In this paper, we propose to align sentence representations from different languages into a unified embedding space, where semantic similarities (both cross-lingual and monolingual) can be computed with a simple dot product. Pre-trained language mode
ls are fine-tuned with the translation ranking task. Existing work (Feng et al., 2020) uses sentences within the same batch as negatives, which can suffer from the issue of easy negatives. We adapt MoCo (He et al., 2020) to further improve the quality of alignment. As the experimental results show, the sentence representations produced by our model achieve the new state-of-the-art on several tasks, including Tatoeba en-zh similarity search (Artetxe and Schwenk, 2019b), BUCC en-zh bitext mining, and semantic textual similarity on 7 datasets.
Building metadata is regarded as the signpost in organizing massive building data. The application of building metadata simplifies the creation of digital representations and provides portable data analytics. Typical metadata standards such as Brick
and Haystack are used to describe the data of the building system. Brick uses standard ontologies to create building metadata. However, neither Haystack nor Brick has provided definitions about the Variable Refrigerant Flow (VRF) system so far. For years, both Brick and Haystack working groups have been discussing how to describe VRF in their schema, mainly about the classification of VRF and the definitions of VRF units. There were no settled solutions for these problems. Meanwhile, the global VRF market is growing increasingly fast because of the energy efficiency and installation simplicity of the VRF system. It is needed to have the metadata to describe VRF units in buildings for data analysis and management. Addressing this challenge, this paper extended Brick Schema with the VRF module and verified the Brick VRF module. Then, the model and the service framework were developed and applied for a building in China. The framework can serve portable energy analysis for different areas. The VRF module of this paper provides a possible solution for the expression of the VRF system in the building semantic web. The works in this paper will support semantic web in automation strategies for building management and scalable building operation.
Beam search is an effective and widely used decoding algorithm in many sequence-to-sequence (seq2seq) text generation tasks. However, in open-ended text generation, beam search is often found to produce repetitive and generic texts, sampling-based de
coding algorithms like top-k sampling and nucleus sampling are more preferred. Standard seq2seq models suffer from label bias due to its locally normalized probability formulation. This paper provides a series of empirical evidence that label bias is a major reason for such degenerate behaviors of beam search. By combining locally normalized maximum likelihood estimation and globally normalized sequence-level training, label bias can be reduced with almost no sacrifice in perplexity. To quantitatively measure label bias, we test the models ability to discriminate the groundtruth text and a set of context-agnostic distractors. We conduct experiments on large-scale response generation datasets. Results show that beam search can produce more diverse and meaningful texts with our approach, in terms of both automatic and human evaluation metrics. Our analysis also suggests several future working directions towards the grand challenge of open-ended text generation.
The proportion of Energy consumption in the building industry is great, as well as the amount of cooling and heating system. Scholars have been working on energy conservation of Heating, ventilation, and air-conditioning and other systems in building
s. The application of occupant behavior data for building energy optimization has started gaining attention from scholars. However, occupant behavior data concerns many aspects of occupants privacy. Different types of occupant behavior data contain occupants private information to different levels. It is crucial to conduct privacy protection of occupant behavior data when using occupant behavior for energy conservation. This paper presents the aspects of privacy issue when using occupant behavior data, and methods to protect data privacy with blockchain technology. Both two options of using blockchain for privacy protection, sending data records as transactions and storing files on the blockchain, are explained and evaluated with temperature records from an open access paper. Sending data as transactions can be used between sensors and local building management system. While storing files on blockchain can be used for collaboration of different building management systems. Advantages, drawbacks, and potentials of using blockchain for data and file transfer are discussed. The results should be helpful for using occupant behavior data for building energy optimization.
