969 research outputs found

    Heuristic Algorithms for Energy and Performance Dynamic Optimization in Cloud Computing

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    Cloud computing becomes increasingly popular for hosting all kinds of applications not only due to their ability to support dynamic provisioning of virtualized resources to handle workload fluctuations but also because of the usage based on pricing. This results in the adoption of data centers which store, process and present the data in a seamless, efficient and easy way. Furthermore, it also consumes an enormous amount of electrical energy, then leads to high using cost and carbon dioxide emission. Therefore, we need a Green computing solution that can not only minimize the using costs and reduce the environment impact but also improve the performance. Dynamic consolidation of Virtual Machines (VMs), using live migration of the VMs and switching idle servers to sleep mode or shutdown, optimizes the energy consumption. We propose an adaptive underloading detection method of hosts, VMs migration selecting method and heuristic algorithm for dynamic consolidation of VMs based on the analysis of the historical data. Through extensive simulation based on random data and real workload data, we show that our method and algorithm observably reduce energy consumption and allow the system to meet the Service Level Agreements (SLAs)

    A White-Box False Positive Adversarial Attack Method on Contrastive Loss-Based Offline Handwritten Signature Verification Models

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    In this paper, we tackle the challenge of white-box false positive adversarial attacks on contrastive loss-based offline handwritten signature verification models. We propose a novel attack method that treats the attack as a style transfer between closely related but distinct writing styles. To guide the generation of deceptive images, we introduce two new loss functions that enhance the attack success rate by perturbing the Euclidean distance between the embedding vectors of the original and synthesized samples, while ensuring minimal perturbations by reducing the difference between the generated image and the original image. Our method demonstrates state-of-the-art performance in white-box attacks on contrastive loss-based offline handwritten signature verification models, as evidenced by our experiments. The key contributions of this paper include a novel false positive attack method, two new loss functions, effective style transfer in handwriting styles, and superior performance in white-box false positive attacks compared to other white-box attack methods.Comment: 8 pages, 3 figure

    Analyzing and evaluating the service quality of foreign banks’ digital offerings in China: a case study of online banking and mobile banking

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    The purpose of this article is to explore the overall performance of the three selected foreign banks’ service quality in digital offerings (online banking and mobile banking) in Chinese retail banking context. The three sample banks were selected based upon accessibility. They are HSBC from UK, Citibank from USA and UOB from Singapore. The service qualities of the banks’ digital platforms were thoroughly examined by adopting a methodology that combines two measurement models known as SERVQUAL and SERVPERF, and seven specific e-BSQ dimensions which are competence, ease-of-use, service variety, reliability, responsiveness, security and awareness. The 3-week field survey, with 313 valid questionnaires competed, was conducted amongst the web-banking and m-banking customers. The findings also indicated that most customers were unaware of their banks digital service functions. They only knew several basic, free and widely-used functions such as balance inquiries, mobile payments and real-time fund transfers. By comparing the three bank’s survey results, insightful information like customer perceptions of service quality and the three banks’ differences in terms of their services and quality, can guide the web-banking and m-banking service providers to improve the effectiveness and efficiency of their digital offerings, and thereby effectively increase the satisfaction and loyalty of the customers. As web-banking and m-banking are electronic platforms which provide services remotely, the empirical findings of this project may also contribute to other distance services or electronic services such as telephone banking

    ContraNorm: A Contrastive Learning Perspective on Oversmoothing and Beyond

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    Oversmoothing is a common phenomenon in a wide range of Graph Neural Networks (GNNs) and Transformers, where performance worsens as the number of layers increases. Instead of characterizing oversmoothing from the view of complete collapse in which representations converge to a single point, we dive into a more general perspective of dimensional collapse in which representations lie in a narrow cone. Accordingly, inspired by the effectiveness of contrastive learning in preventing dimensional collapse, we propose a novel normalization layer called ContraNorm. Intuitively, ContraNorm implicitly shatters representations in the embedding space, leading to a more uniform distribution and a slighter dimensional collapse. On the theoretical analysis, we prove that ContraNorm can alleviate both complete collapse and dimensional collapse under certain conditions. Our proposed normalization layer can be easily integrated into GNNs and Transformers with negligible parameter overhead. Experiments on various real-world datasets demonstrate the effectiveness of our proposed ContraNorm. Our implementation is available at https://github.com/PKU-ML/ContraNorm.Comment: ICLR 202
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