The RGB-Thermal (RGB-T) information for semantic segmentation has been extensively explored in recent years. However, most existing RGB-T semantic segmentation usually compromises spatial resolution to achieve real-time inference speed, which leads to poor performance. To better extract detail spatial information, we propose a two-stage Feature-Enhanced Attention Network (FEANet) for the RGB-T semantic segmentation task. Specifically, we introduce a Feature-Enhanced Attention Module (FEAM) to excavate and enhance multi-level features from both the channel and spatial views. Benefited from the proposed FEAM module, our FEANet can preserve the spatial information and shift more attention to high-resolution features from the fused RGB-T images. Extensive experiments on the urban scene dataset demonstrate that our FEANet outperforms other state-of-the-art (SOTA) RGB-T methods in terms of objective metrics and subjective visual comparison (+2.6% in global mAcc and +0.8% in global mIoU). For the 480 × 640 RGB-T test images, our FEANet can run with a real-time speed on an NVIDIA GeForce RTX 2080 Ti card.

Singlish can be interesting to the computational linguistics community both linguistically, as a major low-resource creole based on English, and computationally, for information extraction and sentiment analysis of regional social media. In our conference paper, Wang et al. (2017), we investigated part-of-speech (POS) tagging and dependency parsing for Singlish by constructing a treebank under the Universal Dependencies scheme and successfully used neural stacking models to integrate English syntactic knowledge for boosting Singlish POS tagging and dependency parsing, achieving the state-of-the-art accuracies of 89.50% and 84.47% for Singlish POS tagging and dependency, respectively. In this work, we substantially extend Wang et al. (2017) by enlarging the Singlish treebank to more than triple the size and with much more diversity in topics, as well as further exploring neural multi-task models for integrating English syntactic knowledge. Results show that the enlarged treebank has achieved significant relative error reduction of 45.8% and 15.5% on the base model, 27% and 10% on the neural multi-task model, and 21% and 15% on the neural stacking model for POS tagging and dependency parsing, respectively. Moreover, the state-of-the-art Singlish POS tagging and dependency parsing accuracies have been improved to 91.16% and 85.57%, respectively. We make our treebanks and models available for further research.

Data-to-text generation models face challenges in ensuring data fidelity by referring to the correct input source.To inspire studies in this area, Wiseman et al. (2017) introduced the RotoWire corpus on generating NBA game summaries from the box-and line-score tables.However, limited attempts have been made in this direction and the challenges remain.We observe a prominent bottleneck in the corpus where only about 60% of the summary contents can be grounded to the boxscore records.Such information deficiency tends to misguide a conditioned language model to produce unconditioned random facts and thus leads to factual hallucinations.In this work, we restore the information balance and revamp this task to focus on fact-grounded data-to-text generation.We introduce a purified and larger-scale dataset, RotoWire-FG (Fact-Grounding), with 50% more data from the year 2017-19 and enriched input tables, hoping to attract more research focuses in this direction.Moreover, we achieve improved data fidelity over the stateof-the-art models by integrating a new form of table reconstruction as an auxiliary task to boost the generation quality.

Singlish can be interesting to the ACL community both linguistically as a major creole based on English, and computationally for information extraction and sentiment analysis of regional social media.We investigate dependency parsing of Singlish by constructing a dependency treebank under the Universal Dependencies scheme, and then training a neural network model by integrating English syntactic knowledge into a state-ofthe-art parser trained on the Singlish treebank.Results show that English knowledge can lead to 25% relative error reduction, resulting in a parser of 84.47% accuracies.To the best of our knowledge, we are the first to use neural stacking to improve cross-lingual dependency parsing on low-resource languages.We make both our annotation and parser available for further research.

This paper proposes a smart grid policy simulation platform, which takes all information affecting the interests of the stakeholders into consideration, to set policy and conduct empirical analysis. The platform can help policy making departments to understand the policy implementation effect (e.g., environmental benefits) more intuitively by using visual analysis and reporting tools, so as to find a best policy combination. The platform uses Python to realize the united simulation of Matlab and Gridview to calculate systematical carbon emissions reduction. Thus, the proposed platform can predict whether the goals of energy conservation and emissions reduction could be achieved. The paper takes IEEE RTS-25 node system as a demonstration case to calculate carbon emission reduction and get policy combination of different targets taking into account the subsidies for wind power, carbon tax, and electricity price of thermal power.

The cipher text-based full-text retrieval technology is proposed to satisfy the pressing demands for high security in massive information sharing applications. Based on the analysis of general technology of full-text indexing, we propose an improved index structure that supports cipher text indexing and retrieval. Then we present approaches to construct, maintain and retrieve cipher text indexes. Meanwhile, we implement a cipher text-based full-text retrieval system, which allows full-text search on the encrypted documents in multiple formats without decryption. Finally, we carry out the experiments and evaluate the performance of cipher text index construction and retrieval.