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- research-articleJuly 2024
On the Opportunities and Challenges of Foundation Models for GeoAI (Vision Paper)
- Gengchen Mai,
- Weiming Huang,
- Jin Sun,
- Suhang Song,
- Deepak Mishra,
- Ninghao Liu,
- Song Gao,
- Tianming Liu,
- Gao Cong,
- Yingjie Hu,
- Chris Cundy,
- Ziyuan Li,
- Rui Zhu,
- Ni Lao
ACM Transactions on Spatial Algorithms and Systems (TSAS), Volume 10, Issue 2Article No.: 11, Pages 1–46https://doi.org/10.1145/3653070Large pre-trained models, also known as foundation models (FMs), are trained in a task-agnostic manner on large-scale data and can be adapted to a wide range of downstream tasks by fine-tuning, few-shot, or even zero-shot learning. Despite their successes ...
- research-articleJune 2024
UFNet: A Multi-scale Fusion Feature based Text Detection Method
ICRSA '23: Proceedings of the 2023 6th International Conference on Robot Systems and ApplicationsSeptember 2023, Pages 163–168https://doi.org/10.1145/3655532.3655558Recently, the field of text detection has witnessed a growing trend, with more and more segmentation-based methods incorporating feature sampling. Segmentation methods possess a natural advantage in detecting text with both regular and irregular shapes ...
- ArticleMay 2024
Keywords and Stops Aware Optimal Routes on Road Networks
AbstractRecently, the keyword-aware routing problem has been increasingly studied, which is to return the optimal route from the starting point s to the destination t, satisfying all the user-specified keyword requirements. Most existing solutions focus ...
- research-articleMay 2024
Can Large Language Models Be Good Companions?: An LLM-Based Eyewear System with Conversational Common Ground
- Zhenyu Xu,
- Hailin Xu,
- Zhouyang Lu,
- Yingying Zhao,
- Rui Zhu,
- Yujiang Wang,
- Mingzhi Dong,
- Yuhu Chang,
- Qin Lv,
- Robert P. Dick,
- Fan Yang,
- Tun Lu,
- Ning Gu,
- Li Shang
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), Volume 8, Issue 2Article No.: 87, Pages 1–41https://doi.org/10.1145/3659600Developing chatbots as personal companions has long been a goal of artificial intelligence researchers. Recent advances in Large Language Models (LLMs) have delivered a practical solution for endowing chatbots with anthropomorphic language capabilities. ...
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- ArticleApril 2024
- research-articleApril 2024
A theoretical understanding of gradient bias in meta-reinforcement learning
NIPS '22: Proceedings of the 36th International Conference on Neural Information Processing SystemsNovember 2022, Article No.: 2252, Pages 31059–31072Gradient-based Meta-RL (GMRL) refers to methods that maintain two-level optimisation procedures wherein the outer-loop meta-learner guides the inner-loop gradient-based reinforcement learner to achieve fast adaptations. In this paper, we develop a ...
- research-articleFebruary 2024
Minimum-time strategy optimization for networked evolutionary games with bankruptcy mechanism
Expert Systems with Applications: An International Journal (EXWA), Volume 237, Issue PAMar 2024https://doi.org/10.1016/j.eswa.2023.121311AbstractIn this paper, the minimum-time strategy optimization problem for NEGs with bankruptcy mechanism is considered based on semi-tensor product of matrices. Firstly, in order to describe the impact of bankruptcy mechanism on evolution, a novel matrix ...
Highlights- The minimum-time control is first introduced into the study of NEGs.
- The introduction of heterogeneous memory expands NEGs with bankruptcy mechanism.
- A new approach is proposed to study NEGs with bankruptcy mechanism.
- The new ...
- research-articleFebruary 2024
Deep-Learning-Assisted Cardiac Electrophysiology Simulation
WSC '23: Proceedings of the Winter Simulation ConferenceDecember 2023, Pages 758–769Simulation built upon partial and ordinary differential equations has been a classic approach to modeling cardiac electrophysiological dynamics. However, mitigating the computational burden of differential equations is still a challenging problem. This ...
- ArticleNovember 2023
TCTV: Trace Clustering Considering Intra- and Inter-cluster Similarity Based on Trace Variants
AbstractAs we know that simply applying existing techniques in process mining will often yield a highly incomprehensible process model that called the spaghetti-like model, because real-life processes are typically less structured and more complex than ...
- research-articleNovember 2023
Closest Pairs Search Over Data Stream
Proceedings of the ACM on Management of Data (PACMMOD), Volume 1, Issue 3Article No.: 205, Pages 1–26https://doi.org/10.1145/3617326k-closest pair (KCP for short) search is a fundamental problem in database research. Given a set of d-dimensional streaming data S, KCP search aims to retrieve k pairs with the shortest distances between them. While existing works have studied continuous ...
- research-articleNovember 2023
GeoKG'2022 Workshop Report: The 1st ACM SIGSPATIAL International Workshop on Geospatial Knowledge Graphs
SIGSPATIAL Special (SIGSPATIAL), Volume 14, Issue 1November 2022, Pages 37–39https://doi.org/10.1145/3632268.3632279The topic of knowledge graphs (KGs) has recently attracted extensive attention in both industry and academia. Knowledge graphs are a new paradigm for representing, retrieving, integrating, and reasoning data from highly heterogeneous and multimodal ...
- ArticleNovember 2023
Searching User Community and Attribute Location Cluster in Location-Based Social Networks
Advanced Data Mining and ApplicationsAug 2023, Pages 389–404https://doi.org/10.1007/978-3-031-46677-9_27AbstractCommunity search is a fundamental problem in analyzing and managing graph data, which is searching for an optimal community based on query nodes. Attribute community search and geosocial community search have been extensively investigated, however,...
- ArticleNovember 2023
Continuous Group Nearest Neighbor Query over Sliding Window
Advanced Data Mining and ApplicationsAug 2023, Pages 225–236https://doi.org/10.1007/978-3-031-46677-9_16AbstractGroup nearest neighbor query(GNN for short) is a classic problem in the spatial database field. Given a data point set D, a query point set Q, the goal of the Group nearest neighbor query(GNN) is to select an object point o in D to minimize the ...
- ArticleNovember 2023
Efficient Regular Path Query Evaluation with Structural Path Constraints
Advanced Data Mining and ApplicationsAug 2023, Pages 308–322https://doi.org/10.1007/978-3-031-46671-7_21AbstractRegular path query is a technique of using a regular expression (regex) on graph data. Classical methods adopt the finite state automaton to match the regular path query on the graph. Their matching results are the sequences of vertex pairs (i.e., ...
- ArticleNovember 2023
A Cross-Region-based Framework for Supporting Car-Sharing
Advanced Data Mining and ApplicationsAug 2023, Pages 614–629https://doi.org/10.1007/978-3-031-46661-8_41AbstractWith the rapid development of mobile Internet and sharing economy, carsharing has attracted a lot of attention around the globe. Many popular taxi-calling service platforms, such as DiDi and Uber, have provided carsharing service to the ...
- ArticleNovember 2023
Deep Reinforcement Learning for Solving the Trip Planning Query
Advanced Data Mining and ApplicationsAug 2023, Pages 569–583https://doi.org/10.1007/978-3-031-46661-8_38AbstractThe Trip Planning Query (TPQ), which returns the optimal path from the starting point to the destination that satisfies multiple types of points of interest (POIs) specified by the user, has attracted more and more attention. The most ...
- research-articleNovember 2023
Knowledge graph completion method based on quantum embedding and quaternion interaction enhancement
Information Sciences: an International Journal (ISCI), Volume 648, Issue CNov 2023https://doi.org/10.1016/j.ins.2023.119548AbstractKnowledge graphs (KG) are used for many downstream tasks in artificial intelligence (AI). However, owing to accuracy issues associated with information extraction, KGs are often incomplete. This has led to the emergence of knowledge graph ...
- research-articleOctober 2023
Modulation recognition based on deep learning network
SPML '23: Proceedings of the 2023 6th International Conference on Signal Processing and Machine LearningJuly 2023, Pages 357–362https://doi.org/10.1145/3614008.3614061The current radio recognition technology has used relatively mature deep learning networks,but the current network learning parameters and computational complexity are relatively high,based on this,a lightweight network with simple computational ...