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arXiv 2023年

作者： Chen, Zixuan Xie, Xiaohua Yang, Lingxiao Lai, Jian-Huang School of Computer Science and Engineering Sun Yat-sen University Guangdong Guangzhou510006 China Guangdong Province Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

Anomaly detectors are widely used in industrial manufacturing to detect and localize unknown defects in query images. These detectors are trained on anomaly-free samples and have successfully distinguished anomalies from most normal samples. However, hard-normal examples are scattered and far apart from most normal samples, and thus they are often mistaken for anomalies by existing methods. To address this issue, we propose Hard-normal Example-aware Template Mutual Matching (HETMM), an efficient framework to build a robust prototype-based decision boundary. Specifically, HETMM employs the proposed Affine-invariant Template Mutual Matching (ATMM) to mitigate the affection brought by the affine transformations and easy-normal examples. By mutually matching the pixel-level prototypes within the patch-level search spaces between query and template set, ATMM can accurately distinguish between hard-normal examples and anomalies, achieving low false-positive and missed-detection rates. In addition, we also propose PTS to compress the original template set for speed-up. PTS selects cluster centres and hard-normal examples to preserve the original decision boundary, allowing this tiny set to achieve comparable performance to the original one. Extensive experiments demonstrate that HETMM outperforms state-of-the-art methods, while using a 60-sheet tiny set can achieve competitive performance and real-time inference speed (around 26.1 FPS) on a Quadro 8000 RTX GPU. HETMM is training-free and can be hot-updated by directly inserting novel samples into the template set, which can promptly address some incremental learning issues in industrial manufacturing. Copyright © 2023, The Authors. All rights reserved.

关键词： Affine transforms

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MLNet: Mutual Learning Network with Neighborhood Invariance for Universal Domain Adaptation

arXiv

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arXiv 2023年

作者： Lu, Yanzuo Shen, Meng Ma, Andy J. Xie, Xiaohua Lai, Jian-Huang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Guangzhou China

Universal domain adaptation (UniDA) is a practical but challenging problem, in which information about the relation between the source and the target domains is not given for knowledge transfer. Existing UniDA methods may suffer from the problems of overlooking intra-domain variations in the target domain and difficulty in separating between the similar known and unknown class. To address these issues, we propose a novel Mutual Learning Network (MLNet) with neighborhood invariance for UniDA. In our method, confidence-guided invariant feature learning with self-adaptive neighbor selection is designed to reduce the intra-domain variations for more generalizable feature representation. By using the cross-domain mixup scheme for better unknown-class identification, the proposed method compensates for the misidentified known-class errors by mutual learning between the closed-set and open-set classifiers. Extensive experiments on three publicly available benchmarks demonstrate that our method achieves the best results compared to the state-of-the-arts in most cases and significantly outperforms the baseline across all the four settings in UniDA. Code is available at https://***/YanzuoLu/MLNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Knowledge management

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A Generalized Model for Modern Hierarchical Memory System

A Generalized Model for Modern Hierarchical Memory System

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Simulation Winter Conference

作者： Hamed Najafi Jason Liu Xiaoyang Lu Xian-He Sun Knight Foundation School of Computing and Information Sciences Florida International University Miami FL USA Department of Computer Science Illinois Institute of Technology Chicago IL USA

ISBN: (纸本)9781665476621

Memory system is critical to architecture design which can significantly impact application performance. Concurrent Average Memory Access Time (C-AMAT) is a model for analyzing and optimizing memory system performance using a recursive definition of the memory access latency along the memory hierarchy. The original C-AMAT model, however, does not provide the necessary granularity and flexibility for handling modern memory architectures with heterogeneous memory technologies and diverse system topology. We propose to augment C-AMAT to take into consideration the idiosyncrasies of individual cache/memory components as well as their topological arrangement in the memory architecture design. Through trace-based simulation, we validate the augmented model and examine the memory system performance with insight unavailable using the original C-AMAT model.

关键词： Performance evaluation Analytical models System performance Memory architecture Cache memory Graphics processing units Machine learning

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Prediction of Transportation Index for Urban Patterns in Small and Medium-sized Indian Cities using Hybrid RidgeGAN Model

arXiv

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arXiv 2023年

作者： Thottolil, Rahisha Kumar, Uttam Chakraborty, Tanujit Spatial Computing Laboratory Center for Data Sciences International Institute of Information Technology Bangalore560100 India Department of Science and Engineering Sorbonne University Abu Dhabi United Arab Emirates School of Business Woxsen University Telengana India

The rapid urbanization trend in most developing countries including India is creating a plethora of civic concerns such as loss of green space, degradation of environmental health, clean water availability, air pollution, traffic congestion leading to delays in vehicular transportation, etc. Transportation and network modeling through transportation indices have been widely used to understand transportation problems in the recent past. This necessitates predicting transportation indices to facilitate sustainable urban planning and traffic management. Recent advancements in deep learning research, in particular, Generative Adversarial Networks (GANs), and their modifications in spatial data analysis such as CityGAN, Conditional GAN, and MetroGAN have enabled urban planners to simulate hyper-realistic urban patterns. These synthetic urban universes mimic global urban patterns and evaluating their landscape structures through spatial pattern analysis can aid in comprehending landscape dynamics, thereby enhancing sustainable urban planning. This research addresses several challenges in predicting the urban transportation index for small and medium-sized Indian cities. A hybrid framework based on Kernel Ridge Regression (KRR) and CityGAN is introduced to predict transportation index using spatial indicators of human settlement patterns. This paper establishes a relationship between the transportation index and human settlement indicators and models it using KRR for the selected 503 Indian cities. This nonlinear KRR model helps in deriving the transportation network for GAN-generated human settlements through the settlement indicators. Our approach leverages human settlement indices, which capture information about demographics and urban land use to predict the transportation index. The proposed hybrid pipeline, we call it RidgeGAN model, can evaluate the sustainability of urban sprawl associated with infrastructure development and transportation systems in sprawling citi

关键词： Land use

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Benchmarking Deep Models on Salient Object Detection

SSRN

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SSRN 2023年

作者： Zhou, Huajun Lin, Yang Yang, Lingxiao Lai, Jianhuang Xie, Xiaohua School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Information Security Technology Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China

Deep network-based methods have continuously refreshed state-of-the-art performance on the Salient Object Detection (SOD) task. However, the performance discrepancy caused by different implementation details may conceal the real progress. In this paper, we construct a SALient Object Detection (SALOD) benchmark to ensure a fair and comprehensive evaluation by unifying the implementation settings of SOD methods. We aim to reveal the reasons behind recent progress by conducting a comprehensive examination of the impact of the network structure, training strategy, and loss function on accuracy. Based on the experimental results, first, we find that U-shaped networks, both older and more recent, still achieve better performance than other recently proposed structures. Second, the ablation study on training strategy proves its significant impact on SOD performance. Third, we provide a comprehensive analysis on existing loss functions and propose a new Edge-Aware (EA) loss function in response. At last, an objectness shifting experiment stands on a new perspective to validate the generalizability of SOD methods. Code is available at https://***/moothes/SALOD. © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Secure computing: NGSCB’s Journey and Windows Security Impact

Secure Computing: NGSCB’s Journey and Windows Security Impa...

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Automation and Computation (AUTOCOM), International Conference on

作者： Kamred Udham Singh Surendra Kumar Umang Deep Mishra Sudha Tiwari Teekam Singh Vikash School of Computing Graphic Era Hill University Dehradun India Departnment of Computer Applications GLBITM Greater Noida Unique Institute of Management & Technology Muradnagar Department of Information Technology Government Engineering College Bilaspur Chhattisgarh India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Appventurez Mobitech PVT LTD Greater Noida India

The Next-Generation Secure computing Base (NGSCB) aimed to improve privacy, boost security, and reinforce system integrity for users of the Windows operating system. The NGSCB’s approach relied on hardware that was specifically developed and constructed to meet the criteria set by the Trusted computing Group, a consortium focused on creating and advocating for secure computing standards. In addition, the programme aimed to include sophisticated functionalities such as hardware-based system isolation, which creates a safe setting for handling sensitive processes, and encrypted routes for user authentication, guaranteeing the secure management and transmission of user credentials. The NGSCB’s dedication to developing a better secure and trustworthy computing experience was emphasised via its diverse approach. It discusses the scepticism and criticism that the NGSCB encountered on the possibility of restrictions being placed on user rights and the eventual reduction in the scope of its goals. The abstract digs deeper into related study fields, such as the management of unstructured data, the creation of safe computing solutions, and the growth of cloud computing. These areas of research are highlighted in order to bring attention to the continuing difficulties of data security, privacy, and effective data management. Furthermore, it presents the possibility of NGSCB capabilities being included into later Microsoft products, which is an indication of the company’s impact on succeeding technology.

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KANsformer for Scalable Beamforming

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IEEE Transactions on Vehicular technology 2025年

作者： Xie, Xinke Lu, Yang Chi, Chong-Yung Chen, Wei Ai, Bo Niyato, Dusit Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation Beijing100044 China Beijing Jiaotong University School of Computer Science and Technology Beijing100044 China National Tsing Hua University Institute of Communications Engineering Department of Electrical Engineering Hsinchu30013 Taiwan Beijing Jiaotong University School of Electronics and Information Engineering Beijing100044 China Nanyang Technological University College of Computing and Data Science 639798 Singapore

This paper proposes an unsupervised deep-learning (DL) approach by integrating Transformer and Kolmogorov-Arnold networks (KAN) termed KANsformer to realize scalable beamforming for mobile communication systems. Specifically, we consider a classic multi-input single-output energy efficiency maximization problem subject to the total power budget. The proposed KANsformer first extracts hidden features via a multi-head self-attention mechanism and then reads out the desired beamforming design via KAN. Numerical results are provided to evaluate the KANsformer in terms of generalization performance, transfer learning and ablation experiment. Overall, the KANsformer outperforms the existing benchmark DL approaches, and is adaptable to the variation in the number of mobile users with real-time and near-optimal inference. © 1967-2012 IEEE.

关键词： Budget control

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VQNet 2.0: A New Generation Machine Learning Framework that Unifies Classical and Quantum

arXiv

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arXiv 2023年

作者： Bian, Huanyu Jia, Zhilong Dou, Menghan Fang, Yuan Li, Lei Zhao, Yiming Wang, Hanchao Zhou, Zhaohui Wang, Wei Zhu, Wenyu Li, Ye Yang, Yang Zhang, Weiming Yu, Nenghai Chen, Zhaoyun Guo, Guoping Origin Quantum Computing Company Limited Hefei230026 China CAS Key Laboratory of Electromagnetic Space Information University of Science and Technology of China Hefei230026 China School of Electronics and Information Engineering Anhui University Anhui Hefei230601 China Anhui Engineering Research Center of Quantum Computing Hefei230026 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Hefei230026 China CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei230026 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China

With the rapid development of classical and quantum machine learning, a large number of machine learning frameworks have been proposed. However, existing machine learning frameworks usually only focus on classical or quantum, rather than both. Therefore, based on VQNet 1.0, we further propose VQNet 2.0, a new generation of unified classical and quantum machine learning framework that supports hybrid optimization. The core library of the framework is implemented in C++, and the user level is implemented in Python, and it supports deployment on quantum and classical hardware. In this article, we analyze the development trend of the new generation machine learning framework and introduce the design principles of VQNet 2.0 in detail: unity, practicality, efficiency, and compatibility, as well as full particulars of implementation. We illustrate the functions of VQNet 2.0 through several basic applications, including classical convolutional neural networks, quantum autoencoders, hybrid classical-quantum networks, etc. After that, through extensive experiments, we demonstrate that the operation speed of VQNet 2.0 is higher than the comparison method. Finally, through extensive experiments, we demonstrate that VQNet 2.0 can deploy on different hardware platforms, the overall calculation speed is faster than the comparison method. It also can be mixed and optimized with quantum circuits composed of multiple quantum computing libraries. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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MobileNet investigation: its application and reproducing edge detectors using depth-wise separable convolution 2

MobileNet investigation: its application and reproducing edg...

引用

2021 2nd International Conference on Machine Learning and Computer Application, ICMLCA 2021

作者： Ai, Lun Luo, Zhen Wang, Chunyang Wu, Yifan International School Beijing University of Posts and Telecommunications Beijing Beijing100088 China Mathematics and The Information Science Department Henan University of Economics and Law Henan Zhengzhou450003 China College of Computer and Information Science College of Software Southwest University Chongqing Chongqing400700 China Information and Computing Science North China University of Science and Technology Hunan Changsha410219 China

ISBN: (纸本)9783800757398

MobileNet has a wide range of applications, and its depth-wise separable convolution deduced the computational cost dramatically compared to traditional convolution methods. In this study, experiments are carried out to examine depth-wise separable convolution's ability in terms of reproducing edge detector kernels. MobileNet's depth-wise separable convolution achieves great performance through reproducing kernels based on our LpP metric. The visualization results show that random initialization can reduce the calculation amount of the model and have higher efficiency. This study also compares three models, including InceptionV3, ResNet50, and MobileNetV2, on face mask detection tasks. The experiment result indicates that the MobileNetV2 model achieved the highest accuracy of 99.08%. A ground truth analysis on a test image containing three different face mask situations is performed to further discuss the model performance of MobileNetV2. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Convolution

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Drunk Driving Detection and Automatic Car Ignition Locking System

Drunk Driving Detection and Automatic Car Ignition Locking S...

引用

Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： T. Senthil Kumar Ramakrishnan Raman M. Karthikeyan Chandrashekhar J Rawandale S. Sasikala S. Murugan School of Information Technology and Engineering VIT University Vellore Tamil Nadu India Symbiosis Institute of Business Management Pune & Symbiosis International (Deemed University) Pune Maharashtra India Department of Computing Technologies Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Symbiosis Law School Noida Uttar Pradesh Symbiosis International (Deemed University) Pune Maharashtra India Department of Information Technology Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

Drunken driving monitoring with vehicle ignition locking was created because drunk driving accounts for most fatalities. By performing automatic, open-source, and non-intrusive alcohol safety checks in vehicles, the Raspberry Pi initiative seeks to alter that. A raspberry pi with alcohol sensors, a dc motor, and an LCD board is used by the device to use. To imitate a car engine, the device employs a Raspberry Pi with a DC motor and an alcohol sensor. The technology continuously checks the accuracy of the alcohol sensor to detect drunk drivers. If the driver is drunk, the processor immediately shuts off the motor to prevent system activation. If the alcohol sensor isn't giving signals, the system allows the engine to function at high alcohol levels. Processing of the data from the alcohol sensor continues on the raspberry pi while a lock is activated on the car's engine to prevent drunk driving. The device can then receive a text message with a link that includes the vehicle's position and is integrated with Google Maps. For other cars to retain their distance from the driver and assess the people are doing, the buzzer sounds, and the automobile slows down simultaneously. To reduce drunken driving accidents, the proposed detecting system has a strong ability to be implemented.

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