摘要：
Visual Dialog aims to answer an appropriate response based on a multi-round dialog history and a given image. Existing methods either focus on semantic interaction, or implicitly capture coarse-grained structural interaction (e.g., pronoun co-references). The fine-grained and explicit structural interaction feature for dialog history is seldom explored, resulting in insufficient feature learning and difficulty in capturing precise context. To address these issues, we propose a structure-aware dual-level graph interactive network (SDGIN) that integrates verb-specific semantic roles and co-reference resolution to explicitly capture context structural features for discriminative and generative tasks in visual dialog. Specifically, we create a novel structural interaction graph that injects syntactic knowledge priors into dialog by introducing semantic role labeling that imply which words are sentence stems. Furthermore, considering the single perspective limitation of previous algorithms, we design a dual-perspective mechanism that learns fine-grained token-level context structure features and coarse-grained utterance-level interactions in parallel. It possess an elegant view to explore precise context interactions, realizing the mutual complementation and enhancement of different granularity features. Experimental results show the superiority of our proposed approach. Compared to other task-specific models, our SDGIN outperforms previous models and achieves a significant improvement on the benchmark dataset VisDial v1.0.
摘要：
We proposed a novel method for predicting the service life of optical cables based on the Autoformer model combined with the calculation method. Leveraging historical weather data from Guangzhou and employing specific cable length calculation techniques, our study comprehensively considers factors impacting cable lifespan. Moreover, through comparative analysis with alternative deep learning models and parameter assessments, our method validates the superiority and stability of the Autoformer model in predicting cable lifespan, which can offer a more reliable approach for ensuring cable technology reliability and the management of associated industries.
摘要：
A detailed theoretical study is conducted on the nonlinear interference in the same-wavelength bidirectional coherent optical fiber communication systems. The Gaussian noise (GN) model used to evaluate nonlinear interference (NLI) in unidirectional systems is applied and extended to bidirectional transmission scenarios. The extended NLI model shows that in a bidirectional transmission communication system, the backward signal almost does not introduce additional nonlinear crosstalk to the forward signal due to the strong walk-off effect between forward and backward transmitted signals. Specifically, the ratio of the nonlinear crosstalk introduced by the forward and backward signals is about 21 dB, which means that the traditional GN model is also applicable in the bidirectional scenario. This conclusion is validated on the platform of a same-wavelength bidirectional coherent optical communication system based on Optisystem software.
摘要：
In the literature, most previous studies on English implicit inter-sentence relation recognition only focused on semantic interactions, which could not exploit the syntactic interactive information in Chinese due to its complicated syntactic structure characteristics. In this paper, we propose a novel and effective model DSGCN-RoBERTa to learn the interaction features implied in sentences from both syntactic and semantic perspectives. To generate a rich contextual sentence embedding, we exploit RoBERTa, a large-scale pre-trained language model based on the transformer unit. DSGCN-RoBERTa consists of two key modules, the syntactic interaction and the semantic interaction modules. Specifically, the syntactic interaction module helps capture the depth-level structure information, including non-consecutive words and their relations, while the semantic interaction module enables the model to understand the context from the whole sentence to the local words. Furthermore, on top of such multi-perspective feature representations, we design a strength-dependent matching strategy that is able to adaptively capture the strong relevant interactive information in a fine-grained level. Extensive experiments demonstrate that the proposed method achieved state-of-the-art results on benchmarks Chinese compound sentence corpus CCCS and Chinese discourse corpus CDTB datasets. We also achieve comparable performance on the English corpus PDTB that demonstrates the superiority of our method.
期刊：
Information Sciences,2022年593:505-526 ISSN：0020-0255
通讯作者：
Yanhong Li
作者机构：
[Wang, Pengfei] School of Computer, Central China Normal University, Wuhan, China;Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, CCNU, Wuhan, China;National Language Resources Monitoring and Research Center for Network Media, CCNU, Wuhan, China;[Li, Yanhong] Department of Computer Science, South-Central University for Nationalities, Wuhan, China;[Zheng, Bolong; Li, Guohui] School of Computer of Science and Technology, Huazhong University of Science and Technology, Wuhan, China
通讯机构：
[Yanhong Li] D;Department of Computer Science, South-Central University for Nationalities, Wuhan, China
关键词：
Keyword attribute;SGI Index;Spatial keyword query;Temporal information
摘要：
The top-k augmented spatial keyword query (TkASKQ) retrieves k objects with the highest scores based on a scoring function, which considers spatial proximity, textual similarity and attribute matching simultaneously. As far as we know, no work has been conducted on answering why-not questions on TkASKQ queries (WTkASKQ). This paper takes the first step to address WTkASKQ queries by adopting a Query Refinement model. Specifically, we propose a hybrid indexing structure, A(k)C, which adopts a two-level partitioning scheme, to efficiently organize the textual, attribute, and spatial information of objects. Based on A(k)C, several filtering strategies are proposed to prune unqualified objects for query processing. To limit the number of refined queries to be explored, we construct new refined queries by sequentially extracting new keywords and attribute-value pairs from missing objects and adding them to the original keyword and attribute-value sets, respectively, so as to efficiently obtain the best refined query with minimal modification cost. In addition, we discuss the applicability of the methods in handling why-not questions on augmented regional queries, ordinary top-k SKQ queries and complex scoring queries. Experimental result shows that our A(k)C-based method has higher query efficiency compared with other baseline methods. (C) 2021 Elsevier B.V. All rights reserved.
摘要：
Extensive efforts have been made to improve the efficiency of the top-k trajectory similarity search(TkTSS), which retrieves k similarity trajectories for a given trajectory with a similarity function. When a user issues a initial query, s/he may find some desired trajectories are not in the result and may question why these expected trajectories are missing. To address this problem, we develop a so-called why-not spatial temporal TkTSS that is able to minimally modify the original top-k result into a result which contains the expected missing trajectories. In this paper, a novel hybrid SGP index is developed to organize the trajectories. Based on this index, an efficient time-first TkTSS framework is proposed to retrieve TkTSS. In order to refine the initial query to make all missing trajectories appear in the result, an innovative trajectory projection approach is designed to transfer the why-not question on TkTSS into a two-dimensional geometrical problem. Two type boundary areas pruned area (PA) and refined area (RA) are calculated to shrink the searching space. By constructing the compact area of RA, the searching space can be shrunk in a further step. Some pruning methods are proposed to accelerate the query process. Finally, extensive experiments with real-world and synthetic data offer evidence that the proposed solution performs much better than its competitors with respect to both effectiveness and efficiency. (C) 2021 Elsevier B.V. All rights reserved.
