A Method for Detecting and Recognizing Yi Character Based on Deep Learning

Aiming at the challenges associated with the absence of a labeled dataset for Yi characters and the complexity of Yi character detection and recognition,we present a deep learning-based approach for Yi character detection and recognition.In the detection stage,an improved Differentiable Binarization Network(DBNet)framework is introduced to detect Yi characters,in which the Omni-dimensional Dynamic Convolution(ODConv)is combined with the ResNet-18 feature extraction module to obtain multi-dimensional complementary features,thereby improving the accuracy of Yi character detection.Then,the feature pyramid network fusion module is used to further extract Yi character image features,improving target recognition at different scales.Further,the previously generated feature map is passed through a head network to produce two maps:a probability map and an adaptive threshold map of the same size as the original map.These maps are then subjected to a differentiable binarization process,resulting in an approximate binarization map.This map helps to identify the boundaries of the text boxes.Finally,the text detection box is generated after the post-processing stage.In the recognition stage,an improved lightweight MobileNetV3 framework is used to recognize the detect character regions,where the original Squeeze-and-Excitation(SE)block is replaced by the efficient Shuffle Attention(SA)that integrates spatial and channel attention,improving the accuracy of Yi characters recognition.Meanwhile,the use of depth separable convolution and reversible residual structure can reduce the number of parameters and computation of the model,so that the model can better understand the contextual information and improve the accuracy of text recognition.The experimental results illustrate that the proposed method achieves good results in detecting and recognizing Yi characters,with detection and recognition accuracy rates of 97.5%and 96.8%,respectively.And also,we have compared the detection and recognition algorithms proposed in this paper with other typical algorithms.In these comparisons,the proposed model achieves better detection and recognition results with a certain reliability.

Lom： Discovering Logic Flaws Within MongoDB-based Web Applications

Logic flaws within web applications will allow malicious operations to be triggered towards back-end database. Existing approaches to identifying logic flaws of database accesses are strongly tied to structured query language （SQL） statement construction and cannot be applied to the new generation of web applications that use not only structured query language （NoSQL） databases as the storage tier. In this paper, we present Lom, a black-box approach for discovering many categories of logic flaws within MongoDB- based web applications. Our approach introduces a MongoDB operation model to support new features of MongoDB and models the application logic as a mealy finite state machine. During the testing phase, test inputs which emulate state violation attacks are constructed for identifying logic flaws at each application state. We apply Lom to several MongoDB-based web applications and demonstrate its effectiveness.

LSN:Long-Term Spatio-Temporal Network for Video Recognition

Although recurrent neural networks(RNNs)are widely leveraged to process temporal or sequential data,they have attracted too little attention in current video action recognition applications.Therefore,this work attempts to model the long-term spatio-temporal information of the video based on a variant of RNN,i.e.,higher-order RNN.Moreover,we propose a novel long-term spatio-temporal network(LSN)for solving this video task,the core of which integrates the newly constructed high-order ConvLSTM(HO-ConvLSTM)modules with traditional 2D convolutional blocks.Specifically,each HO-ConvLSTM module consists of an accumulated temporary state(ATS)module as well as a standard ConvLSTM module,and several previous hidden states in the ATS module are accumulated to one temporary state that will enter the standard ConvLSTM to determine the output together with the current input.The HO-ConvLSTM module can be inserted into different stages of the 2D convolutional neural network(CNN)in a plug-andplay manner,thus well characterizing the long-term temporal evolution at various spatial resolutions.Experiment results on three commonly used video benchmarks demonstrate that the proposed LSN model can achieve competitive performance with the representative models.