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Enhancing Utility in Differential Privacy Models Through Feature Extraction and Knowledge Distillation 2nd

Enhancing Utility in Differential Privacy Models Through Fe...

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2nd International Conference on Data Security and Privacy Protection, DSPP 2024

作者： He, Shuyao Xu, Ting Shen, Fangzhou He, Weijie Yang, Haowei College of Engineering Northeastern University Boston United States Department of Computer Science University of Massachusetts Boston Boston United States Department of Mathematics and Statistics San Jose State University San Jose United States Fielding School of Public Health UCLA Los Angeles United States Cullen College of Engineering University of Houston Houston United States

ISBN: (纸本)9789819785452

In recent years, the privacy security of deep learning models has received widespread attention, and many articles have pointed out that deep learning models are vulnerable to various privacy attacks. As a typical application of data privacy protection technology differential privacy in deep learning models, the Differentially Private Stochastic Gradient Descent (DPSGD) algorithm has gradually become one of the main technologies for privacy security protection of deep learning models and has been widely used in academic and industrial research. However, DPSGD also has some unavoidable shortcomings, that is, when DPSGD provides privacy protection for deep learning models, it will damage the classification accuracy of the model. In this paper, we are inspired by model compression technology and propose a method of using pre-trained models as feature extractor, combined with knowledge distillation technology. Our method can reduce the input dimension of the model, achieve the effect of reducing the model gradient norm. In this way, we can achieve a significant improvement in model classification accuracy under the same privacy budget, especially in complex data sets (such as the ImageNet data set). Extensive experiments demonstrate that our method achieves State-of-the-Art on multiple datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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Directed Brain Network Transformer for Psychiatric Diagnosis 27th

Directed Brain Network Transformer for Psychiatric Diagnosis

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhu, Xu Qi, Zhiwei Yue, Kun Su, Yunshan Duan, Liang School of Information Science and Engineering Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China School of Education Yunnan University Kunming China Department of Radiology Affiliated Hospital of Yunnan University Kunming China

ISBN: (纸本)9783031781940

Human brain is a complex organ that consists of billions of neurons and trillions of connections among the neurons. To describe the correlations among the time series of the brain regions, we model the brain as a functional brain network to diagnose psychosis. However, traditional methods of functional brain network construction are usually noisy and do not consider the causal relationships among brain regions. To obtain the causal relationships and improve diagnosis interpretability, we propose a directed brain network Transformer (DBNT) for psychiatric diagnosis. First, the causal relationships in the blood-oxygen-level-dependent time series of brain regions are extracted to generate a directed brain network. Then, the feature encoding method is proposed to obtain local and global features of the brain networks by using the DBNT. Experimental results demonstrate that the accuracy of DBNT increases by 8.1% and 6.4% compared to state-of-the-art methods on two large-scale brain network datasets. DBNT also highlights the brain regions associated with psychosis and provides interpretation for diagnosis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Network coding

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ShoeTect2.0: Real-Time Activity Recognition Using MobileNet CNN with Multisensory Smart Footwear 9th

ShoeTect2.0: Real-Time Activity Recognition Using MobileNet ...

引用

9th International Workshop on Sensor-Based Activity Recognition and Artificial Intelligence, iWOAR 2024

作者： Schlonsak, Ruben Yang, Tengyunhao Gabrecht, Marco Matthies, Denys J. C. Technical University of Applied Sciences Lübeck Lübeck Germany East China University of Science and Technology Shanghai China Fraunhofer IMTE Lübeck Germany

ISBN: (纸本)9783031808555

In this paper, we introduce a proof-of-concept multi-sensory footwear prototype with artificial intelligence to facilitate human activity recognition. We equipped a shoe with a force sensitive pressure sensor, an accelerometer, and a gyroscope in order to detect human activity. Such sensors allow the system to capture and analyze data about various physical movements, which are further processed in order to detect specific human activities. To achieve accurate activity recognition, we trained and compared several models, which are two types of convolutional neural networks (CNN) and a conventional support vector machine (SVM). The system’s accuracy in identifying activities like standing, sitting, walking, running, and jumping was evaluated, and scored highest using a MobileNet CNN with 83.33% accuracy. With this work, we demonstrate that a somewhat robust real-time activity recognition is feasible with prototypical hardware. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Audio-Visual Wake-up Word Spotting Under Noisy and Multi-person Scenarios 27th

Audio-Visual Wake-up Word Spotting Under Noisy and Multi-pe...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Li, Cancan Su, Fei Liu, Juan Institute of Artificial Intelligence School of Computer Science Wuhan University Wuhan430072 China

ISBN: (纸本)9783031801358

The existing audio-visual wake-up word spotting (AVWWS) methods assume that the audio signal has been aligned with the lip movement video signal of a specific speaker in noisy environments, and are mainly applicable for scenarios with only a single speaker. However, in complex scenarios, there may be multiple people showing up in the video facing the camera simultaneously, and more than one person may be speaking at the same time. Wake-up word spotting in noisy and multi-person scenarios remains relatively under-explored. In this paper, we first propose a Wake-up Word Active Speaker Detection Model (WWASD) to recognize the face that is speaking the wake-up word. Based on the model, we propose two approaches, namely Two-stage detection and Three-stage detection, for audio-visual wake-up word spotting in noisy and multi-person scenarios. We compare the approaches from the perspectives of performance and computational complexity on MISP2021-AVWWS corpus. The best Two-stage detection approach, which contains WWASD and audio-visual wake-up word spotting model, achieves comparable performance against the systems with oracle visual speaker bounding boxes. Three-stage detection, which adds an audio-based single-modality wake-up word model as a front end greatly reduces the computational cost. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Speech recognition

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Deep Evidential Active Learning with Uncertainty-Aware Determinantal Point Process 27th

Deep Evidential Active Learning with Uncertainty-Aware Dete...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhou, Yuxian Yue, Xiaodong Chen, Yufei Xie, Shaorong School of Computer Engineering and Science Shanghai University Shanghai China Artificial Intelligence Institute Shanghai University Shanghai China College of Electronic and Information Engineering Tongji University Shanghai China

ISBN: (纸本)9783031781063

Deep learning method requires a substantial amount of labeled data to achieve the state-of-the-art performance. However, annotating a large volume of data is often costly and impractical. Active Learning is a approach that reduces labeling costs by intelligently selecting and annotating the most crucial data points, which benefits from the integration of uncertainty and diversity as key criteria for sampling. Existing uncertainty-based methods often fall short in capturing the distinct sources of uncertainty, resulting in a diminished quality of uncertainty estimation. Additionally, clustering is commonly used to ensure diversity, which requires multiple iterations but overlooks the global correlations present throughout the entire unlabeled dataset. As a result, a well-designed ad hoc combination is essential to balance uncertainty and diversity. To address above limitations, we propose Evidential Uncertainty-aware Determinantal Point Process active learning method. Specifically, we employ the theory of Subjective Logic to measure multifaceted uncertainty including vacuity and dissonance. On these grounds, we first focus on the samples with high dissonance and employ Determinantal Point Process to select the samples considering both vacuity and diversity. The proposed method explores and exploits the information associated with the latent feature space near the decision boundary to select the most valuable samples. The experimental results on various real-world datasets reveal the superiority of our method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Active learning

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Variational Autoencoder Based Automatic Clustering for Multivariate Time Series Anomaly Detection 18th

Variational Autoencoder Based Automatic Clustering for Mult...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Yan, Li Hu, Hailin Yang, Kun Wang, Gaozhou Guan, Ti Zheng, Yanwei State Grid Shandong Electric Power Company Information and Communication Company Beijing China State Grid Shandong Electric Power Company Beijing China School of Computer Science and Technology Shandong University Shandong China

ISBN: (纸本)9783031714696

Multivariate time series anomaly detection is of great significance in monitoring and ensuring the stable operation of complex systems. The multivariate time series generated in real scenes often have complex dependency patterns, summarized as temporal dynamics and spatial dynamics. Specifically, temporal dynamics manifested as complex dependencies between values at different timestamps, while spatial dynamics refer to uncertain similarity relationships between different sequences. In order to simultaneously model temporal and spatial dynamics, we propose a variational autoencoder based automatic clustering method for multivariate time series anomaly detection (ACVAE), which maps input sequences to latent representations using VAE and reconstructs input sequences based on the latent representations, while detecting anomalies based on reconstruction errors. Specifically, we design an encoder network that combines TCN and GRU to learn multi-scale long short-term temporal dependencies, and introduce the Dirichlet prior to automatically capture the similarity between sequences. Finally, we conduct extensive experiments on two publicly available datasets, and the results show that the ACVAE is superior to other baseline methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Time series

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ConCSE: Unified Contrastive Learning and Augmentation for Code-Switched Embeddings 27th

ConCSE: Unified Contrastive Learning and Augmentation for ...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Jeon, Jangyeong Cho, Sangyeon Ma, Minuk Kim, Junyeong Department of Artificial Intelligence Chung-Ang University Seoul06974 Korea Republic of Department of Computer Science University of British Columbia VancouverBCV6T 1Z4 Canada

ISBN: (纸本)9783031784972

This paper examines the Code-Switching (CS) phenomenon where two languages intertwine within a single utterance. There exists a noticeable need for research on the CS between English and Korean. We highlight that the current Equivalence Constraint (EC) theory for CS in other languages may only partially capture English-Korean CS complexities due to the intrinsic grammatical differences between the languages. We introduce a novel Koglish dataset tailored for English-Korean CS scenarios to mitigate such challenges. First, we constructed the Koglish-GLUE dataset to demonstrate the importance and need for CS datasets in various tasks. We found the differential outcomes of various foundation multilingual language models when trained on a monolingual versus a CS dataset. Motivated by this, we hypothesized that SimCSE, which has shown strengths in monolingual sentence embedding, would have limitations in CS scenarios. We construct a novel Koglish-NLI (Natural Language Inference) dataset using a CS augmentation-based approach to verify this. From this CS-augmented dataset Koglish-NLI, we propose a unified contrastive learning and augmentation method for code-switched embeddings, ConCSE, highlighting the semantics of CS sentences. Experimental results validate the proposed ConCSE with an average performance enhancement of 1.77% on the Koglish-STS(Semantic Textual Similarity) tasks. (Source code available at https://***/jjy961228/ConCSE). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Estimating Causal Effects in Partially Directed Parametric Causal Factor Graphs 16th

Estimating Causal Effects in Partially Directed Parametric ...

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16th International Conference on Scalable Uncertainty Management, SUM 2024

作者： Luttermann, Malte Braun, Tanya Möller, Ralf Gehrke, Marcel Lübeck Germany Data Science Group University of Münster Münster Germany Institute for Humanities-Centered Artificial Intelligence University of Hamburg Hamburg Germany

ISBN: (纸本)9783031762345

Lifting uses a representative of indistinguishable individuals to exploit symmetries in probabilistic relational models, denoted as parametric factor graphs, to speed up inference while maintaining exact answers. In this paper, we show how lifting can be applied to causal inference in partially directed graphs, i.e., graphs that contain both directed and undirected edges to represent causal relationships between directed and undirected edges to represent causal relationships between random variables. We present partially directed parametric causal factor graphs (PPCFGs) as a generalisation of previously introduced parametric causal factor graphs, which require a fully directed graph. We further show how causal inference can be performed on a lifted level in PPCFGs, thereby extending the applicability of lifted causal inference to a broader range of models requiring less prior knowledge about causal relationships. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Undirected graphs

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Adaptive Learning of Centralized and Decentralized Rewards in Multi-agent Imitation Learning 22nd

Adaptive Learning of Centralized and Decentralized Rewards...

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22nd International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2024

作者： Yousif, Yasin M. Müller, Jörg P. Institute of Computer Science Technical University of Clausthal Clausthal-Zellerfeld Germany

ISBN: (纸本)9783031704147

Imitation learning aims at teaching agents to perform desired behaviors by observing expert demonstrations. This approach can be generalized to multi-agent environments, where it is possible to achieve a mutually beneficial policies equilibrium through the use of specialized rewards. One such reward structure can be implemented by using centralized rewards for cooperative agents and decentralized rewards for non-cooperative agents. However, in mixed multi-agent environments, that contain both cooperative and competitive agents, it is necessary to develop a more nuanced approach to reward allocation. In these situations, one part of the reward can be shared among all cooperative agents while another part targets each agent individually. To address this challenge, our work proposes a novel two-component reward model for each agent: a centralized shared component and a decentralized agent-specific component. We conducted several experiments using three different environments to evaluate the performance of our proposed model compared to its individual components. Our results showed that the combined model outperform all of its constituent models in mixed environments, effectively imitating the centralized reward for cooperative environments but exhibiting no improvement in competitive environments. Finally, we tested the transparency of our model by providing representative examples and examining the scalar weights assigned to the centralized and decentralized components within the combined model (code available at: https://***/engyasin/Adaptive_learning_4_MAIL). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Single-Image Driven 3D Viewpoint Training Data Augmentation for Effective Label Recognition 27th

Single-Image Driven 3D Viewpoint Training Data Augmentation ...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Huang, Yueh-Cheng Chen, Hsin-Yi Hung, Cheng-Jui Chuang, Jen-Hui Hwang, Jenq-Neng Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electrical and Computer Engineering University of Washington Seattle United States

ISBN: (纸本)9783031781247

Confronting the critical challenge of insufficient training data in the field of complex image recognition, this paper introduces a novel 3D viewpoint transformation technique initially tailored for label recognition. This technique can be used not only for data augmentation by generating synthetic data from any perspective but also transform photos taken from any angle into a frontal view, thereby reducing the complexity of the recognition task. Given the extensive use of wine-related applications with over 20 million users and the continuous publication of wine label datasets, we decided to focus this study on wine labels. This method enhances deep learning model performance by generating visually realistic training samples from a single real-world label image, overcoming the challenges posed by the intricate combinations of text and logos. Unlike classical Generative Adversarial Network (GAN) methods, which fall short in synthesizing such intricate content combinations and require a large amount of training data to become effective, our proposed solution leverages time-tested computer vision and image processing strategies. By using just a single monocular wine label image, we can expand our training dataset, thereby broadening the range of training samples for deep learning applications. This innovative approach to data augmentation circumvents the constraints of limited training resources. We then utilize the augmented training images through the Vision Transformer (ViT) architecture, performing one-shot recognition of existing wine labels in the training classes or future newly collected wine labels unavailable in the training. Experimental results show a significant increase in recognition accuracy over conventional 2D data augmentation techniques, indicating the potential for broader application in various labeling scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Wine

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