{"id":1540,"date":"2026-01-19T11:58:50","date_gmt":"2026-01-19T11:58:50","guid":{"rendered":"https:\/\/www.kios.ucy.ac.cy\/guardai\/?page_id=1540"},"modified":"2026-01-23T16:14:58","modified_gmt":"2026-01-23T16:14:58","slug":"publications","status":"publish","type":"page","link":"https:\/\/www.kios.ucy.ac.cy\/guardai\/outputs\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"\t\t
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Title<\/th>Publisher<\/th>Year<\/th>Description<\/th>Partner<\/th>Link<\/th>\n<\/tr>\n<\/thead>\n
Uncertainty Quantification for Deep Regression using Contextualised Normalizing Flows<\/td>arXiv<\/td>2025<\/td>This paper introduces a post-hoc method using contextualized normalizing flows to quantify uncertainty in deep regression models by generating calibrated prediction intervals and full predictive distributions without retraining the base model. <\/td>University of York<\/td>Link<\/a><\/td>\n<\/tr>\n
Advancing B5G Security: An AI-Augmented Intrusion Detection System using a Real-Time Attack Generator<\/td>2025 IEEE International Conference on Cyber Security and Resilience (CSR)<\/td>2025<\/td>This IEEE paper presents an analysis of advanced electronic navigation and positioning techniques for maritime and offshore applications, focusing on improving precision and reliability of location determination in challenging ocean environments.<\/td>CERTH<\/td>Link<\/a><\/td>\n<\/tr>\n
Fast and Accurate Outlier-Aware Lidar Super-Resolution for Slam Applications<\/td>2025 IEEE International Conference on Image Processing (ICIP)<\/td>2025<\/td>This paper proposes a fast and accurate outlier-aware LiDAR super-resolution method that enhances point cloud density and reliability to improve SLAM performance in real-time applications.<\/td>Athena<\/td>Link<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

Edit Title Publisher Year Description Partner Link Uncertainty Quantification for Deep Regression using Contextualised Normalizing Flows arXiv 2025 This paper introduces a post-hoc method using contextualized normalizing flows to quantify uncertainty in deep regression models by generating calibrated prediction intervals and full predictive distributions without retraining the base model. University of York Link Advancing B5G […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":161,"menu_order":1,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1540","page","type-page","status-publish","hentry"],"jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.kios.ucy.ac.cy\/guardai\/wp-json\/wp\/v2\/pages\/1540","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.kios.ucy.ac.cy\/guardai\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.kios.ucy.ac.cy\/guardai\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.kios.ucy.ac.cy\/guardai\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.kios.ucy.ac.cy\/guardai\/wp-json\/wp\/v2\/comments?post=1540"}],"version-history":[{"count":10,"href":"https:\/\/www.kios.ucy.ac.cy\/guardai\/wp-json\/wp\/v2\/pages\/1540\/revisions"}],"predecessor-version":[{"id":1593,"href":"https:\/\/www.kios.ucy.ac.cy\/guardai\/wp-json\/wp\/v2\/pages\/1540\/revisions\/1593"}],"up":[{"embeddable":true,"href":"https:\/\/www.kios.ucy.ac.cy\/guardai\/wp-json\/wp\/v2\/pages\/161"}],"wp:attachment":[{"href":"https:\/\/www.kios.ucy.ac.cy\/guardai\/wp-json\/wp\/v2\/media?parent=1540"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}