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MonoPlane: Exploiting Monocular Geometric Cues for Generalizable 3D Plane Reconstruction

MonoPlane: Exploiting Monocular Geometric Cues for Generaliz...

引用

IEEE/RSJ international conference on Intelligent Robots and systems (IROS)

作者： Wang Zhao Jiachen Liu Sheng Zhang Yishu Li Sili Chen Sharon X Huang Yong-Jin Liu Hengkai Guo Department of Computer Science and Technology BNRist MOEKey Laboratory of Pervasive Computing Tsinghua University Beijing China College of Information Sciences and Technology Pennsylvania State University University Park PA USA Bytedance Inc. Beijing China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This paper presents a generalizable 3D plane detection and reconstruction framework named MonoPlane. Unlike previous robust estimator-based works (which require multiple images or RGB-D input) and learning-based works (which suffer from domain shift), MonoPlane combines the best of two worlds and establishes a plane reconstruction pipeline based on monocular geometric cues, resulting in accurate, robust and scalable 3D plane detection and reconstruction in the wild. Specifically, we first leverage large-scale pre-trained neural networks to obtain the depth and surface normals from a single image. These monocular geometric cues are then incorporated into a proximity-guided RANSAC framework to sequentially fit each plane instance. We exploit effective 3D point proximity and model such proximity via a graph within RANSAC to guide the plane fitting from noisy monocular depths, followed by image-level multi-plane joint optimization to improve the consistency among all plane instances. We further design a simple but effective pipeline to extend this single-view solution to sparse-view 3D plane reconstruction. Extensive experiments on a list of datasets demonstrate our superior zero-shot generalizability over baselines, achieving state-of-the-art plane reconstruction performance in a transferring setting. Our code is available at https://***/thuzhaowang/MonoPlane.

关键词： Surface reconstruction Solid modeling Three-dimensional displays Pipelines Fitting Neural networks Surface fitting Noise measurement Image reconstruction Optimization

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Data-Enhanced Prediction with Decomposition and Amplitude-Aware Permutation Entropy in Distributed Computing systems

Data-Enhanced Prediction with Decomposition and Amplitude-Aw...

引用

IEEE international conference on systems, Man and Cybernetics

作者： Haitao Yuan Qinglong Hu Jing Bi Wei Zhang Jia Zhang MengChu Zhou School of Automation Science and Electrical Engineering Beihang University Beijing China Dept. of Computer Science City University of Hong Kong Hong Kong China College of Computer Beijing University of Technology Beijing China Dept. of Computer Science and Engineering University of Connecticut Storrs CT USA Dept. of Computer Science Southern Methodist University Dallas TX USA Dept. of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In recent years, distributed computing has wit-nessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. However, these data often exhibit non-linearity, high volatility, and inter-dependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVI for achieving high-accuracy workload prediction in distributed computing systems. SVI employs the Savitzky-Golay filter and variational mode decomposition for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVI improves the prediction accuracy by 35.4% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVI enhances the prediction accuracy of workload, CPU, and memory by 62.5%, 65.6%, and 69.1 %, respectively.

关键词： Training Accuracy Costs Computational modeling Memory management Organizations Quality of service Predictive models Market research Internet

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modeling and Analysis of Multi-Line Orders in Multi-Tote Storage and Retrieval Autonomous Mobile Robot systems

Modeling and Analysis of Multi-Line Orders in Multi-Tote Sto...

引用

IEEE international conference on Automation Science and Engineering (CASE)

作者： Xiaotao Shan Yichao Jin Peizheng Li Koichi Kondo Bristol Research and Innovation Laboratory Toshiba Europe Ltd. U.K. Corporate Technology Planning Div. Toshiba Corporation Kawasaki Japan

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

As warehouses are emphasizing space utilization and the ability to handle multi-line orders, multi-tote storage and retrieval (MTSR) autonomous mobile robot systems, where robots directly retrieve totes from high shelves, are becoming increasingly popular. This paper presents a novel shared-token, multi-class, semi-open queueing network model to account for multi-line orders with general distribution forms in MTSR systems. The numerical results obtained from solving the SOQN model are validated against discrete-event simulation, with most key performance metrics demonstrating high accuracy. In our experimental setting, results indicate a 12.5% reduction in the minimum number of robots needed to satisfy a specific order arrival rate using the closest retrieval sequence policy compared with the random policy. Increasing the number of tote buffer positions on a robot can greatly reduce the number of robots required in the warehouse.

关键词： measurement Analytical models computer aided software engineering Automation Accuracy Numerical models Mobile robots Autonomous robots

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A Machine Learning Approach for the Detection of Injection Attacks on ADS-B Messaging systems

A Machine Learning Approach for the Detection of Injection A...

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international conference on Computing, Networking and Communications (ICNC)

作者： Joshua Price Hadjar Ould Slimane Khair Al Shamaileh Vijay Devabhaktuni Naima Kaabouch Department of Electrical and Computer Engineering Purdue University Northwest Hammond IN USA School of Electrical Engineering and Computer Science The University of North Dakota Grand Forks ND USA Department of Electrical and Computer Engineering The University of Maine Orono ME USA

This work proposes the use of machine learning (ML) as a candidate for the detection of various types of message injection attacks against automatic dependent surveillance-broadcast (ADSB) messaging systems. Authentic ADS-B messages from a high-traffic area are collected from an open-source platform. These messages are combined with others imposing path modification, ghost aircraft injection, and velocity drift obtained from simulations. Then, ADS-B-related features are extracted from such messages and used to train different ML models for binary classification. For this purpose, authentic ADS-B data is considered as Class 1 (i.e., no attack), while the injection attacks are considered as Class 2 (i.e., presence of attack). The performance of the models is analyzed with metrics, including detection, misdetection, and false alarm rates, as well as validation accuracy, precision, recall, and Fl-score. The resulting models enable identifying the presence of injection attacks with a detection rate of 99.05%, and false alarm and misdetection rates of 0.76% and 1.10%, respectively.

关键词： measurement Analytical models Atmospheric modeling Computational modeling Machine learning Predictive models Feature extraction

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An Association Rule with Split Aggregate Merge Algorithm for Insurance Risk Management

An Association Rule with Split Aggregate Merge Algorithm for...

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Distributed systems, computer Networks and Cybersecurity (ICDSCNC), international conference on

作者： Xiao Zou Xueliang Wu Department of Economics and Finance Zhanjiang University of Science and Technology Zhanjiang China

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Nowadays, Insurance risk management is crucial for minimizing losses in terms of economical perspective. Insurance risk is potential for adverse events that may occur during the policy term which impact the financial stability and ability in paying claims. Despite the rising of insurance buyers, many insurance companies still lack a detailed claim analysis system which leads to delayed settlements, inaccurate assessments and cooperating policyholder satisfaction. To overcome these issues, the methos of Association Rule with Split Aggregate Merge (ARSAM) is proposed to analyses the risk of insurance and incorrect assessment of policyholder’s risk profiles. Also, identify potential risks as well as make informed decisions to optimize their operations improve the individual risk profiles. The proposed ARSAM initially employes with the data collection from The Insurance Company (TIC) Benchmark dataset and extract relevant features from the claim data, which is implemented by the association rule; whereas SAM includes a risk assessment component to identify key metrics from the input data. The proposed ARSAM presents greater results than the existing methods of reciprocal Weibull extension for modeling extreme value distributions on insurance risk management in terms of accuracy (86.52%), precision (91.32%) and recall (90.21%) and f1 score (98.98%) respectively.

关键词： measurement Accuracy Aggregates Insurance Companies Data collection Benchmark testing Feature extraction Association rule learning Risk management

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Choosing the Right Path: A Comparative Study of CNN, RNN, and Transformer Models for Sequential Recommendation systems

Choosing the Right Path: A Comparative Study of CNN, RNN, an...

引用

international conference on Platform Technology and Service (PlatCon)

作者： Yuchen Liu Gangmin Li Terry R. Payne Yong Yue Ka Lok Man Department of Computing Xi'an Jiaotong-Liverpool University Suzhou China HeXie Management Research Centre College of Industry-Entrepreneurs (CIE) Xi'an Jiaotong-Liverpool University Suzhou China Department of Computer Science University of Liverpool Liverpool United Kingdom

ISBN: (数字)9798350367874

ISBN: (纸本)9798350367881

As the Internet continues to evolve rapidly, the accuracy and efficiency of recommendation systems are crucial for Internet platforms. Concurrently, the continuous advancement of deep learning has led to the emergence of various recommendation system models based on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and attention mechanisms. This model diversity presents a challenge for platforms in selecting the appropriate model. This paper comprehensively analyses the key modules within models based on these three architectures. Furthermore, representative models from each architecture are evaluated on a benchmark dataset, comparing both business performance metrics and computational resource consumption. The goal is to guide platforms in choosing the most suitable model based on their specific requirements rather than relying solely on the novelty of the architecture or single performance metrics.

关键词： measurement Accuracy Recurrent neural networks Computational modeling Memory management Graphics processing units Transformers Internet History Recommender systems

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A measurement Study of Wechat Mini-Apps 21

A Measurement Study of Wechat Mini-Apps

引用

2021 ACM SIGMETRICS / international conference on measurement and modeling of computer systems, SIGMETRICS 2021

作者： Zhang, Yue Turkistani, Bayan Yang, Allen Yuqing Zuo, Chaoshun Lin, Zhiqiang The Ohio State University Columbus United States

ISBN: (纸本)9781450380720

A new mobile computing paradigm, dubbed mini-app, has been growing rapidly over the past few years since being introduced by WeChat in 2017. In this paradigm, a host app allows its end-users to install and run mini-apps inside itself, enabling the host app to build an ecosystem around (much like Google Play and Apple AppStore), enrich the host's functionalities, and offer mobile users elevated convenience without leaving the host app. It has been reported that there are over millions of mini-apps in WeChat. However, little information is known about these mini-apps at an aggregated level. In this paper, we present MiniCrawler, the first scalable and open-source WeChat mini-app crawler that has indexed over 1,333,308 mini-apps. It leverages a number of reverse engineering techniques to uncover the interfaces and APIs in WeChat for crawling the mini-apps. With the crawled mini-apps, we then measure the resource consumption, API usage, library usage, obfuscation rate, app categorization, and app ratings at an aggregated level in this work. © 2021 Owner/Author.

关键词： Application programming interfaces (API)

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Embedded Operating System Memory-Related Vulnerabilities Detection Based on Taint Analysis and Graph Neural Networks

Embedded Operating System Memory-Related Vulnerabilities Det...

引用

Frontiers Technology of Information and computer (ICFTIC), IEEE international conference on

作者： Bin Wei Xiangji Xi Zishuai Wu Yanyun Lu Chang Guo Zhenfeng Li Beijing Smartchip Microelectronics Technology Co. Ltd. Beijing China Institute of Microelectronics of The Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Institute of Microelectronics of The Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

Embedded operating systems are highly customizable, with limited public information and severe security vulnerabilities. Among the vulnerabilities, one-third are memory-related. Traditional detection methods are inefficient and costly for addressing these vulnerabilities. Deep learning-based approaches may cause information loss or introduce irrelevant noise during the code representation process. Moreover, memory-related vulnerability patches are often very similar to the source code, making it difficult to represent vulnerability features. To address these issues, this paper proposes a graph neural network-based detection method using taint analysis. By tracking and slicing the representation graph based on taint information, the method can expand effective graph information and remove irrelevant noise. Experimental results show this method achieves an F1 score of 81.82% on an embedded OS memory-related Vulnerability dataset.

关键词： Analytical models Filters Operating systems Source coding Computational modeling Noise Refining Graph neural networks Security Noise measurement

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Multi-Scale Facial Expression Generation Algorithm Based on Spatial Attention Mechanism

Multi-Scale Facial Expression Generation Algorithm Based on ...

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Image Processing, computer Vision and Machine Learning (ICICML), international conference on

作者： Yeming Cong Zitong Yue Qingdong Zhao Huiwen Chen Jianglin Luo Jilin Provincial International Joint Research Center for Digital Animation Technology Jilin Animation Institute Chang Chun China Department of Information Systems Business Statistics and Operations Management The Hong Kong University of Science and Technology Hong Kong China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

With the maturation and application of human-computer interaction and virtual reality technologies, facial expression generation has received widespread attention. However, current technologies often fail to fully capture the fine details of the face, neglecting the differences between various facial parts in an expression, which lowers the quality of the generated expressions. To address this, this paper proposes a facial expression generation algorithm based on spatial attention. The algorithm combines spatial attention mechanisms with Generative Adversarial Networks (GANs), allowing the adversarial network to better focus on important facial details in the image. Additionally, the algorithm effectively addresses the phenomenon of feature differences across multiple scales by introducing convolution layers with varying receptive fields. This forces the neural network to learn facial features at different scales and the differences between them, thereby improving the quality of expression generation. Experimental results demonstrate that the proposed algorithm outperforms current mainstream methods in terms of expression generation quality, enhancing both the efficiency and quality of expression generation and providing new insights for facial expression research.

关键词： measurement Human computer interaction Attention mechanisms Three-dimensional displays Neural networks Virtual reality Feature extraction Generative adversarial networks Data mining Facial features

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Exploring LLM and Neural Networks Towards Malicious Prompt Detection

Exploring LLM and Neural Networks Towards Malicious Prompt D...

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Electronics and Sustainable Communication systems (ICESC), 2020 international conference on

作者： Himani Deshpande Dhawal Chaudhari Tanmay Sarode Ayush Kamath Department of Artificial Intelligence & Data Science Thadomal Shahani Engineering College Mumbai India Department of Computer Science Thadomal Shahani Engineering College Mumbai India Department of Computer Science Thadomal Shahani Engineering College Mumbai India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Atthe forefront of the Artificial Intelligence Revolution is the Generative AI domain which is making splashes in generation of new content from existing Large Language Models. Large Language Models (LLMs) are flexible, effective tools with many uses. On the other hand, they can be tricked by devious provocation. This work explores the detection of fraudulent prompts intended to produce false or damaging outputs using Long Short-Term Memory (LSTM) networks. During this study we analysed the LTSM model against a neural network model. We found a 91.69% accuracy for the LTSM model and a 92.52% accuracy for the simple neural network. However, the simple neural network had a higher F1 score of 0.73 compared to the LTSM score of 0.69. We found that the standard neural model is more efficient at identifying harmful user prompts. Our research highlights the need for taking into account various architectures of models to reduce the risk of malicious prompting in language learning models.

关键词： Training measurement Analytical models Accuracy Computational modeling Large language models Neural networks Security Long short term memory Standards

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