Semantics for consistent activation in context-oriented systems

ContextContext-oriented programming languages provide dedicated programming abstractions to define behavioral adaptations and means to combine those adaptations dynamically according to sensed context changes. Some of these languages feature programming abstractions to explicitly define interaction dependencies among contexts. However, the semantics of context activation and the meaning of dependency relations have been described only informally, which in some cases has led to incorrect specifications, faulty implementations and inconsistent system behavior.ObjectiveWith the aim of avoiding faulty implementations and inconsistencies during system execution, this paper proposes both a formal and run-time model of contexts, context activation and context interaction.MethodAs a formal and computational basis, we introduce context Petri nets, a model based on Petri nets, which we found to match closely the structure of contexts in context-oriented systems. The operational semantics of Petri nets permits the modeling of run-time context activations. Existing Petri net analyses allow us to reason about system properties. As validation, we carried out small and medium-sized case studies.ResultsIn the cases explored, context Petri nets served effectively as underlying run-time model to ensure that declared context interaction constraints remain consistent during context manipulation. Moreover, context Petri nets enabled us to analyze certain properties regarding the activation state of particular contexts.ConclusionContext Petri nets thus proved to be appropriate to encode and manage the semantics of context activation, both formally and computationally, so as to preserve the consistency of context-oriented systems.     

Study on the performance of syntactic foam reinforced by hybrid functionalized carbon nanotubes

Hollow glass microspheres (HGMs)/epoxy syntactic foam were reinforced by hybrid functionalized carbon nanotubes that were synthesized by simultaneous covalent and noncovalent functionalization of carbon nanotubes. The effect of hybrid functionalized carbon nanotubes on density, mechanical properties, and water absorption of HGMs/epoxy syntactic foam was studied. The study indicated that the dispersion of carbon nanotubes in epoxy resin can be improved by hybrid functionalization. The compression strength of syntactic foam reinforced by hybrid functionalized carbon nanotubes was significantly enhanced. The maximum compressive strength of syntactic foam corresponding to chitosan modified carbon nanotubes approached 60 MPa. Hybrid functionalized carbon nanotubes had little effect on the water absorption ability of syntactic foam, and was less than 1%. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48586.     

Automatic recommendation to appropriate log levels

A log statement is one of the key tactics for a developer to record and monitor important run-time behaviors of our system in a development phase and a maintenance phase. It composes of a message for stating log contents, and a log level (eg, debug or warn) to denote the severity of a message and controlling its visibility at run time. In spite of its usefulness, a developer does not tend to deeply consider which log level is appropriate in writing source code, which causes the system to be unmaintainable. To address this issue, this paper proposes an automatic approach to validating the appropriateness of the log level in consideration of the semantic and syntactic features and recommending a proper alternative log level. We first build the semantic feature vector to quantify the semantic similarity among application log messages using the word vector space, and the syntactic feature vector to capture the application context that surrounds the log statement. Based on the feature vectors and machine learning techniques, the log level is automatically validated, and an alternative log level is recommended if the log level is invalid. For the evaluation, we collected 22 open-source projects from three application domains, and obtained the 77% of precision and 75% of recall in validating the log levels. Also, our approach showed 6% higher accuracy than that of the developer group who has 7 to 8 years of work experience, and 72% of the developers accepted our recommendation.     

A method of constructing an inspiration library driven by user-perceived preference evaluation data for biologically inspired design

Biologically inspired design (BID) is one of the common methods for product design. To solve the problem of inaccurate acquisition of inspirational creatures due to the lack of user perception preference analysis, a data-driven intelligent service model for BID considering user perception needs is proposed based on Kansei engineering. Firstly, by extracting the perceptual features of creatures from the semantic source elements of products through mapping and encodes them, we proposed a data acquisition method based on intuitionistic fuzzy sets considering different customer preference distributions, bridging the gap caused by the asymmetry between designers and users. Secondly, the functional relationship between biometric features and user-perceived attributes is identified and predicted, and a predictive model of biodata considering user preferences is obtained by multiple linear regression analysis. Finally, based on the data clustering and reorganization theory to understand the organization and dynamics of the database, the construction of a BID library was completed, and the design resources in the library were used as analyzed knowledge for designers to plan design activities. Taking the bionic design of a UAV product as an example, a prototype of a computer-aided design service system was developed based on the theory proposed in the article, and the analyzed knowledge was used to improve the efficiency and science of the design, effectively verifying the usefulness of this study for design. To a certain extent, this study addresses the problem of cognitive limitations of designers and cognitive differences between designers and users, promotes the application of bioinspiration in product design, and improves the marketability of design solutions.     

建筑语言演进的动力与规律——建筑创新的认识论基础

从建筑语言的系统性与矛盾性,去分析研究建筑语言演进的动力及其内在规律,并随之指出我们在建筑创作中所应确立和坚守的探索方向。     

一种基于深度神经网络的句法要素识别方法

为改进传统特征方法很难获取中文句子中结构信息的问题,提出一种基于深度神经网络的句法要素识别模型。采用Bi-LSTM网络从原始数据中自动抽取句子中的结构信息和语义信息,利用Attention机制自动计算抽象语义特征的分类权重,通过CRF层对输出标签进行约束,输出最优的标注序列。经过对比验证,该模型能有效识别句子中的句法要素,在标注数据集上F₁达到84.85%。     

Quasi-static compressive behaviour of aluminium cenosphere syntactic foams

In this paper, cenosphere particles embedded in AA2014 aluminium matrix are used to fabricate syntactic foam by stir casting method. The particle size is about 100?µm and foam density is about 1990?kg?m^?3. Compression tests at strain rate 0.001/s are performed on foam samples to characterise their mechanical properties which are then used in numerical analysis on commercial finite element analysis software ABAQUS/CAE with isotropic elastic-plastic material model. Experimental and numerical results show good conformity in deformation behaviour with elastic and plateau zones showing average deviations less than 5% and 20%, respectively. Foams showed high yield stress and energy absorption capabilities that can be useful in making blast and impact resistant structures.     

基于桥连接的词典学习方法的语义解析

现阶段的语义解析方法大部分都基于组合语义,这类方法的核心就是词典。词典是词汇的集合,词汇定义了自然语言句子中词语到知识库本体中谓词的映射。语义解析一直面临着词典中词汇覆盖度不够的问题。针对此问题,该文在现有工作的基础上,提出了基于桥连接的词典学习方法,该方法能够在训练中自动引入新的词汇并加以学习,为了进一步提高新学习到的词汇的准确度,该文设计了新的词语—二元谓词的特征模板,并使用基于投票机制的核心词典获取方法。该文在两个公开数据集(WebQuestions和Free917)上进行了对比实验,实验结果表明,该文方法能够学习到新的词汇,提高词汇的覆盖度,进而提升语义解析系统的性能,特别是召回率。     

Feature-based ontological framework for semantic interoperability in product development

An essential requirement in integrating tasks in product development is to have a seamless exchange of product information through the entire product lifecycle. A key challenge in the integration is the exchange of shape semantics in terms of understandable labels and representations. A unified taxonomy is proposed to represent, classify, and extract shape features. This taxonomy is built using the Domain-Independent Form Feature (DIFF) model as the representation of features. All the shape features in a product model are classified under three main classes, namely, volumetric features, deformation features and free-form surface features. Shape feature ontology is developed using the unified taxonomy, which brings the shape features under a single reasoning framework. One-to-many reasoning framework is presented for mapping semantically equivalent information (label and representation) of the feature to be exchanged to target applications, and the reconstruction of the shape model automatically in that target application. An algorithm has been developed to extract the semantics of shape features and construct the model in the target application. The algorithm developed has been tested for shape models taken from literature and test cases are selected based on variations of topology and geometry. Results of exchanging product information are presented and discussed. Finally, the limitations of the proposed method for exchanging product information are explained.     

Syntax and semantics of universal programming languages

This paper deals with certain characterizations of the sets of positive integers which when represented as strings on a finite alphabet, form tree adjunct languages, As the context free languages constitute a subfamily of tree adjunct languages, the results carry over to the former as well.