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Learned Reverse ISP with Soft Supervision 17th

Learned Reverse ISP with Soft Supervision

引用

17th European conference on computer Vision, ECCV 2022

作者： Zou, Beiji Zhang, Yue School of Computer Science and Engineering Central South University Changsha410083 China Hunan Engineering Research Center of Machine Vision and Intelligent Medicine Changsha410083 China

ISBN: (纸本)9783031250620

RAW image serves as the foundation for camera imaging, which resides at the very beginning of the pipeline that generates sRGB images. Unfortunately, owing to special considerations, the information-rich RAW images are forfeited by default in most existing applications. To regain the RAW image, some works attempt to restore RAW images from RGB images. They focus on designing handcrafted model-based methods or complicated networks, however, ignoring the special property of RAW image, i.e., high dynamic range. To make up for this deficiency, we introduce a novel soft supervision, derived from the high dynamic range. Specifically, we propose to soften the original ground-truth as a multivariate Gaussian distribution so that networks could learn much more information. Then, we introduce a soft supervision driven network (SSDNet), based on convolution and transformer, for effectively restoring RAW images from RGB images. Quantitative and qualitative results show the promising restoration performance of RGB-to-RAW. In particular, our method achieved fifth place in the S7 track of AIM Reversed ISP Challenge. The source code will be available at https://***/yuezhang98/Learned-Reverse-ISP-with-Soft-Supervision. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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Range-angle Imaging Based on the Prior of Target Scattering Characteristics in Distributed Arrays 2

Range-angle Imaging Based on the Prior of Target Scattering ...

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2nd IEEE International conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Mei, Yongjie Zhang, Guangbin Huang, Tianyao Wang, Yan Beijng Institute of Technology School of Information and Electronics Beijing China Yangtze Delta Region Academy Beijing Institute of Technology Radar System and Intelligent Signal Processing Jiaxing China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China

ISBN: (纸本)9798331515669

We consider the problem of target range-angle imaging using distributed arrays. high resolution imaging requires a large array aperture. However, due to the limited number of array elements, the subarrays are usually sparsely located, which results in severe under-sampling and ambiguity problem, degrading the imaging performance. To this end, we propose a range-angle imaging method based on the prior of target scattering characteristic to resolve the ambiguity. The main idea is to use the information increment of priors to reduce the dependence on the measurements. Particularly, we first statistically extract the prior distribution of target scattering intensity by data analysis on the target images. Then we use these priors to design weighting parameters and achieve the ambiguity-free images by solving a weighted sparse recovery problem. Finally, numerical simulation results verify the feasibility of the proposed method. © 2024 IEEE.

关键词： ambiguity distributed array Range-angle imaging the prior of target scattering characteristics weight

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The Queen's Guard: A Secure Enforcement of Fine-grained Access Control In Distributed Data Analytics Platforms 23

The Queen's Guard: A Secure Enforcement of Fine-grained Acce...

引用

39th Annual computer Security Applications conference (ACSAC)

作者： Shaon, Fahad Rahaman, Sazzadur Kantarcioglu, Murat Data Secur Technol Dallas TX 75252 USA Univ Arizona Tucson AZ USA

ISBN: (纸本)9798400708862

Distributed data analytics platforms (i.e., Apache Spark, Hadoop) provide high-level APIs to programmatically write analytics tasks that are run distributedly in multiple computing nodes. The design of these frameworks was primarily motivated by performance and usability. Thus, the security takes a back seat. Consequently, they do not inherently support fine-grained access control or offer any plugin mechanism to enable it, making them risky to be used in multi-tier organizational settings. There have been attempts to build "add-on" solutions to enable fine-grained access control for distributed data analytics platforms. In this paper, first, we show that straightforward enforcement of "add-on" access control is insecure under adversarial code execution. Specifically, we show that an attacker can abuse platform-provided APIs to evade access controls without leaving any traces. Second, we designed a two-layered (i.e., proactive and reactive) defense system to protect against API abuses. On submission of a user code, our proactive security layer statically screens it to find potential attack signatures prior to its execution. The reactive security layer employs code instrumentation-based runtime checks and sandboxed execution to throttle any exploits at runtime. Next, we propose a new fine-grained access control framework with an enhanced policy language that supports map and filter primitives. Finally, we build a system named SECUREDL with our new access control framework and defense system on top of Apache Spark, which ensures secure access control policy enforcement under adversaries capable of executing code. To the best of our knowledge, this is the first fine-grained attribute-based access control framework for distributed data analytics platforms that is secure against platform API abuse attacks. performance evaluation showed that the overhead due to added security is low.

关键词： Apache Spark Security Distributed Systems Security Fine-grained Access Control program Analysis

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Vehicular Intrusion Detection System Based on Hybrid Quantum Neural Networks

Vehicular Intrusion Detection System Based on Hybrid Quantum...

引用

2024 IEEE Global Communications conference, GLOBECOM 2024

作者： Zhou, Jianhua Liu, Yueheng Li, Sifan Cao, Yue Chen, Wenkai Ahmed, Naveed Wuhan University School of Cyber Science and Engineering China Wuhan Cyber Security Association China Technology Co. Ltd China Prince Sultan University College of Computer and Information Sciences Riyadh Saudi Arabia

ISBN: (纸本)9798350351255

Traditional Deep Learning (DL) method is increasingly used in vehicular Intrusion Detection Systems (IDSs). However, there are still some limitations. It combines various models, resulting in a massive model size and numerous parameters, requiring more computational resources. Furthermore, real-time performance is crucial in vehicular IDS. Large models typically require more time for detection, failing to meet the timeliness requirements. Quantum computing offers parallel computing capabilities, prompting our proposal of a Hybrid Quantum Neural Network (HQNN)-based IDS to improve the timeliness of intrusion detection. This IDS employs HQNN to enhance feature extraction and speed up traditional convolution. It retains most of the structure of classical Convolutional Neural Network (CNN), comprising attention layers, batch normalization layers, and quantum circuits. Through quantum superposition and entanglement, certain complex non-linear functions are compressed. Furthermore, residual connections enable seamless gradient flow during back propagation. This accelerates computation and expedites convergence. Comparative experiments on the Car-hacking dataset show improved detection time and convergence speed, with exceptionally high accuracy in practical applications. © 2024 IEEE.

关键词： Network intrusion

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Multithreaded Parallelism for Heterogeneous Clusters of QPUs

Multithreaded Parallelism for Heterogeneous Clusters of QPUs

引用

ISC high performance 2024 Research Paper Proceedings (39th International conference)

作者： Philipp Seitz Manuel Geiger Christian B. Mendl Department of Computer Science Technical University of Munich TUM School of Computation Information and Technology Garching Germany Department of Computer Science Technical University of Munich TUM School of Computation Information and Technology TUM Institute for Advanced Study Garching Germany

In this work, we present MILQ, a quantum unrelated parallel machines scheduler and cutter. The setting of unrelated parallel machines considers independent hardware backends, each distinguished by differing setup and processing times. MILQ optimizes the total execution time of a batch of circuits scheduled on multiple quantum devices. It leverages state-of-the-art circuit-cutting techniques to fit circuits onto the devices and schedules them based on a mixed-integer linear program. Our results show a total improvement of up to 26 % compared to a baseline approach.

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Better Clients, Less Conflicts: Hyperledger Fabric Conflict Avoidance 6

Better Clients, Less Conflicts: Hyperledger Fabric Conflict ...

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6th IEEE International conference on Blockchain and Cryptocurrency, ICBC 2024

作者： Ji, Bing-Jie Kuo, Tung-Wei National Chengchi University Department of Computer Science Taipei Taiwan

ISBN: (纸本)9798350316742

Hyperledger Fabric, a prominent permissioned blockchain platform, offers concurrent transaction processing via its Optimistic Concurrency Control (OCC) feature. However, conflicts arise when multiple transactions access the same data, severely impacting Fabric's throughput. To reduce transaction conflicts, we draw inspiration from CSMA/CA, a wireless network medium access control protocol that uses Randomized Exponential Backoff (REB) to avoid packet collisions. Based on REB, we design Fabric/CA, a client-side transaction submission protocol designed to reduce transaction conflicts. By mitigating the computational waste caused by conflicts, Fabric/CA substantially improves Fabric's performance. Our experiments show that under a high-contention workload, Fabric/CA achieves a reduction of over 98 % in transaction conflicts, and boosts Fabric's goodput by more than 10x. © 2024 IEEE.

关键词： Concurrency control

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Hyacinth macaw: a project-based learning program to develop talents in Software Engineering for Artificial Intelligence 23

Hyacinth macaw: a project-based learning program to develop ...

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37th Brazilian Symposium on Software Engineering, SBES 2023, held in conjunction with the Brazilian conference on Software: Theory and Practice, CBSoft 2023

作者： Fontão, Awdren Matsubara, Edson Mongelli, Henrique Medeiros, Marcio Lourenço, Carlos Martins, Henrique Cortez, Igor Borges, Maria Faculty of Computing / Software Engineering Lab Federal University of Mato Grosso Do Sul Brazil PDtec SP Brazil B3 SP Brazil Blk SP Brazil Neoway SP Brazil

ISBN: (纸本)9798400707872

Software Engineering for Artificial Intelligence (SE4A) uses SE principles to design and maintain AI systems, requiring analytical thinking for software complexity, while AI demands mathematical knowledge and algorithm adjustment. The IEEE Curriculum Guidelines for Undergraduate Degree programs in Software Engineering states that extracurricular elements impact students' preparation. This study focuses on the first module of a project-based learning talent development program involving undergraduate students, two expert professors (in AI and SE), and mentors from sponsoring companies. An exploratory case study with 39 students from four courses was conducted, challenging them to deliver an MVP in machine learning within 1.5 months. Results showed high agreement (87.5%) in applying learned skills to future projects, recognizing SE's benefits (96.9%) in AI, and acknowledging the connection between SE and AI (78.1%). Participants applied relevant knowledge in ML performance, data analysis, and software architecture for AI. We share strategies used by students to enhance developer experience. © 2023 ACM.

关键词： Students

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Patchcore-SAM: Rail Defect Unsupervised Detection and Segmentation 27

Patchcore-SAM: Rail Defect Unsupervised Detection and Segmen...

引用

27th IEEE International conference on Intelligent Transportation Systems, ITSC 2024

作者： He, Shuyi Cao, Yuan Wang, Feng Sun, Yongkui Su, Shuai Yang, Weifeng Wang, Wenkun National Engineering Research Center of Rail Transportation Operation Control System School of Automation and Intelligence Beijing Jiaotong University Beijing100044 China Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China Crrc Zhuzhou Institute Co Ltd Zhuzhou412001 China

ISBN: (纸本)9798331505929

The heavy-haul railways plays a crucial role in facilitating the transportation of large-scale goods, yet the challenging operational conditions significantly increase the occurrence of rail defects. Despite the growing importance of computer vision and artificial intelligence in detecting rail defects, current methods face issues of low accuracy and efficiency. The success of the Segment Anything Model (SAM) provides a promising solution to these problems. In this study, a novel two-stage rail defect evaluation methodology called Patchcore-SAM was proposed. Firstly, Patchcore enables the unsupervised detection and localization of defects. Subsequently, the detected anomalies are transformed into anomaly images and prompts, which serve as inputs for the fine-tuned SAM to achieve precise rail defect segmentation. The experimental results demonstrate the superior performance of this method, providing robust support for ensuring the safe operation of heavy-haul railways. © 2024 IEEE.

关键词： Railroads

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STEAM: SVDD-based Anomaly Detection on Attributed Networks via Multi-Autoencoders 24

STEAM: SVDD-based Anomaly Detection on Attributed Networks v...

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24th IEEE International conference on high performance Computing and Communications, 8th IEEE International conference on Data Science and Systems, 20th IEEE International conference on Smart City and 8th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zhang, Junjie Wang, Xinye Yu, Zhenyang Huang, Menglin Duan, Lei Zuo, Jie School of Computer Science Sichuan University Chengdu China

ISBN: (纸本)9798350319934

Anomaly detection on attributed networks is intended to find instances that dramatically different from other instances in terms of attributes or structure. However, most existing methods ignore the adverse effects of abnormal nodes on normal nodes and are unable to capture high-dimensional cross-modal information from the structure and attributes. To tackle this drawback, we propose an SVDD-based anomaly detection framework, named STEAM, which detects abnormal nodes by fusing attributes and structure. First, STEAM uses node structure and its attributes information to learn two independent representations of nodes, respectively. Secondly, to alleviate the interference of abnormal nodes, we introduce Support Vector Data Description (SVDD), which helps us to learn the hypersphere from the structure representations of normal nodes. Thirdly, the two representations of the nodes are fused to reconstruct the attributed networks. Finally, we use a combination of the distance of every node to the hypersphere center and the reconstruction error of the attributed network to determine whether the node is anomalous or not. The performance of STEAM is assessed by 6 public datasets. Numerous experimental results have illustrated that STEAM is effective in detecting anomalous nodes in attributed networks. © 2022 IEEE.

关键词： Anomaly detection

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CAESAR: A CNN Accelerator Exploiting Sparsity and Redundancy Pattern

CAESAR: A CNN Accelerator Exploiting Sparsity and Redundancy...

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2023 International Technical conference on Circuits/Systems, computers, and Communications, ITC-CSCC 2023

作者： Kim, Seongwook Kim, Yongjun Byeon, Gwangeun Hong, Seokin Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of Samsung Lsi Suwon Korea Republic of

ISBN: (纸本)9798350326413

Convolutional Neural Networks (CNN) have shown outstanding performance in many computer vision applications. However, CNN Inference on mobile and edge devices is challenging due to high computation demands. Recently, many prior studies have tried to address this challenge by reducing the data precision with quantization techniques, leading to abundant redundancy in the CNN models. This paper proposes CAESAR, a CNN accelerator that eliminates redundant computations to reduce the computation demands of CNN inference. By analyzing the computation pattern of the convolution layer, CAESAR predicts the location where the redundant computations occur and removes them in the executions. After that, CAESAR remaps the remaining effectual computations on the processing elements originally mapped to the redundant computations so that all processing elements are fully utilized. Based on our evaluation with a cycle-level microarchitecture simulator, CAESAR achieves an overall speedup of up to 2.13x and saves energy by 78% over the TPU-like baseline accelerator. © 2023 IEEE.

关键词： Redundancy

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