Large-scale text-to-image (T2I) diffusion models have revolutionized image generation, enabling the synthesis of highly detailed visuals from textual descriptions. However, these models may inadvertently generate inap...
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作者：
Taisheng ZengXiaodong WuSchool of Mathematics and Computer Science
Quanzhou Normal University Fujian Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian Province University Quanzhou China
Virtual machine placement (VMP) plays a crucial role in optimizing the efficiency and sustainability of data centers. By consolidating virtual machines (VMs) onto fewer physical machines (PMs), VMP can significantly r...
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ISBN:
(数字)9798350377033
ISBN:
(纸本)9798350377040
Virtual machine placement (VMP) plays a crucial role in optimizing the efficiency and sustainability of data centers. By consolidating virtual machines (VMs) onto fewer physical machines (PMs), VMP can significantly reduce power consumption and enhance resource utilization. This not only leads to cost savings but also contributes to environmental sustainability by minimizing energy waste. This paper focuses on addressing the energy-efficient VMP problem, which is critical for modern data centers aiming to balance performance and energy consumption. To tackle this challenge, we propose an improved group-based genetic algorithm (GGA). The GGA employs a novel grouping approach to encode chromosomes, allowing for more effective representation of VM-to-PM assignments. Additionally, optimized crossover and mutation operators are designed to improve the algorithm's performance, ensuring faster convergence and better solutions. Experimental results demonstrate that the proposed algorithm outperforms traditional algorithms in terms of energy-saving efficiency and convergence speed.
Quantum computers can efficiently simulate Lindbladian dynamics, enabling powerful applications in open system simulation, thermal and ground-state preparation, autonomous quantum error correction, dissipative enginee...
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Food producers are under pressure to meet rising demand as the world's population and natural resources diminish. Overuse of pesticides and fertilizers has caused soil erosion and land degradation. Future food nee...
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With the rapid development of information technology, artificial intelligence models have become the core asset of tech giants, highlighting the importance of copyright protection. Black-box watermarking has gained at...
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ISBN:
(数字)9798350356670
ISBN:
(纸本)9798350356687
With the rapid development of information technology, artificial intelligence models have become the core asset of tech giants, highlighting the importance of copyright protection. Black-box watermarking has gained attention due to its ability to verify the legality of models remotely without relying on internal model information. The challenges lie in embedding and verifying watermarks without accessing internal model parameters, while ensuring the robustness and stealth of the watermark. This paper focuses on discussing effective ways to protect the copyright of black-box watermarking models and detect and prevent unauthorized user behavior that infringes on model ownership. Therefore, this paper proposes a multi-ownership verification framework based on invisible watermark and hypothesis testing, which mainly consists of two parts: watermark generation and copyright verification. The watermark generation uses a dynamic watermarking method to generate invisible backdoor watermark and verifies copyright through hypothesis testing. This framework has high assurance and effectiveness under various models of CIFAR-10 and MNIST datasets and shows strong robustness in VGG and Resnet model pruning attacks. This research provides new ideas and methods for the protection of model copyrights in deep neural networks.
Artificial Intelligence is an essential tool for early disease recognition and supporting patient condition monitoring in the future. Timely and exact conclusions about the type of disease are significant for treatmen...
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Recent progress in the field of high-fidelity image synthesis using GANs has shown appealing outcomes, which motivates a series of successful image super-resolution (SR) works. However, most GAN-based SR models apply ...
Recent progress in the field of high-fidelity image synthesis using GANs has shown appealing outcomes, which motivates a series of successful image super-resolution (SR) works. However, most GAN-based SR models apply plain GAN models and are cumbersome compared to CNN-based SR models. In addition, the very advantage of conditional GAN has not yet been explored under the context of SR. In this paper, we develop a lightweight SR-BigGAN with priors for single-image super-resolution (SISR). First, our new model is an extension of BigGAN tailored to deep SR pipeline, retaining both generator and discriminator architectures, but with modifications to accommodate SR tasks. Second, prior knowledge defined as class labels from the low-resolution images, is fully leveraged through the conditional generative model to refine the SR process. Third, the lightweight nature of the model is achieved through knowledge distillation, focusing on reduced computational complexity and memory usage, making it the first practice of this kind in GAN-based SISR modeling. Extensive experiments on DIV2K, Pascal, mini-ImageNet, and SR benchmarks including Set5 and Set14 in an attempt to compare the Structural Similarity (SSIM), Peak Signal-to-Noise Ratio (PSNR) with the state-of-the-art models have shown appealing results. Our model achieves an average PSNR of 34.99 and SSIM of 0.791 across these datasets, demonstrating quantitative improvements over existing methods. The generated high-resolution images offer both perceptual enhancement and improved classification results. Additionally, explicit comparisons with GAN-based SR techniques such as ESRGAN and SRGAN highlight the superiority of our approach in both fidelity and efficiency. In particular, we achieve an average of $$\hbox{PSNR}=34.99$$ and $$\hbox{SSIM}=0.791$$ on several testing datasets.
In this paper, we propose a multi-unmanned aerial vehicle (UAV)-assisted integrated sensing, communication, and computation network. Specifically, the treble-functional UAVs are capable of offering communication and e...
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Blockchain has recently emerged as a research trend,with potential applications in a broad range of industries and *** particular successful Blockchain technology is smart contract,which is widely used in commercial s...
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Blockchain has recently emerged as a research trend,with potential applications in a broad range of industries and *** particular successful Blockchain technology is smart contract,which is widely used in commercial settings(e.g.,high value financial transactions).This,however,has security implications due to the potential to financially benefit from a security incident(e.g.,identification and exploitation of a vulnerability in the smart contract or its implementation).Among,Ethereum is the most active and ***,in this paper,we systematically review existing research efforts on Ethereum smart contract security,published between 2015 and ***,we focus on how smart contracts can be maliciously exploited and targeted,such as security issues of contract program model,vulnerabilities in the program and safety consideration introduced by program execution *** also identify potential research opportunities and future research agenda.
This paper proposes a deep reinforcement learning based on the warehouse environment, the mobile robot obstacle avoidance Algorithm. Firstly, the value function network is improved based on the pedestrian interaction....
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