征稿主题

IC会议涵盖了智能计算机、算法及在计算机科学、民航、医疗、金融、教育、管理等领域的广泛主题。主题包括但不限于以下内容:

AI for data science:

• Machine Learning and Deep Learning for Data Science
• Data Mining and Knowledge Discovery for Data Science
• Natural Language Processing (NLP) for Data Science
• Feature Engineering for Data Science
• AI-Driven Data Visualization

AI for Materials Science and Engineering:

• Property prediction using AI/ML/DL
• Application of AI/ML/DL in traditional MD/DFT
• Materials design guided by AI
• Materials synthesis using AI

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

Intelligent computing system:

• 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
• Observaility 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
• Serverless architecture for AI systems

AI algorithms:

• machine learning (deep learning, statistical learning, etc)
• natural language processing
• computer vision
• data mining
• multiagent systems
• knowledge representation
• robotics
• search, planning, and reasoning

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

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, standardi-zation, 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 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 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 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 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 for Science:

• Learning from acoustics
• Learning physical dynamics from data
• Speeding up physical simulators, samplers and solvers
• Molecular modeling and de novo generation
• Modeling biological systems, genomics, protein, RNA
• Accelerating cosmological simulations
• Improving crop yields through precision agriculture
• Optimizing aerospace product design and development
• Benchmarking related or new tasks (i.e. datasets, sota models, etc.)
• Building tools/infrastructures/platforms for scientific discovery
• Study of science of science/scientific methods

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 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.