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2022 International Conference on Artificial Intelligence, Internet of Things and Cloud Computing Technology, AIoTC 2022

作者： Wang, Rong Duanmu, Chunjiang College of Mathematics and Computer Science Zhejiang Normal University Jin Hua Zhejiang China College of Physics and Electronic Information Engineering Zhejiang Normal University Jin Hua Zhejiang China

In their daily lives, people frequently need to obtain images with a high dynamic range and resolution. Due to technological equipment limitations, high dynamic range images are produced by multi-exposure fusion (MEF) of low dynamic range images, while high resolution images are frequently obtained by super-resolution (SR) of low resolution images. MEF and SR are often analyzed separately. This research examines existing approaches and proposes a coupled feedback network attention network and its method to address the issue that current models cannot achieve high dynamic range and high resolution simultaneously. © 2022 Copyright for this paper by its authors.

关键词： Dynamics

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Demand Clustering and Spectrum Allocation (DCSA) problem in P2MP network with time-varying traffic

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Optical Fiber Technology 2024年 84卷

作者： Din, Der-Rong Department of Computer Science and Information Engineering National Changhua University of Education No. 1 Jin De Road Changhua500 Taiwan

In this paper, the Demand Clustering and Spectrum Allocation (DCSA) problem in a point-to-multipoint (P2MP) network with time-varying traffic is studied. This article introduces several heuristic techniques that can be applied to traffic aggregation for P2MP optical network architectures employing digital subcarrier multiplexing. Based on the transmitter constraint, the algorithms assign individual subcarriers (SCs) to receiving nodes with uncorrelated traffic profiles. The simulation results show that the proposed algorithms outperform the clustering methods. © 2024 Elsevier Inc.

关键词： Heuristic algorithms

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Leveraging CNN-CPU Scheduling for Enhanced Resource Management in Storage Edge Network Frameworks

Leveraging CNN-CPU Scheduling for Enhanced Resource Manageme...

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International Conference on computer engineering and Applications (ICCEA)

作者： Mazen Alwahedi Chunjiang Duanmu College of Computer Science and Technology Zhejiang Normal University Jin Hua City China College of Physics and Electrical Information Engineering Zhejiang Normal University Jin Hua City China

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

Because the landscape of data processing created is likely to rise considerably in the next years due to the proliferation of devices that require data processing at edge computing, it is imperative for data processing issues at the network's edge storage frame. The goal of this study is to find out how deep learning can be used to speed up operation processing. Effective resource management is essential in the age of edge computing to maximize the performance of storage edge networked frameworks. The purpose of this work is to investigate how resource management in such systems could be improved by utilizing CNN-CPU scheduling approaches. Our goal is to optimize job allocation, prioritization, and scheduling in storage edge situations by fusing Convolutional Neural Networks (CNNs) with CPU-based scheduling methods. The suggested method aims to improve overall system performance, reduce latency, and maximize resource use. We illustrate the efficacy of CNN-CPU scheduling in improving resource management in storage edge networked frameworks with experimental validation and performance analysis. This research contributes to advancing the capabilities of edge computing systems by leveraging deep learning techniques for efficient resource allocation and management. The proposed approach seeks to overcome the limitations of traditional scheduling methods by leveraging the power of deep learning. CNNs are renowned for their ability to extract meaningful features from data, and by harnessing this capability, we can optimize resource utilization and minimize latency in storage edge networked frameworks. Through a combination of CNN-based feature extraction and CPU-based scheduling decisions, we strive to achieve efficient and intelligent resource management. This study investigates the potential of CNN-CPU scheduling techniques to address these challenges and enhance resource management in such frameworks. By integrating Convolutional Neural Networks (CNNs) with CPU-based sc

关键词： Deep learning Scheduling algorithms System performance Feature extraction Data processing Scheduling Resource management

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PETAL: physics emulation through averaged linearizations for solving inverse problems 23

PETAL: physics emulation through averaged linearizations for...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Jihui Electrical Etienne Ollivier Richard Touret Matthew McKinley Karim G. Sabra Justin K. Romberg Jin and Computer Engineering Georgia Institute of Technology Atlanta GA Mechanical Engineering Georgia Institute of Technology Atlanta GA Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA

Inverse problems describe the task of recovering an underlying signal of interest given observables. Typically, the observables are related via some non-linear forward model applied to the underlying unknown signal. Inverting the non-linear forward model can be computationally expensive, as it often involves computing and inverting a linearization at a series of estimates. Rather than inverting the physics-based model, we instead train a surrogate forward model (emulator) and leverage modern auto-grad libraries to solve for the input within a classical optimization framework. Current methods to train emulators are done in a black box supervised machine learning fashion and fail to take advantage of any existing knowledge of the forward model. In this article, we propose a simple learned weighted average model that embeds linearizations of the forward model around various reference points into the model itself, explicitly incorporating known physics. Grounding the learned model with physics based linearizations improves the forward modeling accuracy and provides richer physics based gradient information during the inversion process leading to more accurate signal recovery. We demonstrate the efficacy on an ocean acoustic tomography (OAT) example that aims to recover ocean sound speed profile (SSP) variations from acoustic observations (e.g. eigenray arrival times) within simulation of ocean dynamics in the Gulf of Mexico.

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Analysis of Instant Messaging Systems for Users Based on the Go Language 6th

Analysis of Instant Messaging Systems for Users Based on the...

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6th International Conference on Innovative Computing, IC 2023

作者： Lu, Shoulei Ye, Jun Xu, Zheng Hainan University Hainan Haikou570228 China Key Laboratory of Internet Information Retrieval of Hainan Province Haikou China School of Computer and Information Engineering Shanghai Polytechnic University 2360 Jin-Hai Road Pudong District Shanghai201209 China

ISBN: (纸本)9789819922864

With the development of modernization, intelligence, and networking of enterprises, large-scale enterprises continue to emerge, and collaboration and communication between employees within an enterprise are increasingly important. It is time-consuming and labor-intensive, which directly leads to low work efficiency and efficiency. The current popular instant messaging technology provides a solution for this demand, and information and notifications can be released quickly and easily. A large number of instant messaging systems on the market can meet the basic needs of enterprises, but it is difficult to guarantee commercial security and low latency requirements. Therefore, it is necessary to develop an instant messaging system that meets the requirements of enterprises. This article intends to analyze a set of instant messaging systems based on Go language according to actual needs. As a new language, the Go language has some features that many traditional programming languages do not have. It can make full use of the kernel of the device and make the system performance more stable and fast. Based on the above analysis, this paper uses the Go language to design a user instant messaging system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Efficiency

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On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities 24

On the Effectiveness of Function-Level Vulnerability Detecto...

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44th ACM/IEEE International Conference on Software engineering, ICSE 2024

作者： Li, Zhen Wang, Ning Zou, Deqing Li, Yating Zhang, Ruqian Xu, Shouhuai Zhang, Chao jin, Hai Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hong Kong Jin YinHu Laboratory Wuhan China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China University of Colorado Colorado Springs Department of Computer Science Colorado Springs Colorado United States Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798400702174

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter -Procedural Vulnerability Dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers;and (ii) function-level vulner-ability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities. © 2024 ACM.

关键词： Open source software

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Towards Effective and Efficient Error Handling Code Fuzzing Based on Software Fault Injection 31

Towards Effective and Efficient Error Handling Code Fuzzing ...

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31st IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2024

作者： Chen, Kang Wen, Ming Jia, Haoxiang Wu, Rongxin jin, Hai Wuhan430074 China Jin YinHu Laboratory Wuhan430074 China School of Informatics Xiamen University Xiamen361005 China School of Computer Science and Technology HUST Wuhan430074 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security China Cluster and Grid Computing Lab China

ISBN: (纸本)9798350330663

Software systems often encounter various errors or exceptions in practice, and thus proper error handling code is essential to ensure the reliability of software systems. Unfortunately, error handling code is often bug-prone, while sufficiently testing them is challenging as such code often cannot be triggered under normal conditions. Motivated by this, recent studies have proposed to leverage software fault injection (SFI) based fuzzing to discover potential bugs in complicated error handling code. Despite the promising results achieved, their effectiveness and efficiency are still compromised in practice due to the huge search space of error sites, inadequate fuzzing guidance, and the overhead induced by context-sensitive SFI. To achieve effective and efficient testing of error handling code, this study presents AFL-FI, which first utilizes a similarity-based method to identify suspicious error sites, and then incorporates the idea of error site coverage to guide the fuzzing process. Finally, the design of lightweight context-sensitive SFI enables AFL-FI to execute test cases efficiently. We evaluate AFL-FI on eight large-scale open-source projects, and the results show that it can outperform existing state-of-the-art fuzzing tools significantly in terms of branch code coverage. More importantly, AFL-FI has discovered 13 previously unknown bugs, and all of them have been confirmed while 12 of them have been fixed. Besides, our evaluation also demonstrates that all the key designs of AFL-F I are effective that contribute significantly to its overall performance. © 2024 IEEE.

关键词： Errors

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Editorial on cyber security intelligence and analytics 2023

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Neural Computing and Applications 2024年第4期37卷 1727-1727页

作者： Xu, Zheng Loyola-González, Octavio School of Computer and Information Engineering Institute for Artificial Intelligence Shanghai Polytechnic University 2360 Jin Hai Road Shanghai201209 China NTT DATA Novus Building Cam Fuente de La Mora 1 Madrid28050 Spain

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A Self-supervised Learning Method for Denoising Dynamic PET Images from List-mode Data

A Self-supervised Learning Method for Denoising Dynamic PET ...

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2022 IEEE Nuclear Science Symposium, Medical Imaging Conference, and Room Temperature Semiconductor Detector Conference, IEEE NSS MIC RTSD 2022

作者： Ding, Wenxiang Feng, David Dagan Huang, Qiu Shanghai Jiao Tong University Rui Jin Hospital School of Medicine Department of Nuclear Medicine Shanghai China Shanghai Jiao Tong University School of Biomedical Engineering Shanghai China The University of Sydney School of Computer Science SydneyNSW2006 Australia

ISBN: (纸本)9781665488723

Dynamic positron emission tomography (PET) imaging usually suffers from a low signal-to-noise ratio (SNR) due to limited counts in each frame acquired in a short period of time. In order to denoise dynamic PET images, this paper proposes a self-supervised deep learning method in the list-mode data domain. We design a random sampling process to divide data collected along lines of response (LORs) into two groups to reconstruct training image pairs in each iteration. Then the network takes the training image as the input and outputs the denoised image. A simulation study shows that our method performs better in suppressing noise and retaining details than the traditional iterative method followed by denoising filters and the self-supervised denoising method in the image domain. © 2022 IEEE.

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LLM-Assisted Multi-Teacher Continual Learning for Visual Question Answering in Robotic Surgery

LLM-Assisted Multi-Teacher Continual Learning for Visual Que...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Kexin Chen Yuyang Du Tao You Mobarakol Islam Ziyu Guo Yueming jin Guangyong Chen Pheng-Ann Heng Institute of Medical Intelligence and XR CUHK HKSAR China Department of Information Engineering CUHK HKSAR China Jin Lab National University of Singapore (NUS) Singapore Department of Medical Physics and Biomedical Engineering University College London London UK Department of Computer Science Engineering Chinese University of Hong Kong (CUHK) HK-SAR China Zhejiang Lab Zhejiang China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Visual question answering (VQA) can be fundamentally crucial for promoting robotic-assisted surgical education. In practice, the needs of trainees are constantly evolving, such as learning more surgical types and adapting to new surgical instruments/techniques. Therefore, continually updating the VQA system by a sequential data stream from multiple resources is demanded in robotic surgery to address new tasks. In surgical scenarios, the privacy issue of patient data often restricts the availability of old data when updating the model, necessitating an exemplar-free continual learning (CL) setup. However, prior studies overlooked two vital problems of the surgical domain: i) large domain shifts from diverse surgical operations collected from multiple departments or clinical centers, and ii) severe data imbalance arising from the uneven presence of surgical instruments or activities during surgical procedures. This paper proposes to address these two problems with a multimodal large language model (LLM) and an adaptive weight assignment methodology. We first develop a new multi-teacher CL framework that leverages a multimodal LLM as the additional teacher. The strong generalization ability of the LLM can bridge the knowledge gap when domain shifts and data imbalances occur. We then put forth a novel data processing method that transforms complex LLM embeddings into logits compatible with our CL framework. We also design an adaptive weight assignment approach that balances the generalization ability of the LLM and the domain expertise of the old CL model. Finally, we construct a new dataset for surgical VQA tasks. Extensive experimental results demonstrate the superiority of our method to other advanced CL models.

关键词： Continuing education Adaptation models Visualization Instruments Large language models Surgery Transforms

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