Deep Learning for Web Services Classification

Yang, Yu-Bin; Ke, Wei; Wang, Weiru; Zhao, Yongxin

doi:10.1109/icws.2019.00079

Cited by 38 publications

(10 citation statements)

References 8 publications

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“…More efforts should be put in the training and learning of the motion trajectory of various styles of calligraphy with the robot manipulator, as well as the experiments on robots with different degrees of freedom. In order to improve the practicability, the proposed algorithm can also be programmed into formal requirement models [34][35][36] or application systems [37]. Relevant research can be applied to the synthesis of diverse commercial fonts, home calligraphy teaching demonstrations, and on-site demonstrations of high flexibility and accuracy in complex robotic operation.…”

Section: Discussionmentioning

confidence: 99%

Generative adversarial networks based motion learning towards robotic calligraphy synthesis

Wang¹,

Yang

Wang

et al. 2023

CAAI Trans on Intel Tech

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Robot calligraphy visually reflects the motion capability of robotic manipulators. While traditional researches mainly focus on image generation and the writing of simple calligraphic strokes or characters, this article presents a generative adversarial network (GAN)‐based motion learning method for robotic calligraphy synthesis (Gan2CS) that can enhance the efficiency in writing complex calligraphy words and reproducing classic calligraphy works. The key technologies in the proposed approach include: (1) adopting the GAN to learn the motion parameters from the robot writing operation; (2) converting the learnt motion data into the style font and realising the transition from static calligraphy images to dynamic writing demonstration; (3) reproducing high‐precision calligraphy works by synthesising the writing motion data hierarchically. In this study, the motion trajectories of sample calligraphy images are firstly extracted and converted into the robot module. The robot performs the writing with motion planning, and the writing motion parameters of calligraphy strokes are learnt with GANs. Then the motion data of basic strokes is synthesised based on the hierarchical process of ‘stroke‐radical‐part‐character’. And the robot re‐writes the synthesised characters whose similarity with the original calligraphy characters is evaluated. Regular calligraphy characters have been tested in the experiments for method validation and the results validated that the robot can actualise the robotic calligraphy synthesis of writing motion data with GAN.

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Section: Discussionmentioning

confidence: 99%

Generative adversarial networks based motion learning towards robotic calligraphy synthesis

Wang¹,

Yang

Wang

et al. 2023

CAAI Trans on Intel Tech

Self Cite

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“…The pre-trained language model has achieved great success in natural language processing (NLP). Inspired by this, a considerable amount of pre-trained models were proposed and applied for Software Engineering tasks, for example, services classification [16,17], code generation [18], code summarisation [19,20], code completion [21] and clone detection [15], achieving significant progress. In this paper, we adopt CodeT5 [15] as the base model.…”

Section: Pre-trained Language Modelmentioning

confidence: 99%

DeepOCL: A deep neural network for Object Constraint Language generation from unrestricted nature language

Yang

Liu

Bao

et al. 2023

CAAI Trans on Intel Tech

Self Cite

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Object Constraint Language (OCL) is one kind of lightweight formal specification, which is widely used for software verification and validation in NASA and Object Management Group projects. Although OCL provides a simple expressive syntax, it is hard for the developers to write correctly due to lacking knowledge of the mathematical foundations of the first‐order logic, which is approximately half accurate at the first stage of development. A deep neural network named DeepOCL is proposed, which takes the unrestricted natural language as inputs and automatically outputs the best‐scored OCL candidates without requiring a domain conceptual model that is compulsively required in existing rule‐based generation approaches. To demonstrate the validity of our proposed approach, ablation experiments were conducted on a new sentence‐aligned dataset named OCLPairs. The experiments show that the proposed DeepOCL can achieve state of the art for OCL statement generation, scored 74.30 on BLEU, and greatly outperformed experienced developers by 35.19%. The proposed approach is the first deep learning approach to generate the OCL expression from the natural language. It can be further developed as a CASE tool for the software industry.

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“…The recent research uses the approaches based on deep learning to learn the features of web services. For example, Yang et al [39] present a deep neural network, named ServeNet, to the abstract low-level representation of service description to high-level features. Zou et al [43,44] propose DeepWSC to cluster services through automatic feature extraction.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, the bi-SWTM is compared with state-of-the-art methods on service classification, such as the WE-LDA [30] and the ServeNet [39]. Actully, the WE-LDA is tested on service classification with the services from ProgrammableWeb, where the services belong to the top 20 categories.…”

Section: Service Classification On Sentence Levelmentioning

confidence: 99%

“…Specifically, in the case that searches web services by words, phrases, or one sentence, the service retrieval and discovery process is mostly based on keyword matching. For the query text with several sentences (e.g., a paragraph), some approaches based on a topic model [1,30] and a deep neural network [39,43] have been proposed to learn the semantics of a query text and a service description. Topic models, such as Latent Dirichlet Allocation (LDA), have been applied to web service discovery, where the semantics are extracted from rich textual information of services.…”

Section: Introductionmentioning

confidence: 99%

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bi-directional Bayesian probabilistic model based hybrid grained semantic matchmaking for Web service discovery

Luo

Zhao

et al. 2022

World Wide Web

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Web service discovery is a fundamental task in service-oriented architectures which searches for suitable web services based on users’ goals and preferences. In this paper, we present a novel service discovery approach that can support user queries with various-size-grained text elements. Compared with existing approaches that only support semantics matchmaking in single texture granularity (either word level or paragraph level), our approach enables the requester to search for services with any type of query content with high performance, including word, phrase, sentence, or paragraph. Specifically, we present an unsupervised Bayesian probabilistic model, bi-Directional Sentence-Word Topic Model (bi-SWTM), to achieve semantic matchmaking between possible textual types of queries (word, phrase, sentence, paragraph) and the texts in web service descriptions, by mapping words and sentences in the same semantic space. The bi-SWTM captures textual semantics of the words and sentences in a probabilistic simplex, which provides a flexible method to build the semantic links from user queries to service descriptions. The novel approach is validated using a collection of comprehensive experiments on ProgrammableWeb data. The results demonstrate that the bi-SWTM outperforms state-of-the-art methods on service discovery and classification. The visualization of the nearest-neighbored queries and descriptions shows the capability of our model on capturing the latent semantics of web services.

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Deep Learning for Web Services Classification

Cited by 38 publications

References 8 publications

Generative adversarial networks based motion learning towards robotic calligraphy synthesis

Generative adversarial networks based motion learning towards robotic calligraphy synthesis

DeepOCL: A deep neural network for Object Constraint Language generation from unrestricted nature language

bi-directional Bayesian probabilistic model based hybrid grained semantic matchmaking for Web service discovery

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