Mapping semantic knowledge for unsupervised text categorisation

Text categorisation is challenging, due to the complex structure with heterogeneous, changing topics in documents. The performance of text categorisation relies on the quality of samples, effectiveness of document features, and the topic coverage of categories, depending on the employing strategies; supervised or unsupervised; single labelled or multi-labelled. Attempting to deal with these reliability issues in text categorisation, we propose an unsupervised multi-labelled text categorisation approach that maps the local knowledge in documents to global knowledge in a world ontology to optimise categorisation result. The conceptual framework of the approach consists of three modules; pattern mining for feature extraction; feature-subject mapping for categorisation; concept generalisation for optimised categorisation. The approach has been promisingly evaluated by compared with typical text categorisation methods, based on the ground truth encoded by human experts

Sign Inference for Dynamic Signed Networks via Dictionary Learning

Mobile online social network (mOSN) is a burgeoning research area. However, most existing works referring to mOSNs deal with static network structures and simply encode whether relationships among entities exist or not. In contrast, relationships in signed mOSNs can be positive or negative and may be changed with time and locations. Applying certain global characteristics of social balance, in this paper, we aim to infer the unknown relationships in dynamic signed mOSNs and formulate this sign inference problem as a low-rank matrix estimation problem. Specifically, motivated by the Singular Value Thresholding (SVT) algorithm, a compact dictionary is selected from the observed dataset. Based on this compact dictionary, the relationships in the dynamic signed mOSNs are estimated via solving the formulated problem. Furthermore, the estimation accuracy is improved by employing a dictionary self-updating mechanism

Preparation and properties of asphalt binders modified by THFS extracted from direct coal liquefaction residue

This paper aims to study the preparation and viscoelastic properties of asphalt binder modified by tetrahydrofuran soluble fraction (THFS) extracted from direct coal liquefaction residue. The modified asphalt binders, which blended with SK-90 (control asphalt binder) and 4%, 6%, 8% and 10% THFS (by weight of SK-90), were fabricated. The preparation process for asphalt binder was optimized in terms of the orthogonal array test strategy and gray correlation analysis results. The properties of asphalt binder were measured by applying Penetration performance grade and Superpave performance grade specifications. In addition, the temperature step and frequency sweep test in Dynamic Shear Rheometer were conducted to predict the rheological behavior, temperature and frequency susceptibility of asphalt binder. The test results suggested the optimal preparation process, such as 150 °C shearing temperature, 45 min shearing time and 4000 rpm shearing rate. Subsequently, the addition of THFS was beneficial in increasing the high-temperature properties but decreased the low-temperature properties and resistance to fatigue. The content analysis of THFS showed the percentage of 4~6% achieved a balance in the high-and-low temperature properties of asphalt binder. The asphalt binder with higher THFS content exhibited higher resistance to rutting and less sensitivity to frequency and temperature