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检索条件"任意字段=2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022"

共 33684 条记录，以下是241-250 订阅

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Efficient Approach to Improve the Security of PDF Readers Using Transfer learning for Few Shots Sample Scenario

Efficient Approach to Improve the Security of PDF Readers Us...

引用

machine learning and Autonomous Systems (ICMLAS), international conference on

作者： Sonam Bhardwaj Mayank Dave Sumit Dalal School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Engineering National Institute of Technology Kurukshetra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

PDF malware is a significant threat to computer security. The purpose of this study is to introduce a new approach for improving the security of PDF readers. The method utilizes transfer learning by leveraging existing models and a small number of labeled examples, even with limited training samples. The ResNet34 model has been modified for PDF malware detection by replacing the 2D and 3D channels with a 1D channel. Transfer learning is a technique that is employed to enhance the model by utilizing the labeled samples that are available in a scenario where only a few samples are provided. The evaluation of the PDFMal2022 dataset shows that our approach outperforms existing models. It achieves a detection accuracy of 91.6% on source data and 92.4% on target data. This approach effectively utilizes transfer learning and effectively mitigates the risks associated with PDF malware, thereby offering an improved security solution for PDF readers.

关键词： Training Accuracy Three-dimensional displays Computer viruses Computational modeling Transfer learning Training data data models Computer security Viruses (medical)

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Optimizing Resource Allocation in Public Healthcare: A machine learning Approach for Length-of-Stay Prediction 27th

Optimizing Resource Allocation in Public Healthcare: A Mach...

引用

27th international conference on Pattern Recognition, ICPR 2024

作者： Perliti Scorzoni, Paolo Giovanetti, Anita Bolelli, Federico Grana, Costantino Università degli Studi di Modena e Reggio Emilia Modena Italy

ISBN: (纸本)9783031876592

Effective hospital resource management hinges on established metrics such as Length of Stay (LOS) and Prolonged Length of Stay (pLOS). Reducing pLOS is associated with improved patient outcomes and optimized resource utilization (e.g., bed allocation). This study investigates several machine learning (ML) models for both LOS and pLOS prediction. We conducted a retrospective study analyzing data from general inpatients discharged between 2022 and 2023 at a northern Italian hospital. Sixteen regression and twelve classification algorithms were compared in forecasting LOS as either a continuous or multi-class variable (1–3 days, 4–10 days, >10 days). Additionally, the same models were assessed for pLOS prediction (defined as LOS exceeding 8 days). All models were evaluated using two variants of the same dataset: one containing only structured data (e.g., demographics and clinical information), and a second one also containing features extracted from free-text diagnosis. Ensemble models, leveraging the combined strengths of multiple ML algorithms, demonstrated superior accuracy in predicting both LOS and pLOS compared to single-algorithm models, particularly when utilizing both structured and unstructured data extracted from diagnoses. Integration of ML, particularly ensemble models, has the potential to significantly improve LOS prediction and identify patients at high risk of pLOS. Such insights can empower healthcare professionals and bed managers to optimize patient care and resource allocation, promoting overall healthcare efficiency and sustainability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Resource allocation

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Quantization of MobileNetV2 for Resource-Constrained Microcontrollers

Quantization of MobileNetV2 for Resource-Constrained Microco...

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international conference on Applications in Electronics Pervading Industry, Environment and Society, APPLEPIES 2024

作者： Lazzaroni, Luca Bellotti, Francesco Dabbous, Ali Pighetti, Alessandro Berta, Riccardo University of Genoa Via Opera Pia 11a Genova16145 Italy

ISBN: (纸本)9783031840999

The proliferation of IoT and edge computing devices demands for efficient deployment strategies due to their limited computational capabilities and energy constraints. This paper investigates the application of deep learning models in such resource-constrained environments, focusing on the quantization of the MobileNetV2 architecture. We evaluate three primary quantization techniques: dynamic quantization, static quantization, and quantization-aware training. Using a subset of the Vehicle Make and Model Recognition dataset, specifically the Most Stolen Vehicles in the US in 2017, we compare our results with a previous study, highlighting the advancements achieved through MobileNetV2 and its quantization process. Deployment on two STM32 boards, L4 and H7 series, demonstrates the effectiveness of the quantized MobileNetV2 model in achieving efficient, low-power, and low-latency execution on low-power MCUs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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A Triangular Stable Node Network based on Self-supervised learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Qing Gao, Qian Fan, Jun Zhang, Zhiqiang 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 Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd. No.1999 Shunhua road Shandong Jinan250101 China

ISBN: (纸本)9798350368741

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN-a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model's effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets. © 2025 IEEE.

关键词： graph contrastive learning graph neural networks recommender systems self-supervised learning

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Secure and Robust Privacy-Preserving Federated learning For Heterogeneous Resource 26th

Secure and Robust Privacy-Preserving Federated Learning For ...

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26th international conference on Information and Communications Security, ICICS 2024

作者： Garne, Amina El Wei, Yunan Lin, Yucheng Zhao, Shengnan Zhao, Chuan Chen, Zhenxiang School of Information Science and Engineering University of Jinan Jinan250022 China Quan Cheng Laboratory Jinan250103 China

ISBN: (纸本)9789819787975

Privacy-preserving federated learning (PPFL) enables users to conduct tasks cooperatively without sharing their private datasets. Under the setting of the single key held by all users, the security of PPFL frameworks is guaranteed under the fragile assumption that none would reveal the key without authentication and data verification. To protect the data integrity, participants who fail to pass verification are required to be excluded from the current training round. In addition, the dynamic update of users with heterogeneous resources incurs severe degradation of the training performance in the FL process. Therefore, we propose a federated learning framework, named Solar Federated learning (SFL). To protect the users’ privacy, we introduce the BCP cryptosystem to provide a multi-key environment and create a data integrity verification and authentication method based on the bilinear aggregation signature and verifiable secret share. To deal with the impact of the dynamical update under the resource heterogeneity, we design a scheme that enables SFL to tolerate participant dropout during the training while still guaranteeing high accuracy. We evaluated the proposed framework based on the MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100 datasets. The experimental results indicate the practicality of SFL. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Plaintext-Free Deep learning for Privacy-Preserving Medical Image Analysis through Frequency Information Embedding

Plaintext-Free Deep Learning for Privacy-Preserving Medical ...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Sun, Mengyu Yang, Ziyuan Ran, Maosong Wang, Zhiwen Yu, Hui Zhang, Yi School of Cyber Science and Engineering Sichuan University Chengdu China School of Computer Science Sichuan University Chengdu China

ISBN: (纸本)9798350368741

In the fast-evolving field of medical image analysis, deep learning (DL)-based methods have achieved tremendous success. However, these methods require plaintext data for training and inference stages, raising privacy concerns, especially in the sensitive area of medical data. To tackle these concerns, this paper proposes a novel framework that uses surrogate images for analysis, eliminating the need for plaintext images. This approach is called Frequency-domain Exchange Style Fusion (FESF). The framework includes two main components: Image Hidden Module (IHM) and Image Quality Enhancement Module (IQEM). The IHM performs in the frequency domain, blending the features of plaintext medical images into host medical images, and then combines this with IQEM to improve and create surrogate images effectively. During the diagnostic model training process, only surrogate images are used, enabling anonymous analysis without any plaintext data during both training and inference stages. Extensive experiments demonstrate that our framework effectively preserves the privacy of medical images and maintains the diagnostic accuracy of DL models at a relatively high level, proving its effectiveness across various datasets and DL-based models. © 2025 IEEE.

关键词： Deep learning Frequency-Domain Exchange Image Concealment Medical Image Analysis Privacy Preserving

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Hierarchical Knowledge Distillation for Human Activity Recognition 1

Hierarchical Knowledge Distillation for Human Activity Recog...

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1st international conference on Intelligent Manufacturing and Cloud Computing, ICIMCC 2024

作者： Hu, Di Wu, Xingchen Xiong, Dezhu Department of Optoelectronic Science and Technology Ji Hua Laboratory Foshan China Jiushao Institute of AI Algorithm Ji Hua Laboratory Foshan China Intelligent robot research center Ji Hua Laboratory Foshan China

ISBN: (纸本)9781643685779

The widespread adoption of IoT and mobile devices with a variety of sensors has made it possible to develop applications that combine time-series data from multiple sensor inputs. However, current deep neural network architectures for multimodal fusion struggle with consecutive missing data and noise, common in real-world scenarios. We introduce a Hierarchical Knowledge Distillation Framework (HKDF) designed to address noise and data missingness challenges in Human Activity Recognition (HAR). Specifically, we introduce a cross-sample correlation distillation mechanism, leveraging contrastive learning to capture and transfer holistic semantics across samples, thereby enabling effective feature reconstruction. Additionally, a cross-category variation distillation strategy is proposed to learn inter- and intra- category dynamics based on category prototypes to produce robust joint multimodal representations. The superior performance achieved on both datasets demonstrates the remarkable advantages of HKDF. © 2025 The Authors.

关键词： Contrastive learning

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Online Network Inference from Graph-Stationary Signals with Hidden Nodes

Online Network Inference from Graph-Stationary Signals with ...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Buciulea, Andrei Navarro, Madeline Rey, Samuel Segarra, Santiago Marques, Antonio G. Dept. of Signal Theory and Communications Rey Juan Carlos University Madrid Spain Dept. of Electrical and Computer Engineering Rice University HoustonTX United States

ISBN: (纸本)9798350368741

Graph learning is the fundamental task of estimating unknown graph connectivity from available data. Typical approaches assume that not only is all information available simultaneously but also that all nodes can be observed. However, in many real-world scenarios, data can neither be known completely nor obtained all at once. We present a novel method for online graph estimation that accounts for the presence of hidden nodes. We consider signals that are stationary on the underlying graph, which provides a model for the unknown connections to hidden nodes. We then formulate a convex optimization problem for graph learning from streaming, incomplete graph signals. We solve the proposed problem through an efficient proximal gradient algorithm that can run in real-time as data arrives sequentially. Additionally, we provide theoretical conditions under which our online algorithm is similar to batch-wise solutions. Through experimental results on synthetic and real-world data, we demonstrate the viability of our approach for online graph learning in the presence of missing observations. © 2025 IEEE.

关键词： graph signal processing hidden nodes Online graph learning

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Embedding Speech Temporal Information in Pretrained Language Models for Sentiment Analysis 24

Embedding Speech Temporal Information in Pretrained Language...

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5th international conference on Computer Science and Management Technology, ICCSMT 2024

作者： Liu, Xin Wang, Min Huang, Mengtian Dong, Chuang Neusoft Medical Systems Co. Ltd. Liaoning Shenyang China Neusoft Corporation Liaoning Shenyang China School of Computer Science and Engineering Northeastern University Liaoning Shenyang China

ISBN: (纸本)9798400709999

Multimodal Sentiment Analysis is a task aimed at enabling computers to understand human emotions by learning representations from different *** mainstream methods primarily focus on cross-modal interactions, overlooking the impact of diverse data characteristics on sentiment analysis ***, our focus shifts towards the data itself, with the goal of extracting more nuanced sentiment representations directly from raw *** this paper, a Dynamic Speech Temporal Module (D-STEM) is proposed, which employs three speech temporal enhancement steps to extract hierarchical temporal emotion representations from speech ***, we dynamically embed the obtained speech temporal information into a pre-trained language model, resulting in the proposed speech-text sentiment analysis *** just a few training steps, it can adapt to multimodal *** evaluated our approach on the multimodal public sentiment datasets CMU-MOSI and *** results demonstrate that our method outperforms previous baseline models and text-only fine-tuning methods. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Spatio-temporal data

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Correlation Between Satellite Multispectral Imagery and Combine Harvester CANbus data for Corn Yield Assessment

Correlation Between Satellite Multispectral Imagery and Comb...

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international Mid-Term conference of the Italian Association of Agricultural engineering, MID-TERM AIIA 2024

作者： Miralles, Gica Stefanescu Biglia, Alessandro Mattetti, Michele Tortia, Cristina Gay, Paolo Comba, Lorenzo Università degli Studi di Torino Largo Paolo Braccini 2 TO Grugliasco10095 Italy Università degli Studi di Torino Via Pessinetto 12 TO Torino10149 Italy Department of Agricultural and Food Sciences Alma Mater Studiorum – University of Bologna Viale G. Fanin 50 BO Bologna40127 Italy

ISBN: (纸本)9783031842115

Adoption of digital tools has led to relevant advancements in the agricultural sector. In particular, precision agriculture techniques have shown effective applications in farming practices. Integrating multisource data is a crucial task for improving agricultural management, and in this context the reliable assessment of crop yield by remote sensed imagery plays a relevant role. In this work, the correlation between satellite multispectral imagery and corn yield was investigated. Sentinel-2 satellite mission was selected as source of multispectral data, with 10-m spatial resolution and 4–6 days revisit time. A corn field in Ferrara was considered as a case study, with the dataset consisting of 17 multispectral images acquired in days with cloud coverage under 7%. The reference yield map was computed using CANbus data from a combine harvester, and its correlation with NDVI and GRVI indices was explored throughout the whole corn life cycle. In addition, the effect of applying a gaussian filter in the raster CY spatial distribution was explored. Results showed an overall good correlation between remotely sensed tiles and in-field farm data. The Pearson correlation coefficients showed a sharp increase during the vegetative stage of the crop (May and June), followed by a slowly decreasing plateau in July and August dates. Imagery from early June provided the highest correlations. The application of a gaussian filter in the CY map revealed an enhance in correlation of more than 25%. These results pave the path to the development of machine learning based methods exploiting multi-band-multi-temporal data for CY estimation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： CANbus machine learning Precision Agriculture Remote sensing Yield assessment

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