No abstract

This study investigated Chinese English-as-a-foreign-language (EFL) learners' use of vocabulary learning strategies (VLSs) and its relationship with vocabulary knowledge (VK), especially in relation to proficiency, gender, and discipline. Structural equation models were established following exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) procedures, and mediation analyses and multiple-group analyses, as well as analyses of variance, were conducted. Four hundred nineteen sophomores' strategy use frequency, Vocabulary Size Test (VST) scores (indicative of breadth of VK), Word Associates Test (WAT) scores (indicative of depth of VK), College English Test Band-4 scores, and gender and discipline categories were used as data. Proficiency significantly predicted Attention and Guessing positively but was a negative predictor of Socializing (asking others for help). Girls liked making notes while using dictionaries (DictNote) and Socializing, and students of arts also took more notes. Attention and Guessing significantly predicted VST and WAT positively, but Socializing significantly predicted the breadth and depth of VK negatively, and DictNote, Association, and Repetition had no significant relationship. The predictive power of Attention, Guessing, and Socializing, however, was achieved mainly, or for an important part, via the mediating or indirect effects of proficiency. Gender moderated the predictive power of Attention, Socializing, and DictNote over VST, greater for male students, whereas discipline moderated the relationship between Guessing and WAT, stronger for arts students. The findings are related to strategy features, gender characteristics, disciplinary influence, the EFL context and culture, and effective learning. This study reveals the complex relationships among use of VLSs, VK, and learner variables. Attention is called for to third-party factors in understanding VLSs-VK relationships. Given the important mediating effects of proficiency, it is proposed that vocabulary learning be strategically integrated into the accumulative process of English learning.

Vehicular ad-hoc networks (VANETs) aim to provide a comfortable driving experience. Sharing messages in VANETs can help with traffic management, congestion mitigation, and driving safety. However, forged or false messages may undermine the efficiency of VANETs. In this paper, we propose a security scheme based on blockchain technology, where two types of blockchain are constructed based on roadside units (RSUs) and Certificate Authorities (CAs), respectively. The proposed security scheme has multifold goals to identify malicious nodes and detect forged messages based on multiple factors, such as reputation of sender nodes, and time and distance effectiveness of messages. In addition, an incentive mechanism is introduced on the RSU blockchain to encourage RSUs to adopt active behaviors. Extensive simulations show that the proposed scheme exhibits superior performances to existing methods in detecting forged messages and identifying malicious nodes. Meanwhile, it provides privacy protection and improves the efficiency of vehicular networks.

Sentiment analyzing has been used in many fields, such as information security and evaluating products on web. In this paper, we propose a new model of multiple levels using semantics analyzing and the conditional random fields techniques to determine sentiment of a text. Sentiment of a document is divided into two parts in this model: global sentiment which is the sentiment of the entire text and local sentiment which is the sentiment associated with a particular part of the text. All information of local sentiment determine the global sentiment of text. According to this new model, a text is separated to several semantic paragraphs based on semantic similarity, and sentiment of semantic paragraphs is defined as local sentiment. Global sentiment of the text is identified by analyzing local sentiment information. Experiments results demonstrate that the performance of this fine granularity model is better than that of traditional SVM method.

No abstract

Vehicular social networks are emerging hybrid networks that combine traditional vehicular networks and social networks, with two key types of nodes, that is, vehicles and drivers. Since vehicle behaviors are controlled or influenced by drivers, the trustworthiness of a vehicle node is essentially determined by its own communication behaviors and its driver’s social characteristics. Therefore, human factors should be considered in securing the communication in vehicular social networks. In this article, we propose a hybrid trust model that considers both communication trust and social trust. Within the proposed scheme, we first construct a communication trust model to quantify the trust value based on the interactions between vehicle nodes, and then develop a social trust model to measure the social trust based on the social characteristics of vehicle drivers. Based on these two trust models, we compute the combined trust assessment of a vehicle node in vehicular social networks. Extensive simulations show that the proposed hybrid trust model improves the accuracy in evaluating the trustworthiness of vehicle nodes and the efficiency of communication in vehicular social networks.

No abstract

Vehicular delay-tolerant networks are widely used in intelligent transport application. Vehicle nodes exchange and share various information in vehicular delay-tolerant networks. However, current delay-tolerant network routing algorithms do not take into account the dynamic characteristic of traffic flow, and they do not effectively resist cyber attacks, such as black hole attack. To address this issue, we propose a data dissemination mechanism for vehicular delay-tolerant networks. In this mechanism, we develop a combined model to estimate the real-time traffic density. Simultaneously, we propose the metrics which include node interaction dispersion, node interaction freshness, node interaction participation, and node interaction contribution to evaluate behavior of nodes. Based on these metrics, a routing method is constructed. In this routing method, a relay node is selected by evaluating communication interaction behaviors among vehicle nodes. Considering the factors of traffic flow density and communication behaviors of vehicle nodes, a message forwarding strategy scheme is built for different traffic density scenarios. Extensive simulations show that the proposed mechanism exhibits superior performance over existing methods in forwarding traffic information and alleviates negative effects from black hole attacks.