Over recent years, virtualization has worked as the powerhouse of the data centers. To positively influence datacenter utilization, power consumption, and management, live migration presents a technique which must be ...
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We propose a method to reconstruct a personalized hand avatar, representing the user's hand shape and appearance, from a monocular RGB-D video of a hand performing unknown hand poses under unknown illumination. Ou...
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Rice fields all across the world are affected by spikelet sterility, often known as rice spikelet's disease. It is characterized by the improper development of spikelet’s, which lowers grain output and quality. F...
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Ethiopia, known as the birthplace of coffee, relies on coffee exports as a major source of foreign currency. This research paper focuses on developing a hybrid feature mining technique to automatically classify Ethiop...
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Deformable image registration is a fundamental technique in medical image analysis and provide physicians with a more complete understanding of patient anatomy and function. Deformable image registration has potential...
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Machine learning models have been prevalently deployed for malware detection. When properly trained under the training environment, they can deliver highly accurate detection results in the deployment environment prov...
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Machine learning models have been prevalently deployed for malware detection. When properly trained under the training environment, they can deliver highly accurate detection results in the deployment environment provided that the two environments have no distribution shift from each other. Yet, in practice, various factors can cause environment shifts, which lead to degraded malware detection accuracy. In this work, we propose SCRR, a unified training framework to enhance the stability of malware detection models under unknown distribution shifts of the deployment environment. Our method can enhance the stability of the model by filtering out correlations between malicious behaviours and irrelevant features, known as the SC (spurious correlation), which can change significantly across different environments. What is more, SCRR proposes a fine-grained SC filtering strategy to achieve better accuracy performance. We evaluate SCRR in terms of in-distribution accuracy, degradation under environment shifts, and comprehensive detection ability with two real-world Android malware datasets, considering three types of causal factors and four environment shifts. SCRR outperforms the state-of-the-art malware detection training methods by improving the detection accuracy by up to 13.4% under the considered environment shifts. Moreover, it consistently showcases in-distribution accuracy comparable to the best outcomes achieved by baseline methods. IEEE
Software-defined Networking (SDN) is an innovative network architecture tailored to address the modern demands of network virtualization and cloud computing, which require features such as programmability, flexibility...
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Purpose:Assistive technology has been developed to assist the visually impaired individuals in their social *** designed to enhance communication skills,facilitate social engagement and improve the overall quality of ...
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Purpose:Assistive technology has been developed to assist the visually impaired individuals in their social *** designed to enhance communication skills,facilitate social engagement and improve the overall quality of life,conversational assistive technologies include speech recognition APIs,text-to-speech APIs and various communication tools that are *** real-time *** natural language processing(NLP)and machine learning algorithms,the technology analyzes spoken language and provides appropriate responses,offering an immersive experience through voice commands,audio feedback and vibration ***/methodology/approach:These technologies have demonstrated their ability to promote self-confidence and self-reliance in visually impaired individuals during social ***,they promise to improve social competence and foster better *** short,assistive technology in conversation stands as a promising tool that empowers the visually impaired individuals,elevating the quality of their social ***:The main benefit of assistive communication technology is that it will help visually impaired people overcome communication barriers in social *** technology helps them communicate effectively with acquaintances,family,co-workers and even strangers in public *** enabling smoother and more natural communication,it works to reduce feelings of isolation and increase overall quality of ***/value:Research findings include successful activity recognition,aligning with activities on which the VGG-16 model was trained,such as hugging,shaking hands,talking,walking,waving and *** originality of this study lies in its approach to address the challenges faced by the visually impaired individuals in their social interactions through modern *** adds to the body of knowledge in the area of assistive technologies,which contribute to the empowerment and social in
Currently,the main idea of iterative rendering methods is to allocate a fixed number of samples to pixels that have not been fully rendered by calculating the completion *** is obvious that this strategy ignores the c...
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Currently,the main idea of iterative rendering methods is to allocate a fixed number of samples to pixels that have not been fully rendered by calculating the completion *** is obvious that this strategy ignores the changes in pixel values during the previous rendering process,which may result in additional iterative operations.
We present a novel attention-based mechanism to learn enhanced point features for point cloud processing tasks, e.g., classification and segmentation. Unlike prior studies, which were trained to optimize the weights o...
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We present a novel attention-based mechanism to learn enhanced point features for point cloud processing tasks, e.g., classification and segmentation. Unlike prior studies, which were trained to optimize the weights of a pre-selected set of attention points, our approach learns to locate the best attention points to maximize the performance of a specific task, e.g., point cloud classification. Importantly, we advocate the use of single attention point to facilitate semantic understanding in point feature learning. Specifically,we formulate a new and simple convolution, which combines convolutional features from an input point and its corresponding learned attention point(LAP). Our attention mechanism can be easily incorporated into state-of-the-art point cloud classification and segmentation networks. Extensive experiments on common benchmarks, such as Model Net40, Shape Net Part, and S3DIS, all demonstrate that our LAP-enabled networks consistently outperform the respective original networks, as well as other competitive alternatives, which employ multiple attention points, either pre-selected or learned under our LAP framework.
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