An Intelligent Assistant for Converting City Requirements to Formal Specification

Chen, Zirong; Li, Isaac T. S.; Zhang, Haoxiang; Preum, Sarah Masud; Stankovic, John A.; Ma, Meiyi

doi:10.1109/smartcomp55677.2022.00043

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…Question Answering and Large Language Models. In recent years, advanced question-answering systems have evolved across various scenarios (Chen et al 2023(Chen et al , 2022bDiefenbach et al 2018). Black-box abstractive QA systems like mBART and T5 (Chipman et al 2022;Raffel et al 2019) lack output control.…”

Section: Related Workmentioning

confidence: 99%

Auto311: A Confidence-Guided Automated System for Non-emergency Calls

Chen,

Sun,

et al. 2024

AAAI

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Emergency and non-emergency response systems are essential services provided by local governments and critical to protecting lives, the environment, and property. The effective handling of (non-)emergency calls is critical for public safety and well-being. By reducing the burden through non-emergency callers, residents in critical need of assistance through 911 will receive a fast and effective response. Collaborating with the Department of Emergency Communications (DEC) in Nashville, we analyzed 11,796 non-emergency call recordings and developed Auto311, the first automated system to handle 311 non-emergency calls, which (1) effectively and dynamically predicts ongoing non-emergency incident types to generate tailored case reports during the call; (2) itemizes essential information from dialogue contexts to complete the generated reports; and (3) strategically structures system-caller dialogues with optimized confidence. We used real-world data to evaluate the system's effectiveness and deployability. The experimental results indicate that the system effectively predicts incident type with an average F-1 score of 92.54%. Moreover, the system successfully itemizes critical information from relevant contexts to complete reports, evincing a 0.93 average consistency score compared to the ground truth. Additionally, emulations demonstrate that the system effectively decreases conversation turns as the utterance size gets more extensive and categorizes the ongoing call with 94.49% mean accuracy.

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Section: Related Workmentioning

confidence: 99%

Auto311: A Confidence-Guided Automated System for Non-emergency Calls

Chen,

Sun,

et al. 2024

AAAI

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Urban intelligent assistant on the example of the escalator passenger safety management at the subway stations

Tianxing,

Vodyaho,

Zhukova

et al. 2023

Sci Rep

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Intelligent assistants often struggle with the complexity of spatiotemporal models used for understanding objects and environments. The construction and usage of such models demand significant computational resources. This article introduces a novel multilevel spatiotemporal model and a computationally efficient construction method. To facilitate model construction on different levels, we employ a meta-mining technique. Furthermore, the proposed model is specifically designed to excel in foggy environments. As a practical application, we develop an intelligent assistant focused on enhancing subway passenger safety. We present case examples involving jammed objects, such as shoes, in escalator combs. Our results demonstrate the effectiveness of the proposed model and method. Specifically, the accuracy of breakdown detection has improved by 10% compared to existing information systems used in subways. Moreover, the time required to build a spatiotemporal model is reduced by 2.3 times, further highlighting the efficiency of our approach. Our research offers a promising solution for intelligent assistants dealing with complex spatiotemporal modeling, with practical applications in ensuring subway passenger safety.

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An Intelligent Assistant for Converting City Requirements to Formal Specification

Cited by 2 publications

References 4 publications

Auto311: A Confidence-Guided Automated System for Non-emergency Calls

Auto311: A Confidence-Guided Automated System for Non-emergency Calls

Urban intelligent assistant on the example of the escalator passenger safety management at the subway stations

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