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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xia, Tianle Ding, Liang Wan, Guojia Zhan, Yibing Du, Bo Tao, Dacheng School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China The University of Sydney Australia JD Explore Academy College of Computing and Data Science Nanyang Technological University 639798 Singapore

ISBN: (纸本)157735897X

Answering complex queries over incomplete knowledge graphs (KGs) is a challenging task. Most previous works have focused on learning entity/relation embeddings and simulating first-order logic operators with various neural networks. However, they are bottlenecked by the inability to share world knowledge to improve logical reasoning, thus resulting in suboptimal performance. In this paper, we propose a complex reasoning schema over KG upon large language models (LLMs), containing a curriculum-based logical-aware instruction tuning framework, named LACT. Specifically, we augment the arbitrary first-order logical queries via binary tree decomposition, to stimulate the reasoning capability of LLMs. To address the difficulty gap among different types of complex queries, we design a simple and flexible logic-aware curriculum learning framework. Experiments across widely used datasets demonstrate that LACT has substantial improvements (brings an average +5.5% MRR score) over advanced methods, achieving the new state-of-the-art. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Curricula

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A Time-Efficient Distributed Constant Conditional Functional Dependency Discovery Algorithm for data Consistency

A Time-Efficient Distributed Constant Conditional Functional...

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IEEE Annual International Computer software and Applications Conference (COMPSAC)

作者： Junchao Chen Jian-tao Zhou Xinyu Hao Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology College of Computer Science Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

The accuracy of data analysis depends on data quality, and addressing data consistency issues is a key challenge to improve it. Constant Conditional Functional Dependency (CCFD) is an effective approach that ensures data consistency by enforcing bindings of semantically related values, thus providing quality assurance for data analysis and decision-making processes. However, with the growth of data scale, especially the increasing number of data tuples and attributes, existing single-machine CCFD discovery algorithms face issues of low computational efficiency and lengthy computation time. This paper proposes a time-efficient distributed CCFD discovery algorithm (DCCFD). Through the optimization of data preprocessing and index mapping, the data organization structure is enhanced, laying the foundation for the discovery of CCFDs under distributed conditions. The Spark parallel computing framework is used to partition the dataset, which accelerates the parallel loading and processing of data. Additionally, this algorithm ensures accuracy and processing speed when discovering dependencies by efficiently generating frequent itemsets and verifying CCFDs in parallel. Experiments on multiple real datasets show that, especially with the complex Airline dataset, the DCCFD algorithm not only accurately discovers CCFDs, but also reduces the average running time by 75.64% compared with the preCFDMiner algorithm.

关键词： Accuracy data preprocessing software algorithms Distributed databases Parallel processing software Partitioning algorithms

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Deep Reinforcement Learning for Joint Service Placement and Request Scheduling in Mobile Edge Computing Networks

Deep Reinforcement Learning for Joint Service Placement and ...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Yuxuan Deng Xiuhua Li Chuan Sun Jinlong Hao Xiaofei Wang Victor C. M. Leung School of Big Data & Software Engineering Chongqing University Chongqing China Chongqing Changan Automobile Software Technology Co. Ltd Chongqing China TKLAN College of Intelligence & Computing Tianjin University Tianjin China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Department of Electrical & Computer Engineering The University of British Columbia Vancouver Canada

Mobile edge computing aims to provide cloud-like services on edge servers located near Mobile Devices (MDs) with higher Quality of Service (QoS). However, the mobility of MDs makes it difficult to find a global optimal solution for the coupled service placement and request scheduling problem. To address these issues, we consider a three-tier MEC network with vertical and horizontal cooperation. Then we formulate the joint service placement and request scheduling problem in a mobile scenario with heterogeneous services and resource limits, and convert it into two Markov decision processes to decouple decisions across successive time slots. We propose a Cyclic Deep Q-network-based Service placement and Request scheduling (CDSR) framework to find a long-term optimal solution despite future information unavailability. Specifically, to solve the issue of enormous action space, we decompose the system agent and train them cyclically. Evaluation results demonstrates the effectiveness of our proposed CDSR on user-perceived QoS.

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Regression with Cost-based Rejection

arXiv

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arXiv 2023年

作者： Cheng, Xin Cao, Yuzhou Wang, Haobo Wei, Hongxin An, Bo Feng, Lei College of Computer Science Chongqing University China School of Computer Science and Engineering Nanyang Technological University Singapore School of Software Technology Zhejiang University China Department of Statistics and Data Science Southern University of Science and Technology China

Learning with rejection is an important framework that can refrain from making predictions to avoid critical mispredictions by balancing between prediction and rejection. Previous studies on cost-based rejection only focused on the classification setting, which cannot handle the continuous and infinite target space in the regression setting. In this paper, we investigate a novel regression problem called regression with cost-based rejection, where the model can reject to make predictions on some examples given certain rejection costs. To solve this problem, we first formulate the expected risk for this problem and then derive the Bayes optimal solution, which shows that the optimal model should reject to make predictions on the examples whose variance is larger than the rejection cost when the mean squared error is used as the evaluation metric. Furthermore, we propose to train the model by a surrogate loss function that considers rejection as binary classification and we provide conditions for the model consistency, which implies that the Bayes optimal solution can be recovered by our proposed surrogate loss. Extensive experiments demonstrate the effectiveness of our proposed method. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Regression with cost-based rejection 23

Regression with cost-based rejection

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Xin Cheng Yuzhou Cao Haobo Wang Hongxin Wei Bo An Lei Feng College of Computer Science Chongqing University China School of Computer Science and Engineering Nanyang Technological University Singapore School of Software Technology Zhejiang University China Department of Statistics and Data Science Southern University of Science and Technology China

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Byzantine-Robust Federated Learning on Non-IID data via Inversing Artificial Gradients

Byzantine-Robust Federated Learning on Non-IID Data via Inve...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Minyang Li Xiangyun Tang Tao Zhang Guangyuan Liu Yu Weng Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance Minzu University of China China School of Software Engineering Beijing Jiaotong University China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Federated Learning (FL) is a distributed machine learning framework that enhances privacy by enabling multiple participants to train a global model without sharing their raw data. However, FL still faces the threat posed by data and Byzantine attacks (e.g., data poisoning and model poisoning attacks) from malicious clients aimed to decrease the FL global training accuracy. Previous studies have proposed solutions to improve the robustness of FL systems. However, these robust approaches have not effectively defended against Byzantine attacks from malicious clients in non-Independent and Identically Distributed (non-IID) environments, decreasing overall training accuracy. In this work to address this issues, we propose a new defence framework that aims to enhance model accuracy against Byzantine attacks from malicious clients in non-IID environments. In our approach, the central server performs the Inverse Deep Learning Gradient attack using the gradient data submitted by users in each round, obtaining an inversed artificial gradient. We then assess the squared L2 norm difference between this inversed gradient and the actual gradients to detect malicious clients. Simultaneously, we assign weights to each client involved in the aggregation based on differences in the squared L2 norm, aiming to minimize the impact of malicious actors on the overall model. We have experimentally evaluated our method and demonstrated its ability to maintain training accuracy under attack in non-IID environments using standard datasets.

关键词： Training Accuracy Federated learning Distributed databases Robustness data models Servers Global communication Standards Faces

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Design Automation for Continuous-Flow Lab-on-a-Chip Systems: A One-Pass Paradigm

Design Automation for Continuous-Flow Lab-on-a-Chip Systems:...

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作者： Huang, Xing Pan, Youlin Chen, Zhen Guo, Wenzhong Wang, Lu Li, Qingshan Wille, Robert Ho, Tsung-Yi Schlichtmann, Ulf Technical University of Munich Electronic Design Automation Munich80333 Germany Fuzhou University College of Computer and Data Science Fuzhou350116 China Xidian University School of Computer Science and Technology Xi'an710071 China Technical University of Munich Chair for Design Automation Munich80333 Germany Software Competence Center Hagenberg GmbH Hagenberg4232 Austria The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong

Owing to the high complexity of chip architecture and assay protocol, considerable effort has been directed toward the design automation of continuous-flow microfluidics over the past decade. Existing methods, however, perform the corresponding design tasks, including binding, scheduling, placement, and routing separately, leading to serious gaps between different steps and potentially even cause design failure. To overcome these drawbacks, in this article, we propose a one-pass design paradigm for continuous-flow microfluidic lab-on-a-chip systems, integrating all the design steps into an 'organic whole,' which has never been considered in prior work. With the proposed paradigm, all the design tasks can be synchronized seamlessly and performed in a combined manner, thereby eliminating the gaps between design steps. Consequently, optimized biochip architectures can be generated without any design adjustments and modifications. The experimental results demonstrate the effectiveness of the proposed automation flows. © 1982-2012 IEEE.

关键词： Microfluidics

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BIG-MOE: BYPASS ISOLATED GATING MOE FOR GENERALIZED MULTIMODAL FACE ANTI-SPOOFING

arXiv

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arXiv 2024年

作者： Ma, Yingjie Yu, Zitong Lin, Xun Xie, Weicheng Shen, Linlin Great Bay University China College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

In the domain of facial recognition security, multimodal Face Anti-Spoofing (FAS) is essential for countering presentation attacks. However, existing technologies encounter challenges due to modality biases and imbalances, as well as domain shifts. Our research introduces a Mixture of Experts (MoE) model to address these issues effectively. We identified three limitations in traditional MoE approaches to multimodal FAS: (1) Coarse-grained experts’ inability to capture nuanced spoofing indicators;(2) Gated networks’ susceptibility to input noise affecting decision-making;(3) MoE’s sensitivity to prompt tokens leading to overfitting with conventional learning methods. To mitigate these, we propose the Bypass Isolated Gating MoE (BIG-MoE) framework, featuring: (1) Fine-grained experts for enhanced detection of subtle spoofing cues;(2) An isolation gating mechanism to counteract input noise;(3) A novel differential convolutional prompt bypass enriching the gating network with critical local features, thereby improving perceptual capabilities. Extensive experiments on four benchmark datasets demonstrate significant generalization performance improvement in multimodal FAS task. The code is released at https://***/murInJ/BIGMoE. Copyright © 2024, The Authors. All rights reserved.

关键词： HTTP

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APADGCN: Adaptive Partial Attention Diffusion Graph Convolutional Network for Traffic Flow Forecasting 4th

APADGCN: Adaptive Partial Attention Diffusion Graph Convolut...

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4th International Conference on Spatial data and Intelligence, SpatialDI 2023

作者： Zhang, Bowen Li, Bohan Wei, Jinzhan Wen, Hao College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Key Laboratory of Safety-Critical Software Ministry of Industry and Information Technology Beijing China National Engineering Laboratory for Integrated Aero-Space-Ground Ocean Big Data Application Technology Xi’an China School of Electronic Information and Automation Guilin University of Aerospace Technology Guilin541010 China College of Aerospace Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China

ISBN: (纸本)9783031329098

Traffic flow forecasting is a core task of urban governance and plays a vital role in the development of ITS. Because of the complexity and uncertainty of traffic patterns, it is of great challenge to capture spatial-temporal correlations. Recent researches mainly focus on the pre-defined adjacency matrix based on prior knowledge as the basis of spatial-temporal correlation modeling, but the fixed graph structure cannot adequately describe the dependency between traffic sensors. To tackle this issue, a novel deep learning model framework is proposed in this paper: Adaptive Partial Attention Diffusion Graph Convolutional Network(APADGCN), which consists of three main parts: 1) the Multi-Component module that divides the historical traffic flow into recent, daily-periodic, and weekly-periodic, to capture the traffic patterns of different periodic;2) the spatial correlation modeling which can dynamically capture node relationships and model spatial dependency, and enhance the aggregation ability of low-order information;3) the temporal correlation modeling which models long-term time dependencies using convolution and gating. The final result is obtained by the weighted fusion of the results of the multi-components. We compared our APADGCN with various baseline models in the four real datasets from the Caltrans Performance Measurement System (PeMS). The experimental results show that the prediction accuracy of APADGCN is better than that of the baseline models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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Abnormal Crowd Behavior Detection Based on Movement Trajectory 15th

Abnormal Crowd Behavior Detection Based on Movement Trajecto...

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15th Conference on Image and Graphics Technology and Applications, IGTA 2020

作者： Feng, Fujian Huang, Han Zhu, Haofeng Liang, Yihui Wang, Lin College of Software Engineering South China University of Technology Guangzhou510006 China College of Data Science and Information Engineering Guizhou Minzu University Guiyang550025 China Zhongshan Institute University of Electronic Science and Technology of China Zhongshan528400 China

ISBN: (纸本)9789813360327

Abnormal crowd behavior detection on surveillance videos plays an important role in social security and is an important research in computer vision. This work detects crowd abnormal behaviors according to motion trajectory. Specifically, the presented algorithm discovers abnormal direction and abnormal crossing of pedestrians, and uses a novel backtracking method to accurately mark the movement trajectory of abnormal behaviors in the crowd. Experiments were performed on the MOT16 challenge dataset to verify the performance of the proposed method. The experiment shows that the proposed method can effectively detect the abnormal direction, abnormal crossing and movement trajectory of pedestrian with the abnormal behavior. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Trajectories

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