Signal Return Oriented Programming (SROP) is a dangerous code reuse attack method. Recently, defense techniques have been proposed to defeat SROP attacks. In this paper, we leverage the signal nesting mechanism provid...
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In order to solve the problem of insufficient accuracy of Web service QoS prediction, a joint QoS prediction method for Web services based on the deep fusion of features was proposed by considering the hidden environm...
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In order to solve the problem of insufficient accuracy of Web service QoS prediction, a joint QoS prediction method for Web services based on the deep fusion of features was proposed by considering the hidden environmental preference information in QoS and the common characteristics of multi-class QoS. QoS data was modeled as user-service bipartite graph at first, then, multi-component graph convolution neural network was used for feature extraction and mapping, and weighted fusion method was used for the same dimensional mapping of multi-class of QoS features. Subsequently, the attention factor decomposition machine was used to extract the first-order features, second-order interactive features and high-order interactive features of the mapped feature vector. Finally, the results of each part were combined to achieve the joint QoS prediction. The experimental results show that the proposed method is superior to the existing QoS prediction methods in terms of root mean square error (RMSE) and average absolute error (MAE). IEEE
Along with the rapid development of biometric authentication technology,face recognition has been commercially used in many industries in recent ***,it cannot be ignored that face recognition-based authentication tech...
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Along with the rapid development of biometric authentication technology,face recognition has been commercially used in many industries in recent ***,it cannot be ignored that face recognition-based authentication techniques can be easily spoofed using various types of attacks such photographs,videos or forged 3D *** order to solve this problem,this work proposed a face anti-fraud algorithm based on the fusion of thermal infrared images and visible light *** normal temperature distribution of the human face is stable and characteristic,and the important physiological information of the human body can be observed by the infrared thermal ***,based on the thermal infrared image,the pixel value of the pulse sensitive area of the human face is collected,and the human heart rate signal is detected to distinguish between real faces and spoofing *** order to better obtain the texture features of the face,an image fusion algorithm based on DTCWT and the improved Roberts algorithm is ***,DTCWT is used to decompose the thermal infrared image and visible light image of the face to obtain high-and low-frequency ***,the method based on region energy and the improved Roberts algorithm are then used to fuse the coefficients of the high-and low-frequency ***,the DTCWT inverse transform is used to obtain the fused image containing the facial texture *** recognition is carried out on the fused image to realize identity *** results show that this algorithm can effectively resist attacks from photos,videos or *** with the use of visible light images alone for face recognition,this algorithm has higher recognition accuracy and better robustness.
Children's dental caries is a common oral health issue, causing pain and discomfort. Therefore, oral health plays a crucial role in children's growth and development. However, regular dental check-ups and cons...
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In the evolving landscape of Sri Lanka's apparel industry, the predominance of manual methods in the pre-production phase necessitates innovative technological interventions to enhance efficiency. This research ex...
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ISBN:
(数字)9798331517878
ISBN:
(纸本)9798331517885
In the evolving landscape of Sri Lanka's apparel industry, the predominance of manual methods in the pre-production phase necessitates innovative technological interventions to enhance efficiency. This research explores the complexities of garment production, addressing critical challenges such as machine breakdowns, maintenance, and workforce management. By employing advanced technologies such as image recognition, loT devices, and machine learning, this study aims to transform pre-production decision-making, optimize sewing machine performance, implement predictive maintenance strategies, and refine workforce optimization. These initiatives, driven by domain expertise and cutting-edge methodologies, not only aim to bridge existing gaps in the industry but also pave the way for significant advancements, ensuring a future characterized by precision, efficiency, and operational excellence.
Inferring geographic locations via social posts is essential for many practical location-based applications such as product marketing, point-of-interest recommendation, and infector tracking for COVID-19. Unlike image...
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In this paper, an integrated sensing and communication (ISAC) system is investigated. Initially, we introduce a design criterion wherein sensing data acquired from the preceding time slot is employed for instantaneous...
software testing is one of the important ways to ensure the quality of software. It is found that testing cost more than 50% of overall project cost. Effective and efficient software testing utilizes the minimum resou...
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Blended modeling is an emerging paradigm involving seamless interaction between multiple notations for the same underlying modeling language. We focus on a model-driven engineering (MDE) approach based on meta-models ...
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Every day, much multimodal data emerges on social media. Previous studies have focused on the problem of fine-grained sentiment analysis in multimodal data. However, many multimodal data must be annotated for specific...
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ISBN:
(数字)9798350365658
ISBN:
(纸本)9798350365665
Every day, much multimodal data emerges on social media. Previous studies have focused on the problem of fine-grained sentiment analysis in multimodal data. However, many multimodal data must be annotated for specific emotions to train fine-grained emotion prediction models. To efficiently train fine-grained emotion risk prediction models for multimodal data, we propose a few-shot learning-based method for multimodal fine-grained sentiment analysis. This model captures sentiment polarity in text using a pre-trained large language model, annotates a small amount of data, and generates a training dataset. Then, utilizing an intermediate layer fusion strategy, it combines representation vectors of text, images, videos, and audio to ultimately create a fine-grained emotion prediction model based on multimodal data. Exper-imental work on a real multimodal dataset, compared with random fine-grained sentiment analysis methods, shows that our proposed method has significant advantages in handling multimodal fine-grained sentiment analysis tasks and practical value.
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