In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective...
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Cultural evolution is driven by how we choose what to consume and share with others. A common belief is that the cultural artifacts that succeed are ones that balance novelty and conventionality. This balance theory s...
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The ever-increasing competitiveness in the academic publishing market incentivizes journal editors to pursue higher impact factors. This translates into journals becoming more selective, and, ultimately, into higher p...
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Cognitive agents are continuously faced with new problems. To facilitate adaptation, emerging theories of neural reuse propose that evolution might often favor re-purposing existing brain structures for new functions....
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The iris is considered as the biometric trait with the highest unique probability. The iris location is an important task for biometrics systems, affecting directly the results obtained in specific applications such a...
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The iris is considered as the biometric trait with the highest unique probability. The iris location is an important task for biometrics systems, affecting directly the results obtained in specific applications such as iris recognition, spoofing and contact lenses detection, among others. This work defines the iris location problem as the delimitation of the smallest squared window that encompasses the iris region. In order to build a benchmark for iris location we annotate (iris squared hounding boxes) four databases from different biometric applications and make them publicly available to the community. Besides these 4 annotated databases, we include 2 others from the literature. We perform experiments on these six databases, five obtained with near infra-red sensors and one with visible light sensor. We compare the classical and outstanding Daugman iris location approach with two window based detectors: 1) a sliding window detector based on features from Histogram of Oriented Gradients (HOG) and a linear Support Vector Machines (SVM) classifier;2) a deep learning based detector fine-tuned from YOLO object detector. Experimental results showed that the deep learning based detector outperforms the other ones in terms of accuracy and runtime (GPUs version) and should be chosen whenever possible.
The use of iris as a biometric trait is widely used because of its high level of distinction and uniqueness. Nowadays, one of the major research challenges relies on the recognition of iris images obtained in visible ...
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The use of iris as a biometric trait is widely used because of its high level of distinction and uniqueness. Nowadays, one of the major research challenges relies on the recognition of iris images obtained in visible spectrum under unconstrained environments. In this scenario, the acquired iris are affected by capture distance, rotation, blur, motion blur, low contrast and specular reflection, creating noises that disturb the iris recognition systems. Besides delineating the iris region, usually preprocessing techniques such as normalization and segmentation of noisy iris images are employed to minimize these problems. But these techniques inevitably run into some errors. In this context, we propose the use of deep representations, more specifically, architectures based on VGG and ResNet-50 networks, for dealing with the images using (and not) iris segmentation and normalization. We use transfer learning from the face domain and also propose a specific data augmentation technique for iris images. Our results show that the approach using non-normalized and only circle-delimited iris images reaches a new state of the art in the official protocol of the NICE. II competition, a subset of the UBIRIS database, one of the most challenging databases on unconstrained environments, reporting an average Equal Error Rate (EER) of 13.98% which represents an absolute reduction of about 5%.
The increasingly robust High Performance computing (HPC) systems have promoted many researches on fault tolerance mechanisms. However, the efficiency of these techniques is an important challenge, as the data and reso...
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ISBN:
(纸本)9781538692349;9781538692332
The increasingly robust High Performance computing (HPC) systems have promoted many researches on fault tolerance mechanisms. However, the efficiency of these techniques is an important challenge, as the data and resources demand is also getting higher. in this work, we presents how the framework Apache Hadoop implements the Checkpoint and Recovery technique for fault tolerance providing on its distributed file system (Hadoop Distributed File System). The demand of large amounts of data from all applications supported by Hadoop framework can compromise the mechanism efficiency, once its configurations attributes are defined statically. So we present a dynamic configuration mechanism to checkpoint for Apache Hadoop through a resource monitoring, whose goal is to make the Hadoop checkpoint adaptable. The proposed mechanism is then evaluated and submitted to performance tests.
Previous research has demonstrated that various properties of infectious diseases can be inferred from online search behaviour. In this work we use time series of online search query frequencies to gain insights about...
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The Association for the Advancement of Artificial Intelligence 2020 Workshop program included twenty-three workshops covering a wide range of topics in artificial intelligence. This report contains the required report...
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