This repo aims to survey all available NLP+AI Conferences and Journals (mainly for NLPers).
Best wishes for the acception of your papers. Welcome contributions!
| Name | Full Name | Area |
|---|---|---|
| ACL | Annual Meeting of the Association for Computational Linguistics | NLP |
| AAAI | AAAI Conference on Artificial Intelligence | AI |
| IJCAI | International Joint Conference on Artificial Intelligence | AI |
| ICML | International Conference on Machine Learning | ML |
| NeuraIPS | Annual Conference on Neural Information Processing Systems | DL |
| ICLR | International Conference on Learning Representations | DL |
| Name | Full Name | Area |
|---|---|---|
| EMNLP | Conference on Empirical Methods in Natural Language Processing | NLP |
| COLING | International Conference on Computational Linguistics | NLP |
| NAACL | The Annual Conference of the North American Chapter of the Association for Computational Linguistics | NLP |
| ECAI | European Conference on Artificial Intelligence | AI |
| ICASSP | The International Conference on Acoustics, Speech, & Signal Processing | Speech, NLP |
| Name | Full Name | Area |
|---|---|---|
| CoNLL | Conference on Natural Language Learning | NLP |
| AACL | Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics | NLP |
| NLPCC | CCF International Conference on Natural Language Processing and Chinese Computing | NLP |
| IJCNN | International Joint Conference on Neural Networks | DL |
| KSEM | International conference on Knowledge Science,Engineering and Management | NLP |
| Name | Full Name | Area |
|---|---|---|
| TheWebConf | International World Wide Web Conference | AI |
| SIGIR | International Conference on Research on Development in Information Retrieval | IR |
| SIGKDD | ACM Knowledge Discovery and Data Mining | DM |
| Name | Full Name | Area |
|---|---|---|
| CogSci | Cognitive Science Society Annual Conference | AI |
| CIKM | ACM International Conference on Information and Knowledge Management | IR |
| WSDM | ACM International Conference on Web Search and Data Mining | IR |
| ECML-PKDD | European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases | DM |
| DASFAA | Database Systems for Advanced Applications | DM |
CCF-A, 9.235, Q1
https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=69
The scope of the IEEE Transactions on Knowledge and Data Engineering includes the knowledge and data engineering aspects of computer science, artificial intelligence, electrical engineering, computer engineering, and other appropriate fields. This Transactions provides an international and interdisciplinary forum to communicate results of new developments in knowledge and data engineering and the feasibility studies of these ideas in hardware and software. Specific areas to be covered are as follows: Fields and Areas of Knowledge and Data Engineering: (a) Knowledge and data engineering aspects of knowledge based and expert systems, (b) Artificial Intelligence techniques relating to knowledge and data management, (c) Knowledge and data engineering tools and techniques, (d) Distributed knowledge base and database processing, (e) Real-time knowledge bases and databases, (f) Architectures for knowledge and data based systems, (g) Data management methodologies, (h) Database design and modeling, (i) Query, design, and implementation languages, (j) Integrity, security, and fault tolerance, (k) Distributed database control, (l) Statistical databases, (m) System integration and modeling of these systems, (n) Algorithms for these systems, (o) Performance evaluation of these algorithms, (p) Data communications aspects of these systems, (q) Applications of these systems.
CCF-A, 4.797, Q1
https://dl.acm.org/journal/tois
The ACM Transactions on Information Systems (TOIS) publishes papers on information retrieval (such as search engines, recommender systems) that contain:
- new principled information retrieval models or algorithms with sound empirical validation;
- observational, experimental and/or theoretical studies yielding new insights into information retrieval or information seeking;
- accounts of applications of existing information retrieval techniques that shed light on the strengths and weaknesses of the techniques;
- formalization of new information retrieval or information seeking tasks and of methods for evaluating the performance on those tasks;
- development of content (text, image, speech, video, etc) analysis methods to support information retrieval and information seeking;
- development of computational models of user information preferences and interaction behaviors;
- creation and analysis of evaluation methodologies for information retrieval and information seeking; or
- surveys of existing work that propose a significant synthesis.
The information retrieval scope of ACM Transactions on Information Systems (TOIS) appeals to industry practitioners for its wealth of creative ideas, and to academic researchers for its descriptions of their colleagues' work.
CCF-A, 5.41, Q2
The Journal of Machine Learning Research (JMLR), established in 2000, provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online.
JMLR has a commitment to rigorous yet rapid reviewing. Final versions are published electronically (ISSN 1533-7928) immediately upon receipt. Until the end of 2004, paper volumes (ISSN 1532-4435) were published 8 times annually and sold to libraries and individuals by the MIT Press. Paper volumes (ISSN 1532-4435) are now published and sold by Microtome Publishing.
submission:
https://jmlr.org/author-info.html
CCF-A, 14.050, Q1
- COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE - SCIE(Q1)
- www.elsevier.com/wps/find/journaldescription.cws_home/505601/description#description
- ees.elsevier.com/artint/
The journal of Artificial Intelligence (AIJ) welcomes papers on broad aspects of AI that constitute advances in the overall field including, but not limited to, cognition and AI, automated reasoning and inference, case-based reasoning, commonsense reasoning, computer vision, constraint processing, ethical AI, heuristic search, human interfaces, intelligent robotics, knowledge representation, machine learning, multi-agent systems, natural language processing, planning and action, and reasoning under uncertainty. The journal reports results achieved in addition to proposals for new ways of looking at AI problems, both of which must include demonstrations of value and effectiveness.
Papers describing applications of AI are also welcome, but the focus should be on how new and novel AI methods advance performance in application areas, rather than a presentation of yet another application of conventional AI methods. Papers on applications should describe a principled solution, emphasize its novelty, and present an indepth evaluation of the AI techniques being exploited.
Apart from regular papers, the journal also accepts Research Notes, Research Field Reviews, Position Papers, and Book Reviews (see details below). The journal will also consider summary papers that describe challenges and competitions from various areas of AI. Such papers should motivate and describe the competition design as well as report and interpret competition results, with an emphasis on insights that are of value beyond the competition (series) itself.
CCF-B, 5.63, Q1
The Computational Linguistics journal is the primary archival forum for research on computational linguistics and natural language processing. The journal, sponsored by the Association for Computational Linguistics, has been published for the ACL by MIT Press since 1988, and has been Open Access since the beginning of 2009. All issues published by MIT Press are freely available to all at the official MIT Press website for the journal. Issues prior to 1988, as well as the issues published by MIT Press, are also available via the ACL Anthology.
The site you are currently visiting serves as an informal repository for information and resources related to the journal. If there is information that you think it would be useful to have here, drop the Editor an email.
CCF-B, 4.364, Q2
Every manuscript must: provide a clear statement of the problem and what the contribution of the work is to the relevant research community; state why this contribution is significant (what impact it will have); provide citation of the published literature most closely related to the manuscript; and state what is distinctive and new about the current manuscript relative to these previously published works.
For initial submission of the general paper, the manuscript should not exceed 13 pages in two columns (10 point type), including the title; author's name and full contact information; abstract; text; all images, figures and tables, appendices and proofs; and all references. The page count does not include supplementary material and graphic summaries.
For regular papers, revised manuscripts must not exceed 16 double-column pages (10 point font), including titles; author names and their full contact information; abstract; text; all images, figures and tables, appendices and proofs; and All references.
CCF-B, 13.990, Q1
- COMPUTER SCIENCE, CYBERNETICS - SCIE(Q1); COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE - SCIE(Q1)
- www.computer.org/web/tac
- www.computer.org/web/tac/author;jsessionid=754ac76dad2796cda19553ce0570
The IEEE Transactions on Affective Computing is a cross-disciplinary and international archive journal aimed at disseminating results of research on the design of systems that can recognize, interpret, and simulate human emotions and related affective phenomena. The journal publishes original research on the principles and theories explaining why and how affective factors condition interaction between humans and technology, on how affective sensing and simulation techniques can inform our understanding of human affective processes, and on the design, implementation and evaluation of systems that carefully consider affect among the factors that influence their usability. Surveys of existing work are considered for publication when they propose a new viewpoint on the history and the perspective on this domain. The journal covers but is not limited to the following topics: Sensing & analysis: Algorithms and features for the recognition of affective state from face and body gestures; Analysis of text and spoken language for emotion recognition; Analysis of prosody and voice quality of affective speech; Recognition of auditory and visual affect bursts; Recognition of affective state from central (e.g. fMRI, EEG) and peripheral (e.g. GSR) physiological measures; Methods for multi-modal recognition of affective state; Recognition of group emotion; Methods of data collection with respect to psychological issues as mood induction and elicitation or technical methodology as motion capturing; Tools and methods of annotation for provision of emotional corpora. (Cyber) psychology & behavior: Clarification of concepts related to ‘affective computing’ (e.g., emotion, mood, personality, attitude) in ways that facilitate their use in computing; Computational models of human emotion processes (e.g., decision-making models that account for the influence of emotion; predictive models of user emotional state); Studies on cross-cultural, group and cross-language differences in emotional expression; Contributions to standards and markup language for affective computing. Behavior Generation & User Interaction: Computational models of visual, acoustic and textual emotional expression for synthetic and robotic agents; Models of verbal and nonverbal expression of various forms of affect that facilitate machine implementation; Methods to adapt interaction with technology to the affective state of users; Computational methods for influencing the emotional state of people; New methods for defining and evaluating the usability of affective systems and the role of affect in usability; Methods of emotional profiling and adaptation in mid- to long-term interaction; Application of affective computing including education, health care, entertainment, customer service, design, vehicle operation, social agents/robotics, affective ambient intelligence, customer experience measurement, multimedia retrieval, surveillance systems, biometrics, music retrieval and generation.
CCF-B, 14.255, Q1
- ENGINEERING, ELECTRICAL & ELECTRONIC - SCIE(Q1); COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE - SCIE(Q1); COMPUTER SCIENCE, THEORY & METHODS - SCIE(Q1); COMPUTER SCIENCE, HARDWARE & ARCHITECTURE - SCIE(Q1)
- cis.ieee.org/ieee-transactions-on-neural-networks-and-learning-systems.html
- mc.manuscriptcentral.com/tnnls
IEEE Transactions on Neural Networks and Learning Systems publishes technical articles that deal with the theory, design, and applications of neural networks and related learning systems.
CCF-B, 9.588, Q1
- COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE - SCIE(Q1)
- www.springer.com/computer/ai/journal/10462
- www.editorialmanager.com/aire/
Artificial Intelligence Review publishes state-of-the-art research reports and critical evaluations of applications, techniques and algorithms in artificial intelligence, cognitive science and related disciplines. It serves as a forum for the work of researchers and application developers from these fields.
CCF-B, 3.59, Q1
https://www.jair.org/index.php/jair/about
JAIR invites submissions in all areas of AI. Articles published in JAIR must meet the highest quality standards as measured by originality and significance of the contribution. The journal publishes full-length original articles, research notes, survey articles, and special tracks. Research notes are very brief papers that extend or evaluate previous work. Survey articles are tutorials or literature reviews that contribute an analysis or perspective that advances our understanding of the subject matter. Special tracks are collections of articles on a specific subject edited by guest editors.
https://www.jair.org/index.php/jair/about/submissions
https://www.journals.elsevier.com/knowledge-based-systems
CCF-C, 8.038, Q1
Knowledge-based Systems is an international and interdisciplinary journal in the field of artificial intelligence. The journal will publish original, innovative and creative research results in the field, and is designed to focus on research in knowledge-based and other artificial intelligence techniques-based systems with the following objectives and capabilities: to support human prediction and decision-making through data science and computation techniques; to provide a balanced coverage of both theory and practical study in the field; and to encourage new development and implementation of knowledge-based intelligence models, methods, systems, and software tools, with applications in business, government, education, engineering and healthcare.
This journal's current leading topics are but not limited to:
- Machine learning theory, methodology and algorithms
- Data science theory, methodologies and techniques
- Knowledge presentation and engineering
- Recommender systems and E-service personalization
- Intelligent decision support systems, prediction systems and warning systems
- Computational Intelligence systems
- Data-driven optimization
- Cognitive interaction and brain–computer interface
- Knowledge-based computer vision techniques
CCF-C, 6.19, Q1
https://www.sciencedirect.com/journal/neurocomputing
Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.
NEW! Neurocomputing's Software Track allows you to expose your complete Software work to the community through a novel Publication format: the Original Software Publication
Overview:
Neurocomputing welcomes theoretical contributions aimed at winning further understanding of neural networks and learning systems, including, but not restricted to, architectures, learning methods, analysis of network dynamics, theories of learning, self-organization, biological neural network modelling, sensorimotor transformations and interdisciplinary topics with artificial intelligence, artificial life, cognitive science, computational learning theory, fuzzy logic, genetic algorithms, information theory, machine learning, neurobiology and pattern recognition.
Neurocomputing covers practical aspects with contributions on advances in hardware and software development environments for neurocomputing, including, but not restricted to, simulation software environments, emulation hardware architectures, models of concurrent computation, neurocomputers, and neurochips (digital, analog, optical, and biodevices).
Neurocomputing reports on applications in different fields, including, but not restricted to, signal processing, speech processing, image processing, computer vision, control, robotics, optimization, scheduling, resource allocation and financial forecasting.
CCF-B, 14.27, Q1
Transactions of the Association for Computational Linguistics
1th day of every month
https://transacl.org/ojs/index.php/tacl/about/submissions
CCF-None, 12.975, Q1
- COMPUTER SCIENCE, THEORY & METHODS - SCIE(Q1); COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE - SCIE(Q1)
- www.journals.elsevier.com/information-fusion/
- ees.elsevier.com/inffus/
The journal is intended to present within a single forum all of the developments in the field of multi-sensor, multi-source, multi-process information fusion and thereby promote the synergism among the many disciplines that are contributing to its growth. The journal is the premier vehicle for disseminating information on all aspects of research and development in the field of information fusion. Articles are expected to emphasize one or more of the three facets: architectures, algorithms, and applications. Papers dealing with fundamental theoretical analyses as well as those demonstrating their application to real-world problems will be welcome. The journal publishes original papers, letters to the editors and from time to time invited review articles, in all areas related to the information fusion arena including, but not limited to, the following suggested topics:
- Data/Image, Feature, Decision, and Multilevel Fusion
- Multi-classifier/Decision Systems
- Multi-Look Temporal Fusion
- Multi-Sensor, Multi-Source Fusion System Architectures
- Distributed and Wireless Sensor Networks
- Higher Level Fusion Topics Including Situation Awareness And Management
- Multi-Sensor Management and Real-Time Applications
- Adaptive And Self-Improving Fusion System Architectures
- Active, Passive, And Mixed Sensor Suites
- Multi-Sensor And Distributed Sensor System Design
- Fusion Learning In Imperfect, Imprecise And Incomplete Environments
- Intelligent Techniques For Fusion Processing
- Fusion System Design And Algorithmic Issues
- Fusion System Computational Resources and Demands Optimization
- Special Purpose Hardware Dedicated To Fusion Applications
- Mining Remotely Sensed Multi-Spectral/Hyper-Spectral Image Data Bases
- Information Fusion Applications in Intrusion Detection, Network Security, Information Security and Assurance arena
- Applications such as Robotics, Space, Bio-medical, Transportation, Economics, and Financial Information Systems
- Real-World Issues such as Computational Demands, Real-Time Constraints in the context of Fusion systems.
CCF-None, 25.898, Q1
https://www.nature.com/natmachintell/aims
https://www.nature.com/natmachintell/submission-guidelines
Aims & Scope
Nature Machine Intelligence publishes high-quality original research and reviews in a wide range of topics in machine learning, robotics and AI. We also explore and discuss the significant impact that these fields are beginning to have on other scientific disciplines as well as many aspects of society and industry. There are countless opportunities where machine intelligence can augment human capabilities and knowledge in fields such as scientific discovery, healthcare, medical diagnostics and safe and sustainable cities, transport and agriculture. At the same time, many important questions on ethical, social and legal issues arise, especially given the fast pace of developments. Nature Machine Intelligence provides a platform to discuss these wide implications — encouraging a cross-disciplinary dialogue — with Comments, News Features, News & Views articles and also Correspondence.
- 25.898/Q1
- COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS - SCIE(Q1); COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE - SCIE(Q1)
CCF-None, 13.663, Q1
https://www.nature.com/nathumbehav/aims
https://www.nature.com/nathumbehav/submission-guidelines
Drawing from a broad spectrum of social, biological, health, and physical science disciplines, Nature Human Behaviour publishes research of outstanding significance into any aspect of individual or collective human behaviour. How do humans perceive, think, feel, decide, and act? How do they interact with their environments and others? How do these abilities develop and decline over the lifespan? How do they evolve and compare with other species? How do they vary among individuals, groups, and cultures? How are they shaped by socioeconomic and political factors? How are they affected by disease or deprivation? What interventions can influence individual behaviours or outcomes? The journal welcomes research from any discipline that provides significant original insight into these questions.
Nature Human Behaviour features a broad range of topics, including (but not limited to) perception, action, memory, learning, reward, judgment, decision-making, language, communication, emotion, personality, social cognition, social behaviour, neuropsychiatric/neurodevelopmental/neurological disorders, economic & political behaviour, belief systems, social networks, social norms, social structures, behaviour change, collective cognition and behaviour, culture, public policy.
In addition to publishing original research, Nature Human Behaviour publishes Reviews, Perspectives, Comments, News, Features, and Correspondence from across the full range of disciplines concerned with human behaviour.
Ultimately, the journal’s mission is to strengthen the reach and impact of human behaviour research in directly addressing our most pressing social challenges.
Like all Nature-branded journals, Nature Human Behaviour is characterized by a dedicated team of professional editors, a fair and rigorous peer-review process, high standards of copy-editing and production, swift publication and editorial independence.
Disciplines covered in the journal include:
| Anthropology | Evolution |
|---|---|
| Artificial Intelligence | Genetics |
| Business Studies | Geography |
| Cognitive Science | Linguistics |
| Communication | Management |
| Criminology | Neurology |
| Cultural Studies | Neuroscience |
| Ecology | Political Science |
| Economics | Psychiatry |
| Education | Psychology |
| Epidemiology | Public Policy |
| Ethology | Sociology |
CCF-None, 14.136, Q1
https://www.science.org/journal/sciadv
https://www.science.org/content/page/science-advances-information-authors
Science Advances is the American Association for the Advancement of Science’s (AAAS) open access multidisciplinary journal, publishing impactful research papers and reviews in any area of science, in both disciplinary-specific and broad, interdisciplinary areas. The mission of Science Advances is to provide fair, fast, and expert peer review to authors and a vetted selection of research, freely available to readers. Led by a team of distinguished scientists and allowing flexible article formats, Science Advances supports the AAAS mission by extending the capacity of Science magazine to identify and promote significant advances in science and engineering across a wide range of areas. The journal’s use of evolving digital publishing technologies plays a critical role in building and sustaining AAAS’s mission as a global participant and advocate for the communication and use of science to benefit humankind.
CCF-None, 7.558, Q1 Journal Citation Reports (Clarivate, 2022): 29/145 (Computer Science, Artificial Intelligence)9/110 (Computer Science, Theory & Methods) Online ISSN:1942-4795
The objectives of WIREs Data Mining and Knowledge Discovery are to (a) present the current state of the art of data mining and knowledge discovery through an ongoing series of reviews written by leading researchers, (b) capture the crucial interdisciplinary flavor of the field by including articles that address the key topics from the differing perspectives of data mining and knowledge discovery, including a variety of application areas in technology, business, healthcare, education, government and society and culture, (c) capture the rapid development of data mining and knowledge discovery through a systematic program of content updates, and (d) encourage active participation in this field by presenting its achievements and challenges in an accessible way to a broad audience. The content of WIREs DMKD will be useful to upper-level undergraduate and postgraduate students, to teaching and research professors in academic programs, and to scientists and research managers in industry.
The techniques of data mining and knowledge discovery (DMKD) are now being applied in many areas of business and government, such as banking and finance, market research, risk analysis, and counterterrorism. In the sciences, DMKD has become pervasive in such fields as bioinformatics, medical diagnosis, epidemiology, drug discovery, environmental modeling, and meteorological data analysis.
CCF-None, 5.071, Q1 Journal Citation Reports (Clarivate, 2022): 10/90 (Psychology, Experimental) Online ISSN:1939-5086
Cognitive science is the study of how the mind works, addressing functions such as perception and action, memory and learning, language and communication, reasoning and problem solving, artificial intelligence, decision-making, emotion and consciousness. By its very nature, this field highlights the interrelationships among the traditionally self-contained disciplines of Cognitive Biology; Computer Science; Economics; Linguistics; Neuroscience; Philosophy; and Psychology; each of these is thus represented as a major topic covered by WIREs Cognitive Science. This journal will review research from all these fields with the potential to illuminate how the mind is structured, how it has evolved, how it develops through life, and how its functions are instantiated in neural circuits and computations.
CCF-T1, 0.498
投稿须知
1、本刊要求研究论文必须有创新性，内容充实完整；文献综述应由该领域内知名专家结合本人近年研究成果完成，要求有较强的前瞻性和指导性。2、我刊严禁一稿两投，重复内容多次投稿（包括将以不同文种分别投稿）以及抄袭他人论文等现象。一旦发现有上述情况，该作者的稿件将被作退稿处理，同时通知所在单位严肃处理，并向领域兄弟期刊通报。我刊将拒绝发表联系作者作为主要作者的所有投稿。3、上传电子稿件应为WORD（*.doc）。文稿务求论点明确、文字精练、数据可靠。在正文前，各加5个以内"关键词"和300字左右的摘要"。参考文献20条以上，要能反映该学科近年来的发展情况。4、 稿件审查结果一般在1～2月之内通知作者，有个别稿件可能送审时间较长，如果超过2个月后仍未接到审稿结果，作者可与编辑部取得联系后自投它处，不受理未及时间的催审，不能等待可在来信告知编辑部后转投他刊。 5、 稿件的作者必须是直接参与研究工作或对其有重要指导作用的成员（如研究生导师等），协助做实验的人员可放入致谢中。作者人数请控制在6人以下，严禁与论文无关人员挂名。联系人请注明姓名、性别、年龄、职务、职称、学位等自然情况。 6、通过审查后需要修改和补充实验的稿件，最晚不超过4个月将修改稿返回编辑部，如有困难需及时向编辑部说明情况，半年不返回，按自动撤稿处理。 7、 论文录用后刊出前，编辑部还会进行查重等审核，不合格者仍会面临退稿，请务必注意学术不端问题。8、 论文发表后，版权即属编辑部所有，其中包括上网的版权。
CCF-T1, 1.456
《计算机学报》是中国计算机领域权威性学术刊物。其宗旨是报道中国计算机科学技术领域最高水平的科研成果。它由中国计算机学会与中国科学院计算技术研究所主办、科学出版社出版，以中文编辑形式与读者见面，同时以英文摘要形式向国际各大检索系统提供基本内容介绍。
《计算机学报》刊登的内容覆盖计算机领域的各个学科,以论文、技术报告、短文、研究简报、综论等形式报道以下 ，但不仅限于以下方面的科研成果： 计算机系统体系结构、计算机软件、计算机科学与理论、人工智能、 信息安全、数据科学与工程、计算机网络、多媒体、计算机图形学以及其他新技术等。
http://cjc.ict.ac.cn/index.htm
CCF-T1, 6.171
中文稿件投稿地址：http://mc03.manuscriptcentral.com/aas-cn
英文稿件投稿地址：http://mc03.manuscriptcentral.com/ieee-jas
投稿要求
1、稿件要求结构完整、论点明确、论据可靠、论证合理、层次分明、可读性强。
2、稿件为原始稿件，未在国内外公开出版物上发表过，非一稿多投。此外，本刊不接受任何语种的翻译稿件。
3、稿件属作者本人的创造性劳动成果，凡引用或参考了他人的论述、数据、结果，请将文献信息查新查全并在文中明确标引，以示对同行工作的尊重。
4、稿件所述课题应由投稿者单位审核无保密要求，可以在《自动化学报》上公开发表。
5、稿件请用Word排版后转成PDF格式在系统中上传。
6、初次投稿无需使用我刊模板，稿件可以按照任何模板进行排版，只要方便编委会和外审专家评阅即可。
7、本刊实行双盲制审稿，请作者在投稿前删除稿件中的所有作者信息，包括页眉页脚、标题下方、尾注、论文最后和文件名中的姓名、单位、基金号等。
8、投稿前请登录本刊网站“下载中心”下载《自动化学报》作者承诺。所有作者亲笔签名，加盖第一作者单位公章，投稿时需与论文同时提交，编辑部只有在收到作者承诺后才对稿件进行初审。【注意】：承诺书中的作者姓名和顺序必须与系统中保持一致，文章标题不能空缺。如第一作者的单位是国外机构，可以不交作者承诺。
四、作者须知
1、为联系顺畅,请在“在线投稿”—“创建账户”时提供详细的联系方式(包括通讯地址、邮编、E-mail地址和联系电话等)。审稿期间联系方式如有变化,请及时在线更新。
2、《自动化学报》网站上提供了"稿件处理流程",作者可以通过该流程图了解稿件的处理程序和大致时间进度。
3、对于拟录用稿件，编辑部将综合考虑投稿时间和研究方向安排发表。稿件刊发后，我们将赠送当期样刊2本，并在3个月内一次性支付稿酬（包括印刷版、光盘版以及网络版稿酬）。
| 期刊官方网站 | https://www.nature.com/nathumbehav |
|---|---|
| 期刊投稿网址 | https://mts-nathumbehav.nature.com/ |
CCF-T1, 1.901
征稿范围： 计算机体系结构；计算机网络与通信；网络与信息安全；人工智能；软件技术；图形图像、信息检索等应用技术；及其他计算机相关领域。征稿类型：1，学术论文：有创新学术见解的研究成果的完整论述，对该学术领域的发展有积极意义。2，技术报告：面向国民经济和社会发展的、先进实用的开发成果的技术总结。3，综述：对新兴的、活跃的学术研究领域或技术开发领域的评述。4，研究热点论文：对目前国内外计算机前沿热点研究领域的介绍文章。征稿要求：1，本刊只接收中文稿，不受理英文稿。2，要求来稿未在正式出版物上发表过，不存在一稿多投问题。3，作者在投稿时，需向编辑部提交版权转让与投稿声明，如不提供不予受理。对于提交过版权转让协议的来稿，如果发现有侵权行为，本刊将视情节做严肃处理，其后果自负。4，本刊双盲评审，作者在投稿时上传系统的电子版中需要隐去作者、单位、基金、作者简介等信息，初审通过后给编辑部邮寄的纸质版本需要包含前述署名信息。5，稿件一经发表将按国家规定的标准付稿酬，并送当期期刊 2册， 对于未录用稿件，我部将评审意见寄给作者，不退稿，请自留底稿。6，投稿时间超过6个月以上的，在通知编辑部后可另做处理。特别强调：1，请作者在投稿前先准备一份 版权转让与投稿声明 ，上传系统的版本文章编号暂不填写，初审通过后再把编号补在纸版上。该文件需要要求所有作者亲笔签名，并经过主要责任单位审核确认不涉密。签字盖章后扫描存为JPG或PDF格式，在投稿过程中需要上传。未提供有效版权转让协议，将会影响稿件的送审进度。2，请作者确保稿件不涉及一稿多投、造假、抄袭、剽窃等行为，一经发现，将按照本刊“关于涉嫌学术不端行为的稿件处理办法”（点击下载）执行。3，如果是已退稿件修改后再投，需在作者留言中注明，并在文章最后附加一份修稿说明。4，提交稿件时需要填写所有作者的邮件及主要联系方式。5，稿件录用后原则上不同意随意修改作者单位署名信息。6，第一作者为 CCF 会员/高级会员/学生会员者，稿件录用后可享受 8.5 折的版面费优惠。
CCF-T1, 1.153
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CCF-T1, 1.143
半月刊，交叉学科
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