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AWNet: Negative Obstacle Semantic Segmentation Network Based on Adaptive Weight Loss

AWNet: Negative Obstacle Semantic Segmentation Network Based...

引用

IEEE international conference on Industrial Informatics (IndIN)

作者： Jiarui Zhao Guizhen Yu Jie Wang Zhangyu Wang Junjie Zhang Yunsong Feng State Key Lab of Intelligent Transportation System School of Transportation Science and Engineering The Key Laboratory of Autonomous Transportation Technology for Special Vehicles Ministry of Industry and Information Technology Beihang University Beijing China State Key Lab of Intelligent Transportation System Research Institute for Frontier Science Hefei innovation research institute of Beihang university Beihang University Hefei China State Key Laboratory of Pulsed Power Laser Technology National University of Defense Technology Heufei China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

On roads, both vehicles and pedestrians will leave traces after passing by. Slight traces will not cause any trouble to the passing vehicles and pedestrians subsequently. However, there are certain potholes in the road that have been formed by repeated crushing of vehicles. It may cause mechanical damage to following vehicles and lead to accidents as vehicles get stuck in the potholes. Thus, the segmentation of negative obstacles is crucial for the safe driving of self-driving vehicles. This paper proposed an AWNet neural network to this end, and quantitative analyses were conducted based on the publicly available dataset DRNO. Specifically, a random cut-and-joint module (RCJ) was proposed to augment the dataset. The crossentropy loss function was modified by adding an adaptive weight module, i.e., the adaptive weight cross-entropy loss function. It allows the model to focus on negative obstacles during implementation. Furthermore, this paper proposed a post-processing module for adaptive viewpoint conversion based on depth maps, which is capable of fusing depth maps and RGB images to convert the front view into a BEV viewpoint. The mAcc for Negative obstacle semantic segmentation can reach $78.39\%$ , the mIoU can reach $73.21{{\% }}$ , the mF 1 can reach $79.23{{\% }}$ . The experimental results reveal that the AWNet is able to state-of-the-art performance compared with other networks.

关键词： Adaptation models Pedestrians Statistical analysis Roads Semantic segmentation Semantics Neural networks data augmentation data models Informatics

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EdgeMLBalancer: A Self-Adaptive Approach for Dynamic Model Switching on Resource-Constrained Edge Devices

EdgeMLBalancer: A Self-Adaptive Approach for Dynamic Model S...

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IEEE international conference on Software Architecture Workshops (ICSAW)

作者： Akhila Matathammal Kriti Gupta Larissa Lavanya Ananya Vishal Halgatti Priyanshi Gupta Karthik Vaidhyanathan Software Engineering Research Center IIIT Hyderabad India IIIT Hyderabad India

ISBN: (数字)9798331533366

ISBN: (纸本)9798331533373

The widespread adoption of machine learning (ML) on edge devices, such as mobile phones, laptops, IoT devices, etc. has enabled real-time AI applications in resource-constrained environments. Existing solutions for managing computational resources often focus narrowly on accuracy or energy efficiency, failing to adapt dynamically to varying workloads. Furthermore, the existing system lack robust mechanisms to adaptively balance CPU utilization, leading to inefficiencies in resource-constrained scenarios such as real-time traffic monitoring. To address these limitations, we propose a Self-Adaptive approach that optimizes CPU utilization and resource management on edge devices. Our approach EdgeMLBalancer balances between models through dynamic switching, guided by real-time CPU usage monitoring across processor cores. Tested on real-time traffic data, the approach adapts object detection models based on CPU usage, ensuring efficient resource utilization. The approach leverages epsilon-greedy strategy which promotes fairness and prevents resource starvation, maintaining system robustness. The results of our evaluation demonstrate significant improvements by balancing computational efficiency and accuracy, highlighting the approach's ability to adapt seamlessly to varying workloads. This work lays the groundwork for further advancements in self-adaptation for resource-constrained environments.

关键词： Adaptation models Image edge detection Computational modeling Switches Dynamic scheduling Real-time systems Energy efficiency Computational efficiency Resource management Monitoring

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Information Geometry of the Locally Most Powerful Test

Information Geometry of the Locally Most Powerful Test

引用

international conference on Communication Technology (ICCT)

作者： Yongqiang Cheng Xiaoqiang Hua Hao Wu Hongqiang Wang College of Electronic Science and Engineering National University of Defense Technology Changsha China

The locally most powerful (LMP) test for weak signal detection is studied from a information-geometric perspective. In such a framework, the LMP test is identified as the norm of natural whitened gradient on the statistical manifold consisting of a family of parametric probability distributions, which indicates that the LMP test pursues the steepest learning directions from the null hypothesis to the empirical distribution of the observed data on the manifold. A concrete geometrical interpretation of the LMP test in the theory of information geometry is presented, which leads to an immediate extension of the LMP test to a vector valued parameter case. Example of multi-component sinusoidal signal detection under low SNR conditions confirms a practical importance of the extended test.

关键词： Manifolds Information geometry Detectors Probability density function Probability distribution Communications technology Signal detection

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Safety or Not? A Comparative Study for Deep learning Apps on Smartphones

Safety or Not? A Comparative Study for Deep Learning Apps on...

引用

IEEE international conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Jin Au-Yeung Shanshan Wang Yuchen Liu Zhenxiang Chen Information Science and Engineering University of Jinan Jinan China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Recent years have witnessed an astonishing explosion in the evolution of mobile applications powered by deep learning (DL) technologies. Considering that inference of DL models in the cloud requires transferring user data to server, which is prone to the risk of user privacy leakage, many developers choose to deploy the models on local devices for executing the inference process. However, this also raises a number of other security issues, such as adversarial attacks, model stealing attacks, etc. To explore the security issues that exist in deep learning applications (DL apps), we conducted the first comprehensive comparative study of the top 200 apps in each category on Google Play. We built DLApplnspector, a vulnerability detection tool that combines dynamic and static analysis methods for dissecting apps, which helped us automate our empirical study on real-world mobile DL apps. First, we identify DL apps and extract their models by using DL Checker. Subsequently, Static Scoper is provided to detect encryption and reusability of DL models. Finally, within Dynamic Scoper, we use reverse engineering techniques on the network traffic to parse out the packets and collect side-channel information during application runtime. Our research shows that the majority of developers prefer to use open-source models, with almost 92% of models successfully parsed. This suggests that most models are unprotected. DL apps are more likely to upload user behaviour and collect private data than Non-DL apps. We provide security recommendations for developers and users to address the issues discovered.

关键词： Deep learning Privacy Telecommunication traffic Static analysis data models Mobile applications Encryption

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Generating High-Resolution Fire Images with Controllable Attributes via Generative Adversarial Networks

Generating High-Resolution Fire Images with Controllable Att...

引用

international conference on Control, Automation and Systems ( ICCAS)

作者： Nguyen Quoc Dung Hakil Kim Electrical and Computer Engineering Inha University Korea

Obtaining realistic fire images using deep learning models and recent versions of Generative adversarial networks (GAN) has been proven to be a difficult task due to the unnatural appearance of the generated results. This paper provides a novel approach based on StarGANv2 to generate fire kernels from any input provided as a reference. In addition, a deep learning-based image blending technique performs the migration of the fire kernels to the target scenes. By using any input as a reference, the generated fire image could be controlled to accommodate different environmental factors, resulting in a diverse but equally pseudo-real synthetic dataset. The proposed method generates images that achieve better FID and LPIPS values than StarGANv2 for both a public dataset (AI Hub) and a privately-owned dataset (Visionin). In addition, YOLOv4 is used as a fire detection model to evaluate the synthetic data on improving the performance of the detected network. Compared to the model trained on the real data, the model trained on the combined dataset outperforms 2%~14% higher.

关键词： Training Shape Image synthesis Generative adversarial networks data models Environmental factors Kernel

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Explainable Pre-Trained Models for Skin Cancer Classification

Explainable Pre-Trained Models for Skin Cancer Classificatio...

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IEEE SSD international Multi-conference on Systems, Signals and Devices

作者： Abdelkader Alrabai Amira Echtioui Fathi Kallel Advanced Technologies for Medicine and Signals Laboratory ‘ATMS’ National Engineering School of Sfax (ENIS) Sfax University Sfax Tunisia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Skin cancers represent a significant health issue, posing considerable challenges in early detection and accurate classification. This study investigated the effectiveness of Deep learning (DL) models for skin cancers classification using dermoscopic images. Two pre-trained models, DenseNet169 and VGG16, were employed. The DenseNet169 model delivered the highest accuracy, reaching 90.61% while, the VGG16 model achieved an accuracy of 84.24%. The two models were assessed using key performance metrics to conduct a comprehensive evaluation of their effectiveness and overall performance. This analysis provided significant insights on various aspects of each model, allowing a detailed comparison. It also provided a clearer comprehension of the strengths and weaknesses of both models, aiding in informed decision-making and driving further optimization. In addition, three explainable AI (XAI) techniques were applied to deliver perceptions into the decision-making process. These techniques enhance model interpretability by emphasizing the regions of the input images that contribute most to the classification decision, thus improving the transparency and reliability of the model's predictions. Reliable skin cancer classification has the potential to assist healthcare specialists, particularly dermatologists, in making early diagnoses, facilitating timely interventions, and ultimately improving patient outcomes. This study offers valuable contributions for enhancing skin cancer detection and classification, supporting accurate diagnosis and treatment development for medical practitioners.

关键词： Measurement Accuracy Medical services Predictive models data models Skin Reliability Optimization Medical diagnostic imaging Skin cancer

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Threat Detection Using MLP for IoT Network 25th

Threat Detection Using MLP for IoT Network

引用

25th international conference on Internet Computing and IoT, ICOMP 2024, and 22nd international conference on Embedded Systems, Cyber-physical Systems, and Applications, ESCS 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied Computing, CSCE 2024

作者： Taylor, Genea Johnson, David Roy, Kaushik Department of Computer Science North Carolina Agricultural and Technical State University GreensboroNC27411 United States

ISBN: (纸本)9783031859229

While the popularity of IoT networks has grown significantly, they remain highly vulnerable to various cyber-attacks. These attacks can disrupt services, compromise sensitive data, and damage the integrity of IoT ecosystems. Machine learning (ML) techniques, particularly deep learning (DL) models, have been employed to effectively detect and mitigate such threats by identifying abnormal patterns in network traffic. In this paper, we propose utilizing a multilayer perceptron (MLP)-based machine learning model to detect cyber-attacks by using the NF-ToN-IoT dataset, which contains a diverse set of cyber-attacks. The MLP model has been trained and optimized to distinguish between normal and malicious activity. Our results demonstrate the effectiveness of this approach, with the MLP achieving a training accuracy of approximately 97% and a test accuracy of around 95–97%. This high accuracy indicates the model’s capability to generalize well across different attacks while creating a robust solution for real-time threat detection and mitigation in IoT networks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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A Comprehensive Machine learning Methodology for Embedded Systems PHM

A Comprehensive Machine Learning Methodology for Embedded Sy...

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IEEE international conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Juliano Pimentel Alistair A. McEwan Hong Qing Yu School of Computing and Engineering University of Derby Derby UK

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Techniques of Machine learning (ML) have recently been widely used in several applications, but not much for embedded systems, particularly those of safety-critical functionality, and are expected to help solve complex PHM and RUL which are difficult to be overcome with traditional approaches. This paper proposes a novel approach to encompass data-driven system-level health management, which comprises three main processes: an exhaustive offline training using machine learning techniques to derive a model capable of predicting the system of interest within fixed performance criteria; followed by its deployment on the targeted embedded system for real-time health assessment and finally the ability to provide a multi-step ahead forecasting for the system *** ability to monitor the current state of the systems and predict their behaviour becomes essential. Therefore, techniques to establish fault tolerance and fault prediction are required. Condition monitoring (CM), prognostics and health management (PHM) and remaining useful life (RUL) are enablers for fault detection, fault progression and system *** paper presents a ML-based methodology for the PHM of embedded systems, aiming at generalization and real-time performance.

关键词： Training Embedded systems Taxonomy Machine learning Real-time systems Security Prognostics and health management

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An Empirical Study on Software Requirements Classification Method based on Mobile App User Comments

An Empirical Study on Software Requirements Classification M...

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IEEE international conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Huan Jin Hongyan Wan Ziruo Li Wenxuan Wang School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan China School of Information Engineering Wuhan University of Technology Wuhan China

User comments are one of the main ways for IT companies to obtain software evolution requirements. There are two major methods used to classify the software requirements: the traditional user requirements mining method and the user comments requirements mining. The advantage of traditional user requirements mining is that it can communicate with users face to face, but it is time consuming and the results may not be accurate. Therefore, in this paper, we use the user comments requirements mining method to compare the labeling effect of classification methods on the data set of 19,673 comments. The experimental results show that the combination of TF-IDF and logistic regression (LR) works best on the labeled dataset. This experiment combined with word cloud map has excellent effect on obtaining user requirements.

关键词： Machine learning algorithms Software quality Tag clouds Libraries Mobile applications Software reliability data mining

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An Energy-Constrained Optimization-Based Structured Pruning Method for Deep Neural Network Compression

An Energy-Constrained Optimization-Based Structured Pruning ...

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international conference on Communication Technology (ICCT)

作者： Siping Liu Qiang Li Bo Peng Yanping Qiao Maosong Lin Qiang Zeng School of Information Engineering Southwest University of Science and Technology Mianyang China Science and Technology on Altitude Simulation Laboratory Mianyang China School of Computer Science and Technology Southwest University of Science and Technology Mianyang China

Deep neural networks are widely used in modern intelligent applications due to their superior ability to express reality, and these intelligent applications run on highly energy-constrained edge devices. Neural network structured pruning is an efficient method to reduce the energy consumption of neural networks. This paper proposes an energy-constrained optimization-based structured pruning method to compress deep neural network. This method uses the energy budget provided by edge devices as a constraint for neural network pruning. The key idea is to formulate deep neural network structured pruning as an optimization problem, in which the energy estimate of each layer is used as the Frobenius norm optimization constraint for the convolution kernel weights. Then the importance of all convolution kernels in the network can be evaluated, thereby the neural network structure can be pruned according to the importance to reduce the energy consumption and memory size of the neural network. Compared with traditional heuristic structured pruning methods, our proposal enables neural networks to achieve higher accuracy with the same or lower energy budget.

关键词： Deep learning Performance evaluation Energy consumption Convolution Neural networks Memory management Proposals

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