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This article addresses the challenge of collective decision making among the smart components in medical waste transportation. We search for an optimized alternation to generate a commitment according to the capabilities belonging to heterogeneous agents under ever-changing context conditions. we propose a Goal-Capability-Commitment based Executable Tree (GCC-ETree) approach to generate an adaptive protocol. We illustrate our approach in a real-world medical waste automated guided vehicle transportation scenario and evaluate the feasibility of our approach by comparison with other common approaches. Our work makes two main contributions. First, we show how ontology-based matching and calculation can be used to enact a semantic understanding of the alignment of environment, goal, capability and commitment. Second, we introduce run-time goal modeling into collective decision making to capture the real-time requirements and provide execution flow information for generating a decision flow at runtime.

Semantic information exchange is currently facing the heterogeneity and consistency across languages context in a smart city. Thus, this paper highlights the challenges that smart city is confronting in terms of semantic document exchange and proposes a novel Machine Natural Language Mediation (MNLM) framework which provides a sentence-based machine natural language (MNL) as a kind of mediation language, where each sentence as a compound concept is a set of atomic concepts to be compatible with all languages achieving a global semantic transformation. The MNL enables the sentence computer-readable and understandable through unique iid and eiid without semantic ambiguity, and simultaneously MNLM will eliminate the semantic inconsistency and improve the accurate meaning interpretation across conversational contexts. The overall aim of the framework is to offer a better solution to communicate and exchange information or document in a smart city.

In the Industry 4.0/5.0 era, information integration is employed to fuse information from different companies to facilitate interoperation. However, information extraction is an important preprocessing phase that must be performed prior to integrating data from different contexts. Relation extraction, which is an element of information extraction, is typically the basis of many upper-level applications, e.g., information visualization and inference. Some current models may not fully consider the complementary effect of information at different levels of granularity featured by different neural networks. In this paper, two improvement relation extraction strategies based on the graph convolutional model on tree structure (GCNTree) are proposed. The first strategy integrates a hierarchical attention mechanism and correlation analysis between subjects and objects to generate sentence and entity vectors, respectively. The second strategy merges a named-entity recognition subnetwork with GCNTree to realize joint learning of relation and entity extraction. Experimental results demonstrate that the proposed strategies are comparable to state-of-the-art methods. 1

Data mining techniques play an important role in clinical decision making, which provides physicians with accurate, reliable and quick predictions through building different models. This paper presents an improved classification approach for the prediction of diseases based on the classical Iterative Dichotomiser 3 (Id3) algorithm. The improved Id3 algorithm overcomes multi-value bias problem when selecting test/split attributes, solves the issue of numeric attribute discretization and stores the classifier model in the form of rules by using a heuristic strategy for easy understanding and memory savings. Experiment results show that the improved Id3 algorithm is superior to the current four classification algorithms (J48, Decision Stump, Random Tree and classical Id3) in terms of accuracy, stability and minor error rate.

Internet is a common information space populated with many entities (e.g., Internet of Things) with different information system types. Each of them has its own context of how to build and process documents (e.g., form documents). This leads to heterogeneous documents in terms of syntax and semantics, which are difficult to make information fusion from one context to another. To resolve this problem, this paper uses semantic interoperability technique which consists of two automatic stages including consistent data understanding and reasonable data usage. To implement semantic interoperability, this paper proposes a novel automatic tabular document exchange (DocEx) framework comprised of a new tabular document model (TabDoc) and a semantic inference scheme to fit the two stages above respectively. In this TabDoc model, a new Tabular Document Language (DocLang) as a communication medium between users and devices is provided, which is not only an information representation language but also a rule language for semantic inference as well. Abstract sub-tree-based semantic relations constructing the logical structure of a tabular document are separated from their presentational structures, clarifying the relationship between semantic groups (e.g., a cell or a block) with the help of a common dictionary CONEX. Besides, this paper proposes a semantic inference algorithm (SIA) executing the inference procedure on received tabular documents created by a Table Designer system which integrates with SIA. Finally, the proposed framework is applied to the processing of flight ticket booking in a realistic e-business scenario. The results show that the proposed method in this paper improves the performance of information fusion among different information systems on the Internet. Information fusion is realized by consistent interpretation and usage of documents.The proposed DocLang syntax only defines an element and will not restrict user input.Automatic document understanding and processing is implemented by a rule language.TabDoc model contains syntactic and semantic representation for document reusability.Tabdoc rule syntax is used for semantic extraction in a declarative manner.

In e-marketplace, semantic document exchange is a methodology of providing exchangeable semantic documents, which ensures document writer, writer's computer, reader's computer and document reader to share a same understanding in meaning on any exchanged document, that is, a semantic document is exchangeable across any heterogeneous contexts. This paper illustrates two kinds of semantic input method for Chinese word senses such as Word-based word sense input and Sentence-based word sense input. These two kind of word sense input methods are based on the statistic word sense representation and disambiguation. The prototype system shows both our Word-based and Sentence-based word sense Pinyin methods are promising in the text edit system. These two kinds of Chinese word sense input methods are designed for our semantic document edit: syntactic file and semantic file aliment system, designed for the semantic document exchange for e-business.

Document classification has become an indispensable technology to realize intelligent information services. This technique is often applied to the tasks such as document organization, analysis, and archiving or implemented as a submodule to support high-level applications. It has been shown that semantic analysis can improve the performance of document classification. Although this has been incorporated in previous automatic document classification methods, with an increase in the number of documents stored online, the use of semantic information for document classification has attracted greater attention as it can greatly reduce human effort. In this present paper, we propose two semantic document classification strategies for two types of semantic problems: (1) a novel semantic similarity computation (SSC) method to solve the polysemy problem and (2) a strong correlation analysis method (SCM) to solve the synonym problem. Experimental results indicate that compared with traditional machine learning, n-gram, and contextualized word embedding methods, the efficient semantic similarity and correlation analysis allow eliminating word ambiguity and extracting useful features to improve the accuracy of semantic document classification for texts in Chinese.

This article presents pioneering research on virtual money exchange (VMX) to protect the wealth generated in virtual worlds. This goal is realized by designing and implementing a VMX system through which virtual money exchange rates between virtual worlds are generated based on a redistribution strategy. Moreover, this strategy aims to re-value the total intrinsic value and the total exchangeable value of virtual currencies at a series of Pareto exchange points. We have designed, implemented, proved and extended a novel VMX exchange rate algorithm (VERA) algorithm, which implements the redistribution strategy. In order to observe the behavior of virtual money exchange rate generations, we built a VMX simulator to simulate various cases of virtual money exchange. Experiments on our VMX simulator show that minimum acceptable virtual exchange rates set by exchange requestors have a great impact on the success or failure of a virtual money exchange. This suggests the need for further research to reconcile the contradiction between presenting fair virtual currency exchange rates and achieving low fluctuations of virtual currency exchange rates. Finally, this research contributes to a better understanding of many aspects relating to virtual money, virtual currency exchange and virtual wealth protection. These are important for the future design and implementation of integrated virtual worlds.

Brachydactyly type A-1 (BDA1) was, in 1903, the first recorded example of a human anomaly with Mendelian autosomal dominant inheritance. Two large families, the affected members of which were radiographed, were recruited in the study we describe here. Two-point linkage analysis for pedigree 1 (maximum LOD score [Zmax] 6.59 at recombination fraction [theta] 0.00) and for pedigree 2 (Zmax=5.53 at straight theta=0.00) mapped the locus for BDA1 in the two families to chromosome 2q. Haplotype analysis of pedigree 1 confined the locus for family 1 within an interval of8.1 cM flanked by markers D2S2248 and D2S360, which was mapped to chromosome 2q35-q36 on the cytogenetic map. Haplotype analysis of pedigree 2 confined the locus for family 2 within an interval of28. 8 cM flanked by markers GATA30E06 and D2S427, which was localized to chromosome 2q35-q37. The two families had no identical haplotype within the defined region, which suggests that the two families were not related.