It has become increasingly thorny for computer vision competitions to preserve fairness when participants intentionally fine-tune their models against the test datasets to improve their performance. To mitigate such unfairness, competition organizers restrict the training and evaluation process of participants' models. However, such restrictions introduce massive computation overheads for organizers and potential intellectual property leakage for participants. Thus, we propose Themis, a framework that trains a noise generator jointly with organizers and participants to prevent intentional fine-tuning by protecting test datasets from surreptitious manual labeling. Specifically, with the carefully designed noise generator, Themis adds noise to perturb test sets without twisting the performance ranking of participants' models. We evaluate the validity of Themis with a wide spectrum of real-world models and datasets. Our experimental results show that Themis effectively enforces competition fairness by precluding manual labeling of test sets and preserving the performance ranking of participants' models.
Model data-driven ontology and knowledge presentation for evolving semantic Asian social networks (OK-ASN) is a critical strategy for web of things (WoT) services. Meanwhile, Deep Neural Network (DNN) based OK-ASN service in WoT is growing rapidly. However, most DNN-based services can not utilize the potential of WoT fully, as heterogeneity exists in WoT. Therefore, this paper proposes a novel framework called Web-Based Heterogeneous Hierarchical Distributed Deep Neural Network (
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) to deploy the DNNs for OK-ASN services on WoT, overcoming the heterogeneity. The architecture of the system and the designed Edge-Cloud-Joint execute scheme utilize heterogeneous devices to make DNN inference ubiquitous and output two types of results to meet various requirements. To bring robustness to OK-ASN services, a global scheduling is designed to arrange the workflow dynamically. The results of our experiments prove the efficiency of the execute scheme and the global scheduling in the system.
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