人工智能 | 会议 / 期刊约稿信息3条

全文截稿: 2021-01-12

开会时间: 2021-05-23

会议难度: ★★

CCF分类: 无

会议地点: Virtual

网址：https://rose-workshops.github.io/rose2021/

Increasingly, challenging domains employ robotic applications. Yet, Robotics still is one of the most challenging domains for software engineering. Deploying robotics applications requires integrating solutions from experts of various domains, including navigation, path planning, manipulation, localization, human-robot interaction, etc. Integration of modules contributed by respective domain experts is one of the key challenges in engineering software-centric systems, yet only one of the cross-cutting software concerns crucial to robotics. As robots often operate in dynamic, partially observable environments additional challenges include adaptability, robustness, safety, and security.
The goal of RoSE 2021 is to bring together researchers from participating domains with practitioners to identify new frontiers in robotics software engineering, discuss challenges raised by real-world applications, and transfer the latest insights from research to industry. RoSE 2021 will solicit contributions from both academic and industrial participants, thus fostering active synergy between the two communities.
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Topics include, but are not limited to
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- Analysis of challenges in robotic software engineering
- Architectures that lead to reusable robotic software
- Challenges for defining and integrating domain-specific languages for the
design of robotic systems
- Continuous integration and deployment in robotics
- Identification and analysis of design principles promoting quality of
service (e.g., performance, energy efficiency)
- Engineering the collaboration of multiple (heterogeneous) robots
- Machine learning for safety-critical robotic systems
- Metrics to measure non-functional properties (e.g., robustness,
availability, etc.) and their application in robotic software
- Best practices in engineering robotic software
- Variability, modularity, and reusability in robotic software
- Validation and verification of robotic software
- Processes and tools supporting the engineering and development of robotic
systems
- State-of-the-art research projects, innovative ideas, and field-based
studies in robotic software engineering
- Lessons learned in the engineering and deployment of large-scale, real-world
integrated robot
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Submission
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RoSE'21 seeks contributions in the form of
- research papers presenting novel contributions on advancing software
engineering in robotics (max. 8 pages)
- challenge showcase papers describing robotics challenges considered
insufficiently addressed from an industry perspective (max. 6 pages)
- lessons learned papers describing lessons learned in the collaboration
between the two communities of SE and robotics (max. 6 pages)
- vision papers on future of software engineering in robotics (max. 4 pages)
- tool & project papers on software engineering in robotics (max. 4 pages)
Workshop papers must follow the ICSE 2021 Format and Submission Guidelines but will use a single-blind submission process. All submitted papers will be reviewed based on technical quality, relevance, significance, and clarity by the program committee. Accepted papers will become part of the workshop proceedings. All papers should be submitted electronically in PDF format through the EasyChair workshop website: https://easychair.org/conferences/?conf=rose2021.

全文截稿: 2021-03-15

开会时间: 2021-06-10

会议难度: ★★

CCF分类: 无

会议地点: Mexico City, Mexico

网址：https://sdproc.org/

** Introduction **

Although scientific literature plays a major part in research and policy-making, these texts represent an underserved area of NLP. NLP can play a role in addressing research information overload, identifying disinformation and its effect on people and society, and enhancing the reproducibility of science. The unique challenges of processing scholarly documents necessitate the development of specific methods and resources optimized for this domain. The Scholarly Document Processing (SDP) workshop provides a venue for discussing these challenges, bringing together stakeholders from different communities including computational linguistics, text mining, information retrieval, digital libraries, scientometrics, and others to develop and present methods and resources in support of these goals.

This workshop builds on the success of prior workshops: the 1st SDP workshop held at EMNLP 2020 and the 1st SciNLP workshop held at AKBC 2020. In addition to having broad appeal within the NLP community, we hope the SDP workshop will attract researchers from other relevant fields including meta-science, scientometrics, data mining, information retrieval, and digital libraries, bringing together these disparate communities within ACL.

** Topics of Interest **

We invite submissions from all communities demonstrating usage of and challenges associated with natural language processing, information retrieval, and data mining of scholarly and scientific documents. Relevant tasks include:
* Representation learning
* Information extraction
* Summarization
* Generation
* Question answering
* Discourse modeling and argumentation mining
* Network analysis
* Bibliometrics, scientometrics, and altmetrics
* Reproducibility
* Peer review
* Search and indexing
* Datasets and resources
* Document parsing
* Text mining
* Research infrastructure, and others.

We specifically invite research on important and/or underserved areas, such as:
* Identifying/mitigating scientific disinformation and its effects on public policy and behavior
* Reducing  information  overload  through  summarization   and   aggregation   of   information within and across documents
* Improving  access  to  scientific  papers  through multilingual scholarly document processing

** Submission Information **

Authors are invited to submit full and short papers with unpublished, original work. Submissions will be subject to a double-blind peer review process. Accepted papers will be presented by the authors at the workshop either as a talk or a poster. All accepted papers will be published in the workshop proceedings.

The submissions should be in PDF format and anonymized for review. All submissions must be written in English and follow the NAACL 2021 formatting requirements: https://2021.naacl.org/calls/style-and-formatting/

We follow the same policies as NAACL 2021 regarding preprints and double-submissions (https://2021.naacl.org/calls/papers/). The anonymity period for SDP 2021 is from February 15, 2021 to April 15, 2021.

Long paper submissions: up to 8 pages of content, plus unlimited references.
Short paper submissions: up to 4 pages of content, plus unlimited references.

Final versions of accepted papers will be allowed 1 additional page of content so that reviewer comments can be taken into account.

全文截稿: 2021-04-01

影响因子: 5.898

CCF分类: B类

中科院JCR分区:

• 大类 : 工程技术 - 2区

• 小类 : 计算机：人工智能 - 2区

• 小类 : 工程：电子与电气 - 2区

网址: http://www.journals.elsevier.com/pattern-recognition/

The special issue will focus on the recent advance in learning to solve the combinatorial optimization problem, especially for problems related to pattern recognition. The capability of efficiently solving the challenging combinatorial optimization tasks, which are often NP-hard, is key to success of many business areas, ranging from transportation, aerospace industry, to industrial engineering etc. However, the traditional solvers are often based on rules and specific design based on human knowledge and experience, and the computing is often iterative and serialized on CPU, suffering limitation in scalability, adaptation ability, speed and accuracy.