In recent research, slight performance improvement is observed from automatic speech recognition systems to audio-visual speech recognition systems in end-to-end frameworks with low-quality videos. Unmatching convergence rates and specialized input representations between audio-visual modalities are considered to cause the problem. In this paper, we propose two novel techniques to improve audio-visual speech recognition (AVSR) under a pre-training and fine-tuning training framework. First, we explore the correlation between lip shapes and syllable-level subword units in Mandarin through a frame-level subword unit classification task with visual streams as input. The fine-grained subword labels guide the network to capture temporal relationships between lip shapes and result in an accurate alignment between video and audio streams. Next, we propose an audio-guided Cross-Modal Fusion Encoder (CMFE) to utilize main training parameters for multiple cross-modal attention layers to make full use of modality complementarity. Experiments on the MISP2021-AVSR data set show the effectiveness of the two proposed techniques. Together, using only a relatively small amount of training data, the final system achieves better performances than state-of-the-art systems with more complex front-ends and back-ends. The code is released at1.

In online systems, including e-commerce platforms, many users resort to the reviews or comments generated by previous consumers for decision making, while their time is limited to deal with many reviews. Therefore, a review summary, which contains all important features in user-generated reviews, is expected. In this article, we study “how to generate a comprehensive review summary from a large number of user-generated reviews.” This can be implemented by text summarization, which mainly has two types of extractive and abstractive approaches. Both of these approaches can deal with both supervised and unsupervised scenarios, but the former may generate redundant and incoherent summaries, while the latter can avoid redundancy but usually can only deal with short sequences. Moreover, both approaches may neglect the sentiment information. To address the above issues, we propose comprehensive Review Summary Generation frameworks to deal with the supervised and unsupervised scenarios. We design two different preprocess models of re-ranking and selecting to identify the important sentences while keeping users’ sentiment in the original reviews. These sentences can be further used to generate review summaries with text summarization methods. Experimental results in seven real-world datasets (Idebate, Rotten Tomatoes Amazon, Yelp, and three unlabelled product review datasets in Amazon) demonstrate that our work performs well in review summary generation. Moreover, the re-ranking and selecting models show different characteristics.

In currently popular access control models, the security policies and regulations never change in the running system process once they are identified, which makes it possible for attackers to find the vulnerabilities in a system, resulting in the lack of ability to perceive the system security status and risks in a dynamic manner and exposing the system to such risks. By introducing the maximum entropy (MaxENT) models into the rule optimization for the Bell-LaPadula (BLP) model, this paper proposes an improved BLP model with the self-learning function: MaxENT-BLP. This model first formalizes the security properties, system states, transformational rules, and a constraint model based on the states transition of the MaxENT. After handling the historical system access logs as the original data sets, this model extracts the user requests, current states, and decisions to act as the feature vectors. Second, we use k -fold cross validation to divide all vectors into a training set and a testing set. In this paper, the model training process is based on the Broyden-Fletcher-Goldfarb-Shanno algorithm. And this model contains a strategy update algorithm to adjust the access control rules dynamically according to the access and decision records in a system. Third, we prove that MaxENT-BLP is secure through theoretical analysis. By estimating the precision, recall, and F1-score, the experiments show the availability and accuracy of this model. Finally, this paper provides the process of model training based on deep learning and discussions regarding adversarial samples from the malware classifiers. We demonstrate that MaxENT-BLP is an appropriate choice and has the ability to help running information systems to avoid more risks and losses.

Latent Dirichlet Allocation (LDA), has been recently used to automatically generate text corpora topics, and applied to sentences extraction based multi-document summarization algorithms. In this paper, we propose a novel approach to automatic generation of aspect-oriented summaries from multiple documents. Our approach is to combine the traditional summary generation algorithm and the the abstract generation algorithm based on deep learning.We employ the improved traditional summary generation algorithm to convert multiple documents into a single document, and then using the resulting single document with the deep learning method to extract the final summary. At first, we apply improved LDA model to cluster sentences in all documents. Second, We employ the extended LexRank algorithm to sort the sentences in each cluster. Third, we use extended Hedge Trimmer algorithm for sentence compression. Fourth, We apply Integer Linear Programming for sentence selection, and in this step ,we get the single document. Finally, We employ the textum on TensorFlow to get the final abstract. The experiments showed that the proposed algorithm achieved better performance compared the other state-of-the-art algorithms on DUC2005 and TAC2010 corpus.