Automatic recognition of signals occupies an essential place in railway self-driving systems. Signal recognition systems help drivers avoid human factor accidents from fatigue and distraction of train drivers. In computer vision, developing object recognition algorithms is an important area of research related to transportation. This field is evolving exponentially with the help of deep learning, high-performance cameras, and reliable datasets. Nevertheless, the complexity of the states and combinations of railway signals has led traditional object recognition algorithms based on a single image as a dataset to add more structure to the recognition of one of the states of the signals described as "Blinking". This paper investigates a neural network based on YOLOv8 with a recurrent mechanism that extracts spatio-temporal contextual features expressed by scintillation signals. This research compensates for the lacunae in the railway transportation industry in terms of deep learning based approach in recognizing scintillation traffic signals. We enhance the training speed with a dual-channel parallel training structure and design an Ultra Fast Feature Extraction Module. Our network model implements real-time inference on 20fps video.

Concerns about privacy in outsourced cloud databases have grown recently and many efficient and scalable query processing methods over encrypted data have been proposed. However, there is very limited work on how to securely process top-k ranking queries over encrypted databases in the cloud. In this paper, we propose the first efficient and provably secure top-k query processing construction that achieves adaptive CQA security. We develop an encrypted data structure called EHL and describe several secure sub-protocols under our security model to answer top-k queries. Furthermore, we optimize our query algorithms for both space and time efficiency. Finally, we empirically evaluate our protocol using real world datasets and demonstrate that our construction is efficient and practical.

Node similarity is fundamental in graph analytics. However, node similarity between nodes in different graphs (inter-graph nodes) has not received enough attention yet. The inter-graph node similarity is important in learning a new graph based on the knowledge extracted from an existing graph (transfer learning on graphs) and has applications in biological, communication, and social networks. In this paper, we propose a novel distance function for measuring inter-graph node similarity with edit distance, called NED . In NED, two nodes are compared according to their local neighborhood topologies which are represented as unordered k -adjacent trees, without relying on any extra information. Due to the hardness of computing tree edit distance on unordered trees which is NP-Complete, we propose a modified tree edit distance, called TED* , for comparing unordered and unlabeled k -adjacent trees. TED* is a metric distance, as the original tree edit distance, but more importantly, TED* is polynomially computable. As a metric distance, NED admits efficient indexing, provides interpretable results, and shows to perform better than existing approaches on a number of data analysis tasks, including graph deanonymization. Finally, the efficiency and effectiveness of NED are empirically demonstrated using real-world graphs.

Privacy preserving issues of time series databases in finan-cial, medical and transportation applications have become more and more important recently. A key problem in time series databases is to compute the similarity between two different time series. Despite some recent work on time se-ries security and privacy, there is very limited progress on securely computing the similarity between two time series. In this paper, we consider exactly this problem in a two-party setting (client and server). In particular, we want to compute the similarity between two time series, one from the client and the other from the server, without revealing the actual time series to the other party. Only the value of the similarity should be revealed to both parties at the end. At the same time, we want to do the computation as

This paper proposes a general framework for matching similar subsequences in both time series and string databases. The matching results are pairs of query subsequences and database subsequences. The framework finds all possible pairs of similar subsequences if the distance measure satisfies the "consistency" property, which is a property introduced in this paper. We show that most popular distance functions, such as the Euclidean distance, DTW, ERP, the Frechét distance for time series, and the Hamming distance and Levenshtein distance for strings, are all "consistent". We also propose a generic index structure for metric spaces named "reference net". The reference net occupies O ( n ) space, where n is the size of the dataset and is optimized to work well with our framework. The experiments demonstrate the ability of our method to improve retrieval performance when combined with diverse distance measures. The experiments also illustrate that the reference net scales well in terms of space overhead and query time.