{"id":226927,"date":"2026-06-17T02:25:41","date_gmt":"2026-06-17T02:25:41","guid":{"rendered":"https:\/\/cisose.fit.ac.jp\/2026\/?page_id=226927"},"modified":"2026-06-17T02:30:07","modified_gmt":"2026-06-17T02:30:07","slug":"accepted-tutorials","status":"publish","type":"page","link":"https:\/\/cisose.fit.ac.jp\/2026\/accepted-tutorials\/","title":{"rendered":"Accepted Tutorials"},"content":{"rendered":"\t\t
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\r\n IEEE CISOSE 2026 will feature three half-day tutorials (approximately 3 hours each),\r\n delivered in person at Fukuoka Institute of Technology. Session times will be announced\r\n in the final program.\r\n <\/p>\r\n\r\n

\r\n

\r\n INTRODUCTORY SURVEY \u00b7 HALF DAY (~3 HOURS) \u00b7 ENGLISH\r\n <\/p>\r\n

Tutorial 1 \u2014 From Test Automation to Agentic Test Automation for Smart Systems<\/h3>\r\n

\r\n Presenter:<\/strong>\r\n Jerry Zeyu Gao (San Jose State University, California, USA)\r\n <\/p>\r\n

\r\n According to Global Market Insights, the automation-testing market is projected to grow from\r\n US$15B in 2020 to more than US$40B by 2027, and the rapid rise of AI-powered systems is driving\r\n strong demand for AI system test automation. This survey tutorial reviews AI system testing and\r\n automation \u2014 test modeling and analysis, test generation and augmentation, smart test validation,\r\n and the use of LLMs in test automation \u2014 then examines the major problems and needs, including\r\n intelligence-quality validation and test-coverage evaluation. It closes with a roadmap that\r\n transforms test automation into agentic AI test automation across three smart-system domains:\r\n AI systems, smart robots, and autonomous vehicles. Intended for AI system test engineers,\r\n managers, researchers, students, and industry practitioners.\r\n <\/p>\r\n

Schedule: TBA.<\/p>\r\n <\/article>\r\n\r\n

\r\n

\r\n HANDS-ON \/ TOOL DEMO \u00b7 HALF DAY (~3 HOURS) \u00b7 ENGLISH\r\n <\/p>\r\n

Tutorial 2 \u2014 Building and Verifying AI Systems with Agentic AI<\/h3>\r\n

\r\n Presenters:<\/strong>\r\n Chia-Kai Chang (National Central University, Taiwan);\r\n Kuei-Hao Li (National Tsing Hua University, Taiwan);\r\n Teng-Yok Lee (Mitsubishi Electric, Japan)\r\n <\/p>\r\n

\r\n Generative and agentic AI have collapsed the cost of building real-world, multi-user AI\r\n systems \u2014 but not the cost of understanding or trusting them. This hands-on tutorial teaches\r\n a domain-agnostic method in three repeating moves: build a system with spec-driven, agentic AI\r\n development; make it legible with data visualization; and use that visualization to test it.\r\n Attendees leave with a layered AI-testing rubric (code, pipeline, behavior, guardrail,\r\n governance, and drift), a runnable LLM-as-Judge harness with versioned prompts and cross-model\r\n reproducibility, visualization methods (CAM-style contribution maps and visual anomaly detection),\r\n and a redacted, IRB-governed data-handling template. A production AI education platform serves as\r\n the running example, but the method is the takeaway \u2014 not a tour of one platform.\r\n <\/p>\r\n

\r\n Tutorial page:<\/strong>\r\n https:\/\/uedu.tw\/cisose-2026-tutorial\/<\/a>\r\n <\/p>\r\n

Schedule: TBA.<\/p>\r\n <\/article>\r\n\r\n

\r\n

\r\n INTRODUCTORY \/ FOUNDATIONS \u00b7 HALF DAY (~3 HOURS) \u00b7 ENGLISH\r\n <\/p>\r\n

Tutorial 3 \u2014 Foundations of Physical AI: Sensing, Calibration, Communication, and Data Validation<\/h3>\r\n

\r\n Presenter:<\/strong>\r\n Winncy Du (San Jos\u00e9 State University, California, USA)\r\n <\/p>\r\n

\r\n Physical AI systems \u2014 autonomous robots, drones, intelligent manufacturing systems, and\r\n cyber-physical infrastructures \u2014 depend fundamentally on the quality and reliability of sensor\r\n data. While recent advances in AI have focused heavily on machine learning models, the\r\n performance of Physical AI ultimately begins with sensing, data acquisition, communication,\r\n and validation: inaccurate measurements, poor calibration, communication failures, and\r\n low-quality data can significantly degrade AI-driven decision-making. This tutorial provides\r\n a comprehensive introduction to the foundational technologies that enable trustworthy Physical\r\n AI \u2014 sensor selection and characterization, calibration and uncertainty analysis, sensor\r\n networking and communication protocols, data quality assessment, and validation techniques for\r\n real-world deployments \u2014 along with sensor fusion, edge intelligence, and emerging approaches\r\n for integrating AI with sensing systems. Through practical examples and case studies from\r\n robotics, unmanned aerial systems, smart manufacturing, and IoT applications, attendees will\r\n gain insights into designing end-to-end sensing architectures that support reliable AI\r\n operations. Intended for researchers, practitioners, and graduate students interested in\r\n trustworthy Physical AI and the critical role of sensing and data quality in AI-enabled\r\n cyber-physical environments.\r\n <\/p>\r\n

Schedule: TBA.<\/p>\r\n <\/article>\r\n\r\n

\r\n All tutorials are presented in person at Fukuoka Institute of Technology, Japan;\r\n remote or hybrid delivery is not permitted.\r\n <\/p>\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":"

IEEE CISOSE 2026 will feature three half-day tutorials (approximately 3 hours each), delivered in person at Fukuoka Institute of Technology. […]<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"site-sidebar-layout":"no-sidebar","site-content-layout":"","ast-site-content-layout":"full-width-container","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"disabled","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"class_list":["post-226927","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/cisose.fit.ac.jp\/2026\/wp-json\/wp\/v2\/pages\/226927","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cisose.fit.ac.jp\/2026\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/cisose.fit.ac.jp\/2026\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/cisose.fit.ac.jp\/2026\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/cisose.fit.ac.jp\/2026\/wp-json\/wp\/v2\/comments?post=226927"}],"version-history":[{"count":3,"href":"https:\/\/cisose.fit.ac.jp\/2026\/wp-json\/wp\/v2\/pages\/226927\/revisions"}],"predecessor-version":[{"id":226930,"href":"https:\/\/cisose.fit.ac.jp\/2026\/wp-json\/wp\/v2\/pages\/226927\/revisions\/226930"}],"wp:attachment":[{"href":"https:\/\/cisose.fit.ac.jp\/2026\/wp-json\/wp\/v2\/media?parent=226927"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}