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23rd International Conference of the Italian Association for Artificial Intelligence, AIxIA 2024

作者： Chatterjee, Soumick Xu, Jiahua Kuzhipathalil, Adarsh Nürnberger, Andreas Faculty of Computer Science Otto von Guericke University Magdeburg Magdeburg Germany Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Magdeburg Germany Genomics Research Centre Human Technopole Milan Italy Hertie Institute for Clinical Brain Research Tuebingen Germany XenomatiX NV Leuven Belgium

ISBN: (纸本)9783031806063

In recent years, the field of autonomous vehicles and driverless technology has seen remarkable advancements, driven by contributions from mainstream automotive manufacturers and open-source projects. This research aims to develop a pipeline for road scene understanding through semantic segmentation. The proposed pipeline utilises a multi-modal segmentation model, incorporating greyscale images and point cloud data from Xenolidar, specifically designed to capture the structural priors of highway road scenes. The fusion of input modalities and the design of an encoder-decoder architecture with a novel attention scheme called HaWANet is introduced, which focuses on the height and width contextual information to improve the accuracy of road segmentation, are the primary aspects explored for the proposed model. The output of the encoder is a two-dimensional point cloud, which effectively represents the road’s planar nature, and is crucial for improving the accuracy of road segmentation, particularly in edge cases, addressing current challenges in autonomous driving research. This research, aimed at addressing the segmentation problem for multimodal sensor data, has presented significant performance improvement over single-modal approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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Enhancing Continuous Cognitive Diagnosis with Fuzzy Strategy-Based Hybrid Genetic Algorithm 3rd

Enhancing Continuous Cognitive Diagnosis with Fuzzy Strateg...

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3rd International Conference on Cyberspace Simulation and Evaluation, CSE 2024

作者： He, Chenlong Hu, Xuegang Cao, Zhiyong Bu, Chenyang Luo, Wenjian Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Hefei China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Harbin China

ISBN: (纸本)9789819645053

Continuous cognitive diagnosis models (CDMs) are vital tools for assessing students’ mastery of knowledge points. However, traditional probability-based CDMs are prone to falling into local optima due to their use of single-point search methods, which can affect the accuracy of the models. To address this issue, we propose a hybrid genetic algorithm (HGA) enhanced with a fuzzy strategy to improve continuous cognitive diagnosis. This approach introduces the multidimensional item response theory (MIRT) as a local search operator to boost diagnostic precision. Additionally, considering the limitation on the number of local searches within a finite time, we introduce a fuzzy strategy that dynamically adjusts the number of local searches by evaluating the similarity between the current population and the elite set, thus balancing global and local search. Experimental results on three real-world datasets demonstrate that our method significantly outperforms six existing comparison models, validating the effectiveness of the fuzzy strategy and continuous CDM. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Continuous cognitive diagnosis Educational data mining Evolutionary algorithm Local search

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Effective Instruction Parsing Plugin for Complex Logical Query Answering on knowledge Graphs 34

Effective Instruction Parsing Plugin for Complex Logical Que...

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34th ACM Web Conference, WWW 2025

作者： Zhuo, Xingrui Wang, Jiapu Wu, Gongqing Pan, Shirui Wu, Xindong Hefei University of Technology Anhui Hefei China Beijing University of Technology Beijing China Griffith University Gold Coast QLD Australia The Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) School of Computer Science and Information Engineering Hefei University of Technology China

ISBN: (纸本)9798400712746

knowledge Graph Query Embedding (KGQE) aims to embed First-Order Logic (FOL) queries in a low-dimensional KG space for complex reasoning over incomplete KGs. To enhance the generalization of KGQE models, recent studies integrate various external information (such as entity types and relation context) to better capture the logical semantics of FOL queries. The whole process is commonly referred to as Query Pattern Learning (QPL). However, current QPL methods typically suffer from the pattern-entity alignment bias problem, leading to the learned defective query patterns limiting KGQE models’ performance. To address this problem, we propose an effective Query Instruction Parsing Plugin (QIPP) that leverages the context awareness of Pre-trained Language Models (PLMs) to capture latent query patterns from code-like query instructions. Unlike the external information introduced by previous QPL methods, we first propose code-like instructions to express FOL queries in an alternative format. This format utilizes textual variables and nested tuples to convey the logical semantics within FOL queries, serving as raw materials for a PLM-based instruction encoder to obtain complete query patterns. Building on this, we design a query-guided instruction decoder to adapt query patterns to KGQE models. To further enhance QIPP’s effectiveness across various KGQE models, we propose a query pattern injection mechanism based on compressed optimization boundaries and an adaptive normalization component, allowing KGQE models to utilize query patterns more efficiently. Extensive experiments demonstrate that our plug- and-play method¹ improves the performance of eight basic KGQE models and outperforms two state-of-the-art QPL methods. © 2025 Copyright held by the owner/author(s).

关键词： Complex Query Answering knowledge Graph Pre-trained Language Model Query Pattern Learning

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arXiv

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arXiv 2025年

作者： Li, Yangning Kang, Hui Li, Jiahui Sun, Geng Sun, Zemin Wang, Jiacheng Zhao, Changyuan Niyato, Dusit College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University 639798 Singapore College of Computing and Data Science Nanyang Technological University Singapore

An expansion of Internet of Things (IoTs) has led to significant challenges in wireless data harvesting, dissemination, and energy management due to the massive volumes of data generated by IoT devices. These challenges are exacerbated by data redundancy arising from spatial and temporal correlations. To address these issues, this paper proposes a novel data-driven collaborative beamforming (CB)-based communication framework for IoT networks. Specifically, the framework integrates CB with an overlap-based multi-hop routing protocol (OMRP) to enhance data transmission efficiency while mitigating energy consumption and addressing hot spot issues in remotely deployed IoT networks. Based on the data aggregation to a specific node by OMRP, we formulate a node selection problem for the CB stage, with the objective of optimizing uplink transmission energy consumption. Given the complexity of the problem, we introduce a softmax-based proximal policy optimization with long-short-term memory (SoftPPO-LSTM) algorithm to intelligently select CB nodes for improving transmission efficiency. Simulation results show that the proposed OMRP improves network lifetime by 17% compared to benchmark routing protocols, while the SoftPPO-LSTM method for CB node selection achieves an 8.3% increase in throughput over benchmark algorithms. The results also reveal that the combined OMRP with the SoftPPO- Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Joint Double Auction-Based Channel Selection in Wireless Monitoring Networks

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IEEE Transactions on Network and Service Management 2025年

作者： Xia, Na Chen, Lei Li, Meng Yin, Yutao Zhang, Ke Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data China State Grid Electric Power Research Institute Co. Ltd Hefei230088 China

In wireless networks, utilizing sniffers for fault analysis, traffic traceback, and resource optimization is a crucial task. However, existing centralized algorithms cannot be applied to high-density wireless networks. Therefore, distributed optimization of channel selection to maximize the monitoring rate of sensors in Wireless Monitoring Networks (WMNs) is a challenge. This paper proposes a joint double auction-based distributed channel selection algorithm (J2A-CS) to maximize overall quality of monitoring (QoM). First, sniffers are redundantly deployed in WMNs, and an initial channel allocation strategy is formulated. Subsequently, sniffers collectively act as buyers and sellers at different stages. Finally, buyers bid asynchronously, and sellers settle synchronously to maximize the seller's marginal revenue and update the channel selection scheme. As a distributed channel selection algorithm, J2A-CS addresses the highest overall QoM issue in WMNs, demonstrating high scalability and fault tolerance. Simulation results show that J2A-CS significantly improves QoM compared to existing distributed algorithms and outperforms centralized algorithms in high-density scenarios. © 2004-2012 IEEE.

关键词： Sales

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Accurate, Secure, and Efficient Semi-Constrained Navigation Over Encrypted City Maps

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2642-2658页

作者： Li, Meng Chen, Yifei Gao, Jianbo Wu, Jingyu Zhang, Zijian He, Jialing Zhu, Liehuang Conti, Mauro Lin, Xiaodong Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei230002 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230002 China Hefei University of Technology Anhui Province Key Laboratory of Industry Safety and Emergency Technology Intelligent Interconnected Systems Laboratory of Anhui Province Hefei230002 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100811 China University of Science and Technology of China School of Computer Science and Technology Hefei230026 China Beijing Institute of Technology Southeast Institute of Information Technology Fujian Putian351100 China Chongqing University College of Computer Science Chongqing400044 China University of Padua Department of Mathematics HIT Center Padua35131 Italy Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft2628 CD Netherlands University of Guelph School of Computer Science GuelphONN1G 2W1 Canada

Navigation services enable users to find the shortest path from a starting point S to a destination D, reducing time, gas, and traffic congestion. Still, navigation users risk the exposure of their sensitive location data. Our motivation arises from how users can accurately, securely, and efficiently navigate from S to Dwhile passing through k unordered stops, i.e., midway locations with a non-fixed visiting order. In this work, we formally define Semi-Constrained Navigation (SCN) and present a novel scheme Hermestoachieveaccurate,secure, andefficient SCN. Specifically, weproposeadivide-and-conquerapproachtostrikeagoodbalance betweenaccuracyandefficiency. It recursively depth-first-searches the whole area (a navigation tree) and invokes five carefully-crafted strategies stop-by-stop to compute three subpaths in three se quential subareas. We construct a path-distance oracle to encrypt the road graph and securely implement the strategies by using homomorphic encryption and garble circuits. We formally prove the security in the random oracle model and analyze the search complexity to be less than O(k2). We experiment over a real-world city map and compare with six baselines. Results show that path search with k = 4 among N = 1000 intersections requires 5.58 seconds with a 3.2% distance deviation rate and an 82.5% path similarity. © 2004-2012 IEEE.

关键词： Traffic congestion

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Predicting microRNA-Disease Associations Through Multi-View Feature Fusion and Matrix Completion on Graph Convolutional Networks

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第1期22卷 151-159页

作者： Shifei Ding Chuangui Cao Fan Sun Xuan Li Xindong Wu School of Computer Science and Technology China University of Mining and Technology Xuzhou China Mine Digitization Engineering Research Center of Ministry of Education Xuzhou China Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology Hefei China

Since the regulatory roles of microRNAs in complex human diseases have been gradually demonstrated, more and more enlightening models were developed for predicting microRNA-disease associations. These models can inform bio-logical studies on the differential expression of microRNAs. In this paper, a graph convolutional network-based model using multi-view feature fusion and matrix completion, FMCGCN for brevity, is proposed. First of all, graph autoencoders are used to learn multiple embeddings from different networks. For convenience, attention mechanisms are used to fuse them. As a result, multi-view features constructed from different perspectives are aggregated. Then, the fused embedding is fed into the graph convolutional network to aggregate local information. This fused embedding is thought to facilitate feature extraction for graph convolutional networks. Finally, feature and nuclear norm minimization, a method for matrix completion, is used to obtain the prediction matrix. Additionally, evaluation results under 5-fold cross-validation prove that FMCGCN outperforms current models in several metrics. Furthermore, case studies for esophageal neoplasms and pancreatic neoplasms also demonstrate the validity of our model.

关键词： Diseases Semantics Predictive models Kernel Topology Graph convolutional networks Feature extraction Learning systems Biological system modeling Reliability

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IEEE Internet of Things Journal 2025年

作者： Li, Yangning Kang, Hui Li, Jiahui Sun, Geng Sun, Zemin Wang, Jiacheng Zhao, Changyuan Niyato, Dusit Jilin University College of Computer Science and Technology Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Nanyang Technological University College of Computing and Data Science 639798 Singapore

An expansion of Internet of Things (IoTs) has led to significant challenges in wireless data harvesting, dissemination, and energy management due to the massive volumes of data generated by IoT devices. These challenges are exacerbated by data redundancy arising from spatial and temporal correlations. To address these issues, this paper proposes a novel data-driven collaborative beamforming (CB)-based communication framework for IoT networks. Specifically, the framework integrates CB with an overlap-based multi-hop routing protocol (OMRP) to enhance data transmission efficiency while mitigating energy consumption and addressing hot spot issues in remotely deployed IoT networks. Based on the data aggregation to a specific node by OMRP, we formulate a node selection problem for the CB stage, with the objective of optimizing uplink transmission energy consumption. Given the complexity of the problem, we introduce a softmax-based proximal policy optimization with long-short-term memory (SoftPPO-LSTM) algorithm to intelligently select CB nodes for improving transmission efficiency. Simulation results show that the proposed OMRP improves network lifetime by 17% compared to benchmark routing protocols, while the SoftPPO-LSTM method for CB node selection achieves an 8.3% increase in throughput over benchmark algorithms. The results also reveal that the combined OMRP with the SoftPPO-LSTM method effectively mitigates hot spot problems and offers superior performance compared to traditional strategies. © 2014 IEEE.

关键词： Deep reinforcement learning

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UAV-Assisted Joint Mobile Edge Computing and data Collection via Matching-Enabled Deep Reinforcement Learning

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IEEE Internet of Things Journal 2025年

作者： Wang, Boxiong Kang, Hui Li, Jiahui Sun, Geng Sun, Zemin Jilin University College of Computer Science and Technology Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Nanyang Technological University College of Computing and Data Science 639798 Singapore

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) and data collection (DC) have been popular research issues. Different from existing works that consider MEC and DC scenarios separately, this paper investigates a multi-UAV-assisted joint MEC-DC system. Specifically, we formulate a joint optimization problem to minimize the MEC latency and maximize the collected data volume. This problem can be classified as a non-convex mixed integer programming problem that exhibits long-term optimization and dynamics. Thus, we propose a deep reinforcement learning-based approach that jointly optimizes the UAV movement, user transmit power, and user association in real time to solve the problem efficiently. Specifically, we reformulate the optimization problem into an action space-reduced Markov decision process (MDP) and optimize the user association by using a two-phase matching-based association (TMA) strategy. Subsequently, we propose a soft actor-critic (SAC)-based approach that integrates the proposed TMA strategy (SAC-TMA) to solve the formulated joint optimization problem collaboratively. Simulation results demonstrate that the proposed SAC-TMA is able to coordinate the two subsystems and can effectively reduce the system latency and improve the data collection volume compared with other benchmark algorithms. © 2025 IEEE. All rights reserved,

关键词： Unmanned aerial vehicles (UAV)

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NI-GDBA: Non-Intrusive Distributed Backdoor Attack Based on Adaptive Perturbation on Federated Graph Learning 34

NI-GDBA: Non-Intrusive Distributed Backdoor Attack Based on ...

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34th ACM Web Conference, WWW 2025

作者： Li, Ken Shi, Bin Wei, Jiazhe Dong, Bo School of Computer Science and Technology Xi’an Jiaotong University Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an China School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an China School of Distance Education Xi’an Jiaotong University Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an China

ISBN: (纸本)9798400712746

Federated Graph Learning (FedGL) is an emerging Federated Learning (FL) framework that learns the graph data from various clients to train better Graph Neural Networks(GNNs) model. Owing to concerns regarding the security of such framework, numerous studies have attempted to execute backdoor attacks on FedGL, with a particular focus on distributed backdoor attacks. However, all existing methods posting distributed backdoor attack on FedGL only focus on injecting distributed backdoor triggers into the training data of each malicious client, which will cause model performance degradation on original task and is not always effective when confronted with robust federated learning defense algorithms, leading to low success rate of attack. What’s more, the backdoor signals introduced by the malicious clients may be smoothed out by other clean signals from the honest clients, which potentially undermining the performance of the attack. To address the above significant shortcomings, we propose a non-intrusive graph distributed backdoor attack(NI-GDBA) that does not require backdoor triggers to be injected in the training data. Our attack trains an adaptive perturbation trigger generator model for each malicious client to learn the natural backdoor from the GNN model downloading from the server with the malicious client’s local data. In contrast to traditional distributed backdoor attacks on FedGL via trigger injection in training data, our attack on different datasets such as Molecules and Bioinformatics have higher attack success rate, stronger persistence and stealth, and has no negative impact on the performance of the global GNN model. We also explore the robustness of NI-GDBA under different defense strategies, and based on our extensive experimental studies, we show that our attack method is robust to current federated learning defense methods, thus it is necessary to consider non-intrusive distributed backdoor attacks on FedGL as a novel threat that requires custom d

关键词： Backdoor Attacks Federated Graph Learning

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