Abstract It may be difficult for existing methods to make full use of the correlation and complementarity of various kinds of information when processing multi-source information. In order to accurately perceive the security situation of distribution automation and ensure the safe and stable operation of distribution network, the multi-source information fusion distribution automation security situation awareness technology based on risk transmission path is studied. Based on the risk transmission path, the distribution automation security situational awareness factors are analyzed, and the main factors affecting the distribution automation security situation are divided into two dimensions: internal source and external source, and eight main awareness factors; Different types of sensors are set in the main areas of security situational awareness factors to collect data of different awareness factors. Using ant colony algorithm to optimize DS evidence fusion method, data with different perception factors are fused, and data fusion results with different perception factors are obtained. The distribution automation security situational awareness model is constructed, and the security situational awareness results are obtained based on the data fusion results of the awareness factors. If the results are higher than the set threshold, the abnormal signal can be output to determine the area where the distribution automation abnormal equipment is located. The experimental results show that the multi-source data fusion effect of this method is good, and it can accurately perceive the security status of different nodes of the experimental object at different time nodes.

In neural machine translation (NMT), most sequence-to-sequence (seq2seq) models are trained only with the teacher-forcing paradigm, where the ground truth history is used to predict the next ground truth word. At the inference stage, however, the decoder predicts the next token solely based on history generated from scratch. Both using ground truth history and predicting ground truth words potentially lead to exposure bias. On the one hand, to alleviate the issue of exposure bias caused by using ground truth history, we propose contextual augmentation by allowing substitution, insertion, and deletion of words. The contextual augmentation applies to target sequence to generate non-ground truth and natural history when predicting next words. On the other hand, to alleviate the exposure bias caused by predicting ground truth words, we further apply self distillation to guide the model to carry out optimization according to smoothed prediction distribution, i.e, enable the model to predict not only ground truth words, but also other potentially correct and reasonable words. Experimental results on WMT14 English$\leftrightarrow$German and IWSLT14 German$\rightarrow$English translation tasks demonstrate that our approach achieves significant improvements over Transformer on standard benchmarks. Detailed experimental analyses further reveal the effectiveness of our proposed approach on improving the translation quality.

BACKGROUND: Protein-protein interaction (PPI) is vital for life processes, disease treatment, and drug discovery. The computational prediction of PPI is relatively inexpensive and efficient when compared to traditional wet-lab experiments. Given a new protein, one may wish to find whether the protein has any PPI relationship with other existing proteins. Current computational PPI prediction methods usually compare the new protein to existing proteins one by one in a pairwise manner. This is time consuming. RESULTS: In this work, we propose a more efficient model, called deep hash learning protein-and-protein interaction (DHL-PPI), to predict all-against-all PPI relationships in a database of proteins. First, DHL-PPI encodes a protein sequence into a binary hash code based on deep features extracted from the protein sequences using deep learning techniques. This encoding scheme enables us to turn the PPI discrimination problem into a much simpler searching problem. The binary hash code for a protein sequence can be regarded as a number. Thus, in the pre-screening stage of DHL-PPI, the string matching problem of comparing a protein sequence against a database with M proteins can be transformed into a much more simpler problem: to find a number inside a sorted array of length M. This pre-screening process narrows down the search to a much smaller set of candidate proteins for further confirmation. As a final step, DHL-PPI uses the Hamming distance to verify the final PPI relationship. CONCLUSIONS: The experimental results confirmed that DHL-PPI is feasible and effective. Using a dataset with strictly negative PPI examples of four species, DHL-PPI is shown to be superior or competitive when compared to the other state-of-the-art methods in terms of precision, recall or F1 score. Furthermore, in the prediction stage, the proposed DHL-PPI reduced the time complexity from [Formula: see text] to [Formula: see text] for performing an all-against-all PPI prediction for a database with M proteins. With the proposed approach, a protein database can be preprocessed and stored for later search using the proposed encoding scheme. This can provide a more efficient way to cope with the rapidly increasing volume of protein datasets.

Due to different types of inputs, diverse text generation tasks may adopt different encoder-decoder frameworks. Thus most existing approaches that aim to improve the robustness of certain generation tasks are input-relevant, and may not work well for other generation tasks. Alternatively, in this paper we present a universal approach to enhance the language representation for text generation on the base of generic encoder-decoder frameworks. This is done from two levels. First, we introduce randomness by randomly masking some percentage of tokens on the decoder side when training the models. In this way, instead of using ground truth history context, we use its corrupted version to predict the next token. Then we propose an auxiliary task to properly recover those masked tokens. Experimental results on several text generation tasks including machine translation (MT), AMR-to-text generation, and image captioning show that the proposed approach can significantly improve over competitive baselines without using any task-specific techniques. This suggests the effectiveness and generality of our proposed approach.

Mining communities or clusters in networks is valuable in analyzing, designing, and optimizing many natural and engineering complex systems, e.g., protein networks, power grid, and transportation systems. Most of the existing techniques view the community mining problem as an optimization problem based on a given quality function(e.g., modularity), however none of them are grounded with a systematic theory to identify the central nodes in the network. Moreover, how to reconcile the mining efficiency and the community quality still remains an open problem. In this paper, we attempt to address the above challenges by introducing a novel algorithm. First, a kernel function with a tunable influence factor is proposed to measure the leadership of each node, those nodes with highest local leadership can be viewed as the candidate central nodes. Then, we use a discrete-time dynamical system to describe the dynamical assignment of community membership; and formulate the serval conditions to guarantee the convergence of each node's dynamic trajectory, by which the hierarchical community structure of the network can be revealed. The proposed dynamical system is independent of the quality function used, so could also be applied in other community mining models. Our algorithm is highly efficient: the computational complexity analysis shows that the execution time is nearly linearly dependent on the number of nodes in sparse networks. We finally give demonstrative applications of the algorithm to a set of synthetic benchmark networks and also real-world networks to verify the algorithmic performance.