applications such as Plagiarism Detection (PD), Text Paraphrasing and Information Retrieval (IR). Current methods for STS rely on statistical machine learning. Recent studies showed that neural networks for STS presented promising experimental results. In this paper, we propose an Attentive Siamese Long Short-Term Memory (LSTM) network for measuring Semantic Textual Similarity. Instead of external resources and handcraft features, raw sentence pairs and pre-trained word embedding are needed as input. Attention mechanism is utilized in LSTM network to capture high-level semantic information. We demonstrated the effectiveness of our model by applying the architecture in different tasks: three corpora and three language tasks. Experimental results on all tasks and languages show that our method with attention mechanism outperforms the baseline model with a higher correlation with human annotation.

No abstract

Multipath TCP (MPTCP) is one of the most important extensions to TCP that enables the use of multiple paths in data transmissions for a TCP connection. In MPTCP, the end hosts transmit data across a number of TCP subflows simultaneously on one connection. MPTCP can sufficiently utilize the bandwidth resources to improve the transmission efficiency while providing TCP fairness to other TCP connections. Meanwhile, it also offers resilience due to multipath data transfers. MPTCP attracts tremendous attention from the academic and industry field due to the explosive data growth in recent times and limited network bandwidth for each single available communication interface. The vehicular Internet-of-Things systems, such as cooperative autonomous driving, require reliable high speed data transmission and robustness. MPTCP could be a promising approach to solve these challenges. In this paper, we first conduct a brief survey of existing MPTCP studies and give a brief overview to multipath routing. Then we discuss the significance technical challenges in applying MPTCP for vehicular networks and point out future research directions.

In order to enable emerging vehicular Internet of Things (IoT) applications, including fully autonomous driving, more efforts should be done in collecting driving experiences in different road situations. This requires the exchange of information between vehicles as each vehicle has very limited experience. Due to the decentralized feature of vehicular environment, an efficient management of collaborative behaviors among the vehicles becomes particularly important. Blockchain has been attracting great interest recently because it provides a way to reach consensus in decentralized systems. However, existing blockchain systems assume high communication capabilities for vehicles, which is difficult to achieve in a decentralized vehicular environment. Existing studies also assume the existence of networking infrastructure, such as roadside units (RSU). In this paper, we propose a scheme to empower blockchain in vehicular environments without depending on the existing networking infrastructure. The proposed scheme uses a distributed clustering approach to select some vehicles as edge nodes, and the edge nodes maintain the blockchain used to record transactions in a decentralized way. The proposed scheme employs a distributed approach that guides vehicle clustering with the consideration of multiple metrics based on a fuzzy logic algorithm. By using the edge nodes, the proposed scheme solves the communication problem of maintaining a blockchain in a totally decentralized vehicular environment. We use computer simulations to clarify the performance of the proposed scheme in terms of communication performance by comparing it with existing baselines. INDEX TERMS Vehicular IoT, Blockchain technology, Edge computing, Fuzzy logic.

No abstract