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Region-adaptive Concept Aggregation for Few-shot Visual Recognition

Machine Intelligence Research 2023年第4期20卷 554-568页

作者： Mengya Han Yibing Zhan Baosheng Yu Yong Luo Han Hu Bo Du Yonggang Wen Dacheng Tao School of Computer Science National Engineering Research Center for Multimedia SoftwareWuhan UniversityWuhan430072China Hubei Luojia Laboratory Wuhan430072China School of Computer Science The University of SydneySydney2006Australia JD Explore Academy Beijing101116China School of Information and Electronics Beijing Institute of TechnologyBeijing100081China School of Computer Science and Engineering Nanyang Technological UniversitySingapore639798Singapore

Few-shot learning (FSL) aims to learn novel concepts from very limited examples. However, most FSL methods suffer from the issue of lacking robustness in concept learning. Specifically, existing FSL methods usually ignore the diversity of region contents that may contain concept-irrelevant information such as the background, which would introduce bias/noise and degrade the performance of conceptual representation learning. To address the above-mentioned issue, we propose a novel metric-based FSL method termed region-adaptive concept aggregation network or RCA-Net. Specifically, we devise a region-adaptive concept aggregator (RCA) to model the relationships of different regions and capture the conceptual information in different regions, which are then integrated in a weighted average manner to obtain the conceptual representation. Consequently, robust concept learning can be achieved by focusing more on the concept-relevant information and less on the conceptual-irrelevant information. We perform extensive experiments on three popular visual recognition benchmarks to demonstrate the superiority of RCA-Net for robust few-shot learning. In particular, on the Caltech-UCSD Birds-200-2011 (CUB200) dataset, the proposed RCA-Net significantly improves 1-shot accuracy from 74.76% to 78.03% and 5-shot accuracy from 86.84% to 89.83% compared with the most competitive counterpart.

关键词： Few-shot learning metric-based meta learning concept learning region-adaptive concept-aggregation

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Improving Complex Reasoning over Knowledge Graph with Logic-Aware Curriculum Tuning 39

Improving Complex Reasoning over Knowledge Graph with Logic-...

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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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Activation Distance and Capacity Analysis for Ambient Backscatter Communications with Sensitivity Constraint and Beamforming

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China Communications 2024年第11期21卷 257-266页

作者： Mu Yunping Fan Dian Wang Gongpu Xu Yongjun Kuang Lei Engineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijing 100044China China Academy of Information and Communications Technology Beijing 100191China School of Communication and Information Engineering Chongqing University of Posts and TelemcommunicationsChongqing 400065China Shanghai Key Laboratory of Multidimensional Information Processing Engineering Center of SHMEC for Space Information and GNSSSchool of Information Science and TechnologyEast China Normal UniversityShanghai 200241China

Circuit sensitivity of sensors or tags without battery is one practical constraint for ambient backscatter communication *** letter considers using beamforming to reduce the sensitivity constraint and evaluates the corresponding performance in terms of the tag activation distance and the system ***,we derive the activation probabilities of the tag in the case of single-antenna and multi-antenna ***,we obtain the capacity expressions for the ambient backscatter communication system with beamforming and illustrate the power allocation that maximizes the system capacity when the tag is ***,simulation results are provided to corroborate our proposed studies.

关键词： activation distance ambient backscatter communications beamforming channel capacity power allocation sensitivity

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A Robust Key Exchange and Tamper-Resistant Protocol for HAN and NAN networks in Smart Grids

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IEEE Internet of Things Journal 2025年第12期12卷 21394-21403页

作者： Faizan Ayub, Muhammad Li, Xiong Mahmood, Khalid Shamshad, Salman Kumar Das, Ashok Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Korea University College of Informatics Department of Computer Science and Engineering Seoul02841 Korea Republic of Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China

Smart grids (SGs) rely on home area networks (HANs) and neighborhood area networks (NANs) to ensure efficient power distribution, real-time monitoring, and seamless communication between smart devices. Despite these advantages, the use of public communication channels in HAN and NAN networks introduces critical challenges, such as vulnerability to impersonation, physical tampering, and scalability issues in resource-constrained environments. These issues compromise the stability, reliability, and security of SG environments. Existing protocols often fail to adequately address these challenges, particularly in ensuring resistance to physical tampering of supply lines and impersonation attacks. Additionally, they struggle to minimize the computation, communication, and energy costs associated with securing a resource-constrained SG environment. Therefore, we propose a robust key exchange and tamper-resistant protocol for HAN and NAN networks to address these limitations. The proposed protocol leverages the physical unclonable function (PUF) mechanism to offer physical tampering resistance to smart meters. We validate our protocol formally using the Random or Real (RoR) model, which confirms its security robustness. Additionally, our informal security analysis highlights the protocol’s resilience against impersonation, physical tampering attacks, etc. We analyze and compare the performance of the proposed protocol, which further demonstrates our protocol’s effectiveness and security compared to competing protocols. Moreover, the proposed protocol achieves resource efficiency with 45.99% and 58.85% substantial reductions in computation overhead and energy overhead, respectively, compared to competing protocols. These results showcase the protocol’s enhanced security and practicality for resource-constrained SG environments. © 2014 IEEE.

关键词： Authentication Protocol

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Knowledge Graph Augmented network Towards Multiview Representation Learning for Aspect-Based Sentiment Analysis

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IEEE Transactions on Knowledge and Data engineering 2023年第10期35卷 10098-10111页

作者： Zhong, Qihuang Ding, Liang Liu, Juhua Du, Bo Jin, Hua Tao, Dacheng Wuhan University National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Hubei Wuhan430072 China Jd Explore Academy at *** Beijing100101 China Wuhan University Research Center for Graphic Communication Printing and Packaging Institute of Artificial Intelligence Hubei Wuhan430072 China Hospital of Kunming University of Science and Technology Kunming650032 China

Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis. To better comprehend long complicated sentences and obtain accurate aspect-specific information, linguistic and commonsense knowledge are generally required in this task. However, most current methods employ complicated and inefficient approaches to incorporate external knowledge, e.g., directly searching the graph nodes. Additionally, the complementarity between external knowledge and linguistic information has not been thoroughly studied. To this end, we propose a knowledge graph augmented network (KGAN), which aims to effectively incorporate external knowledge with explicitly syntactic and contextual information. In particular, KGAN captures the sentiment feature representations from multiple different perspectives, i.e., context-, syntax- and knowledge-based. First, KGAN learns the contextual and syntactic representations in parallel to fully extract the semantic features. Then, KGAN integrates the knowledge graphs into the embedding space, based on which the aspect-specific knowledge representations are further obtained via an attention mechanism. Last, we propose a hierarchical fusion module to complement these multi-view representations in a local-to-global manner. Extensive experiments on five popular ABSA benchmarks demonstrate the effectiveness and robustness of our KGAN. Notably, with the help of the pretrained model of RoBERTa, KGAN achieves a new record of state-of-the-art performance among all datasets. © 1989-2012 IEEE.

关键词： Semantics

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A Data Consistency Insurance Method for Smart Contract

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computers, Materials & Continua 2023年第9期76卷 3783-3795页

作者： Jing Deng Xiaofei Xing Guoqiang Deng Ning Hu Shen Su Le Wang Md Zakirul Alam Bhuiyan School of Computer Science and Cyber Engineering Guangzhou UniversityGuangzhou510006China Information Network Engineering and Reasearch Center South China University of TechnologyGuangzhou510640China Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China Department of Computer and Information Sciences Fordham UniversityNew York10458USA

As one of the major threats to the current DeFi(Decentralized Finance)ecosystem,reentrant attack induces data inconsistency of the victim smart contract,enabling attackers to steal on-chain assets from DeFi projects,which could terribly do harm to the confidence of the blockchain ***,protecting DeFi projects from the reentrant attack is very difficult,since generating a call loop within the highly automatic DeFi ecosystem could be very *** researchers mainly focus on the detection of the reentrant vulnerabilities in the code testing,and no method could promise the non-existent of reentrant *** this paper,we introduce the database lock mechanism to isolate the correlated smart contract states from other operations in the same contract,so that we can prevent the attackers from abusing the inconsistent smart contract *** to the existing resolutions of front-running,code audit,andmodifier,our method guarantees protection resultswith better *** we further evaluate our method on a number of de facto reentrant attacks observed from *** results prove that our method could efficiently prevent the reentrant attack with less running cost.

关键词： Blockchain smart contract data consistency reentrancy attack

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The Parables of the Mustard Seed and the Yeast: Extremely Low-Budget, High-Performance Nighttime Semantic Segmentation 39

The Parables of the Mustard Seed and the Yeast: Extremely Lo...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Shiqin Xu, Xin Chen, Haoyang Jiang, Kui Wang, Zheng School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)157735897X

Nighttime Semantic Segmentation (NSS) is essential to many cutting-edge vision applications. However, existing technologies overly rely on massive labeled data, whose annotation is time-consuming and laborious. In this paper, we pioneer a new task focusing on exploring the potential of training strategy and framework design with limited annotation to achieve high-performance NSS. Insufficient information at very low labeling budgets can easily lead to under-optimization or overfitting of the model. Our solution comprises two main components: i) a novel region-based active sampling strategy called Contextual-Aware Region Query (CARQ), which identifies highly informative target nighttime regions for labeling;and ii) an innovative Fragmentation Synergy Active Domain Adaptation framework (FS-ADA), which progressively broadcasts the limited annotation to the unlabeled regions, achieving high performance with a minimal annotation budget. Extensive experiments demonstrate that our method outperforms state-of-the-art UDA-NSS & ADA-SS methods across four day-to-nighttime benchmarks, and generalizes well to foggy, rainy, & snowy scenes. In particular only with 1% target nighttime data annotation, our method is on par with the mainstream fully-supervised methods on the BDD100K-Night val dataset. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Budget control

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Robust Imagined Speech Production Using AI-Generated Content network for Patients with Language Impairments

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 1402-1411页

作者： Xu, Xu Fu, Chong Dalian University of Technology School of Software Dalian116621 China Northeastern University School of Computer Science and Engineering Shenyang110004 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Ministry of Education Engineering Research Center of Security Technology of Complex Network System Shenyang110004 China

Imagined speech production is critical for brain-computer interface systems. It is able to provide the communication ability for patients with language impairments. Nowadays, many studies have developed algorithms for this purpose. However, they often struggle with an issue that is the noise in physiological signals from various devices. This makes current methods have low robustness when performing speech generation. To this end, we propose a framework to robustly producing imagined speech for patients with language impairments. It is based on artificial intelligent-generated content (AIGC). In specific, this work develops a diffusion model to remove noise from brain signals, which are then converted and normalized into log-mel spectrograms. Next, the product feeds into a transformer-based model to extract local and global features. Finally, these enhanced features are inputted into full connected layers to generate the imagined speech. The effectiveness of our model is validated using the high-quality, i.e., single-word-production-Dutch-iBIDS dataset. It achieves Pearson correlation scores above 0.8 and standard deviations below 0.2 across different volunteers. Experimental results well demonstrate that our model is robust and has advantages over peer methods. © 2024 IEEE.

关键词： Deep learning

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Twin delayed deep deterministic policy gradient-based intelligent computation offloading for IoT

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Digital Communications and networks 2023年第4期9卷 836-845页

作者： Siguang Chen Bei Tang Kun Wang School of Internet of Things Nanjing University of Posts and TelecommunicationsNanjing210003China Jiangsu Engineering Research Center of Communications and Network Technology Nanjing University of Posts and TelecommunicationsNanjing210003China Department of Electrical and Computer Engineering University of CaliforniaLos AngelesCA90095USA

In view of the randomness distribution of multiple users in the dynamic large-scale Internet of Things(IoT)scenario,comprehensively formulating available resources for fog nodes in the area and achieving computation services at low cost have become great *** a result,this paper studies an efficient and intelligent computation offloading mechanism with resource ***,an optimization problem is formulated to minimize the total energy consumption of all tasks under the joint optimization of computation offloading decisions,bandwidth resources and transmission ***,a Twin Delayed Deep Deterministic Policy Gradient-based Intelligent Computation Offloading(TD3PG-ICO)algorithm is proposed to solve this optimization *** combining the concept of the actor critic algorithm,the proposed algorithm designs two independent critic networks that can avoid the subjective prediction of a single critic network and better guide the policy network to generate the global optimal computation offloading ***,this algorithm introduces a continuous variable discretization operation to select the target offloading node with random *** available resources of the target node are dynamically allocated to improve the model decision-making ***,the simulation results show that this proposed algorithm has faster convergence speed and good *** can always approach the greedy algorithm with respect to the lowest total energy ***,compared with full local and Deep Q-learning network(DQN)-based computation offloading schemes,the total energy consumption can be reduced by an average of 15.53%and 6.41%,respectively.

关键词： Fog computing Computation offloading Deep reinforcement learning Resource allocation

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Driving Scene Understanding with Traffic Scene-Assisted Topology Graph Transformer 24

Driving Scene Understanding with Traffic Scene-Assisted Topo...

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32nd ACM International Conference on multimedia, MM 2024

作者： Rong, Fu Peng, Wenjin Lan, Meng Zhang, Qian Zhang, Lefei National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China School of Cyber Science and Engineering Wuhan University Wuhan China Hong Kong University of Science and Technology Hong Kong Hong Kong Horizon Robotics Beijing China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Hubei Luojia Laboratory Wuhan China

ISBN: (纸本)9798400706868

Driving scene topology reasoning aims to understand the objects present in the current road scene and model their topology relationships to provide guidance information for downstream tasks. Previous approaches fail to adequately facilitate interactions among traffic objects and neglect to incorporate scene information into topology reasoning, thus limiting the comprehensive exploration of potential correlations among objects and diminishing the practical significance of the reasoning results. Besides, the lack of constraints on lane direction may introduce erroneous guidance information and lead to a decrease in topology prediction accuracy. In this paper, we propose a novel topology reasoning framework, dubbed TSTGT, to address these issues. Specifically, we design a divide-and-conquer topology graph Transformer to respectively infer the lane-lane and lane-traffic topology relationships, which can effectively aggregate the local and global object information in the driving scene and facilitate the topology relationship learning. Additionally, a traffic scene-assisted reasoning module is devised and combined with the topology graph Transformer to enhance the practical significance of lane-traffic topology. In terms of lane detection, we develop a point-wise matching strategy to infer lane centerlines with correct directions, thereby improving the topology reasoning accuracy. Extensive experimental results on Openlane-V2 benchmark validate the superiority of our TSTGT over state-of-the-art methods and the effectiveness of our proposed modules. The code is available at https://***/rongfu-dsb/TSTGT. © 2024 ACM.

关键词： Graph theory

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