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Structure- and Logic-Aware Heterogeneous Graph Learning for Recommendation

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Structure- and Logic-Aware Heterogeneous Graph Learning for ...

International Conference on Data engineering

作者： Anchen Li Bo Yang Huan Huo Farookh Khadeer Hussain Guandong Xu College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China Aalto University Espoo Finland School of Computer Science University of Technology Sydney Sydney Australia Education University of Hong Kong Hong Kong China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Recently, there has been a surge in recommendations based on heterogeneous information networks (HINs), attributed to their ability to integrate complex and rich semantics. Despite this advancement, most HIN-based recommenders overlook two critical aspects. First, they often fail to consider HIN's heterophily nature, hindering the capture of non-local structures in HINs. Second, most methods lack the capability for logical reasoning. In this paper, we propose a novel structure- and logic-aware heterogeneous graph learning framework for recommender systems (SLHRec). Our SLHRec contains a structure-aware module and a logic-aware module. The former uses network geometry to construct non-local neighborhoods for nodes in HINs, and then introduces a graph neural network to integrate constructed neighbors for modeling the heterophily of HINs. The logic-aware module uses the Markov logic network (MLN) to infuse logic rules into heterogeneous graph learning, thereby boosting logic reasoning in recommendations. Furthermore, we utilize contrastive learning to model cooperative signals between modules, enabling them to complement each other. In the prediction stage, both modules contribute to generating recommendations. Compared with several strong recommender baselines, our SLHRec achieves superior performance on four real-world datasets.

关键词： Geometry Semantics Contrastive learning Data engineering Boosting Cognition Graph neural networks

Beam Tracking for High-Speed UAV Via Generative Diffusion Model-Enabled Joint Optimization Approach

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IEEE Transactions on Vehicular Technology 2025年

作者： Zhang, Jing Zhang, Chaofeng Feng, Xin Yang, Hongwei Liang, Shuang Sun, Geng Wang, Jiacheng Niyato, Dusit Changchun University of Science and Technology College of Computer Science and Technology Changchun130022 China Jilin University College of Communication Engineering Changchun130012 China Northeast Normal University School of Information Science and Technology Changchun130117 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Jilin University College of Computer Science and Technology Changchun130012 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Nanyang Technological University 639798 Singapore

Beam tracking is crucial for maintaining stable data transmission in unmanned aerial vehicle (UAV) communications. However, a communication link can be disrupted by frequent switching of narrow beams between a base station and a UAV at certain moments. In this study, we propose a position prediction-based beam tracking algorithm with adaptive beam reconstruction (PPBT-AR) for high-speed UAV. Specifically, long short-term memory (LSTM) recurrent neural networks are utilized to predict the nonlinear flight trajectory during high-speed flight. Moreover, we employ the generative diffusion model (GDM) to jointly optimize the beam width and signal strength, thereby reducing the number of beam switches. In addition, we design an adaptive beam reconstruction (ABR) mechanism to mitigate communication interruptions caused by prediction errors. Simulation results demonstrate that the proposed PPBT-AR reduces the number of beam switches by 66.6% and 76.9% in low-speed and high-speed scenarios compared to the traditional phased array beam tracking algorithm, respectively. © 1967-2012 IEEE.

关键词： Long short-term memory

PhysReason: A Comprehensive Benchmark towards Physics-Based Reasoning

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arXiv 2025年

作者： Zhang, Xinyu Dong, Yuxuan Wu, Yanrui Huang, Jiaxing Jia, Chengyou Fernando, Basura Shou, Mike Zheng Zhang, Lingling Liu, Jun School of Computer Science and Technology Xi’an Jiaotong University China Institute of High-Performance Computing Agency for Science Technology and Research Singapore Show Lab National University of Singapore Singapore Ministry of Education Key Laboratory of Intelligent Networks and Network Security China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering China

Large language models demonstrate remarkable capabilities across various domains, especially mathematics and logic reasoning. However, current evaluations overlook physics-based reasoning, a complex task requiring physics theorems and constraints. We present PhysReason, a 1,200-problem benchmark comprising knowledge-based (25%) and reasoning-based (75%) problems, where the latter are divided into three difficulty levels (easy, medium, hard). Notably, problems require an average of 8.1 solution steps, with hard problems requiring 15.6, reflecting the complexity of physics-based reasoning. We propose the Physics Solution Auto Scoring Framework, incorporating efficient answer-level and comprehensive step-level evaluations. Top-performing models like Deepseek-R1, Gemini-2.0-Flash-Thinking, and o3-mini-high achieve less than 60% on answer-level evaluation, with performance dropping from knowledge questions (75.11%) to hard problems (31.95%). Through step-level evaluation, we identify four key bottlenecks: Physics Theorem Application, Physics Process Understanding, Calculation, and Physics Condition Analysis. These findings position PhysReason as a novel and comprehensive benchmark for evaluating physics-based reasoning capabilities in large language models. Our code and data will be published at https://***/PhysReason/. © 2025, CC BY-NC-SA.

关键词： knowledge based systems

Generative AI for Advanced UAV Networking

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IEEE Network 2024年

作者： Sun, Geng Xie, Wenwen Niyato, Dusit Du, Hongyang Kang, Jiawen Wu, Jing Sun, Sumei Zhang, Ping Jilin University College of Computer Science and Technology Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun130012 China Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong999077 Hong Kong Guangdong University of Technology School of Automation Guangzhou510006 China Agency for Science Technology and Research Institute for Infocomm Research Singapore639798 Singapore Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China

With the impressive achievements of chatGPT and Sora, generative artificial intelligence (GAI) has received increasing attention. Not limited to the field of content generation, GAI is also widely used to solve the problems in wireless communication scenarios due to its powerful learning and generalization capabilities. Therefore, we discuss key applications of GAI in improving unmanned aerial vehicle (UAV) communication and networking performance in this article. Specifically, we first review the key technologies of GAI and the important roles of UAV networking. Then, we show how GAI can improve the communication, networking, and security performances of UAV systems. Subsequently, we propose a novel framework of GAI for advanced UAV networking, and then present a case study of UAV-enabled spectrum map estimation and transmission rate optimization based on the proposed framework to verify the effectiveness of GAI-enabled UAV systems. Finally, we discuss some important open directions. © 2024 IEEE.

关键词： Aircraft communication

A Lightweight Api Recommendation Method for App Development Based on Multi-Objective Evolutionary Algorithm

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SSRN

SSRN 2022年

作者： Li, Xun Liu, Lei Liu, Yuzhou Liu, Huaxiao College of Computer Science and Technology Jilin University Jilin130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun Jilin University Jilin130012 China

Context: API is important in daily programming activities during app development, but finding appropriate APIs is time-consuming for developers. To simplify this process, many researchers pay attention to overcoming the task-API knowledge gap using semantic information mined from the large-scale dataset for API recommendation. However, only semantic information is not enough since API descriptions and developer's query may not share similar words, meanwhile, large-scale data mining brings huge time cost and causes knowledge fixed problem (i.e. data cannot be timely updated). This limits the efficiency of existing API commendation methods. Objective: In this work, we aim at proposing a lightweight API recommendation method for app development based on the data in app store. Method: We model API recommendation as a multi-objective optimization problem by considering both structural and semantic information of API, and use the genetic algorithm to gain optimal solutions for overcoming limitations of the small-scale dataset. Specially, we collect APK files of related apps and API descriptions to establish a lightweight dataset, and extract the structural and semantic information of API from them with graph embedding and NLP techniques respectively. Then, we get the recommended APIs satisfying structural and semantic objectives according to the developers’ target functionality with a genetic algorithm. Finally, we give the interpretation of our recommendation information to help developers understand the results easily. Results and Conclusion: We conduct a series of experiments based on apps in Google Play and Q&As in Stack Overflow. The results show that Precision@N of our recommendation can be up to 0.89, MAP@N is up to 0.50, and MRR reaches 0.69, and our method achieves such good performance only utilizing less than 1/19 of the project number of our compared machine learning method. Besides, we conduct a survey and most of the participants confirm the understandabilit

关键词： Multiobjective optimization

Lightweight Deep Learning for Resource-Constrained Environments: A Survey

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arXiv 2024年

作者： Liu, Hou-I Galindo, Marco Xie, Hongxia Wong, Lai-Kuan Shuai, Hong-Han Li, Yung-Yui Cheng, Wen-Huang Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu300 Taiwan College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130000 China Faculty of Computing and Informatics Multimedia University Cyberjaya63100 Malaysia Hon Hai Research Institute Taipei114 Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei106 Taiwan

Over the past decade, the dominance of deep learning has prevailed across various domains of artificial intelligence, including natural language processing, computer vision, and biomedical signal processing. While there have been remarkable improvements in model accuracy, deploying these models on lightweight devices, such as mobile phones and microcontrollers, is constrained by limited resources. In this survey, we provide comprehensive design guidance tailored for these devices, detailing the meticulous design of lightweight models, compression methods, and hardware acceleration strategies. The principal goal of this work is to explore methods and concepts for getting around hardware constraints without compromising the model's accuracy. Additionally, we explore two notable paths for lightweight deep learning in the future: deployment techniques for TinyML and Large Language Models. Although these paths undoubtedly have potential, they also present significant challenges, encouraging research into unexplored areas. Copyright © 2024, The Authors. All rights reserved.

关键词： Computer hardware

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning

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arXiv 2025年

作者： Sun, Geng Xiao, Jian Li, Jiahui Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China affiliated with the College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Guangzhou510641 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. However, the limited onboard energy and antenna power of a UAV restrict its communication range and transmission capability. To address these limitations, this work employs collaborative beamforming through a UAV-enabled virtual antenna array to improve transmission performance from the UAV to terrestrial mobile users, under interference from non-associated BSs and dynamic channel conditions. Specifically, we introduce a memory-based random walk model to more accurately depict the mobility patterns of terrestrial mobile users. Following this, we formulate a multi-objective optimization problem (MOP) focused on maximizing the transmission rate while minimizing the flight energy consumption of the UAV swarm. Given the NP-hard nature of the formulated MOP and the highly dynamic environment, we transform this problem into a multi-objective Markov decision process and propose an improved evolutionary multi-objective reinforcement learning algorithm. Specifically, this algorithm introduces an evolutionary learning approach to obtain the approximate Pareto set for the formulated MOP. Moreover, the algorithm incorporates a long short-term memory network and hyper-sphere-based task selection method to discern the movement patterns of terrestrial mobile users and improve the diversity of the obtained Pareto set. Simulation results demonstrate that the proposed method effectively generates a diverse range of non-dominated policies and outperforms existing methods. Additional simulations demonstrate the scalability and robustness of the proposed CB-based method under different system parameters and various unexpected circumstances. © 2025, CC0.

关键词： Unmanned aerial vehicles (UAV)

Causal-Inspired Multitask Learning for Video-Based Human Pose Estimation

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arXiv 2025年

作者： Chen, Haipeng Wu, Sifan Wang, Zhigang Yin, Yifang Jiao, Yingying Lyu, Yingda Liu, Zhenguang College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China College of Computer Science and Technology Zhejiang Gongshang University China A*STAR Singapore Public Computer Education and Research Center Jilin University China The State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China

Video-based human pose estimation has long been a fundamental yet challenging problem in computer vision. Previous studies focus on spatio-temporal modeling through the enhancement of architecture design and optimization strategies. However, they overlook the causal relationships in the joints, leading to models that may be overly tailored and thus estimate poorly to challenging scenes. Therefore, adequate causal reasoning capability, coupled with good interpretability of model, are both indispensable and prerequisite for achieving reliable results. In this paper, we pioneer a causal perspective on pose estimation and introduce a causal-inspired multitask learning framework, consisting of two stages. In the first stage, we try to endow the model with causal spatio-temporal modeling ability by introducing two self-supervision auxiliary tasks. Specifically, these auxiliary tasks enable the network to infer challenging keypoints based on observed keypoint information, thereby imbuing causal reasoning capabilities into the model and making it robust to challenging scenes. In the second stage, we argue that not all feature tokens contribute equally to pose estimation. Prioritizing causal (keypoint-relevant) tokens is crucial to achieve reliable results, which could improve the interpretability of the model. To this end, we propose a Token Causal Importance Selection module to identify the causal tokens and non-causal tokens (e.g., background and objects). Additionally, non-causal tokens could provide potentially beneficial cues but may be redundant. We further introduce a non-causal tokens clustering module to merge the similar non-causal tokens. Extensive experiments show that our method outperforms state-of-the-art methods on three large-scale benchmark datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： Self-supervised learning

Aerial Secure Collaborative Communications under Eavesdropper Collusion in Low-altitude Economy: A Generative Swarm Intelligent Approach

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arXiv 2025年

作者： Li, Jiahui Sun, Geng Wu, Qingqing Liang, Shuang Wang, Jiacheng Niyato, Dusit Kim, Dong In College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China School of Information Science and Technology Northeast Normal University Changchun130117 China Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

The rapid development of the low-altitude economy (LAE) has significantly increased the utilization of autonomous aerial vehicles (AAVs) in various applications, necessitating efficient and secure communication methods among AAV swarms. In this work, we aim to introduce distributed collaborative beamforming (DCB) into AAV swarms and handle the eavesdropper collusion by controlling the corresponding signal distributions. Specifically, we consider a two-way DCB-enabled aerial communication between two AAV swarms and construct these swarms as two AAV virtual antenna arrays. Then, we minimize the two-way known secrecy capacity and maximum sidelobe level to avoid information leakage from the known and unknown eavesdroppers, respectively. Simultaneously, we also minimize the energy consumption of AAVs when constructing virtual antenna arrays. Due to the conflicting relationships between secure performance and energy efficiency, we consider these objectives by formulating a multi-objective optimization problem, which is NP-hard and with a large number of decision variables. Accordingly, we design a novel generative swarm intelligence (GenSI) framework to solve the problem with less overhead, which contains a conditional variational autoencoder (CVAE)-based generative method and a proposed powerful swarm intelligence algorithm. In this framework, CVAE can collect expert solutions obtained by the swarm intelligence algorithm in other environment states to explore characteristics and patterns, thereby directly generating high-quality initial solutions in new environment factors for the swarm intelligence algorithm to search solution space efficiently. Simulation results show that the proposed swarm intelligence algorithm outperforms other state-of-the-art baseline algorithms, and the GenSI can achieve similar optimization results by using far fewer iterations than the ordinary swarm intelligence algorithm. Experimental tests demonstrate that introducing the CVAE mechanism ach

关键词： Multiobjective optimization