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Symmetric Private Shortest Path Retrieval for LM-Enabled Route Services on Consumer Devices

IEEE Transactions on Consumer Electronics 2025年

作者： Liu, Xuefeng Lyu, Fake Lei, Jing Yang, Mengmeng Yau, Kok-Lim Alvin Lin, Yangfei Xidian University School of Cyber Engineering Shaanxi Xi’an710071 China Shaanxi Key Laboratory of Blockchain and Secure Computing China Commonwealth Scientific and Industrial Research Organization Data61 Black MountainACT2601 Australia Universiti Tunku Abdul Rahman Department of Internet Engineering and Computer Science Selangor Kajang43200 Malaysia University of Tsukuba Department of Computer Science Tsukuba305-8577 Japan

Route optimization is widely utilized in consumer electronics like Google Maps and Facebook connections. However, due to the limitations of consumer devices, users often rely on online service providers to process large-model-enabled route services, which may involve sensitive information such as location and social relationships. This raises privacy concerns, necessitating the need for a system that ensures expected utility while protecting the privacy of both consumers and service providers. This problem, known as Symmetric Private Shortest Path Retrieval, has been studied in the literature, either focusing on specific data types or a simple private setting where the data user and owner are the same. In this paper, we propose a low-latency symmetric private shortest path query for generic graph data in a more realistic public service model. We propose several novel techniques, including a chained encryption method and an oblivious key transfer mechanism to efficiently reveal the queried shortest path without leaking additional information to either the consumers or service provider. Experimental results demonstrate that our scheme outperforms the state-of-the-art by being approximately 46 to 53 times faster and consuming roughly 36 to 74 times less bandwidth for a city navigation service. © 1975-2011 IEEE.

关键词： Sensitive data

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Optimization for Task Offloading and Downloading in UAV-Assisted MEC Systems with Aerial to Aerial Collaboration

Optimization for Task Offloading and Downloading in UAV-Assi...

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International Conference on Computer Supported Cooperative Work in Design

作者： Jiawei Jiang Xiang Tian Yubing Han Chunyu Hu Bin Feng Jiguo Yu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China School of Information Science and Technology Taishan University Taian China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Big Data Institute Qilu University of Technology Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Owing to the easy deployment and mobile flexibility, Unmanned Aerial Vehicle (UAV) assisted Mobile Edge computing (MEC) has been deemed as one potential technology for handling the computation-intensive tasks at terminal devices (TDs). In this work, a MEC architecture assisted by UAVs is designed which achieves efficient offloading, computing, and downloading for tasks from multiple TDs via aerial to aerial collaboration of two UAVs. In this architecture, the task offloading process contains two parts, i.e., the offloading from TDs to a mobile UAV which flies around TDs, and the offloading from the mobile UAV to a hovering UAV which hovers in the air. The computing tasks from TDs will be divided into three parts allocated to the TDs themselves, and both two UAVs. Upon completion of computation, the computation results are downloaded to the TDs. The optimization objective is to seek for an optimal task division strategy to attain the weighted total energy consumption minimization for all devices. Since the formulated optimization problem is not convex, we develop a two-step iteration algorithm which jointly optimizes computing frequency, task allocation volume, as well as UAV's trajectory based on the method of block coordinate descent. Simulation results confirm the effectiveness and performance advantages of the designed algorithm.

关键词： Energy consumption Simulation Collaboration Computer architecture Autonomous aerial vehicles Minimization Resource management Jamming Trajectory optimization Radio spectrum management

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Generative AI for Deep Reinforcement Learning: Framework, Analysis, and Use Cases

arXiv

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arXiv 2024年

作者： Sun, Geng Xie, Wenwen Niyato, Dusit Mei, Fang Kang, Jiawen Du, Hongyang 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 College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Guangzhou510006 China Department of Electrical and Electronic Engineering The University of Hong Kong 999077 Hong Kong Department of Electrical and Computer Engineering Auburn University Auburn36830 United States

As a form of artificial intelligence (AI) technology based on interactive learning, deep reinforcement learning (DRL) has been widely applied across various fields and has achieved remarkable accomplishments. However, DRL faces certain limitations, including low sample efficiency and poor generalization. Therefore, we present how to leverage generative AI (GAI) to address these issues above and enhance the performance of DRL algorithms in this paper. We first introduce several classic GAI and DRL algorithms and demonstrate the applications of GAI-enhanced DRL algorithms. Then, we discuss how to use GAI to improve DRL algorithms from the data and policy perspectives. Subsequently, we introduce a framework that demonstrates an actual and novel integration of GAI with DRL, i.e., GAI-enhanced DRL. Additionally, we provide a case study of the framework on UAV-assisted integrated near-field/far-field communication to validate the performance of the proposed framework. Moreover, we present several future directions. Finally, the related code is available at: https://***/GAI-enhanced-DRL. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Semi-Supervised Clustering Framework for Fine-grained Scene Graph Generation 39

Semi-Supervised Clustering Framework for Fine-grained Scene ...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yang, Jiarui Wang, Chuan Zhang, Jun Wu, Shuyi Jinjing, Zhao Liu, Zeming Yang, Liang Shanghai Key Lab of Intell. Info. Processing School of Computer Science Fudan University China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China Institute of Information Engineering CAS China School of Computer Science and Technology Beijing JiaoTong University China Guangdong Provincial Key Lab of Intell. Info. Processing & Shenzhen Key Lab of Media Security Shenzhen University China Information Research Center of Military Science PLA Academy of Military Science China National Key Laboratory of Science and Technology on Information System Security China School of Computer Science and Engineering Beihang University China School of Artificial Intelligence Hebei University of Technology China

ISBN: (纸本)157735897X

Scene Graph Generation (SGG) aims to detect all objects and identify their pairwise relationships existing in the scene. Considering the substantial human labor costs, existing scene graph annotations are often sparse and biased, which result in confusion training with low-frequency predicates. In this work, we design a Semi-Supervised Clustering framework for Scene Graph Generation (SSC-SGG) that uses the sparse labeled data to guide the generation of effective pseudo-labels from unlabeled object pairs, thus enriching the labeled sample space, especially for low-frequency interaction samples. We approach from the perspective of clustering, reducing the problem of confirmation bias in a self-training manner. Specifically, we first enhance the model’s robustness to feature extraction via prototype-based clustering, aggregating different relationship augmented features onto the same prototype. Secondly, we design a dynamic pseudo-label assignment algorithm based on a mini-batch, which adjusts the detection sensitivity to different frequency samples from the historical assignment. Finally, we conduct joint training on the pseudo-labels and the labeled data. We conduct experiments on various SGG models and achieve substantial overall performance improvements, demonstrating the effectiveness of SSC-SGG. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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MagLive: Robust Voice Liveness Detection on Smartphones Using Magnetic Pattern Changes

arXiv

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arXiv 2024年

作者： Sun, Xiping Chen, Jing Wu, Cong He, Kun Xu, Haozhe Feng, Yebo Du, Ruiying Chen, Xianhao Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan430072 China Rizhao Institute of Information Technology Wuhan University Rizhao276800 China School of Computer Science and Engineering Nanyang Technological University Singapore Collaborative Innovation Center of Geospatial Technology Wuhan430079 China The Department of Electrical and Electronic Engineering HKU Musketeers Foundation Institute of Data Science University of Hong Kong Pok Fu Lam Hong Kong

Voice authentication has been widely used on smartphones. However, it remains vulnerable to spoofing attacks, where the attacker replays recorded voice samples from authentic humans using loudspeakers to bypass the voice authentication system. In this paper, we present MagLive, a robust voice liveness detection scheme designed for smartphones to mitigate such spoofing attacks. MagLive leverages the differences in magnetic pattern changes generated by different speakers (i.e., humans or loudspeakers) when speaking for liveness detection, which are captured by the built-in magnetometer on smartphones. To extract effective and robust magnetic features, MagLive utilizes a TF-CNN-SAF model as the feature extractor, which includes a time-frequency convolutional neural network (TF-CNN) combined with a self-attention-based fusion (SAF) model. Supervised contrastive learning is then employed to achieve user-irrelevance, device-irrelevance, and content-irrelevance. MagLive imposes no additional burden on users and does not rely on active sensing or specialized hardware. We conducted comprehensive experiments with various settings to evaluate the security and robustness of MagLive. Our results demonstrate that MagLive effectively distinguishes between humans and attackers (i.e., loudspeakers), achieving an average balanced accuracy (BAC) of 99.01% and an equal error rate (EER) of 0.77%. Copyright © 2024, The Authors. All rights reserved.

关键词： Smartphones

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Normal Distribution Function on Descriptor Extraction 11th

Normal Distribution Function on Descriptor Extraction

引用

11th International Conference on Image and Graphics, ICIG 2021

作者： Yin, Jianhua Liu, Cong Jiang, Jun Wen, Jie Yang, Lihang Zhu, Shilong School of Computer Science and Technology Harbin Institute of Technology Shenzhen518055 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518055 China Peng Cheng Laboratory Shenzhen518055 China Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen518055 China College of Big Data and Information Engineering Guizhou University Guizhou Guiyang550025 China

ISBN: (纸本)9783030873578

The triplet-loss function is widely used in fields of descriptor extraction in recent years and owing to its good performance in various databases. However, some recent works make less effort on the relationship of adjacent descriptors from the same sample, which leads to the instability of descriptors and results in the mismatching problem in practical applications. To solve this problem, we introduce the topological relationship with the Normal Distribution Function (NDF) into the triplet loss function. The loss function establishes the relationship of descriptors from the same sample and considers the interrelation of descriptors from different types of samples. Furthermore, to increase the calculation speed, we normalize the algorithm NDF. Finally, we propose the triplet-loss function on three databases. These results demonstrate that our algorithm obtains better performance than state-of-the-art methods. © 2021, Springer Nature Switzerland AG.

关键词： Distribution functions

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Enhancing Coarse-Grained and Fine-Grained Image-Text Retrieval Models via Multi-Scale Modal Distance Learning

Enhancing Coarse-Grained and Fine-Grained Image-Text Retriev...

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Kaili Zhou Yushui Geng Jing Zhao Yiyun Xing Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Graduate School Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

The current image-text cross-modal retrieval faces challenges due to the heterogeneous nature of different modalities. This paper proposes an improved model based on multi-scale modal distance learning to enhance the performance of coarse-grained and fine-grained image-text matching. In the image encoding part, an improved ResNet architecture is adopted, utilizing upsampling techniques to better preserve key semantic information. For text encoding, BERT and Bi-LSTM models are combined to extract shallow and deep textual features, respectively. We design a coarse-fine granularity interaction mechanism, leveraging a bidirectional stacked Transformer to achieve local similarity interaction between image regions and text words, while also using Bi-LSTM-extracted contextual features to interact with global image features for global similarity matching. Additionally, this paper introduces a novel multi-scale modal contrastive loss, which dynamically adjusts the distance between samples of different modalities, thereby improving the model's sensitivity to semantic similarity. Experimental results on the COCO 5K and Flickr30K datasets demonstrate that the proposed method outperforms existing mainstream methods in both retrieval accuracy and efficiency, achieving complementary advantages between coarse- and fine-grained features. This provides an effective solution and new research direction for cross-modal retrieval.

关键词： Computer aided instruction Image coding Accuracy Sensitivity Semantics Bidirectional control Feature extraction Transformers Encoding Cross modal retrieval

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Hierarchical Text Classification Method Driven by Dynamic Hierarchical Fusion and Prompt Embedding

Hierarchical Text Classification Method Driven by Dynamic Hi...

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Yiyun Xing Kaili Zhou Jianqiang Zhang Gongpeng Song Qin Lu Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Computer Science and Technology Shandong Engineering Research Center of Big Data Applied Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Hierarchical Text Classification, as a critical task in natural language processing, has broad applications in scenarios with complex label structures and limited samples. However, existing methods still face challenges in capturing hierarchical label information and text interactions, resulting in suboptimal classification performance. This study proposes a few-shot hierarchical text classification method based on prompt embeddings and dynamically fused hierarchical features. Building on pre-trained language models, the method encodes hierarchical structure information as prompts through an adaptive prompt embedding model and dynamically fuses hierarchical features to generate more expressive text representations. Additionally, a positive sample generation strategy based on contrastive learning is introduced, enabling the model to effectively distinguish hierarchical features across different categories, further enhancing classification performance. Experimental results demonstrate that this method significantly improves classification accuracy on multiple public datasets, exhibiting stronger generalization ability and robustness compared to mainstream methods. This approach offers a novel and effective solution for hierarchical text classification tasks.

关键词： Adaptation models Accuracy Federated learning Computational modeling Text categorization Buildings Contrastive learning Robustness Natural language processing Faces

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Multimodal Knowledge Graph Completion Method Based on Integrated Modality Adversarial Training and Relation-Enhanced Attention Mechanism

Multimodal Knowledge Graph Completion Method Based on Integr...

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Ruijia Zhang Zheng Dong Jianqiang Zhang Gongpeng Song Qin Lu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Negative sampling (NS) is widely used in knowledge graph completion (KGC) to generate negative triples for contrastive learning during training. However, existing NS methods are not suitable when multimodal information is incorporated into KGC models. Due to their complex design, these methods are also inefficient. In this paper, we propose the integration of Modality-Aware Adversarial Training (IMAT) to generate higher-quality negative samples for Multimodal Knowledge Graph Completion (MMKGC), and introduce a Relation-Enhanced Cross-modal Attention (RECA) mechanism to evaluate bidirectional attention weights between multimodal features using relational information, thereby improving the model's ability to identify hard negative samples. Our approach represents a joint design of MMKGC models and training strategies, surpassing 16 recent MMKGC methods and achieving new state-of-the-art results on three public MMKGC benchmarks.

关键词： Training Federated learning Design methodology Computational modeling Semantics Focusing Knowledge graphs Contrastive learning Sampling methods Real-time systems

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Source-free domain adaptation via avatar prototype generation and adaptation

arXiv

引用

arXiv 2021年

作者： Qiu, Zhen Zhang, Yifan Lin, Hongbin Niu, Shuaicheng Liu, Yanxia Du, Qing Tan, Mingkui School of Software Engineering South China University of Technology School of Computing National University of Singapore Singapore Key Laboratory of Big Data and Intelligent Robot Ministry of Education Pazhou Laboratory

We study a practical domain adaptation task, called source-free unsupervised domain adaptation (UDA) problem, in which we cannot access source domain data due to data privacy issues but only a pre-trained source model and unlabeled target data are available. This task, however, is very difficult due to one key challenge: the lack of source data and target domain labels makes model adaptation very challenging. To address this, we propose to mine the hidden knowledge in the source model and exploit it to generate source avatar prototypes (i.e., representative features for each source class) as well as target pseudo labels for domain alignment. To this end, we propose a Contrastive Prototype Generation and Adaptation (CPGA) method. Specifically, CPGA consists of two stages: (1) prototype generation: by exploring the classification boundary information of the source model, we train a prototype generator to generate avatar prototypes via contrastive learning. (2) prototype adaptation: based on the generated source prototypes and target pseudo labels, we develop a new robust contrastive prototype adaptation strategy to align each pseudo-labeled target data to the corresponding source prototypes. Extensive experiments on three UDA benchmark datasets demonstrate the effectiveness and superiority of the proposed method. Copyright © 2021, The Authors. All rights reserved.

关键词： data privacy

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