We study a novel problem in this manuscript, that is, if a modern text-to-image diffusion model can tailor any image classifier across domains and categories. Existing domain adaption works exploit both source and tar...
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A fuzzy visual image denoising algorithm based on Bayesian estimation is proposed to address the problems of poor denoising performance and long denoising time in traditional image denoising algorithms. First, analyse...
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Heterogeneous Graph Neural Networks are an efficient and powerful tool for modeling graph structure data in recommendation systems. However, existing heterogeneous graph neural networks often fail to model the depende...
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In numerical computation, the inherent rounding errors of floating-point operations often affect the precision of mathematical functions. The use of high-precision achieved through software-dependent simulation for pr...
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In numerical computation, the inherent rounding errors of floating-point operations often affect the precision of mathematical functions. The use of high-precision achieved through software-dependent simulation for precision compensation may result in significant performance overhead. Error-free transformations (EFT) technology, based on hardware-supported precision to approximate high-precision implementation, can effectively balance accuracy and performance. However, enhancing the precision of mathematical functions is a very complex and challenging issue. There is a lack of relevant research on when EFT technology can be used to improve the precision of mathematical functions, what effects can be achieved, and what impact it may have on program performance. In this work, we present an empirical study on the applicability and effectiveness of using error-free transformations (EFT) in floating-point computation to assess their potential and limitations in improving precision over mathematical functions. We select 42 mathematical functions from the GNU Scientific Library (GSL), known for significant rounding errors. We evaluate the EFT techniques from three aspects: the applicability of EFT for different mathematical functions (especially at the maximum error point and its vicinity), the precision improvement of EFT in input domains near the error-triggering input, and the performance of EFT compared with the high-precision versions. Experimental results show that EFT has advantages in reducing floating-point errors across 27 functions. Furthermore, while improving the accuracy of mathematical functions within specific input ranges near the maximum error input, EFT achieves a 10.92× speedup compared to long double precision and a 2426.3× speedup compared to mpmath. These findings suggest that EFT achieves computational accuracy to the real results with much lower overhead than conventional high-precision calculations, which makes EFT a promising technology for balan
The rapid proliferation of connected vehicles has significantly expanded the attack surface of the Internet of Vehicles (IoV), introducing severe security risks. In such resource-constrained environments, developing l...
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State-of-the-art recommender systems are increasingly focused on optimizing implementation efficiency, such as enabling on-device recommendations under memory constraints. Current methods commonly use lightweight embe...
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State-of-the-art recommender systems are increasingly focused on optimizing implementation efficiency, such as enabling on-device recommendations under memory constraints. Current methods commonly use lightweight embeddings for users and items or employ compact embeddings to enhance reusability and reduce memory usage. However, these approaches consider only the coarse-grained aspects of embeddings, overlooking subtle semantic nuances. This limitation results in an adversarial degradation of meta-embedding performance, impeding the system's ability to capture intricate relationships between users and items, leading to suboptimal recommendations. To address this, we propose a novel approach to efficiently learn meta-embeddings with varying grained and apply fine-grained meta-embeddings to strengthen the representation of their coarse-grained counterparts. Specifically, we introduce a recommender system based on a graph neural network, where each user and item is represented as a node. These nodes are directly connected to coarse-grained virtual nodes and indirectly linked to fine-grained virtual nodes, facilitating learning of multi-grained semantics. Fine-grained semantics are captured through sparse meta-embeddings, which dynamically balance embedding uniqueness and memory constraints. To ensure their sparseness, we rely on initialization methods such as sparse principal component analysis combined with a soft thresholding activation function. Moreover, we propose a weight-bridging update strategy that aligns coarse-grained meta-embedding with several fine-grained meta-embeddings based on the underlying semantic properties of users and items. Comprehensive experiments demonstrate that our method outperforms existing baselines. The code of our proposal is available at https://***/htyjers/C2F-MetaEmbed.
Improving website security to prevent malicious online activities is crucial,and CAPTCHA(Completely Automated Public Turing test to tell computers and Humans Apart)has emerged as a key strategy for distinguishing huma...
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Improving website security to prevent malicious online activities is crucial,and CAPTCHA(Completely Automated Public Turing test to tell computers and Humans Apart)has emerged as a key strategy for distinguishing human users from automated ***-based CAPTCHAs,designed to be easily decipherable by humans yet challenging for machines,are a common form of this ***,advancements in deep learning have facilitated the creation of models adept at recognizing these text-based CAPTCHAs with surprising *** our comprehensive investigation into CAPTCHA recognition,we have tailored the renowned UpDown image captioning model specifically for this *** approach innovatively combines an encoder to extract both global and local features,significantly boosting the model’s capability to identify complex details within CAPTCHA *** the decoding phase,we have adopted a refined attention mechanism,integrating enhanced visual attention with dual layers of Long Short-Term Memory(LSTM)networks to elevate CAPTCHA recognition *** rigorous testing across four varied datasets,including those from Weibo,BoC,Gregwar,and Captcha 0.3,demonstrates the versatility and effectiveness of our *** results not only highlight the efficiency of our approach but also offer profound insights into its applicability across different CAPTCHA types,contributing to a deeper understanding of CAPTCHA recognition technology.
data centers struggle with growing cluster sizes and rising submissions of short-lived, high-frequency tasks that cause performance bottlenecks in task scheduling. Existing centralized and distributed scheduling syste...
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作者：
Han, XinhuiPan, HaoyuanWang, ZhaoruiLi, JianqiangShenzhen University
College of Computer Science and Software Engineering Shenzhen China
Future Network of Intelligence Institute The School of Science and Engineering Shenzhen China Shenzhen University
National Engineering Laboratory for Big Data System Computing Technology The College of Computer Science and Software Engineering Shenzhen China
We investigate the timely status update in linear multi-hop wireless networks, where a source tries to deliver status update packets to a destination through a sequence of half-duplex relays. Timeliness is measured by...
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SaaS (Software-as-a-Service) is a service model provided by cloud computing. It has a high requirement for QoS (Quality of Software) due to its method of providing software service. However, manual identification and ...
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SaaS (Software-as-a-Service) is a service model provided by cloud computing. It has a high requirement for QoS (Quality of Software) due to its method of providing software service. However, manual identification and diagnosis for performance issues is typically expensive and laborious because of the complexity of the application software and the dynamic nature of the deployment environment. Recently, substantial research efforts have been devoted to automatically identifying and diagnosing performance issues of SaaS software. In this survey, we comprehensively review the different methods about automatically identifying and diagnosing performance issues of SaaS software. We divide them into three steps according to their function: performance log generation, performance issue identification and performance issue diagnosis. We then comprehensively review these methods by their development history. Meanwhile, we give our proposed solution for each step. Finally, the effectiveness of our proposed methods is shown by experiments.
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