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WBA Releases New Standardized Technical Framework for Operator-Managed ...

We address privacy and latency issues in edge-cloud computing environments where the neural network training is centralized. This paper considers the scenario where the edge devices are the only data sources for the deep learning model to be trained on the central server. Improper access to the massive amounts of data generated by edge devices could lead to privacy concerns. As a result, existing solutions for preserving privacy and reducing network latency in the edge environment rely on auxili...

Researchers used historical traffic data to develop a model that calculates the most efficient routes for police and medical personnel traveling from di...

Dublin, Ohio (PRWEB) May 09, 2023 -- Today, Infoverity, a leading provider of information management solutions, has launched its new MDM Cloud

Online metric learning (OML) has been widely applied in classification and retrieval. It can automatically learn a suitable metric from data by restricting similar instances to be separated from dissimilar instances with a given margin. However, the existing OML algorithms have limited performance in real-world classifications, especially, when data distributions are complex. To this end, this article proposes a multilayer framework for OML to capture the nonlinear similarities among instances. ...

The core issue in semi-supervised learning (SSL) lies in how to effectively leverage unlabeled data, whereas most existing methods tend to put a great emphasis on the utilization of high-confidence samples yet seldom fully explore the usage of low-confidence samples. In this article, we aim to utilize low-confidence samples in a novel way with our proposed mutex-based consistency regularization, namely MutexMatch. Specifically, the high-confidence samples are required to exactly predict “what ...

Columbus, Ohio (PRWEB) February 16, 2023 -- FOR IMMEDIATE RELEASEDublin, Ohio – Month Date, 2023 –Infoverity Inc. today announced that Informatica

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IoT industry enthusiasts and specialists will all be heading to RAI Amsterdam on the 20-21 September 2022

Person re-identification (Re-ID) has achieved great success in the supervised scenario. However, it is difficult to directly transfer the supervised model to arbitrary unseen domains due to the model overfitting to the seen source domains. In this paper, we aim to tackle the generalizable multi-source person Re-ID task (i.e., there are multiple available source domains, and the testing domain is unseen during training) from the data augmentation perspective, thus we put forward a novel method, t...