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OpenLS-DGF: An Adaptive Open-Source Dataset Generation Framework for Machine Learning Tasks in Logic Synthesis

IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Ni, Liwei Wang, Rui Liu, Miao Meng, Xingyu Lin, Xiaoze Liu, Junfeng Luo, Guojie Chu, Zhufei Qian, Weikang Yang, Xiaoyan Xie, Biwei Li, Xingquan Li, Huawei Chinese Academy of Sciences State Key Lab of Processors Institute of Computing Technology Beijing100190 China Pengcheng Laboratory Shenzhen518055 China University of Chinese Academy of Sciences Beijing101408 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing100049 China Peking University School of Computer Science Center for Energy-Efficient Computing and Applications Beijing100871 China Ninbo University Faculty of Electrical Engineering and Computer Science Ninbo315211 China Shanghai Jiao Tong University University of Michigan-Shanghai Jiao Tong University Joint Institute MoE Key Laboratory of Artificial Intelligence Shanghai200240 China Hangzhou Dianzi University School of Electronics and Information Engineering Hangzhou311121 China

This paper introduces OpenLS-DGF, an adaptive logic synthesis dataset generation framework, to enhance machine learning (ML) applications within the logic synthesis process. Previous dataset generation flows were tailored for specific tasks or lacked integrated ML capabilities. While OpenLS-DGF supports various machine learning tasks by encapsulating the three fundamental steps of logic synthesis: Boolean representation, logic optimization, and technology mapping. It preserves the original information in both Verilog and ML-friendly GraphML formats. The Verilog files offer semi-customizable capabilities, enabling researchers to insert additional steps and incrementally refine the generated dataset. Furthermore, OpenLS-DGF includes an adaptive circuit engine that facilitates the final dataset management and downstream tasks. The generated OpenLS-D-v1 dataset comprises 46 combinational designs from established benchmarks, totaling over 966,000 Boolean circuits. OpenLS-D-v1 supports integrating new data features, making it more versatile for new tasks. This paper demonstrates the versatility of OpenLS-D-v1 through four distinct downstream tasks: circuit classification, circuit ranking, quality of results (QoR) prediction, and probability prediction. Each task is chosen to represent essential steps of logic synthesis, and the experimental results show the generated dataset from OpenLSDGF achieves prominent diversity and applicability. The source code and datasets are available at https://***/Logic-Factory/ACE/blob/master/OpenLS-DGF. © 1982-2012 IEEE.

关键词： Information management

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Top-Down Target Object Detection Through Context

Top-Down Target Object Detection Through Context

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International Conference on Image and Vision Computing New Zealand, IVCNZ

作者： Ibrahim M. H. Rahman Christopher Hollitt Mengjie Zhang Osama Rehman Aisha Ajmal Simon Jigwan Park Information Technology The Open Polytechnic of New Zealand Wellington New Zealand School of Engg. and Computer Science Victoria University of Wellington Wellington New Zealand Department of Software Engineering Bahria University Karachi Pakistan

ISBN: (数字)9798331518776

ISBN: (纸本)9798331518783

Visual attention is crucial for identifying the most salient regions in an image. However, when the objective involves higher-level visual tasks, such as target object detection, it becomes necessary to incorporate high-level information to guide the search for the target. This process, known as top-down saliency detection, leverages guidance sources like contextual information and target features to identify regions of interest that are more likely to contain the target object. In this paper, we propose a model that generates top-down saliency maps by adjusting the feature map weights of a universal visual attention model based on contextual information. While contextual information has traditionally been used to understand the gist of an image, it has not been integrated into the creation of saliency maps for target object detection. We demonstrate that incorporating contextual information into a visual attention model enhances target object detection performance. The proposed model, tested on six datasets, shows significant improvements in detecting target objects compared to models that do not utilize contextual information.

关键词： Weight measurement Visualization Accuracy Object detection Feature extraction Search problems Tuning Optimization Context modeling Saliency detection

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Shape-intensity knowledge distillation for robust medical image segmentation

arXiv

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arXiv 2024年

作者： Dong, Wenhui Du, Bo Xu, Yongchao National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China Institute of Artificial Intelligence School of Computer Science Wuhan University Wuhan430072 China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430072 China Medical Artificial Intelligence Research Institute of Renmin Hospital Wuhan University Wuhan430072 China

Many medical image segmentation methods have achieved impressive results. Yet, most existing methods do not take into account the shape-intensity prior information. This may lead to implausible segmentation results, in particular for images of unseen datasets. In this paper, we propose a novel approach to incorporate joint shape-intensity prior information into the segmentation network. Specifically, we first train a segmentation network (regarded as the teacher network) on class-wise averaged training images to extract valuable shape-intensity information, which is then transferred to a student segmentation network with the same network architecture as the teacher via knowledge distillation. In this way, the student network regarded as the final segmentation model can effectively integrate the shape-intensity prior information, yielding more accurate segmentation results. Despite its simplicity, experiments on five medical image segmentation tasks of different modalities demonstrate that the proposed Shape-Intensity Knowledge Distillation (SIKD) consistently improves several baseline models (including recent MaxStyle and SAMed) under intra-dataset evaluation, and significantly improves the cross-dataset generalization ability. Code is available at https://***/whdong-whu/SIKD. © 2024, CC BY.

关键词： Image segmentation

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A Novel Steganography Algorithm Based on Instance Segmentation

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computers, Materials & Continua 2020年第4期63卷 183-196页

作者： Ruohan Meng Qi Cui Zhili Zhou Chengsheng Yuan Xingming Sun School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Jiangsu Engineering Centre of Network Monitoring Nanjing210044China Department of Electrical and Computer Engineering University of WindsorWindsorCanada

Information hiding tends to hide secret information in image area where is rich texture or high frequency,so as to transmit secret information to the recipient without affecting the visual quality of the image and arousing *** take advantage of the complexity of the object texture and consider that under certain circumstances,the object texture is more complex than the background of the image,so the foreground object is more suitable for steganography than the *** the basis of instance segmentation,such as Mask R-CNN,the proposed method hides secret information into each object's region by using the masks of instance segmentation,thus realizing the information hiding of the foreground object without *** method not only makes it more efficient for the receiver to extract information,but also proves to be more secure and robust by experiments.

关键词： Steganography object detection mask R-CNN irregular region

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Virtual Target Based Multi-agent Surrounding Approach

Virtual Target Based Multi-agent Surrounding Approach

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Weiping Zhu Haoyang Li Ning Ding Yukang Chen Yufei Sun Chao Ma Wei Li School of Computer Science Wuhan University P. R. China School of Cyber Science and Engineering Wuhan University P. R. China School of Artificial Intelligence and Computer Science Jiangsu Key Laboratory of Media Design and Software Technology Science Center for Future Foods Jiangnan University P. R. China

ISBN: (数字)9798350358261

ISBN: (纸本)9798350358278

Multi-agent surrounding is a collaborative task that uses multiple agents to surround a stationary or moving target. Multi-agent surrounding has a wide range of applications, such as area monitoring of unmanned ships, environmental monitoring, and exploration of unknown environments. Existing work pay attention to the case of one-to-one surrounding of targets by agents, but there is a lack of consideration for the case where agents are not one-to-one with the target. In this paper, we propose the concept of virtual target, which is used as a mediator to realize the generic multi-agent surrounding a target. The main idea is to surround the actual target with the virtual target, while the agents surround the virtual target, where the generation of the virtual target is based on any given surrounding graph and random sampling, and the performance of the surrounding is ensured by the virtual target control algorithm and the agent controlling algorithm. The Lyapunov stability analysis and simulation results show that the proposed approach can make the virtual target fit the actual target effectively, and the agents can surround the actual target with the shape of the virtual target effectively.

关键词： Shape Simulation Fitting Stability analysis Sensors Reliability Environmental monitoring

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Detecting Backdoors During the Inference Stage Based on Corruption Robustness Consistency

Detecting Backdoors During the Inference Stage Based on Corr...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Xiaogeng Liu Minghui Li Haoyu Wang Shengshan Hu Dengpan Ye Hai Jin Libing Wu Chaowei Xiao School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Software Engineering Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan University School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab Arizona State University

Deep neural networks are proven to be vulnerable to backdoor attacks. Detecting the trigger samples during the inference stage, i.e., the test-time trigger sample detection, can prevent the backdoor from being triggered. However, existing detection methods often require the defenders to have high accessibility to victim models, extra clean data, or knowledge about the appearance of backdoor triggers, limiting their practicality. In this paper, we propose the test-time corruption robustness consistency evaluation (TeCo) 1 1 https://***/CGCL-codes/TeCo, a novel test-time trigger sample detection method that only needs the hard-label outputs of the victim models without any extra information. Our journey begins with the intriguing observation that the backdoor-infected models have similar performance across different image corruptions for the clean images, but perform discrepantly for the trigger samples. Based on this phenomenon, we design TeCo to evaluate test-time robustness consistency by calculating the deviation of severity that leads to predictions' transition across different corruptions. Extensive experiments demonstrate that compared with state-of-the-art defenses, which even require either certain information about the trigger types or accessibility of clean data, TeCo outperforms them on different backdoor attacks, datasets, and model architectures, enjoying a higher AUROC by 10% and 5 times of stability.

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zsLLMCode: An Effective Approach for Code Embedding via LLM with Zero-Shot Learning

arXiv

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arXiv 2024年

作者： Xian, Zixiang Huang, Rubing Cui, Chenhui Fang, Chunrong Chen, Zhenyu School of Computer Science and Engineering Macau University of Science and Technology Macau Taipa999078 China Macau University of Science and Technology Zhuhai MUST Science and Technology Research Institute Guangdong Zhuhai519099 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210093 China

The advent of large language models (LLMs) has greatly advanced artificial intelligence (AI) in software engineering (SE), with code embeddings playing a critical role in tasks like code-clone detection and code clustering. However, existing methods for code embedding, including those based on LLMs, often depend on costly supervised training or fine-tuning for domain adaptation. This paper proposes a novel zero-shot approach, zsLLMCode, to generate code embeddings by using LLMs and sentence embedding models. This approach attempts to eliminate the need for task-specific training or fine-tuning, and to effectively address the issue of erroneous information commonly found in LLM-generated outputs. We conducted a series of experiments to evaluate the performance of the proposed approach by considering various LLMs and embedding models. The results have demonstrated the effectiveness and superiority of our method zsLLMCode over state-of-the-art unsupervised approaches such as SourcererCC, Code2vec, InferCode, and TransformCode. Our findings highlight the potential of zsLLMCode to advance the field of SE by providing robust and efficient solutions for code embedding tasks. © 2024, CC BY.

关键词： Zero-shot learning

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High Efficiency Quantum Image Steganography Protocol Based on ZZW Framework 7th

High Efficiency Quantum Image Steganography Protocol Based o...

引用

7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Sun, Hanrong Qu, Zhiguo School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (纸本)9783030786113

This paper proposes an efficient quantum image steganography protocol based on Zhang Weiming, Zhang Xinpeng and Wang Shuozhong’s steganography framework (ZZW). Based on the high embedding efficiency of the classical ZZW steganography framework, the new protocol combined with the quantum carrier, not only greatly improves the embedding efficiency of the existing quantum image steganography protocols, but also has high security. In order to reflect the practicability of the new protocol, we also designed dedicated quantum circuits to embed and extract secret information. In addition, the simulation results based on Matlab also show that the new protocol not only has good imperceptibility, but also has very high embedding efficiency. © 2021, Springer Nature Switzerland AG.

关键词： Steganography

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Lane marking detection algorithm based on high-precision map and multisensor fusion

Lane marking detection algorithm based on high-precision map...

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作者： Yao, Haichang Chen, Chen Liu, Shangdong Li, Kui Ji, Yimu Huang, Guangyan Wang, Ruchuan School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Computer and Software Nanjing Institute of Industry Technology Nanjing China Industrial Software Engineering Technology Research and Development Center of Jiangsu Education Department Nanjing Institute of Industry Technology Nanjing China Jiangsu HPC and Intelligent Processing Engineer Research Center Nanjing China Institute of High Performance Computing and Bigdata Nanjing University of Posts and Telecommunications Nanjing China School of Information Technology Deakin University Melbourne Australia

In case of sharp road illumination changes, bad weather such as rain, snow or fog, wear or missing of the lane marking, the reflective water stain on the road surface, the shadow obstruction of the tree, and mixed lane markings and other signs, missing detection or wrong detection will occur for the traditional lane marking detection algorithm. In this manuscript, a lane marking detection algorithm based on high-precision map and multisensor fusion is proposed. The basic principle of the algorithm is to use the centimeter-level high-precision positioning combined with high-precision map data to complete the detection of lane markings. In the process of generating high-precision maps or in the uncovered areas of high-precision maps, LIDAR (LIght Detection And Ranging) is used to estimate the curvature of the road to assist in lane marking detection. The experimental results show that the algorithm has lower false detection rate in case of bad road conditions, and the algorithm is robust. © 2020 John Wiley & Sons, Ltd.

关键词： Roads and streets

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Research on Resource Scheduling and Optimization Strategies of Edge Computing-based 5G Networks in Smart City Applications 4

Research on Resource Scheduling and Optimization Strategies ...

引用

4th International Symposium on computer engineering and Intelligent Communications, ISCEIC 2023

作者： Wang, Yongzhe Wang, Dongxu Lin, Guofei Zheng, Bowen Luo, Lanxi Li, Shuai Sichuan University College of Water Resource and Hydropower Sichuan Chengdu China Tongji University No.4800 Caoan Road Jiading Shanghai China Xiamen University Longhai School of Accounting and Finance Jiageng College Fujian Zhangzhou China Hong Kong University of Science and Technology School of Engineering Clear water bay New Territories Sai Kung Hong Kong Minzu University of China Haidian Department of Software Engineering Beijing China Minzu University of China Haidian Department of Computer Science and Technology Beijing China

ISBN: (纸本)9798350306323

In this paper, we explore in detail resource scheduling strategies in cloud computing environments. First, we introduce the basic concepts and background of cloud computing, and then delve into the importance and challenges of resource scheduling. The article then describes our proposed new resource scheduling strategy in detail and verifies its performance through experiments. The experimental results show that our strategy outperforms other strategies in terms of task completion time, resource utilization, and the percentage of tasks that satisfy latency requirements. © 2023 IEEE.

关键词： 5G mobile communication systems

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