Measuring the similarity of PML documents with RFID-based sensors

The Electronic Product Code (EPC) Network is an important part of the Internet of Things. The Physical Mark-Up Language (PML) is to represent and de-scribe data related to objects in EPC Network. The PML documents of each component to exchange data in EPC Network system are XML documents based on PML Core schema. For managing theses huge amount of PML documents of tags captured by Radio frequency identification (RFID) readers, it is inevitable to develop the high-performance technol-ogy, such as filtering and integrating these tag data. So in this paper, we propose an approach for meas-uring the similarity of PML documents based on Bayesian Network of several sensors. With respect to the features of PML, while measuring the similarity, we firstly reduce the redundancy data except information of EPC. On the basis of this, the Bayesian Network model derived from the structure of the PML documents being compared is constructed.Comment: International Journal of Ad Hoc and Ubiquitous Computin

Revisiting Pre-Trained Models for Chinese Natural Language Processing

Bidirectional Encoder Representations from Transformers (BERT) has shown marvelous improvements across various NLP tasks, and consecutive variants have been proposed to further improve the performance of the pre-trained language models. In this paper, we target on revisiting Chinese pre-trained language models to examine their effectiveness in a non-English language and release the Chinese pre-trained language model series to the community. We also propose a simple but effective model called MacBERT, which improves upon RoBERTa in several ways, especially the masking strategy that adopts MLM as correction (Mac). We carried out extensive experiments on eight Chinese NLP tasks to revisit the existing pre-trained language models as well as the proposed MacBERT. Experimental results show that MacBERT could achieve state-of-the-art performances on many NLP tasks, and we also ablate details with several findings that may help future research. Resources available: https://github.com/ymcui/MacBERTComment: 12 pages, to appear at Findings of EMNLP 202

Language Inclusion Checking of Timed Automata with Non-Zenoness

Ministry of Education, Singapore under its Academic Research Funding Tier

Longitudinal survey of microbiome associated with particulate matter in a megacity.

BackgroundWhile the physical and chemical properties of airborne particulate matter (PM) have been extensively studied, their associated microbiome remains largely unexplored. Here, we performed a longitudinal metagenomic survey of 106 samples of airborne PM2.5 and PM10 in Beijing over a period of 6 months in 2012 and 2013, including those from several historically severe smog events.ResultsWe observed that the microbiome composition and functional potential were conserved between PM2.5 and PM10, although considerable temporal variations existed. Among the airborne microorganisms, Propionibacterium acnes, Escherichia coli, Acinetobacter lwoffii, Lactobacillus amylovorus, and Lactobacillus reuteri dominated, along with several viral species. We further identified an extensive repertoire of genes involved in antibiotic resistance and detoxification, including transporters, transpeptidases, and thioredoxins. Sample stratification based on Air Quality Index (AQI) demonstrated that many microbial species, including those associated with human, dog, and mouse feces, exhibit AQI-dependent incidence dynamics. The phylogenetic and functional diversity of air microbiome is comparable to those of soil and water environments, as its composition likely derives from a wide variety of sources.ConclusionsAirborne particulate matter accommodates rich and dynamic microbial communities, including a range of microbial elements that are associated with potential health consequences

Lowering emissivity of concrete roof tile\u27s underside cuts down heat entry to the building

Buildings in Southern China widely use a double-skin roof to reduce heat entry through the roof to the building interior during summertime. Concrete roof tiles are preferably installed as the outmost layer of the double-skin roof due to their resistance to hail and wind damages and their attractive price. However, after construction, the tile’s top tends to be darkened by dust deposit and algae growth, increasing the heat entry through the roof to the building. Here, we show that this heat entry can be curtailed by lowering the emissivity at the tile’s underside. Temperatures and heat fluxes at different elevations of a double-skin roof with concrete tiles as the outmost layer of the roof are monitored. The underside of each concrete tile is coated with a specific paint to get a unique emissivity. Observations reveal that lowering the emissivity of concrete roof tiles could cut down the summer heat gain of buildings in tropical regions