A5100725604- Metaheuristic Optimization Algorithms Research
- Advanced Multi-Objective Optimization Algorithms
- Evolutionary Algorithms and Applications
- Advanced Software Engineering Methodologies
- Robotic Path Planning Algorithms
- Formal Methods in Verification
- Chinese history and philosophy
- Vehicle Routing Optimization Methods
- Model-Driven Software Engineering Techniques
- Service-Oriented Architecture and Web Services
- Artificial Immune Systems Applications
- Privacy-Preserving Technologies in Data
- Software Engineering Research
- Resource-Constrained Project Scheduling
- Distributed and Parallel Computing Systems
- Language, Metaphor, and Cognition
- Distributed Control Multi-Agent Systems
- BIM and Construction Integration
- Advanced Algorithms and Applications
- Manufacturing Process and Optimization
- Advanced Optimization Algorithms Research
- Natural Language Processing Techniques
- Advanced Measurement and Metrology Techniques
- Discourse Analysis in Language Studies
- Modular Robots and Swarm Intelligence
UNSW Sydney
2011-2024
University of Canberra
2017-2024
Xidian University
2004-2023
Shenzhen University
2014-2023
Kashi University
2016-2023
Ministry of Natural Resources
2023
Beijing University of Civil Engineering and Architecture
2023
Ministry of Water Resources of the People's Republic of China
2023
Beijing Jiaotong University
2023
University of Maryland, College Park
2022
In this paper, multiagent systems and genetic algorithms are integrated to form a new algorithm, algorithm (MAGA), for solving the global numerical optimization problem. An agent in MAGA represents candidate solution problem hand. All agents live latticelike environment, with each fixed on lattice-point. order increase energies, they compete or cooperate their neighbors, can also use knowledge. Making of these agent-agent interactions, realizes purpose minimizing objective function value....
Abstract In recent years, many researchers have attempted to determine the mechanisms of how corporate social responsibility (CSR) brings financial benefits a firm. However, chief officers (CFOs) throughout world are uncertain about strategic value CSR, and no consensus has been reached on defining CSR creates value. Drawing signaling theory, we explore effects multidimensional construct organizational performance by examining relationships among reputation, customer satisfaction,...
Yizhong Wang, Kai Liu, Jing Wei He, Yajuan Lyu, Hua Wu, Sujian Li, Haifeng Wang. Proceedings of the 56th Annual Meeting Association for Computational Linguistics (Volume 1: Long Papers). 2018.
Fine-tuning large language models on private data for downstream applications poses significant privacy risks in potentially exposing sensitive information. Several popular community platforms now offer convenient distribution of a variety pre-trained models, allowing anyone to publish without rigorous verification. This scenario creates threat, as can be intentionally crafted compromise the fine-tuning datasets. In this study, we introduce novel poisoning technique that uses...
Planning problems, such as mission capability planning in defense, can traditionally be modeled a resource investment project scheduling problem (RIPSP) with unconstrained resources and cost. This formulation is too abstract some real-world applications. In these applications, the durations of tasks depend on allocated resources. this paper, we first propose new version RIPSPs, namely extended RIPSPs (ERIPSPs), which are function Moreover, introduce proportion coefficient to manifest...
Multimodal large language models (MLLMs) have attracted widespread interest and rich applications. However, the inherent attention mechanism in its Transformer structure requires quadratic complexity results expensive computational overhead. Therefore, this work, we propose VL-Mamba, a multimodal model based on state space models, which been shown to great potential for long-sequence modeling with fast inference linear scaling sequence length. Specifically, first replace transformer-based...
In this paper, we focus on solving non-linear programming (NLP) problems using quantum-behaved particle swarm optimization (QPSO). After a brief introduction to the original (PSO), describe origin and development of QPSO, penalty function method for constrained NLP problems. The performance QPSO is tested some unconstrained benchmark functions compared with PSO inertia weight (PSO-In) constriction factor (PSO-Co). experimental results show that outperforms traditional PSOs promising algorithm.
Responsive teaching is a highly effective strategy that promotes student learning. In math classrooms, teachers might {emph{funnel} students towards normative answer or {emph{focus} to reflect on their own thinking depending understanding of concepts. When focus, they treat students' contributions as resources for collective sensemaking, and thereby significantly improve achievement confidence in mathematics. We propose the task computationally detecting funneling focusing questions...
One of the major challenges in field evolutionary algorithms (EAs) is to characterise which kinds problems are easy and not. Researchers have been attracted predict behaviour EAs different domains. We introduce fitness landscape networks (FLNs) that formed using operators satisfying specific conditions define a new predictive measure we call motif difficulty (MD) for comparison-based EAs. Because it impractical exhaustively search whole network, propose sampling technique calculating an...