In this paper, an interactive multitask learning method for Chinese text sentiment classification is proposed. Here, the classic BiLSTM + attention + CRF model is used to obtain full use of the interaction relationship between tasks, and it simultaneously solves the two tasks of emotional dictionary expansion and sentiment classification. The proposed method divides text sentiment classification and emotional dictionary expansion into primary task and subtask, and it adopts the Enhanced Language Representation with Informative Entities (ERNIE) model as the text representation learning model for the primary task. Then, through the maximum pooling layer and the fully connected layer, the text sentiment classification task is completed. Meanwhile, the classical BiLSTM + attention + CRF model is used to extract emotional words from the text in the subtask. In addition, the multitask information interaction mechanism is used, and the prediction information on the autonomous subtask is fed back into the potential representation of the two tasks. After iterative training, the performance of the two tasks is further optimized. Micro-blogs with COVID-19 are used here as the subject to form the experimental data set. The results demonstrate the superiority of the proposed method over other approaches, and they further verify the superiority of ERNIE over BERT, RoBERTa and XLNet for the sentiment classification of Chinese text.

In order to obtain high quality and large-scale labelled data for information security research, we propose a new approach that combines a generative adversarial network with the BiLSTM-Attention-CRF model to obtain labelled data from crowd annotations. We use the generative adversarial network to find common features in crowd annotations and then consider them in conjunction with the domain dictionary feature and sentence dependency feature as additional features to be introduced into the BiLSTM-Attention-CRF model, which is then used to carry out named entity recognition in crowdsourcing. Finally, we create a dataset to evaluate our models using information security data. The experimental results show that our model has better performance than the other baseline models.

A join order directly affects database query performance and computational overhead. Deep reinforcement learning (DRL) can explore efficient query plans while not exhausting the search space. However, the deep Q network (DQN) suffers from the overestimation of action values in query optimization, which can lead to limited query performance. In addition, $\epsilon$-greedy exploration is not efficient enough and does not enable deep exploration. Accordingly, in this paper, we propose a dynamic double DQN (DDQN) order selection method(DDOS) for join order optimization. First, the method models the join query as a Markov decision process (MDP), then solves the DRL model by integrating the network model DQN and DDQN weighting into the DRL model’s estimation error problem in query joining, and finally improves the quality of developing query plans. And actions are selected using a dynamic progressive search strategy to improve the randomness and depth of exploration and accumulate a high information gain of exploration. The performance of the proposed method is compared with those of dynamic programming, heuristic algorithms, and DRL optimization methods based on the query set Join Order Benchmark (JOB). The experimental results show that the proposed method can effectively improve the query performance with a favorable generalization ability and robustness, and outperforms other baselines in multi-join query applications.

In the field of information security, a gap exists in the study of coreference resolution of entities. A hybrid method is proposed to solve the problem of coreference resolution in information security. The work consists of two parts: the first extracts all candidates (including noun phrases, pronouns, entities, and nested phrases) from a given document and classifies them; the second is coreference resolution of the selected candidates. In the first part, a method combining rules with a deep learning model (Dictionary BiLSTM-Attention-CRF, or DBAC) is proposed to extract all candidates in the text and classify them. In the DBAC model, the domain dictionary matching mechanism is introduced, and new features of words and their contexts are obtained according to the domain dictionary. In this way, full use can be made of the entities and entity-type information contained in the domain dictionary, which can help solve the recognition problem of both rare and long entities. In the second part, candidates are divided into pronoun candidates and noun phrase candidates according to the part of speech, and the coreference resolution of pronoun candidates is solved by making rules and coreference resolution of noun phrase candidates by machine learning. Finally, a dataset is created with which to evaluate our methods using information security data. The experimental results show that the proposed model exhibits better performance than the other baseline models.

Increasingly, more administrators (defenders) are using defense strategies with deception such as honeypots to improve the IoT network security in response to attacks. Using game theory, the signaling game is leveraged to describe the confrontation between attacks and defenses. However, the traditional approach focuses only on the defender; the analysis from the attacker side is ignored. Moreover, insufficient analysis has been conducted on the optimal defense strategy with deception when the model is established with the signaling game. In our work, the signaling game model is extended to a novel two-way signaling game model to describe the game from the perspectives of both the defender and the attacker. First, the improved model is formally defined, and an algorithm is proposed for identifying the refined Bayesian equilibrium. Then, according to the calculated benefits, optimal strategies choice for both the attacker and the defender in the game are analyzed. Last, a simulation is conducted to evaluate the performance of the proposed model and to demonstrate that the defense strategy with deception is optimal for the defender.