July 31, 2023
July 31, 2023
IC 2023 Calls for Papers
2023 BenchCouncil International Symposium on Intelligent Computers, Algorithms, and Applications (IC 2023)
In conjunction with Federated Intelligent Computing and Chip Conference (FICC 2023)
Paper Submission: July 31, 2023, at 11:59 PM AoE
Notification: September 30, 2023, at 11:59 PM AoE
Final Papers Due: October 31, 2023, at 11:59 PM AoE
Conference Date: December 4-6, 2023
Venue: Sanya, Hainan Province, People’s Republic of China.
Please note that citizens from up to 59 nations can visit Sanya, Hainan without a Visa from the Chinese Government. Sanya is a beautiful seaside city, well known as Hawaii in China.
Submission website: https://ic2023.hotcrp.com/
The mission of IC 2023 is to provide a pioneering technology map through searching and advancing state-of-the-art and state-of-the-practice in processors, systems, algorithms, and applications for machine learning, deep learning, spiking neural network and other AI techniques across multidisciplinary and interdisciplinary areas. The BenchCouncil staff will invite worldwide contributors to showcase their superior chips, systems, algorithms and applications. IC 2023 also solicits manuscripts describing original work in the above areas.
IC 2023 invites manuscripts describing original work in the above areas and topics. All accepted papers will be presented at the IC 2023 conference and published by Springer CCIS (Indexed by EI).
With generous support from BenchCouncil, IC 2023 will offer travel grants for students to defray a portion of their travel cost. The size and number of these grants will vary depending on funding availability, the number of student applicants, and their respective priority. Grant awards will be made before the early registration deadline; expenses will be reimbursed after the conference; grant recipients will be asked to submit original receipts to verify their expenditures as well as a 1-page summary of their involvement during the conference. While we encourage all in need of a travel grant to apply, the selection process will give higher priority to students who would otherwise not be able to attend the conference. We strongly encourage applications from students that belong to under-represented groups.
Call for papers
The IC conference encompasses a wide range of topics in intelligent computers, algorithms, and applications in computer science, civil aviation, medicine, finance, education, etc. IC’s multidisciplinary and interdisciplinary emphasis provides an ideal environment for developers and researchers from different areas and communities to discuss practical and theoretical work. The topics of interest include, but are not limited to the following:
- AI Algorithms
- machine learning (deep learning, statistical learning, etc)
- natural language processing
- computer vision
- data mining
- multi-agent systems
- knowledge representation
- search, planning, and reasoning
- AI Systems
- Scalable and distributed AI systems
- High-performance computing for AI
- System-level optimization for deep learning
- Efficient hardware architectures for AI
- Model compression and acceleration techniques
- Memory management and resource allocation in AI systems
- Real-time and edge AI systems
- AutoML and automated system design
- Benchmarking and evaluation of AI systems
- Observability of AI systems
- Edge computing for AI systems
- Reliability of AI systems
- GPU sharing
- Intelligent Operations of AI systems
- Graph computing systems
- Domain specific AI systems
- Server-less architecture for AI systems
- AI for Ocean Science and Engineering
- Ocean Front Detection
- Mesoscale Eddy Recognition
- Underwater Image Enhancement
- Underwater Image Super-Resolution
- Underwater Object Recognition, Detection and Tracking
- Sea Surface Height Estimation
- Sea Surface Temperature Estimation
- Internal Wave Identification
- Wave Height Estimation
- AI in Finance
Applications of AI in finance: such as capital markets, investment and financing in real economy, risk management, investment decision-making, transaction execution, etc.Impact of AI on the financial industry: discuss the influence of AI in the financial industry, such as improving efficiency, reducing risks, and optimizing customer experience.
Challenges and opportunities for AI: Explore the technical, ethical, regulatory, and other challenges faced by AI in the financial field, and how to overcome them.
Sustainable development of intelligent finance: explore how to promote the development of finance industry with extensive AI application while maintaining the principles of sustainable development.
Ethics and transparency: explore the ethical and transparency issues raised by AI in the financial field.
- AI for Education
- Position papers on AI for education
- Large language models for education
- AI models of teaching and learning
- AI-assisted education
- Innovative applications of AI technologies in education
- Evaluation of AI technologies in education
- Intelligent tutoring systems
- Human-computer collaborative education systems
- Ethics and AI in education
- Impacts of AI technologies on education
- AI for Law
- Argument mining on legal texts
- Automatic classification and summarization of legal text
- Computational methods for negotiation and contract formation
- Computer-assisted dispute resolution
- Computable representations of legal rules and domain specific languages for the law
- Decision support systems in the legal domain
- Deep learning on data and text from the legal domain
- E-discovery, e-disclosure, e-government, e-democracy and e-justice
- Ethical, legal, fairness, accountability, and transparency subjects arising from the use of AI systems in legal practice, access to justice, compliance, and public administration
- Explainable AI for legal practice, data, and text analytics
- Formal and computational models of legal reasoning (e.g., argumentation, case-based reasoning), including deontic logics)
- Formal and computational models of evidential reasoning
- Formal models of norms and norm-governed systems
- Information extraction from legal databases and texts
- Information retrieval, question answering, and literature recommendation in the legal domain
- Intelligent support systems for forensics
- Interdisciplinary applications of legal informatics methods and systems
- Knowledge representation, knowledge engineering, and ontologies in the legal domain
- Legal design involving AI techniques
- Machine learning and data analytics applied to the legal domain
- Normative reasoning by autonomous agents
- Open and linked data in the legal domain
- Smart contracts and application of blockchain in the legal domain
- Visualization techniques for legal information and data
- AI for Materials Science and Engineering
- AI for materials chemistry
- AI for materials physics
- AI for materials characterization
- AI for materials design
- AI for materials manufacturing and processing
- AI for materials in industry
- AI for Science
Applications of machine learning in scientific research: Explore the application of machine learning algorithms in scientific data analysis, pattern recognition, classification, and prediction. This includes innovative research in emerging fields such as quantum computing, materials science, climate change, drug discovery, genomics, physics simulation, environmental protection, sustainable energy, and healthcare. For example, using AI techniques to construct complex models and simulate the behavior of natural systems, exploring scientific questions related to climate simulation, cosmological simulation, molecular dynamics simulation, and more.
Assisting experiment design and optimization: Utilize AI to optimize experiment design and parameter optimization, improving experiment efficiency. For example, rapidly determining optimal experimental conditions and reducing the time and cost of experiments.
Natural language processing and scientific literature mining: Explore the application of natural language processing techniques in scientific literature analysis, knowledge graph construction, text summarization, and information extraction, accelerating the dissemination and discovery of scientific knowledge.
Data visualization and scientific communication: Discuss the latest methods and tools for visualizing scientific data and presenting scientific results using AI technology, promoting the communication and sharing of scientific research findings. AI plays a critical role in scientific data analysis. Machine learning and statistical methods can extract useful information and patterns from large-scale scientific datasets, assisting scientists in data mining, feature extraction, data dimensionality reduction, and other tasks.
- AI for Civil Aviation
- AI in Aircraft Maintenance, Repair and Overhaul (MRO)
- AI in Operations Management and Revenue Optimization against safety control
- AI in Customer Service and Engagement
- AI in Aircraft Design Optimization
- AI in Identification of Passengers
- Pitfalls of using AI in Aviation
- The integrity, Metadata integration architecture, effectiveness, consistency, standardization, openness and sharing management of the civil aviation data
- Digital Business of civil aviation, quality management of Civil Aviation data
- Digital Air-Control Management and Digital Surveillance Management of Civil Aviation
- AI for Medicine
- Medical AI and Interpretable Medical Models
- AI, Block Chain, Cloud, and Data Techniques for Medicine
- Big Medical data and Privacy Protection
- Artificial Intelligence and Medical Image Analysis
- Internet-based Medical Diagnosis
- Medical Robot
- Drug discovery and Computer-aided Design
- Artificial Intelligence in Medical Diagnosis
- Medical Data and AI Practice and Case Study
- AI for Space Science and Engineering
- Space science target prediction, detection and feature extraction based on AI technology
- Uncertain analysis of AI models in space science
- Physics-informed machine learning in space science
- AI surrogate of the physics models
- How to gain new knowledge from the space science AI models
- Foundation models in space science
- Use AI technology to assist in space mission planning and scheduling
- AI-assisted space satellite anomaly detection and emergency decision-making
- AI for High Energy Physics
- Machine learning methods or models for HEP, including event triggering, particle identification, fast simulation, event reconstruction, noise filtering, detector monitoring, and experimental control.
- Utilizing high-performance computing for implementing machine learning methods in HEP, such as feature detection, feature engineering, usability, interpretability, robustness, and uncertainty quantification.
- Optimizing machine learning models on large-scale HEP simulation or experimental datasets.
- Deepening the modeling and simulation of HEP scientific problems using machine learning techniques.
- Harnessing emerging hardware (e.g., GPUs, NPUs, FPGAs) to accelerate machine learning processes for HEP data.
- Applications of large-scale language models in machine learning for HEP.
- Applications of quantum machine learning in machine learning for HEP.
- AI and Security
- Security and Privacy of AI
- Fairness, interpretability, and explainability for AI
- AI Regulations
- Adversarial learning
- Membership inference attacks
- Data poisoning & backdoor attacks
- Security of deep learning systems
- Robust statistics
- Differential privacy & privacy-preserving data mining
- AI for security and privacy
- Computer forensics
- Spam detection
- Phishing detection and prevention
- Botnet detection
- Intrusion detection and response
- Malware identification and analysis
- Intelligent vulnerability fuzzing
- Automatic security policy management & evaluation
- Big data analytics for security
Papers must be submitted in PDF. For a full paper, the page limit is 15 pages in the CCIS format, not including references. For a short paper, the page limit is 8 pages in the CCIS format, not including references. Authors are also encouraged to submit a 4-page extended abstract and make an extension after acceptance.
The review process follows a strict double-blind policy. The submissions will be judged based on the merit of the ideas rather than the length. After the conference, the proceeding will be published by Springer CCIS (Indexed by EI). Please note that the CCIS format is the final one for publishing.
At least one author must pre-register for the conference, and at least one author must attend the conference to present the paper. Papers for which no author is pre-registered will be removed from the proceedings.
Please make sure your submission satisfies ALL of the following requirements:
- All authors and affiliation information must be anonymized.
- Paper must be submitted in printable PDF format.
- Please number the pages of your submission.
- The submission must be formatted for black-and-white printers. Please make sure your figures are readable when printed in black and white.
- The submission must describe unpublished work that is not currently under review of any other conference or journal venues.
LNCS latex template: https://www.benchcouncil.org/file/llncs2e.zip
Weiping Li, Civil Aviation Flight University of China, China
Tao Tang, BNU-HKBU United International College, China
Frank Werner, Institute of Mathematical Optimization, Otto-von-Guericke-University, German
Christophe Cruz, Université de Bourgogne, France
Yanchun Zhang, Victoria University, Australia
Wanling Gao, ICT, Chinese Academy of Sciences, China
Jungang Xu, University of Chinese Academy of Sciences, China
Yucong Duan, Hainan University, China