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检索条件"主题词=detection algorithms"

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SMART: Serverless Module Analysis and Recognition Technique for Managed Applications

SMART: Serverless Module Analysis and Recognition Technique ...

引用

IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID)

作者： Adi Ashkenazi Edita Grolman Aviad Elyashar Dudu Mimran Oleg Brodt Yuval Elovici Asaf Shabtai Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Israel Cyber Labs@BGU Ben-Gurion University of the Negev Israel Department of Computer Science Shamoon College of Engineering Beer-Sheva Israel

ISBN: (数字)9798350395662

ISBN: (纸本)9798350395679

Serverless Function-as-a-Service (FaaS) environments enable developers to build and run cloud applications without the need to manage the underlying servers and computing infrastructure, allowing them to focus on implementing the application logic. Such environments contain numerous functions and dynamic resources, e.g., APIs and databases, making it challenging to gain insight and context of internal events i.e., recognize modules. Module in a serverless application is a set of functions and resources, that represents a functional unit that shares logical context. This paper presents SMART, a method for automatic analysis and recognition of modules for managed serverless applications. The proposed method creates an event-based graph by analyzing the standard serverless logs that document events involving the application’s functions and resources and utilizes well-known community detection algorithms (such as Louvain), with graph centrality metrics (such as degree centrality) to recognize the modules. SMART enables high-level visibility of the application’s structure and logical context which can facilitate security analysis and contribute to improved decision-making of incident response handlers, who typically do not have direct access to the application’s design and code, which can lead to challenges in fully understanding the system’s intricacies. We focused on the popular Amazon Web Services (AWS) Lambda serverless computing platform and evaluated the proposed method on three different demo applications (Airline Booking, VOD, and E-commerce). We compared SMART’s performance to four overlapping community detection algorithms and showed that it outperformed them in the task of module recognition, with a maximum improvement of 61% on the omega index metric compared to the Speaker-Listener Label Propagation algorithm. In addition, we demonstrate that the use of large language models (LLMs) with the knowledge gained by SMART can enrich security analysis insights.

关键词： Measurement Web services Serverless computing Computer architecture Security Indexes Electronic commerce detection algorithms Standards Airline industry

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Construction of Classification Model for Traditional Facial Color Diagnosis of Hypertension in Chinese Medicine

Construction of Classification Model for Traditional Facial ...

引用

IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)

作者： Helin Zhang Bingbing Wang Huashan Ye Jiangxi University of Chinese Medicine Jiangxi China

ISBN: (数字)9798350396133

ISBN: (纸本)9798350396140

Facial color diagnosis is an important method in traditional Chinese medicine diagnosis and treatment, with a rich theoretical basis and clinical experience. We designed an “Improved Multi-task Cascaded Convolutional Neural Network (MTCNN) detection Algorithm” to define the facial color of hypertensive patients, and constructed a classification model for facial color diagnosis of hypertension using the Error Backpropagation Training (BP network) algorithm.

关键词： Hypertension Training Image recognition Image color analysis Neural networks Feature extraction Multitasking detection algorithms Sustainable development Medical diagnostic imaging

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Two-stage approach based on combination of one-class SVM and CNN for epileptic seizure identification

Two-stage approach based on combination of one-class SVM and...

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School on Dynamics of Complex Networks and their Application in Intellectual Robotics (DCNAIR)

作者： Sergei Nazarikov Vadim Grubov Nikita Utashev Oleg Karpov Baltic Center for Neurotechnology and Artificial Intelligence Immanuel Kant Baltic Federal University Kaliningrad Russia National Medical and Surgical Center named after N.I. Pirogov of the Ministry of Health of the Russian Federation Moscow Russia

ISBN: (数字)9798350390834

ISBN: (纸本)9798350390841

Many automated EEG seizure detection methods are able to perform well in terms of precision but fail to keep reasonable recall producing a lot of false positives. To solve this, we propose a two-stage framework combining classical outlier detection methods and neural networks, utilizing real-world EEG data. The first stage uses a one-class support vector machine to filter non-seizure activity, followed by a convolutional neural network to distinguish between false positives and true seizures. Evaluation of metrics tailored for epilepsy detection showed improved precision, with a slight decrease in recall for the proposed approach, suggesting that it may improve the performance of clinical decision support systems.

关键词： Support vector machines Measurement Decision support systems Filtering Neural networks Epilepsy Complex networks Electroencephalography Convolutional neural networks detection algorithms

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Scalable Fire Classification with MobileNetV2-Driven Convolutional Neural Networks

Scalable Fire Classification with MobileNetV2-Driven Convolu...

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Communication, Computing and Signal Processing (IICCCS), IEEE International Conference on

作者： Poonam Shourie Vatsala Anand Deepak Upadhyay Swati Devliyal Sheifali Gupta Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science & Engineering Graphic Era Deemed to Be University Dehradun Uttarakhand India

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

Public safety and the avoidance of extensive damage depend on the prompt and precise identification of fire. The creation of effective and trustworthy fire classification systems is required due to the rising frequency and intensity of wildfires throughout the world. Climate change is making wildfires a more serious concern, therefore finding creative solutions that support the sustainable development goals is imperative. In order to achieve high accuracy and efficiency, this article provide a fire classification model that uses Convolutional Neural Networks (CNNs) with MobileNetV2 architecture. Taking use of MobileNetV2's lightweight design and depth wise separable convolutions, our model is optimized for real-time applications with limited computational resources, like those present in mobile and embedded devices. The proposed model was trained and assessed using a wide range of datasets that included both fire and non-fire photos. The suggested model successfully distinguished between fire and non-fire events, as evidenced by its remarkable 98% classification accuracy. The model's strong feature extraction capabilities and good cross-environment and cross-condition generalization led to this high accuracy. In addition, the model is refined for low-latency inference, which qualifies it for use in real-world scenarios where prompt and accurate fire detection is critical. The potential of the MobileNetV2-based CNN as a workable solution for real-time fire detection in safety-critical applications is highlighted by the combination of high precision and computational economy.

关键词： Accuracy Wildfires Computational modeling Feature extraction Real-time systems Public security Telecommunication computing Convolutional neural networks Sustainable development Low latency communication Climate change Safety Fires detection algorithms

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A Novel Technique of Android Stealth Rooting

A Novel Technique of Android Stealth Rooting

引用

International Conference on Developments in eSystems Engineering (DESE)

作者： Bilal Ahmed Kamal Abdul Basit Shahid Syed Muhammad Sajjad Kashif Kifayat Khwaja Mansoor Ul Hassan Department of Cyber Security Air University Islamabad Pakistan University of Khorfakkan Khorfakkan Sharjah UAE Department of Information Security National University of Science and Technology Islamabad Pakistan

ISBN: (数字)9798350368697

ISBN: (纸本)9798350368703

This study investigates the altered landscape following the rooting of Android devices, focusing on subtle yet significant changes in application data, system functionalities, and unexpected shifts in device behavior. These changes, often underestimated, pose potential vulnerabilities that compromise device security, even enabling the bypassing of banking apps and other root-checking utilities. By highlighting these aspects, the research aims to provide a comprehensive understanding of the implications of rooting in Android 13. This study not only reveals the extent of security risks but also lays the groundwork for developing effective mitigation strategies and improving device security in rooted Android 13 environments.

关键词： Prevention and mitigation Focusing Banking Real-time systems Data systems Mobile applications Security detection algorithms Artificial intelligence

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Detecting OOD Samples via Optimal Transport Scoring Function

Detecting OOD Samples via Optimal Transport Scoring Function

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Heng Gao Zhuolin He Jian Pu ISTBI Fudan University China School of Computer Science Fudan University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

To deploy machine learning models in the real world, researchers have proposed many OOD detection algorithms to help models identify unknown samples during the inference phase and prevent them from making untrustworthy predictions. Unlike methods that rely on extra data for outlier exposure training, post hoc methods detect Out-of-Distribution (OOD) samples by developing scoring functions, which are model agnostic and do not require additional training. However, previous post hoc methods may fail to capture the geometric cues embedded in network representations. Thus, in this study, we propose a novel score function based on the optimal transport theory, named OTOD, for OOD detection. We utilize information from features, logits, and the softmax probability space to calculate the OOD score for each test sample. Our experiments show that combining this information can boost the performance of OTOD with a certain margin. Experiments on the CIFAR-10 and CIFAR-100 benchmarks demonstrate the superior performance of our method. Notably, OTOD outperforms the state-of-the-art method GEN by 7.19% in the mean FPR@95 on the CIFAR-10 benchmark using ResNet-18 as the backbone, and by 12.51% in the mean FPR@95 using WideResNet-28 as the backbone. In addition, we provide theoretical guarantees for OTOD. The code is available in https://***/HengGao12/OTOD.

关键词： Training Machine learning Predictive models Benchmark testing Signal processing Probability Feature extraction Safety Speech processing detection algorithms

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Level Set Reinitialization in Device Boundary Evolution Based on Inverse Design

Level Set Reinitialization in Device Boundary Evolution Base...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Junyu Gao Jifang Qiu Ruize Gao Lan Wu HongXiang Guo Jian Wu School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing P.R. China

ISBN: (数字)9798350379266

ISBN: (纸本)9798350379273

We propose a level set reinitialization algorithm in the inverse design framework to avoid numerical errors in boundary evolution caused by kinks, which lead to stagnation in structural optimization. When applied to a $1\times 2$ wavelength demultiplexer, this method reduces the number of kinks detected by the kink detection algorithm from 1873 to 1745, resulting in continued performance improvement. Moreover, the algorithm demonstrates good generalizability across different devices. We validate this approach by applying it to another $1\times 2$ power splitter and a 6-channel spatial light receiver.

关键词： Performance evaluation Couplings Level set Asia Receivers Linear programming Optical fiber communication detection algorithms Optimization Photonics

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A Study on Lightweight algorithms for Detecting Helmet-Wearing in Electric Scooters

A Study on Lightweight Algorithms for Detecting Helmet-Weari...

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Robotics and Computer Vision (ICRCV), International Conference on

作者： Po Hu Haihui Wang Yuzhuo Wang Chenghui Wang School of Computer Science and Engineering and School of Artificial Intelligence Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798331527426

ISBN: (纸本)9798331527433

In urban transportation, electric bicycles serve as a primary mode of non-motorized transport, leading to increasing concerns about traffic safety, especially the helmet-wearing habits of riders. Helmets are crucial for rider safety, effectively reducing injuries in traffic accidents. Existing helmet detection algorithms struggle with small objects, occluded targets, and complex environments. To address these issues, we propose a helmet-wearing detection algorithm based on YOLOv8n. We enhance the model's feature extraction by incorporating the BiFormer attention mechanism before the first detection head, P3, improving the detection of small objects and overall performance. Additionally, in the Neck part of YOLOv8n, we replace traditional convolution modules with the lightweight G-GSConv module for efficient feature aggregation without sacrificing performance. Building on G-GSConv, we introduce the G-Ghost-Bottleneck layer, using a one-shot aggregation method to design the cross-level part network (GSCSP) module, G-GhostCSP-Bottleneck, replacing the original three CSP modules to reduce model parameters. We further integrate the BSAM attention mechanism based on the Transformer architecture to enhance the detection of dense small objects, thereby improving model accuracy. We also refine the loss function using the MDPIOU method to enhance detection flexibility and robustness. Experimental results on the SHWD dataset show that our improved model achieves an average precision of 88.9%, a 3.6% improvement over the original model. The proposed algorithm effectively improves helmet-wearing detection accuracy in complex traffic scenarios, demonstrating its potential for enhancing urban traffic safety management.

关键词： Attention mechanisms Accuracy Convolution Computational modeling Transportation Feature extraction Transformers Robustness Neck detection algorithms

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Determining the Colliding Vehicle in Traffic Accidents Using Hybrid Machine Learning Algorithm

Determining the Colliding Vehicle in Traffic Accidents Using...

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Sustainable Technology and Engineering (i-COSTE), International Conference on

作者： Rhodessa J. Cascaro Patrick D. Cerna Hernan Rey. N. Jugar Matthew Ira C. Senon College of Cmputer and Information Science Mapua Malayan College Mindanao Davao City Philippines

ISBN: (数字)9798331517335

ISBN: (纸本)9798331517342

In a world rife with vehicular accidents and traffic incidents, it is known that drivers are more likely than not to shift the blame in an accident rather than admit it. Other than that, there is a noticeable lack of models in the academic sector that allow neural networks to differentiate colliding vehicles from one another and are instead fixated on tracking and detecting traffic accidents as a whole. As such, the researchers propose a way of detecting colliding vehicles and classifying both vehicles as either the 'colliding' vehicle or the 'collided' vehicle. The processes in this machine learning pipeline are split into three main parts: crash detection-to which the model would use a crash detection algorithm; footage tracking-of which the model would utilise DeepSORT; and lastly a colliding vehicle classification algorithm that uses Gated Recurrent Units (GRUs), all of which will be combined to form a novel machine learning pipeline. The model exhibits very mixed performances when detecting both Vehicle 1 and Vehicle 2 in our testing phase. When detecting Vehicle 1, the model provides a very poor recall and F1-score, meanwhile the detection of Vehicle 2 exhibits a decent amount of precision, recall, and F1-score. Overall, the model provides an accuracy of 42 % with a macro average precision of 0.45, a macro average recall of 0.29, and a macro $F 1$ -score of about 0.30.

关键词： Machine learning algorithms Pipelines Neural networks Machine learning Object detection Object tracking detection algorithms Accidents Vehicles Testing

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Implementation of Atrous Convolution for Cloud Segmentation in Grayscale & RGB Images from Landsat 8

Implementation of Atrous Convolution for Cloud Segmentation ...

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IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES)

作者： Ammar Abdurrauf Lina Zulaikah Devi Handaya Computer Engineering Marmara University Istanbul Türkiye Computer Science Istanbul Technical University Istanbul Türkiye Electric and Electronics Engineering Istanbul University-Cerrahpasa Istanbul Türkiye Mechanical Engineering Politeknik Negeri Jakarta Depok Indonesia

ISBN: (数字)9798331542016

ISBN: (纸本)9798331542023

Advancements in remote sensing over the past two decades have enabled the production of high-resolution multispectral imagery, necessitating improved analytical methods. Object-Based Image Analysis (OBIA) has become a principal approach with image segmentation being a critical initial step. This study examines various image segmentation methods, particularly focusing on semantic segmentation for cloud images. Accurate cloud detection in satellite imagery is vital for numerous applications, leading to the development of Cloud-Net, an end-to-end cloud detection algorithm utilizing a Fully Convolutional Network (FCN) based on a U-Net architecture. This research implements atrous convolution on Cloud-Net in both contracting and expanding blocks, reinstating vanilla skip connections to reduce complexity. Experimental results using Landsat 8 imagery demonstrate that while the proposed Atrous Cloud-Net improves precision and specific metrics evaluation for grayscale images, the original Cloud-Net performs better for RGB images. The findings highlight the trade-offs between different segmentation methods and the importance of multi-channel imagery for higher accuracy in cloud detection tasks. Future work will focus on further refining segmentation models and exploring additional channels to enhance performance.

关键词： Image segmentation Accuracy Convolution Semantic segmentation Landsat Gray-scale Predictive models Satellite images detection algorithms Remote sensing

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