Topic models can extract consistent themes from large corpora for research purposes. In recent years, the combination of pretrained language models and neural topic models has gained attention among scholars. However, this approach has some drawbacks: in short texts, the quality of the topics obtained by the models is low and incoherent, which is caused by the reduced word frequency (insufficient word co-occurrence) in short texts compared to long texts. To address these issues, we propose a neural topic model based on SBERT and data augmentation. First, our proposed easy data augmentation (EDA) method with keyword combination helps overcome the sparsity problem in short texts. Then, the attention mechanism is used to focus on keywords related to the topic and reduce the impact of noise words. Next, the SBERT model is trained on a large and diverse dataset, which can generate high-quality semantic information vectors for short texts. Finally, we perform feature fusion on the augmented data that have been weighted by an attention mechanism with the high-quality semantic information obtained. Then, the fused features are input into a neural topic model to obtain high-quality topics. The experimental results on an English public dataset show that our model generates high-quality topics, with the average scores improving by 2.5% for topic coherence and 1.2% for topic diversity compared to the baseline model.

Contemporary large-scale and systematic agricultural operations demand the collaborative efforts of multiple agricultural machines with distinct functionalities. However, the failure of a single agricultural machine during collaborative operations jeopardizes the entire undertaking. To address this challenge, this paper proposes a multi-machine collaborative dynamic job allocation method based on the improved ant colony algorithm. Initially, the improved ant colony algorithm is employed to determine the optimal solution for harvester scheduling. This solution is then fed into the data conversion algorithm to acquire the necessary unloading point information for transport vehicle scheduling. Subsequently, the improved ant colony algorithm is once again utilized to optimize the transport vehicle scheduling. In cases of agricultural machinery failure or changes in the operating environment, two distinct methods are employed based on the situation. The first involves double-layer rescheduling of both harvester and transport vehicles, while the second employs single-layer rescheduling exclusively for the transport vehicles, yielding the respective rescheduling results. The outcomes demonstrate that the proposed solution method effectively identifies the current optimal scheduling plan for both the harvester and transport vehicle in the event of malfunctions. Moreover, under the premise that the unproductive waiting time of the harvester is reduced to zero, and the number of transport vehicles is minimized, it achieves the minimization of operating time cost and transportation cost. This method exhibits significant potential for seamless integration into the practical application of unmanned farms, providing a foundation for addressing scheduling and management challenges in multi-agricultural machinery collaborative operations within complex farmland operating environments.

In recent years, with the development of the Internet, the attribution classification of APT malware remains an important issue in society. Existing methods have yet to consider the DLL link library and hidden file address during the execution process, and there are shortcomings in capturing the local and global correlation of event behaviors. Compared to the structural features of binary code, opcode features reflect the runtime instructions and do not consider the issue of multiple reuse of local operation behaviors within the same APT organization. Obfuscation techniques more easily influence attribution classification based on single features. To address the above issues, (1) an event behavior graph based on API instructions and related operations is constructed to capture the execution traces on the host using the GNNs model. (2) ImageCNTM captures the local spatial correlation and continuous long-term dependency of opcode images. (3) The word frequency and behavior features are concatenated and fused, proposing a multi-feature, multi-input deep learning model. We collected a publicly available dataset of APT malware to evaluate our method. The attribution classification results of the model based on a single feature reached 89.24% and 91.91%. Finally, compared to single-feature classifiers, the multi-feature fusion model achieves better classification performance.

When an entity contains one or more entities, these particular entities are referred to as nested entities. The Layered BiLSTM-CRF model can use multiple BiLSTM layers to identify nested entities. However, as the number of layers increases, the number of labels that the model can learn decreases, and it may not even predict any entities, thereby causing the model to stop stacking. Furthermore, the model will be constrained by the one-way propagation of information from the lower layer to the higher layer. The incorrect entities extracted by the outer layer will affect the performance of the inner layer. We propose a novel neural network for nested named entity recognition (NER) that dynamically stacks flat NER layers to address these issues. Each flat NER layer captures contextual information based on a pretrained model with more robust feature extraction capabilities. The model parameters of a flat NER layer and its input are entirely independent. The input of each layer is all of the word representations generated by the input sequence through the embedding layer. The independent input ensures that different flat NER layers will not be interfered with by other flat NER layers during model training and testing to reduce error propagation. Experiments show that our model obtains F1 scores of 76.9%, 78.1%, and 78.0% on the ACE2004, ACE2005, and GENIA datasets, respectively.