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To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jss.2010.08.018 Byline: Zijing Tan (a), Chengfei Liu (b), Wei Wang (a), Baile Shi (a) Keywords: XML; inconsistent; Query; Constraint Abstract: When data sources are virtually integrated, there is no common and centralized method to maintain global consistency, so inconsistencies with regard to global integrity constraints are very likely to occur. In this paper, we consider the problem of defining and computing consistent query answers when queries are posed to virtual XML data integration systems, which are specified following the local-as-view approach. We propose a powerful XML constraint model to define global constraints, which can express keys and functional dependencies, and which also extends the newly introduced conditional functional dependencies to XML. We provide an approach to defining XML views, which supports not only edge-path mappings but also data-value bindings to express the join operator. We give formal definitions of repair and consistent query answers with the XML data integration settings. Given a query on the global system, we present a two-step method to compute consistent query answers. First, the given query is transformed using the global constraints, such that to run the transformed query on the original global system will generate exactly the consistent query answers. Because the global instance is not materialized, the query on the global instance is then rewritten in the form of queries on the underlying data sources by reversing rules in view definitions. We illustrate that the XPath query transformations can be implemented in XQuery. Finally, we implement prototypes of our method and evaluate our algorithms in the experiments. Author Affiliation: (a) School of Computer Science, Fudan University, Shanghai, China (b) Faculty of Information and Communication Technologies, Swinburne University of Technology, Australia Article History: Received 11 December 2009; Revised 6 August 2010; Accepted 9 August 2010

Abstract Forecasting the short-term speed of moving vehicles on roads plays a vital role on traffic control and trip planning, which however still remains a challenging task when the high accuracy is required. In this paper, we propose a novel approach to the short-term traffic speed forecasting, which takes into account the influence of different traffic attributes, such as traffic flow, traffic speed, road occupancy and traffic density, of adjacent roads on the traffic speed. In addition, in order to obtain the more accurate relation between traffic speed and traffic attributes, we employ the idea of piecewise correlation function and adopt the Jenks clustering method with dynamic programming to determine the segment intervals of relation. We validate our approach based on the real data collected from Wenzhou and Hangzhou, two large cities located in eastern China. The extensive experimental results show that, compared with the state-of-the-art approaches, our approach has the higher stability and accuracy, especially for 5-minute and 10-minute speed prediction. Highlights • We consider only traffic attributes most relevant to speed and also their weights. • We consider the differences of impacts on speed among adjacent roads. • We employ piecewise correlation function between traffic speed and attributes. • We adopt Jenks clustering with dynamic programming to determine segment intervals. • We validate our approach based on the real data collected from two large cities. [ABSTRACT FROM AUTHOR]

Road traffic networks are rapidly growing in size with increasing complexities. To simplify their analysis in order to maintain smooth traffic, a large urban road network can be considered as a set of small sub-networks, which exhibit distinctive traffic flow patterns. In this paper, we propose a robust framework for spatial partitioning of large urban road networks based on traffic measures. For a given urban road network, we aim to identify the different sub-networks or partitions that exhibit homogeneous traffic patterns internally, but heterogeneous patterns to others externally. To this end, we develop a two-stage algorithm (referred as FaDSPa ) within our framework. It first transforms the large road graph into a well-structured and condensed density peak graph (DPG) via density based clustering and link aggregation using traffic density and adjacency connectivity, respectively. Thereafter we apply our spectral theory based graph cut (referred as α -Cut) to partition the DPG and obtain the different sub-networks. Thus the framework applies the locally distributed computations of density based clustering to improve efficiency and the centralized global computations of spectral clustering to improve accuracy. We perform extensive experiments on real as well as synthetic datasets, and compare its performance with that of an existing road network partitioning method. Our results show that the proposed method outperforms the existing normalized cut based method for small road networks and provides impressive results for much larger networks, where other methods may face serious problems of time and space complexities. [ABSTRACT FROM AUTHOR]

Abstract: The current business environment changes rapidly, dictated by user requirements and market opportunities. Organisations are therefore driven to continuously adapt their business processes to new conditions. Thus, management of business process schema evolution, particularly process version control, is in great demand to capture the dynamics of business process schema changes. This paper aims to facilitate version control for business process schema evolution, with an emphasis on version compatibility, co-existence of multiple versions and dynamic version shifts. A multi-level versioning approach is established to specify dependency between business process schema evolutions, and a novel version preserving graph model is proposed to record business process schema evolutions. A set of business process schema updating operations is devised to support the entire set of process change patterns. By maintaining sufficient and necessary schema and version information, our approach provides comprehensive support for navigating process instance executions of different and changing versions, and deriving the process schema of a certain version. A prototype is also implemented for the proof-of-concept purpose. [Copyright &y& Elsevier]

Abstract: Searching XML data with a structured XML query can improve the precision of results compared with a keyword search. However, the structural heterogeneity of the large number of XML data sources makes it difficult to answer the structured query exactly. As such, query relaxation is necessary. Previous work on XML query relaxation poses the problem of unnecessary computation of a big number of unqualified relaxed queries. To address this issue, we propose an adaptive relaxation approach which relaxes a query against different data sources differently based on their conformed schemas. In this paper, we present a set of techniques that supports this approach, which includes schema-aware relaxation rules for relaxing a query adaptively, a weighted model for ranking relaxed queries, and algorithms for adaptive relaxation of a query and top-k query processing. We discuss results from a comprehensive set of experiments that show the effectiveness and the efficiency of our approach. [Copyright &y& Elsevier]

• The application of Self-supervised learning reduces the time and effort of manual labeling. • Contrastive learning frameworks can generate powerful feature extractors to improve generalization. • Apply microseismic dataset from different practical engineering monitoring systems for generalization testing. The identification of valid microseismic events is a crucial stage in the processing of microseismic data and plays a central role in subsequent monitoring and early warning of rockburst disasters. However, obtaining microseismic data is difficult, and manually identifying effective microseismic signals is even more challenging. Therefore, exploring high-precision intelligent classification methods with limited labeled data is necessary. In this study, we applied a self-supervised learning method based on contrastive learning to classify small samples of labeled data. Firstly, use large unlabeled data to pre-train SimCLR (a simple contrastive learning framework) for transfer learning to classify small sample labeled data. The classification accuracy was 87.14%, an increase of 11.3% compared to the supervised-deep-learning method. Furthermore, we directly applied the pre-trained SimCLR to the microseismic data of various projects, achieving a classification accuracy of 86.70%. This demonstrates strong classification performance and generalization and provides valuable technical support for the subsequent monitoring and warning of rockburst disasters. [ABSTRACT FROM AUTHOR]

Due to the large amount of brominated flame retardants contained in waste printed circuit boards, their incineration treatment has caused great harm to the environment. Conventional heat pyrolysis is used to treat used circuit boards, resulting in more contaminating waste due to the production of brominated gases and dioxins. In this paper, the steam atmosphere heating method is used to pyrolyze the crushed waste circuit board under optimized conditions (700 °C, 20 °C/min, 60 min, 2 mL/min). Pyrolysis residues, pyrolytic oils and pyrolysis gases were obtained, and the mass ratios were 65.47 wt%, 27.32 wt% and 7.21 wt% respectively. The bromine content in the residue is reduced to 0.005 wt%, and the content of metallic copper is 28.69 wt%, which can be used as a raw material for the collaborative smelting of copper. About 94 wt% of the bromine in the raw material is transferred to water, which can be effectively recovered as a raw material. These results result in a reduction in hazardous pollutants. Compared with conventional heating pyrolysis, steam atmosphere heating not only reduces energy consumption, but also reduces the production of brominated products and toxic substances, and promotes the subsequent utilization of pyrolysis products. This paper reports on the heating pyrolysis of steam atmospheres on waste printed circuit boards, and this study provides a new method for removing bromine from the pyrolysis process. [ABSTRACT FROM AUTHOR]

Individuals with an Eating Disorder (ED) are typically reluctant to seek help via traditional means (e.g., psychologists). However, recent evidence suggests that many individuals seek assistance via social media for weight and diet related concerns. Sophisticated approaches are needed to better distinguish those who may be in need of help for an ED from those who are simply commenting on ED in online social environments. In order to facilitate effective communication between individuals with or at-risk of an ED and healthcare professionals, this research exploits a deep learning model to differentiate the users with ED engagement (e.g., ED sufferers, healthcare professionals or communicators) over social media. For this purpose, a collection of Twitter data is compiled using Twitter application programming interface (API) on the Australian Research Data Commons (ARDC) Nectar research cloud. After collecting 1,400,000 Twitter biographies in total, a subset of 4000 biographies are annotated manually. This annotation enables the differentiation of users engaged with ED-focused language on social media into five categories: ED-user , healthcare professional , communicator , healthcare professional-communicator , and other. Based on these annotated categories, a predictive deep learning model based on bidirectional encoder representations from transformers (BERT) and long short-term memory (LSTM) is developed. The model achieves an F 1 score of 98.19% and an accuracy of 98.37%. It demonstrates the viability of detecting the individuals with possible ED risk and distinguishes them from other categories using their biography data. We further conducted a network analysis for investigating the communication network between these categories. Our analysis shows that ED-users are more secretive and self-protective, whereas the healthcare professionals and communicators frequently interact with each other and a wide range of other people. To the best of our knowledge, our research is the first of its kind for identifying the different user categories engaged with ED-focused communications on social media. • Twitter biographies of 1.4 million users with ED engagement are collected. • A subset of the biographies is manually annotated with user categories. • The proposed model achieves an F1 score of 98.19% and an accuracy of 98.37%. • Our network analysis suggests that ED-users maintain restricted interactions. • Healthcare professionals and communicators play an essential role in addressing EDs. [ABSTRACT FROM AUTHOR]