The increasing amount of fake news is a growing problem that will progressively worsen in our interconnected world. Machine learning, particularly deep learning, is being used to detect misinformation; however, the models employed are essentially black boxes, and thus are uninterpretable. This paper presents an overview of explainable fake news detection models. Specifically, we first review the existing models, datasets, evaluation techniques, and visualization processes. Subsequently, possible improvements in this field are identified and discussed.

With the rapid development of technology and economy, the number of cars is increasing rapidly, which brings a series of traffic problems. To solve these traffic problems, the development of intelligent transportation systems are accelerated in many cities. While vehicles and their detailed information detection are great significance to the development of urban intelligent transportation system, the traditional vehicle detection algorithm is not satisfactory in the case of complex environment and high real-time requirement. The vehicle detection algorithm based on motion information is unable to detect the stationary vehicles in video. At present, the application of deep learning method in the task of target detection effectively improves the existing problems in traditional algorithms. However, there are few dataset for vehicles detailed information, i.e. driver, car inspection sign, copilot, plate and vehicle object, which are key information for intelligent transportation. This paper constructs a deep learning dataset containing 10,000 representative images about vehicles and their key information detection. Then, the SSD (Single Shot MultiBox Detector) target detection algorithm is improved and the improved algorithm is applied to the video surveillance system. The detection accuracy of small targets is improved by adding deconvolution modules to the detection network. The experimental results show that the proposed method can detect the vehicle, driver, car inspection sign, copilot and plate, which are vehicle key information, at the same time, and the improved algorithm in this paper has achieved better results in the accuracy and real-time performance of video surveillance than the SSD algorithm.

In this paper, the authors present a CAM-based Information Detection Hardware System for fast, exact and approximate image matching on 2-D data, using FPGA. The proposed system can be potentially applied to fast image matching with various required search patterns, without using search principles. In designing the system, we take advantage of Content Addressable Memory (CAM) which has parallel multi-match mode capability and has been designed, using dual-port RAM blocks. The system has a simple structure, and does not employ any Central Processor Unit (CPU) or complicated computations.

A new information detection method has been proposed for a very fast and efficient search engine. This method is implemented on hardware system using FPGA. We take advantages of Content Addressable Memory (CAM) which has an ability of matching mode for designing the system. The CAM blocks have been designed using available memory blocks of the FPGA device to save access times of the whole system. The entire memory can return multi-match results concurrently. The system operates based on the CAMs for pattern matching, in a parallel manner, to output multiple addresses of multi-match results. Based on the parallel multi-match operations, the system can be applied for pattern matching with various required constraint conditions without using any search principles. The very fast multi-match results are achieved at 60 ns with the operation frequency 50 MHz. This increases the search performance of the information detection system which uses this method as the core system.