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arXiv 2022年

作者： Saoudi, Oussama Singh, Ishwar Mahyar, Hamidreza The Department of Computing and Software McMaster University Canada W Booth School of Engineering Practice and Technology Faculty of Engineering McMaster University Canada

Autonomous vehicles are the culmination of advances in many areas such as sensor technologies, artificial intelligence (AI), networking, and more. This paper will introduce the reader to the technologies that build autonomous vehicles. It will focus on open-source tools and libraries for autonomous vehicle development, making it cheaper and easier for developers and researchers to participate in the field. The topics covered are as follows. First, we will discuss the sensors used in autonomous vehicles and summarize their performance in different environments, costs, and unique features. Then we will cover Simultaneous Localization and Mapping (SLAM) and algorithms for each modality. Third, we will review popular open-source driving simulators, a cost-effective way to train machine learning models and test vehicle software performance. We will then highlight embedded operating systems and the security and development considerations when choosing one. After that, we will discuss Vehicle-to-Vehicle (V2V) and Internet-of-Vehicle (IoV) communication, which are areas that fuse networking technologies with autonomous vehicles to extend their functionality. We will then review the five levels of vehicle automation, commercial and open-source Advanced Driving Assistance Systems, and their features. Finally, we will touch on the major manufacturing and software companies involved in the field, their investments, and their partnerships. These topics will give the reader an understanding of the industry, its technologies, active research, and the tools available for developers to build autonomous vehicles. Copyright © 2022, The Authors. All rights reserved.

关键词： software testing

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LWS: A Framework for Log-based Workload Simulation in Session-based SUT

arXiv

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arXiv 2023年

作者： Han, Yongqi Du, Qingfeng Xu, Jincheng Zhao, Shengjie Chen, Zhekang Cao, Li Yin, Kanglin Pei, Dan School of Software Engineering Tongji University Shanghai201804 China Bizseer Beijing100083 China Department of Computer Science and Technology Tsinghua University Beijing100084 China

Artificial intelligence for IT Operations (AIOps) plays a critical role in operating and managing cloud-native systems and microservice-based applications but is limited by the lack of high-quality datasets with diverse scenarios. Realistic workloads are the premise and basis of generating such AIOps datasets, with the session-based workload being one of the most typical examples. Due to privacy concerns, complexity, variety, and requirements for reasonable intervention, it is difficult to copy or generate such workloads directly, showing the importance of effective and intervenable workload simulation. In this paper, we formulate the task of workload simulation and propose a framework for Log-based Workload Simulation (LWS) in session-based systems. LWS extracts the workload specification including the user behavior abstraction based on agglomerative clustering as well as relational models and the intervenable workload intensity from session logs. Then LWS combines the user behavior abstraction with the workload intensity to generate simulated workloads. The experimental evaluation is performed on an open-source cloud-native application with both well-designed and public real-world workloads, showing that the simulated workload generated by LWS is effective and intervenable, which provides the foundation of generating high-quality AIOps datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Abstracting

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Diffusion-based Reinforcement Learning for Edge-enabled AI-Generated Content Services

TechRxiv

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TechRxiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Huang, Huawei Mao, Shiwen School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore School of Software Engineering Sun Yat-Sen University Zhuhai China Department of Electrical and Computer Engineering Auburn University Auburn United States

As Metaverse emerges as the next-generation Internet paradigm, the ability to efficiently generate content is paramount. AI-Generated Content (AIGC) emerges as a key solution, yet the resource-intensive nature of large Generative AI (GAI) models presents challenges. To address this issue, we introduce an AIGC-as-a-Service (AaaS) architecture, which deploys AIGC models in wireless edge networks to ensure broad AIGC services accessibility for Metaverse users. Nonetheless, an important aspect of providing personalized user experiences requires carefully selecting AIGC Service Providers (ASPs) capable of effectively executing user tasks, which is complicated by environmental uncertainty and variability. Addressing this gap in current research, we introduce the AI-Generated Optimal Decision (AGOD) algorithm, a diffusion model-based approach for generating the optimal ASP selection decisions. Integrating AGOD with Deep Reinforcement Learning (DRL), we develop the Deep Diffusion Soft Actor-Critic (D2SAC) algorithm, enhancing the efficiency and effectiveness of ASP selection. Our comprehensive experiments demonstrate that D2SAC outperforms seven leading DRL algorithms. Furthermore, the proposed AGOD algorithm has the potential for extension to various optimization problems in wireless networks, positioning it as a promising approach for future research on AIGC-driven services. The implementation of our proposed method is available at: https://***/Lizonghang/AGOD. © 2023, CC BY.

关键词： Reinforcement learning

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Efficient Region-Aware Neural Radiance Fields for High-Fidelity Talking Portrait Synthesis

Efficient Region-Aware Neural Radiance Fields for High-Fidel...

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International Conference on Computer Vision (ICCV)

作者： Jiahe Li Jiawei Zhang Xiao Bai Jun Zhou Lin Gu School of Computer Science and Engineering State Key Laboratory of Software Development Environment Jiangxi Research Institute Beihang University School of Information and Communication Technology Griffith University RIKEN AIP The University of Tokyo

This paper presents ER-NeRF, a novel conditional Neural Radiance Fields (NeRF) based architecture for talking portrait synthesis that can concurrently achieve fast convergence, real-time rendering, and state-of-the-art performance with small model size. Our idea is to explicitly exploit the unequal contribution of spatial regions to guide talking portrait modeling. Specifically, to improve the accuracy of dynamic head reconstruction, a compact and expressive NeRF-based Tri-Plane Hash Representation is introduced by pruning empty spatial regions with three planar hash encoders. For speech audio, we propose a Region Attention Module to generate region-aware condition feature via an attention mechanism. Different from existing methods that utilize an MLP-based encoder to learn the cross-modal relation implicitly, the attention mechanism builds an explicit connection between audio features and spatial regions to capture the priors of local motions. Moreover, a direct and fast Adaptive Pose Encoding is introduced to optimize the head-torso separation problem by mapping the complex transformation of the head pose into spatial coordinates. Extensive experiments demonstrate that our method renders better high-fidelity and audio-lips synchronized talking portrait videos, with realistic details and high efficiency compared to previous methods. Code is available at https://***/Fictionarry/ER-NeRF.

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ALOFT: A Lightweight MLP-Like Architecture with Dynamic Low-Frequency Transform for Domain Generalization

ALOFT: A Lightweight MLP-Like Architecture with Dynamic Low-...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Jintao Guo Na Wang Lei Qi Yinghuan Shi State Key Laboratory for Novel Software Technology Nanjing University National Institute of Healthcare Data Science Nanjing University School of Computer Science and Engineering Southeast University

Domain generalization (DG) aims to learn a model that generalizes well to unseen target domains utilizing multiple source domains without re-training. Most existing DG works are based on convolutional neural networks (CNNs). However, the local operation of the convolution kernel makes the model focus too much on local representations (e.g., texture), which inherently causes the model more prone to overfit to the source domains and hampers its generalization ability. Recently, several MLP-based methods have achieved promising results in supervised learning tasks by learning global interactions among different patches of the image. Inspired by this, in this paper, we first analyze the difference between CNN and MLP methods in DG and find that MLP methods exhibit a better generalization ability because they can better capture the global representations (e.g., structure) than CNN methods. Then, based on a recent lightweight MLP method, we obtain a strong baseline that outperforms most state-of-the-art CNN-based methods. The baseline can learn global structure representations with a filter to suppress structureirrelevant information in the frequency space. Moreover, we propose a dynAmic LOw-Frequency spectrum Transform (ALOFT) that can perturb local texture features while preserving global structure features, thus enabling the filter to remove structure-irrelevant information sufficiently. Extensive experiments on four benchmarks have demonstrated that our method can achieve great performance improvement with a small number of parameters compared to SOTA CNN-based DG methods. Our code is available at https://***/lingeringlight/ALOFT/.

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DomainDrop: Suppressing Domain-Sensitive Channels for Domain Generalization

DomainDrop: Suppressing Domain-Sensitive Channels for Domain...

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International Conference on Computer Vision (ICCV)

作者： Jintao Guo Lei Qi Yinghuan Shi State Key Laboratory for Novel Software Technology Nanjing University National Institute of Healthcare Data Science Nanjing University School of Computer Science and Engineering Southeast University

Deep Neural Networks have exhibited considerable success in various visual tasks. However, when applied to unseen test datasets, state-of-the-art models often suffer performance degradation due to domain shifts. In this paper, we introduce a novel approach for domain generalization from a novel perspective of enhancing the robustness of channels in feature maps to domain shifts. We observe that models trained on source domains contain a substantial number of channels that exhibit unstable activations across different domains, which are inclined to capture domain-specific features and behave abnormally when exposed to unseen target domains. To address the issue, we propose a DomainDrop framework to continuously enhance the channel robustness to domain shifts, where a domain discriminator is used to identify and drop unstable channels in feature maps of each network layer during forward propagation. We theoretically prove that our framework could effectively lower the generalization bound. Extensive experiments on several benchmarks indicate that our framework achieves state-of-the-art performance compared to other competing methods. Our code is available at https://***/lingeringlight/DomainDrop.

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An Effective Moving Cast Shadow Detection Framework for Surveillance Video Analysis

SSRN

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SSRN 2022年

作者： Dai, Jiangyan Zhang, Huihui Gao, Jin Yi, Yugen Wang, Bingshu School of Computer Engineering Weifang University Weifang China School of Computer Science and Information Engineering HeFei University of Technology Hefei China School of Software Jiangxi Normal University Nanchang China School of Software Taicang Campus Northwestern Polytechnical University Suzhou China

Moving object detection is crucial in intelligent surveillance system, such as object tracking, object classification, and so on. However, moving cast shadows seriously degrade the accuracy of moving object detection and are often detected as objects, resulting in object merging, object loss or shape distortion. To overcome the drawbacks, we propose an original moving shadow detection approach by incorporating the broad learning system. Firstly, according to the color constancy and texture consistency of shadows, the color, texture and gradient features are extracted by input frames and a background. Then, the broad learning system is introduced, which can map input samples into a more appropriate feature space. Meanwhile, the BLS offers an alternative way, which makes it classify moving cast shadows effectively and efficiently. Finally, a post-processing is designed for keeping complete moving objects. Extensive experiments verify the proposed method’s effectiveness and generalization ability on outdoor and indoor surveillance scenes. © 2022, The Authors. All rights reserved.

关键词： Textures

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Redesign Visual Transformer For Small Datasets

Redesign Visual Transformer For Small Datasets

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Jingjie Wang Xiang Wei Siyang Lu Mingquan Wang Xiaoyu Liu Wei Lu School of Software Engineering Beijing Jiaotong University Beijing China School of Computer and Information Technology Beijing Jiaotong University Beijing China

Nowadays, the self-attention mechanism has become a resound of visual feature extraction along with convolution. The transformer network composed of self-attention has developed rapidly and made remarkable achievements in visual tasks. The self-attention shows the potential to replace convolution as the primary method of visual feature extraction in ubiquitous intelligence. Nevertheless, the development of the Visual Transformer still suffer from the following problems: a) The self-attention mechanism has a low inductive bias, which leads to large data demand and a high training cost. b) The Transformer backbone network cannot adapt well to the low visual information density and performs unsatisfactorily under low resolution and small-scale datasets. To tackle the abovementioned two problems, this paper proposes a novel algorithm based on the mature Visual Transformer architecture, which is dedicated to exploring the performance potential of the Transformer network and its kernel self-attention mechanism on small-scale datasets. Specifically, we first propose a network architecture equipped with multi-coordination strategy to solve the self-attention degradation problem inherent in the existing Transformer architecture. Secondly, we introduce consistent regularization into the Transformer to make the self-attention mechanism acquire more reliable feature representation ability in the case of insufficient visual features. In the experiments, CSwin Transformer, the mainstream visual model, is selected to verify the effectiveness of the proposed method on the prevalent small datasets, and superior results are achieved. In particular, without pre-training, our accuracy on the CIFAR-100 dataset is improved by 1.24% compared to CSwin.

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Extended Abstract of Candidate Test Set Reduction for Adaptive Random Testing: An Overheads Reduction Technique

Extended Abstract of Candidate Test Set Reduction for Adapti...

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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Rubing Huang Haibo Chen Weifeng Sun Dave Towey Faculty of Information Technology Macau University of Science and Technology Taipa Macau China School of Computer Science and Communication Engineering Jiangsu University Zhenjiang Jiangsu China School of Big Data and Software Engineering Chongqing University Chongqing China School of Computer Science University of Nottingham Ningbo China Ningbo Zhejiang China

This document 1 is an extended abstract of a Science of Computer Programming paper, "Candidate Test Set Reduction for Adaptive Random Testing: An Overheads Reduction Technique," presented as a J1C2 (Journal publication first, Conference presentation following) at the 30th IEEE International Conference on software Analysis, Evolution and Reengineering (Saner 2023).The paper presents a candidate set reduction strategy to enhance the Fixed-Sized-Candidate-Set version of Adaptive Random Testing (FSCS-ART). The proposed method reduces the number of randomly-generated candidate test cases by retaining valuable, unused candidates from previous iterations. As the computational costs associated with a stored/retained candidate are less than the costs associated with a randomly-generating one, the overall computational overheads of FSCS-ART are reduced. The reported experimental studies show that the proposed method has a comparable failure-detection effectiveness to FSCS-ART, but less computational overheads.

关键词： Costs Programming software Power capacitors Computational efficiency Testing

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Global and local complementary multi-path feature fusion network for the classification of crop remote sensing images 4

Global and local complementary multi-path feature fusion net...

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2024 4th International Conference on Artificial Intelligence and Industrial technology Applications, AIITA 2024

作者： Zhang, Keqian Cui, Tengfei Wu, Wei Zhang, Yichi Zhang, Ziqian Si, Qiliang Song, Xiangyang Cheng, Gang Henan Polytechnic University College of Surveying and Land Information Engineering 2001 Century Road Jiaozuo Henan China Boston University Metropolitan College 1010 Commonwealth Avenue BostonMA United States Guilin University of Technology College of Geomatics and Geoinformation No. 319 Yanshan Street Guangxi Guilin China Hohai University School of Artificial Intelligence and Automation No. 8 Focheng West Road Jiangning District Jiangsu Nanjing China Henan University of Science and Technology School of Software No. 48 Xiyuan Road Jianxi District Henan Luoyang China

The accuracy and efficiency of crop distribution information extraction are pivotal in ensuring global food security. In long-time-series optical satellite data, most existing methods focus on extracting spatial features using Convolutional Neural Networks (CNNs), which do not adequately mine and model the spatial-temporal information. The development of the attention mechanism allows for the extraction of global features in remote-sensing images of long temporal sequences. To extract global attentional features with complementary features in crop remote sensing images, we propose a Global and Local Complementary Multi-path Feature Fusion Network (GLMP), which is capable of extracting global features from remote sensing images of long temporal sequence, that enhances the local characteristics of crop images derived from CNNs, thus obtaining more effective multi-scale complementary features. This extraction of features enhances the comprehension of crop images, thereby boosting the performance of associated tasks. Within GLMP, we introduce two pivotal modules: the Hybrid Attention and Convolutional Paths Module (HACM) and the Multi-path Feature Fusion Module (MPFM). These modules synergistically converge multi-path features, yielding more discriminative feature information. Experimental results on the ZueriCrop dataset show that the proposed GLMP technique is effective;it performs promisingly having a total accuracy of 90.2% and an F1 value of 62.5%. Furthermore, the ablation study verifies the substantial improvement in classification accuracy for remote sensing crop images of long-time series in nature, specifically attributed to the HACM and MPFM modules. © Published under licence by IOP Publishing Ltd.

关键词： Convolutional neural networks

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