Cognitive Computing Track @ ACM SAC 2017 SACCOCO 2017

征稿已开启

已截稿

注册已开启

已截止

活动简介

Cognitive Computing investigates the development of self-learning systems, that naturally interact with humans in complex environments, and are capable to adapt to context and changes in language and meaning. These systems mimic the human mind processes, elaborating huge amounts of data, finding correlations, testing hypotheses, making predictions and inferences, drawing conclusions, and so on. Cognitive Computing is a new and emerging research area fostering the synergetic application of multidisciplinary approaches from various disciplines, such as Natural Language Processing, Knowledge Representation and Reasoning, Audio and Video Analysis, Computer-Human Interaction, Neuroscience and Cognition, and so on. A couple of related workshop initiatives have been recently proposed. Aim of the track is to promote a forum for scientists, engineers and practitioners, in academia and industry, to share and discuss latest advances, breakthrough results, and real-world experiences in the Cognitive Computing area.

征稿信息

重要日期

2016-09-15

初稿截稿日期

2016-11-10

初稿录用日期

2016-11-25

终稿截稿日期

The SAC2017 special track on Cognitive Computing aims to promote a forum for scientists, engineers and practitioners, in academia and industry, to share and discuss latest advances, breakthrough results, and real-world experiences in the Cognitive Computing area. This is an interdisciplinary emerging research area, at the core of Artificial Intelligence, investigating the development of self-learning systems, that naturally interact with humans in complex environments, and are capable to adapt to context and changes in language and meaning.

征稿范围

Topics of interest include, but are not limited to:

Cognitive Knowledge Acquisition
- Frameworks for acquiring Cognitive Knowledge
- Tools and Methodologies for Cognitive Knowledge acquisition
- Natural Language Processing for Cognitive Knowledge acquisition
- Sentiment Analysis and Opinion Mining
- Linguistic Resources and Cognitive Knowledge Acquisition
- Machine Learning techniques (e.g., Deep Learning) for Cognitive Knowledge acquisition
- Acquisition from media other than text (e.g., audio, video, images)
- Linked Data and Cognitive Knowledge Acquisition
- Emerging approaches for Cognitive Knowledge acquisition (e.g., crowd-based approaches).
- Principled evaluation of acquired Cognitive Knowledge
Representing, Formalizing and Reasoning with Cognitive Knowledge
- Ontologies for Cognitive Systems
- Representation languages for Cognitive Knowledge
- Reasoning about Cognitive Knowledge
Development of Cognitive Systems
- Architectures for Cognitive Systems
- Scalability of Cognitive Systems
- Complex Cognitive Systems
- Fuzzy Cognitive Systems
Applications
- Real-world applications that exploit Cognitive Knowledge
- Cognitive Systems for Big Data scenarios
- Cognitive Techniques for Deep Learning
- Cognitive Techniques for Persuasion and Recommender Systems
- Cognitive Robotics
- Cognitive Techniques for Information Retrieval
- Deployment of Cognitive Systems in specific domains. For example:
  - Digital Humanities and Social Sciences
  - eGovernment and public administration
  - Life sciences, health and medicine
  - Social Media, News, and Data Streams

作者指南

Research papers and experience reports related to the above topics are solicited. Submissions must not have been published or be concurrently considered for publication elsewhere. Papers should be submitted in PDF with no more than 6 pages using the ACM-SAC proceedings format via the SAC 2017 website. Authors' names and affiliations should be entered separately at the submission site and not appear in the submitted papers. Each submission will be reviewed in a DOUBLE-BLIND process according to the ACM-SAC Regulations. Student Research Competition submissions are welcome.