Vehicular Ad Hoc Networks (VANETs) are characterized by high mobility of nodes and volatility, which make privacy, trust management, and security challenging issues in VANETs' design. In such networks, data can be exposed to a variety of attacks, the most dangerous is false information dissemination, which threatens the safety and efficiency of transportation systems. False emergency messages can be injected by inside attackers to announce fake incidents such as traffic accidents, resulting in a false information attack. As the data in VANET is based on events, any trust mechanism must first identify the true events. To address these security challenges, a blockchain‐based authentication scheme and trust management model are proposed for VANETs. Using the authentication scheme, vehicles are enabled to send messages anonymously to the roadside units (RSUs) and the identity privacy of vehicles is protected. Besides, the proposed trust management model is designed to detect and deal with false information by evaluating the trustworthiness of vehicles and data. Using the trust model, when vehicles report an incident to the nearest RSU, the RSU is able to verify whether or not the incident took place. This mechanism ensures that RSUs send only verified event notifications. Finally, RSUs participate in updating the trust values of vehicles and store these values in the blockchain. The efficiency of the proposed authentication scheme is validated through analysis while the trust model is validated through simulations. The results obtained show that the proposed authentication scheme and the trust model provide better performance than other state‐of‐the‐art models where malicious vehicles can be identified efficiently and RSUs are enabled to broadcast only legitimate events.

Lifting capacity charts are tabulated and provided to operators and practitioners by mobile crane manufacturers. These charts are structured based on predetermined crane configurations, which consist of boom/jib length, lifting radius, main boom angle to ground, and jib angle to ground or its offset to its main boom centerline. It is a tedious job that lifting planners select cranes for construction projects based on a large number of lifting capacity charts. This paper presents a newly developed algorithm for selecting mobile cranes on construction sites, which takes into account the lifting capacity, the geometrical characteristics of the crane, the dimensions of equipments and riggings, and the ground bearing pressure. The algorithm is incorporated into a three-dimensional (3D) computer-aided system that integrates crane selection module, crane modeling module, 3D-simulation module, 3D computer-aided design modeling module, rigging calculation module, and data management module. At last, a case is repr...

Sarcasm is a form of figurative language where the literal meaning of words cannot hold, and instead the opposite interpretation is intended in a text. Sarcasm detection is a significant task to mine fine-grained information, which is a much more difficult challenge for sentiment analysis. Both industry and academia have realized the importance of sarcasm detection. However, most existing methods do not work very well. Using a neural architecture, we propose a novel multi-dimension question answering (MQA) network in order to detect sarcasm. MQA not only introduces the abundant semantic information to understand the ambiguity of sarcasm by multi-dimension representations, but also builds the conversation context information by deep memory question answering network based on bidirectional LSTM and attention mechanism to discover sarcasm. The experimental results show that our model has ability to obviously outperform other state-of-the-art methods, and then further examples also verifies the advancement and effectiveness of our proposed network for detecting sarcasm.

As a part of the intelligent transportation system, vehicular ad hoc networks (VANETs) provide timely information about road events and traffic to improve road safety and traffic efficiency. However, VANETs face many challenges, such as attacks from malicious vehicles, identity privacy leakage, and the absence of trust between vehicular nodes. In addition, vehicles nearby an event usually lack the motivation to participate in the traffic event validation whenever it occurs, which requires the cooperation of vehicles on the network. To solve these problems, a blockchain-enabled incentive trust model with a privacy-preserving threshold ring signature scheme for VANETs is proposed. Firstly, a threshold ring signature scheme is designed in order to allow participants in the non-trusted environment to anonymously witness the message’s authenticity and reliability while guaranteeing the vehicle’s privacy. Second, a blockchain-enabled incentive trust management model is presented to enable the roadside units (RSUs) to thwart various attacks and guarantee the trustworthiness of event messages transmitted in VANETs and also motivate the senders of the traffic information and their witnesses with incentives. Finally, to improve efficiency, a practical Byzantine fault-tolerant consensus mechanism is used. Our proposed system is demonstrated to be effective and secure for VANETs, according to both security analysis and performance evaluation.

In Deep Learning, recent works show that neural networks have a high potential in the field of biometric security. The advantage of using this type of architecture, in addition to being robust, is that the network learns the characteristic vectors by creating intelligent filters in an automatic way, grace to the layers of convolution. In this paper, we propose an algorithm “FM_net” for iris recognition by using Fully Convolutional Network (FCN) and Multi-scale Convolutional Neural Network (MCNN). By taking into considerations the property of Convolutional Neural Networks to learn and work at different resolutions, our proposed iris recognition method overcomes the existing issues in the classical methods which only use handcrafted features extraction, by performing features extraction and classification together. Our proposed algorithm shows better classification results as compared to the other state-of-the-art iris recognition approaches.

Image dehazing plays a pivotal role in numerous computer vision applications such as object recognition, surveillance systems, and security systems, where it can be considered as an introductory stage. Recently, many proposed learning-based works address this significant task; however, most of them neglect the atmospheric light estimation and fail to produce accurate transmission maps. To address such a problem, in this paper, we propose a two-stage dehazing system. The first stage presents an accurate atmospheric light algorithm labeled “A-Est” that employs hazy image blurriness and quadtree decomposition. Te second stage represents a cascaded multi-scale CNN model called CMT ${}_{net}$ that consists of two subnetworks, one for calculating rough transmission maps (CMCNN ${}_{tr}$ ) and the other for its refinement (CMCNN ${}_t$ ). Each subnetwork is composed of three-layer D-units (D indicates dense). Experimental analysis and comparisons with state-of-the-art dehazing methods revealed that the proposed system can estimate AL and t efficiently and accurately by achieving high-quality dehazing results and outperforms state-of-the-art comparative methods according to SSIM and MSE values, where our proposed achieves the best scores of both (91% average SSIM and 0.068 average MSE).