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Titlebook: Security, Privacy, and Anonymity in Computation, Communication, and Storage; SpaCCS 2019 Internat Guojun Wang,Jun Feng,Rongxing Lu Conferen

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41#
發(fā)表于 2025-3-28 17:41:30 | 只看該作者
Yanlong Li,Hongbing Qiu,Xiao Chen,Jielin Fu,Junyi Wang,Yitao Zhangand well-being, towards a more critical praxis that involves students’ own affective and critical engagement . well-being. This praxis is what I call pedagogy of ‘inreach and outreach’. The great challenge of this pedagogic approach lies in seeing teacher and student well-being as inherently relatio
42#
發(fā)表于 2025-3-28 19:51:49 | 只看該作者
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發(fā)表于 2025-3-29 02:30:03 | 只看該作者
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發(fā)表于 2025-3-29 05:41:06 | 只看該作者
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發(fā)表于 2025-3-29 09:07:19 | 只看該作者
A Framework to Identify People in Unstructured Environments Incorporating Biometricshow how our framework applies. Recently, biometrics has gained the limelight as a means to identify individuals, but much else may be available for this task, including sensor data, witness reports, and data on file. To our knowledge, this is the only framework that in principle can accommodate any
46#
發(fā)表于 2025-3-29 13:25:55 | 只看該作者
Continuous Authentication Using Mouse Clickstream Data Analysiscan distinguish between a genuine user and an impostor with a relatively high degree of accuracy. In the verification mode, all the classifiers achieve a perfect accuracy of 100%. In authentication mode, all three classifiers achieved the highest accuracy (ACC) and Area Under Curve (AUC) from scenar
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發(fā)表于 2025-3-29 18:38:06 | 只看該作者
Presentation Attack Detection Using Wavelet Transform and Deep Residual Neural Netpped image sets. The datasets used in this research contain images that are captured both in a controlled and uncontrolled environment along with different resolution and sizes. We obtained the best accuracy of 93% on the ATVS Iris dataset. For CASIA two class and CASIA cropped datasets, we achieved
48#
發(fā)表于 2025-3-29 20:52:51 | 只看該作者
49#
發(fā)表于 2025-3-30 03:10:33 | 只看該作者
Limited Memory Eigenvector Recursive Principal Component Analysis in Sensor-Cloud Based Adaptive Ope principal component analysis (LMERPCA) based OMA method is designed to reduce the runtime and memory requirements and facilitate online process in conjunction with the cloud computing. This approach combines moving window technology and eigenvector recursive principal component analysis method and
50#
發(fā)表于 2025-3-30 05:46:05 | 只看該作者
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