{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T21:10:37Z","timestamp":1776114637000,"version":"3.50.1"},"reference-count":60,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2024,4,15]],"date-time":"2024-04-15T00:00:00Z","timestamp":1713139200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"DS Chair of AI fellowship to Srikanta Bedathur"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2024,6,30]]},"abstract":"<jats:p>\n            Human beings always engage in a vast range of activities and tasks that demonstrate their ability to adapt to different scenarios. These activities can range from the simplest daily routines, like walking and sitting, to multi-level complex endeavors such as cooking a four-course meal. Any human activity can be represented as a temporal sequence of actions performed to achieve a certain goal. Unlike the time series datasets extracted from electronics or machines, these action sequences are highly disparate in their nature\u2014the time to finish a sequence of actions can vary between different persons. Therefore, understanding the dynamics of these sequences is essential for many downstream tasks such as activity length prediction, goal prediction, and next action recommendation. Existing neural network based approaches that learn a continuous-time activity sequence are limited to the presence of only visual data or are designed specifically for a particular task (i.e., limited to next action or goal prediction). In this article, we present\n            <jats:sc>ProActive<\/jats:sc>\n            , a neural marked temporal point process framework for modeling the continuous-time distribution of actions in an activity sequence while simultaneously addressing three high-impact problems: next action prediction, sequence goal prediction, and\n            <jats:italic>end-to-end<\/jats:italic>\n            sequence generation. Specifically, we utilize a self-attention module with temporal normalizing flows to model the influence and the inter-arrival times between actions in a sequence. Moreover, for time-sensitive prediction, we perform an\n            <jats:italic>early<\/jats:italic>\n            detection of sequence goal via a constrained margin-based optimization procedure. This in turn allows\n            <jats:sc>ProActive<\/jats:sc>\n            to predict the sequence goal using a limited number of actions. In addition, we propose a novel addition over the\n            <jats:sc>ProActive<\/jats:sc>\n            model, called\n            <jats:sc>ProActive++<\/jats:sc>\n            , that can handle variations in the order of actions (i.e., different methods of achieving a given goal). We demonstrate that this variant can learn the order in which the person or actor prefers to do their actions. Extensive experiments on sequences derived from three activity recognition datasets show the significant accuracy boost of our\n            <jats:sc>ProActive<\/jats:sc>\n            and\n            <jats:sc>ProActive++<\/jats:sc>\n            over the state of the art in terms of action and goal prediction, and the first-ever application of end-to-end action sequence generation.\n          <\/jats:p>","DOI":"10.1145\/3650045","type":"journal-article","created":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T12:26:44Z","timestamp":1709209604000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Tapestry of Time and Actions: Modeling Human Activity Sequences Using Temporal Point Process Flows"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2626-8852","authenticated-orcid":false,"given":"Vinayak","family":"Gupta","sequence":"first","affiliation":[{"name":"University of Washington, Seattle, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3949-2175","authenticated-orcid":false,"given":"Srikanta","family":"Bedathur","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology Delhi, New Delhi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,4,15]]},"reference":[{"key":"e_1_3_2_2_2","article-title":"SenseGen: A deep learning architecture for synthetic sensor data generation","author":"Alzantot Moustafa","year":"2017","unstructured":"Moustafa Alzantot, Supriyo Chakraborty, and Mani B. 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