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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhao, Shuyuan Chen, Wei Shi, Boyan Zhou, Liyong Lin, Shuohao Wan, Huaiyu School of Computer Science & Technology Beijing Jiaotong University Beijing China Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence Beijing China

ISBN: (纸本)157735897X

The takeaway recommendation system aims to recommend users' future takeaway purchases based on their historical purchase behaviors, thereby improving user satisfaction and boosting merchant sales. Existing methods focus on incorporating auxiliary information or leveraging knowledge graphs to alleviate the sparsity issue of user purchase sequences. However, two main challenges limit the performance of these approaches: (1) capturing dynamic user preferences on complex geospatial information and (2) efficiently integrating spatial-temporal knowledge from both graphs and sequence data with low computational costs. In this paper, we propose a novel spatial-temporal knowledge distillation model for takeaway recommendation (STKDRec) based on the two-stage training process. Specifically, during the first pre-training stage, a spatial-temporal knowledge graph (STKG) encoder is trained to extract high-order spatial-temporal dependencies and collaborative associations from the STKG. During the second spatial-temporal knowledge distillation (STKD) stage, a spatial-temporal Transformer (ST-Transformer) is employed to comprehensively model dynamic user preferences on various types of fine-grained geospatial information from a sequential perspective. Furthermore, the STKD strategy is introduced to transfer graph-based spatial-temporal knowledge to the ST-Transformer, facilitating the adaptive fusion of rich knowledge derived from both the STKG and sequence data while reducing computational overhead. Extensive experiments on three real-world datasets show that STKDRec significantly outperforms the state-of-the-art baselines. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Knowledge graph

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A Traffic Target Detection Algorithm Based on Hybrid Model Compression Method 4

A Traffic Target Detection Algorithm Based on Hybrid Model C...

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2022 4th International Conference on Electronic Engineering and Informatics, EEI 2022

作者： Yu, Wenze Liang, Quan Hu, Jinjing School of Computer Science and Mathematics Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China

ISBN: (纸本)9783800759231

With the development of the computer vision field, convolutional neural networks are widely used in object detection algorithms. However, the deep learning network model has high computational complexity, occupies a large storage space, and is difficult to deploy on low-computing devices. Aiming at the difficulty of deploying traffic target detection algorithms in practical scenarios, this paper proposes a target detection network compression method based on fusion of pruning and quantization. First, based on the Yolo series target detection algorithm, we perform an unstructured pruning operation with a variable threshold for the network model to be optimized, which can remove more information than the structure and reduce the parameters of the network model. Then, the network is quantized into 8-bit integer data through quantization-aware training to compress the network size. Experiments show that the optimized network model has less accuracy loss after variable threshold pruning and data quantization, and the model size is greatly compressed, which can achieve high detection capability even in the case of limited resources. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Object detection

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First Token Probability Guided RAG for Telecom Question Answering

arXiv

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arXiv 2025年

作者： Chen, Tingwei Chen, Jiayi Zhao, Zijian Chen, Haolong Zhang, Liang Zhu, Guangxu Shenzhen Research Institute of Big Data Shenzhen China Shenzhen China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

Large Language Models (LLMs) have garnered significant attention for their impressive general-purpose capabilities. For applications requiring intricate domain knowledge, Retrieval-Augmented Generation (RAG) has shown a distinct advantage in incorporating domain-specific information into LLMs. However, existing RAG research has not fully addressed the challenges of Multiple Choice Question Answering (MCQA) in telecommunications, particularly in terms of retrieval quality and mitigating hallucinations. To tackle these challenges, we propose a novel first token probability guided RAG framework. This framework leverages confidence scores to optimize key hyperparameters, such as chunk number and chunk window size, while dynamically adjusting the context. Our method starts by retrieving the most relevant chunks and generates a single token as the potential answer. The probabilities of all options are then normalized to serve as confidence scores, which guide the dynamic adjustment of the context. By iteratively optimizing the hyperparameters based on these confidence scores, we can continuously improve RAG performance. We conducted experiments to validate the effectiveness of our framework, demonstrating its potential to enhance accuracy in domain-specific MCQA tasks. Copyright © 2025, The Authors. All rights reserved.

关键词： Question answering

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Lost in UNet: Improving Infrared Small Target Detection by Underappreciated Local Features

arXiv

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arXiv 2024年

作者： Quan, Wuzhou Zhao, Wei Wang, Weiming Xie, Haoran Wang, Fu Lee Wei, Mingqiang The School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China The Shenzhen Research Institute Nanjing University of Aeronautics and Astronautics Shenzhen China The School of Science and Technology Hong Kong Metropolitan University Hong Kong The School of Data Science Lingnan University Hong Kong

Many targets are often very small in infrared images due to the long-distance imaging meachnism. UNet and its variants, as popular detection backbone networks, downsample the local features early and cause the irreversible loss of these local features, leading to both the missed and false detection of small targets in infrared images. We propose HintU, a novel network to recover the local features lost by various UNet-based methods for effective infrared small target detection. HintU has two key contributions. First, it introduces the "Hint" mechanism for the first time, i.e., leveraging the prior knowledge of target locations to highlight critical local features. Second, it improves the mainstream UNet-based architecture to preserve target pixels even after downsampling. HintU can shift the focus of various networks (e.g., vanilla UNet, UNet++, UIUNet, MiM+, and HCFNet) from the irrelevant background pixels to a more restricted area from the beginning. Experimental results on three datasets NUDT-SIRST, SIRSTv2 and IRSTD1K demonstrate that HintU enhances the performance of existing methods with only an additional 1.88 ms cost (on RTX Titan). Additionally, the explicit constraints of HintU enhance the generalization ability of UNet-based methods. Code is available at https://***/Wuzhou-Quan/HintU. Copyright © 2024, The Authors. All rights reserved.

关键词： Pixels

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A Symbolic Rule Integration Framework with Logic Transformer for Inductive Relation Prediction 24

A Symbolic Rule Integration Framework with Logic Transformer...

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33rd ACM Web Conference, WWW 2024

作者： Pan, Yudai Liu, Jun Zhao, Tianzhe Zhang, Lingling Lin, Yun Dong, Jin Song School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Xi'an China National Engineering Lab for Big Data Analytics Xi'an Jiaotong University Shaanxi Xi'an China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Shaanxi Xi'an China Shanghai Jiao Tong University Shanghai China National University of Singapore Singapore Singapore

ISBN: (纸本)9798400701719

Relation prediction in knowledge graphs (KGs) aims at predicting missing relations in incomplete triples, whereas the dominant paradigm by KG embeddings has a limitation to predict the relation between unseen entities. This situation is called an inductive setting, which is more common in the real-world scenario. To handle this issue, implicit symbolic rules have shown great potential in capturing the inductive capability. However, it is still challenging to obtain precise representations of logic rules from KGs. The argument variability and predicate non-commutativity in symbolic rule integration make the modeling of component symbols difficult. To this end, we propose a novel inductive relation prediction model named SymRITa with a logic transformer integrating rules. SymRITa firstly extracts the subgraph, whose embeddings are captured by a graph network. Meanwhile, symbolic rule graphs in the subgraph can be generated. Then, the symbolic rules are modeled by a proposed logic transformer. Specifically, the input format based on the subgraph-based embeddings is to focus on the argument variability in symbolic rules. In addition, a conjunction attention mechanism in the logic transformer can resolve predicate non-commutativity in the symbolic rule integration process. Finally, the subgraph-based and symbol-based embeddings obtained from the previous steps are combined for the training regime, and prediction results as well as rules explaining the reasoning process are explicitly output. Extensive experiments on twelve inductive datasets show that SymRITa achieves outstanding effectiveness compared to state-of-the-art inductive baselines. Moreover, the logic rules with corresponding confidences provide an interpretable paradigm. © 2024 ACM.

关键词： Forecasting

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Word reuse and combination support efficient communication of emerging concepts

arXiv

引用

arXiv 2024年

作者： Xu, Aotao Kemp, Charles Frermann, Lea Xu, Yang Department of Computer Science University of Toronto Canada School of Computing and Information Systems University of Melbourne Australia School of Psychological Sciences University of Melbourne Australia Cognitive Science Program University of Toronto Canada

A key function of the lexicon is to express novel concepts as they emerge over time through a process known as lexicalization. The most common lexicalization strategies are the reuse and combination of existing words, but they have typically been studied separately in the areas of word meaning extension and word formation. Here we offer an information-theoretic account of how both strategies are constrained by a fundamental tradeoff between competing communicative pressures: word reuse tends to preserve the average length of word forms at the cost of less precision, while word combination tends to produce more informative words at the expense of greater word length. We test our proposal against a large dataset of reuse items and compounds that appeared in English, French and Finnish over the past century. We find that these historically emerging items achieve higher levels of communicative efficiency than hypothetical ways of constructing the lexicon, and both literal reuse items and compounds tend to be more efficient than their non-literal counterparts. These results suggest that reuse and combination are both consistent with a unified account of lexicalization grounded in the theory of efficient communication. © 2024, CC BY-NC-ND.

关键词： Information theory

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Linking Multi-Scale Brain Connectivity with Vigilance, Working Memory, and Behavior in Adolescents 22

Linking Multi-Scale Brain Connectivity with Vigilance, Worki...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Bajracharya, Prerana Ballem, Ram Chen, Jiayu Andrés-Camazón, Pablo Khasayeva, Nigar Calhoun, Vince Liu, Jingyu Iraji, Armin Tri-Institutional Center for Translational Research in Neuroimaging and Data Science Atlanta United States Georgia State University Department of Computer Science Atlanta United States Institute of Psychiatry and Mental Health Hospital General Universitario Gregorio Marañón IiSGM Cibersam Isciii School of Medicine Universidad Complutense Madrid Spain

ISBN: (纸本)9798331520526

This study examines how multi-scale intrinsic connectivity networks (ICNs) relate to cognitive and behavioral functions in adolescents, focusing on attention/vigilance, working memory, and behavioral regulation. Leveraging the NeuroMark 2.2 multi-scale ICN template obtained from over 100,000 subjects, we obtained multi-scale ICNs from baseline resting-state fMRI data from the ABCD Study. For this study, we are interested in 'the fronto-thalamo-cerebellar (FTC) circuitry' and choose the subdomains of Neuromark 2.2 that cover it: Cerebellar (CB), Subcortical - Extended Thalamic (SC-ET), Higher Cognition - Insular Temporal (HC-IT), and Higher Cognition - Frontal (HC-FR), previously identified as relevant to cognitive and behavioral functions. Employing a multivariate approach combining principal component analysis (PCA) and canonical correlation analysis (CCA), we examined associations between these multi-scale ICNs and cognitive-behavioral outcomes. Our findings revealed significant associations, particularly for one of the estimated canonical components, linking multi-scale ICNs to cognitive and behavioral measures across both discovery and replication sets. This connectivity pattern may serve as a potential marker for attention, working memory, and behavioral regulation, offering new insights into a wide spectrum of neurodevelopmental disorders including Attention-Deficit/Hyperactivity Disorder (ADHD). © 2025 IEEE.

关键词： Behavioral research

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PresRecCD: A Novel Herbal Prescription Recommendation Framework with Cross-Domain Learning and Neural Collaborative Filtering

PresRecCD: A Novel Herbal Prescription Recommendation Framew...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Wansong Dong, Xin Yang, Kuo Zhang, Lei Wang, Xinyu Huang, Rouye Zhang, Runshun Zhou, Xuezhong Beijing Key Lab of Traffic Data Analysis and Mining School of Computer Science & Technology Beijing Jiaotong University Institute of Medical Intelligence Department of Artificial Intelligence Beijing100044 China China Academy of Chinese Medical Sciences National Data Center of Traditional Chinese Medicine Beijing China Guang'Anmen Hospital China Academy of Chinese Medical Sciences Beijing China

ISBN: (纸本)9798350386226

Herbal prescriptions hold significant importance in Traditional Chinese Medicine (TCM) diagnosis and treatment, embodying millennia of clinical case summaries and wisdom. Despite numerous proposed methods for herbal prescription recommendation (HPR), significant challenges persist due to the lack of comprehensive clinical data, particularly regarding the relationships between symptoms and herbs. This scarcity poses considerable hurdles for effective HPR modeling. In this study, we introduced a novel herbal prescription recommendation framework with cross-domain learning and neural collaborative filtering (termed PresRecCD). The cross-domain learning mechanism is introduced to learn the noise-reduced cross-domain features of herbs and symptoms in the unified space, alleviating the sparsity of data, and neural collaborative filtering is utilized to carry out prescription recommendations. Comprehensive experiments demonstrate the superiority of the proposed PresRecCD model over the SOTA model. This study contributes to enhancing the performance of the HPR model, ultimately benefiting the efficiency and precision of clinical treatment. © 2024 IEEE.

关键词： Medicinal chemistry

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SRLinear: Lightweight Long-Term Time Series Forecasting via Symbolic Regression

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Zhao, Hongbo Zhang, Hengzhe Tian, Chutian Wei, Zhi Zhou, Aimin East China Normal University Lab of Artificial Intelligence for Education School of Computer Science and Technology Shanghai200062 China Victoria University of Wellington Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand New Jersey Institute of Technology Department of Computer Science NewarkNJ07102 United States

Long-term Time Series Forecasting (LTSF) plays a crucial role in various fields, including energy consumption and weather prediction. Recent advancements in deep learning, particularly transformer models, have significantly improved LTSF performance. However, these models often face criticism for their lack of transparency, limited interpretability, and high computational requirements, which can hinder their practical application. Simple linear models have emerged as a promising alternative, demonstrating excellent performance in LTSF while offering greater simplicity. Yet, as these models become more complex, they tend to lose their advantages. To address these challenges, we introduce SRLinear, a lightweight and interpretable LTSF framework based on symbolic regression. Our approach first breaks down time series data into seasonal and trend components, then divides these into smaller segments. We use symbolic regression to extract meaningful and interpretable features from these components, which are then combined and processed through a single linear layer for future predictions. This lightweight design ensures computational efficiency, while symbolic regression generates symbolic expressions, providing clear insights into the feature extraction process and enhancing model interpretability. We tested SRLinear on 8 real-world datasets, achieving state-of-the-art results on multiple benchmarks. Importantly, our framework produces highly interpretable features that can be easily visualized, enhancing the overall transparency of the prediction process. © 2020 IEEE.

关键词： Deep learning

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Mobile Application for Elderly Care 2

Mobile Application for Elderly Care

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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Jose, Fernando Livaldo Mahardhika, Mozan Hafiz Sukiman, Nathanael Adolf Warnars, Harco Leslie Hendric Spits Ramadhan, Arief Utomo, Wiranto Herry Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Bina Nusantara Univerisity Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia President University Faculty of Computing Department of Magister Science of Information Technology West Java17530 Indonesia

ISBN: (纸本)9781665400527

This paper discusses the design and implementation process of mobile applications used by nurses to communicate with the elderly or with people appointed to represent the elderly in using this mobile application. This mobile application is expected to help caregivers or nurses monitor the health condition of the elderly, and this mobile application has several functions that make it complete. These functions include submitting weekly reports, communicating with others via forums, and providing relevant information about the elderly and how to deal with them, including news and government regulations on elderly care. This mobile application uses case diagrams to describe business processes and class diagrams to illustrate database design. As for the implementation, android studio and MySQL database are used for database storage. This paper is limited to 9 references for literature review previous similar research by searching for publication titles with the sentences 'mobile application'+elderly+care using quotation marks and without quotation marks for the words elderly and care for all years of publication. © 2022 IEEE.

关键词： Mobile computing

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