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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhou, Ziqi Song, Yufei Li, Minghui Hu, Shengshan Wang, Xianlong Zhang, Leo Yu Yao, Dezhong Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Computer Science and Technology Huazhong University of Science and Technology China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversarial perturbation (UAP) have not been thoroughly investigated yet. In this paper, we propose DarkSAM, the first prompt-free universal attack framework against SAM, including a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. We first divide the output of SAM into foreground and background. Then, we design a shadow target strategy to obtain the semantic blueprint of the image as the attack target. DarkSAM is dedicated to fooling SAM by extracting and destroying crucial object features from images in both spatial and frequency domains. In the spatial domain, we disrupt the semantics of both the foreground and background in the image to confuse SAM. In the frequency domain, we further enhance the attack effectiveness by distorting the high-frequency components (i.e., texture information) of the image. Consequently, with a single UAP, DarkSAM renders SAM incapable of segmenting objects across diverse images with varying prompts. Experimental results on four datasets for SAM and its two variant models demonstrate the powerful attack capability and transferability of DarkSAM. Our codes are available at: https://***/CGCL-codes/DarkSAM. © 2024 Neural information processing systems foundation. All rights reserved.

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Epileptic Seizure Detection Using the EEG Signal Empirical Mode Decomposition and Machine Learning

Epileptic Seizure Detection Using the EEG Signal Empirical M...

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2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Nassir, Jana Alasabi, Majeda Qaisar, Saeed Mian Khan, Muzammil Effat University Computer Science Department Jeddah22332 Saudi Arabia Effat University Electrical and Computer Engineering Department Jeddah22332 Saudi Arabia Cesi Engineering School Lineact Laboratory Villeurbanne69100 France University of Swat Department of Computer and Software Technology Swat Pakistan

ISBN: (纸本)9798350347050

Epileptic seizures affect millions of people worldwide. Medical treatments exist to help lessen the severity of the damage caused by these seizures. However, people with epilepsy still struggle with unexpected seizures. People who experience epileptic seizures have Electroencephalogram (EEG) signals that show different features in comparison to a healthy brain. In this study, EEG signals are studied to detect the seizures. The incoming signals are denoised by using linear phase filters. In next step these are divided in fix-length segments. Then, each segment is broken down using the Empirical Mode Decomposition (EMD) into Intrinsic Mode Functions (IMFs). For an automatic identification of EEG signals, features are extracted from the collected IMFs and then processed using machine learning techniques. A dataset on epilepsy that is available to the public is used for evaluation. To determine which is the best predictor for the under-consideration dataset, four different classification methods are performed and the results are examined. The system achieves a classification accuracy of 96.70%. © 2023 IEEE.

关键词： Empirical mode decomposition

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An Unsupervised Convolutional Adversarial Anomaly Detection Model for IoT Data Infrastructure 24

An Unsupervised Convolutional Adversarial Anomaly Detection ...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Wen, Peian Yang, Zhenyu Wu, Lei Qi, Sibo Chen, Juan School of Computer and Software Engineering Xihua University Chengdu China School of Mathematical Sciences University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350319934

Anomaly detection is critical to ensure the IoT (Internet of Things) data infrastructures' Quality of Service. However, due to the complexity of incon-spicuous(indistinct) anomalies, high dynamicity, and lack of anomaly labels in the operational IoT systems and cloud infrastructures, multivariate time series anomaly detection becomes more difficult. Existing approaches are rarely effective in meeting these challenges. In this paper, We propose a novel convolutional adversarial model based on CNN adversarial training with POT (Peak Over Threshold) dynamic threshold selection (CAT-POT) for multivariate time series anomaly detection in the IoT data. We build on the original autoencoder, replacing the fundamental linear changes with a convolutional neural network and adding a convolutional decoder to make an adversarial training architecture. The ability of the model to identify 'slight' anomalies is enhanced through adversarial training, combined with POT dynamically selected thresholds to detect anomalies. We conduct ex-periments on five subsets of various open datasets, and the overall performance of CAT-POT is better than that of the baseline method. The average F1 value of CAT-POT on all datasets is 0.889, which is 5.1% higher than the suboptimal model, with the best improvement reaching 21.5%. © 2022 IEEE.

关键词： Internet of things

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Hybrid Parallel Inference for Large Model on Heterogeneous Clusters for High Throughput 29

Hybrid Parallel Inference for Large Model on Heterogeneous C...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Xu, Chaonong Kong, Weiming Liu, Min Zhang, Mingming Li, Chao Gong, Luqi Beijing Key Lab of Petroleum Data Mining Beijing China Institute of Intelligent Computing Zhejiang Lab Hangzhou China Bupt School of Computer Science National Pilot Software Engineering School Beijing China

ISBN: (纸本)9798350330717

In high-throughput intelligent computing scenarios, multi-device parallelism strategies based on data parallelism or pipeline parallelism have been extensively utilized to accelerate large deep neural network model inference. Data parallelism offers nearly linear improvement in inference speed, but it is limited by the memory capacity of a single device which constrains the model size. On the other hand, pipeline parallelism can support larger models, but the total communication of the activations among devices is high, which limits the improvement of the inference speed. To address the demand for efficient model inference in high-throughput heterogeneous scenarios, we proposes a hybrid parallelism strategy that combines data parallelism and pipeline parallelism. The strategy involves grouping heterogeneous device clusters and then employing inter-group data parallelism along with intra-group pipeline parallelism. Moreover, we propose an algorithm to find an optimal hybrid parallel inference strategy with maximum throughput. The control variables of the strategy includes the number of groups, group-device assignments and model partition ratios. Our experimental evaluation demonstrates that compared to PipeEdge, a pipeline parallel inference framework for heterogeneous cluster, our strategy can achieve 1.7× -3.4× acceleration in an 8-device heterogeneous cluster without loss of accuracy. © 2023 IEEE.

关键词： data parallelism heterogeneous cluster hybrid parallelism model inference pipeline parallelism

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Emergency Vehicle Navigation in Connected Autonomous Systems Using Enhanced Traffic Management System

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IEEE Internet of Things Journal 2025年第12期12卷 20670-20677页

作者： Raja, Gunasekaran Anbalagan, Sudha Gurumoorthy, Sugeerthi Jegathesan, Darshini Girivel, Niveditha Subramanian Sundaram, Varsha Mani Shanmuga Khowaja, Sunder Ali Dev, Kapal Anna University NGNLab Department of Computer Technology MIT Campus Chennai600044 India Vellore Institute of Technology Chennai Centre for Smart Grid Technologies School of Computer Science and Engineering Chennai600127 India Citicorp Services Private Ltd Software Development Chennai600113 India Dublin City University School of Computing Faculty of Engineering and Computing Glasnevin Campus DublinD09 V209 Ireland Munster Technological University Department of Computer Science Bishopstown CorkT12 P928 Ireland University of Johannesburg Department of Institute of Intelligent Systems Johannesburg2006 South Africa Chitkara University Institute of Engineering and Technology Centre for Research Impact Outcome Chitkara University Punjab Rajpura140401 India

Autonomous Vehicle (AV) usage has become predominant in the rapidly evolving landscape of urban transportation. Integrating AVs and non-AVs in the existing traffic infrastructure has significantly increased the complexity of traffic patterns. This research work primes the enhanced Traffic Management System (e-TMS), a solution implemented to expedite Emergency Vehicle (EV) travel in the context of Connected AVs (CAVs) and ensured secured communication employing Public Key Infrastructure (PKI) among the Internet of Vehicles (IoV) framework. When an EV is detected, the IoV system verifies the EV's signal using PKI, ensuring its authenticity and integrity by encrypting the communication between the EV and Road Side Units (RSUs). Further, the e-TMS activates the platooning process where CAVs in the EV's lane shift to the adjacent lanes and dynamically form platoons to create a dedicated lane for the EV. To further optimize the platooning process, an Adaptive Smart Leader Selection (ASLS) algorithm is employed to select a leader vehicle spontaneously among the AVs based on the proximity to the EV, communication reliability, and lane position. Radio Detection and Ranging devices aid this platooning process by providing distance and target velocity, which are needed to maintain the required distance between the AVs and ensure safe platoon formation. The e-TMS enhances EV response times and overall traffic flow efficiency and resulted in 27.8% increase in mean speed compared to the traditional methods. © 2014 IEEE.

关键词： Emergency vehicles

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Model Predictive Control for Automated Network Assurance in Intent-Based Networking enabled Service Function Chains 36

Model Predictive Control for Automated Network Assurance in ...

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36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023

作者： Avgeris, Marios Leivadeas, Aris Athanasopoulos, Nikolaos Lambadaris, Ioannis Falkner, Matthias Carleton University Department of Systems and Computer Engineering Ottawa Canada Ácole de Technologie Supérieure Department of Software and It Engineering Montreal Canada Queen's University School of Electronics Electrical Engineering and Computer Science Belfast United Kingdom Cisco Systems Ottawa Canada

ISBN: (纸本)9781665477161

Recent trends in Network Function Virtualization (NFV) combined with Internet of Things (IoT) and 5G applications have reshaped the network service offering. In particular, Service Function Chains (SFCs) can associate network functions with physical and virtual resources towards providing a complete network service. Concurrently, the management of a continuously expanding network and the fulfillment of the applications' requirements pave the way for autonomic network solutions. Intent Based Networking (IBN) is a novel paradigm that aims to achieve the automatic orchestration of network services and the assurance of their performance. Accordingly, in this paper, we propose a novel automated network assurance model, based on Model Predictive Control, to guarantee the Quality of Service (QoS) and security requirements of multi-tenant and IBN-enabled SFCs. In this context, corrective decisions are proactively taken, in the form of incoming intent relocations among the SFCs. The results reveal that our model can assure with high probability the application requirements and minimize QoS violations. © 2023 IEEE.

关键词： Network function virtualization

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Anomaly Detection in Spatial Information Networks: A Security Knowledge Graph-based Approach

Anomaly Detection in Spatial Information Networks: A Securit...

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Intelligent Computing and Robotics (ICICR), International Conference on

作者： Yayue Tan Yaqi Zhang Hui Liu Yongqing Tang College of Computer and Information Science School of Software Southwest University Chongqing China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350372274

ISBN: (纸本)9798350372281

Anomaly detection stands as a critical element in securing space information networks (SINs). This paper delves into the realm of anomaly detection within dynamic networks, shedding light on established methodologies. The focus lies on scrutinizing various anomaly detection strategies in space information networks, conducting a comprehensive assessment of their practical applications, advantages, and drawbacks. Moreover, it introduces a novel anomaly detection approach driven by deep learning principles. This innovative method merges graph convolution techniques with a network security-oriented knowledge graph, presenting a cutting-edge solution. As a final contribution, the paper extends its purview to the future landscape of anomaly detection within space information networks, offering valuable insights into potential advancements and forthcoming trends in this domain. This holistic investigation seeks to enhance the understanding of anomaly detection paradigms within the unique context of space information networks, aiming to fortify their security measures and operational resilience.

关键词： Knowledge engineering Convolution Network topology Knowledge graphs Market research computer security Anomaly detection

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Deep Learning-based Classification of Lung CT Scan for Accurate Cancer Diagnosis 7

Deep Learning-based Classification of Lung CT Scan for Accur...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Sujatha, D.Christy Lakshmi, T.R. Vijaya Surya, G. Surendar, U. Rajendiran, M. Maranan, Ramya Periyar Maniammai Institute of Science And Technology Department of Software Engineering Tamail Nadu Thanjavur India Mahatma Gandhi Institute of Technology Department of Electronics And Communication Engineering Telangana Hyderabad India K. Ramakrishnan College of Engineering Department of Computer Science And Engineering Tamil Nadu Tiruchirappalli India Srm Institute of Science And Technology Department of Electronics And Communication Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of Computer Science And Engineering Chennai Tamil Nadu Poonamallee India Saveetha School of Engineering SIMATS Department of Research And Innovation Tamil Nadu Chennai India

ISBN: (纸本)9798350359299

This paper investigates the suitability of advanced deep learning models for precise diagnosis of lung cancer from MRI images. Recurrent neural networks (RNN), K-Nearest Neighbors (KNN), ResNet50, and convolutional neural networks (CNN) were all carefully evaluated to determine their unique contributions. The CNN showed off its good performance and capacity to recognize intricate patterns in lung images, achieving an accuracy of 92.3%. KNN demonstrated competitive results, demonstrating the adaptability of non-parametric methods for medical image classification. Remarkably, ResNet50 fared extremely well, exhibiting a remarkable accuracy of 94.8% and verifying the value of deep residual networks in differentiating between intricate features. RNNs gave the analysis a temporal dimension and contributed to its 89.5% accuracy. Information from confusion matrices containing comprehensive classification results was useful in refining the model. Spatial representations of expected cancer cell locations showed the effectiveness of the models by giving doctors visual cues for targeted interventions. Comparisons with the literature show that the results are in line with recent developments in deep learning for medical image analysis. Because of its comprehensive assessment of different deep learning architectures, which provides fresh perspectives that advance the field of lung cancer detection technologies, this work is an invaluable resource for future research. © 2024 IEEE.

关键词： computerized tomography

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Makeup Transfer Based On Laplacian Pyramid Network 34

Makeup Transfer Based On Laplacian Pyramid Network

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Pattern Recognition and Tracking XXXIV 2023

作者： Chen, Yan Gong, Wenhui Alam, Mohammad S. Sang, Jun School of Big Data & Software Engineering Chongqing University Chongqing401331 China Department of Electrical Engineering and Computer Science Texas A&M University - Kingsville KingsvilleTX78363-8202 United States

ISBN: (数字)9781510661691

ISBN: (纸本)9781510661684

Makeup transfer aims to extract a specific makeup from a face and transfer it to another face, which can be widely used in portrait beauty, and cosmetics marketing. At present, existing methods can achieve the transfer of the entire facial makeup, but the quality of makeup transfer is not excellent because there may be a mismatch between the two images. In this paper, we propose a facial makeup transfer network based on the Laplacian pyramid, which can better preserve the facial structure from the source image and achieve high-quality transfer results. The model consists of three parts: makeup feature extraction, facial structure feature extraction, and makeup fusion. The makeup extraction part is used to extract the facial makeup from the reference image. And the facial structure feature extraction part is used to extract facial structure from the source image, in order to solve the loss of facial details when extracting facial structural features, we used the method based on Laplacian Pyramid. The makeup fusion part will fuse the facial makeup with facial structure features. Many experiments on the MT dataset have shown that this method can transfer makeup successfully without changing the original facial structure, and achieve advanced performance in various makeup styles. © 2023 SPIE.

关键词： Feature extraction

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Flexible Capacitive Pressure Sensors and Their Applications in Human-Machine Interactions 2

Flexible Capacitive Pressure Sensors and Their Applications ...

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2nd International Symposium on Sensor Technology and Control, ISSTC 2023

作者： Fang, Chenyu Fu, Tianli Shi, Yao Changzhou No.1 High School Changzhou China Wuhan University of Technology School of Mechanical and Electronic Engineering Wuhan China Southwest University College of Computer and Information Science College of Software Chongqing China

ISBN: (纸本)9798350314465

Flexible capacitive pressure sensors have shown excellent application potential in human-machine interactions, wearable electronic devices, biological and medical, and electronic skin. Compared with piezoresistive and piezoelectric pressure sensors, capacitive pressure sensors with advantages such as high flexibility, high stability, low consumption, and simple construction. Because of these advantages, flexible capacitive pressure sensors have become the key research direction of pressure sensors in recent years. This paper introduces the working principle of flexible capacitive pressure sensors and discusses numerous research on improving the performance of flexible capacitive pressure sensors. Then two methods of improving the dielectric layer structures to improve the sensitivity of the sensor and some related works are reviewed. Finally, the flexible capacitive pressure sensors' huge potential for advances in human-machine interactions areas such as health care and motion monitoring is also discussed. The paper concludes with an overview of future research directions of improving the performance of flexible capacitive pressure sensors. © 2023 IEEE.

关键词： Pressure sensors

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