Browsing by Author "Tai, Chih-Hua"

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A Mental Disorder Early Warning Approach by Observing Depression Symptom in Social Diary
(2014)
Fang, Ying-En
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Tai, Chih-Hua
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Chang, Yue-Shan
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Fan, Chih-Tien
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資訊工程學系
With the advances of information technology, there are increasing researches aiming at assisting depression diagnosis and treatment. In most of them the user is necessarily actively joining the diagnosis and treatment program while he has perceived mental disorder himself. In order to early prevent the mental disorder, in this paper we propose an early warning mechanism that observes and mines user diary published on social network platform, and generates a score of getting mental disordered or depressed. If the score is large than a threshold, the system can notify the user and his friends on the social network to take care about the friend. We have conducted experiments to evaluate the proposed approach, and the results show that the proposed approach is effective.
Adaptive Block Partition of Geographic Map Based on Road Coverage in Urban Environment
(2015-01-01)
Wu, Guang-Ting
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Sheu, Ruey-Kai
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Fan, Chih-Tien
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Chang, Yue-Shan
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Tai, Chih-Hua
Smart cities try to deploy new sensors or controller to existing hardware infrastructure to improve the efficiency of city life. How to effectively utilize geographic information to deploy ubiquitous facilities (such as street light control unit) in the urban environment increasingly becomes an important issue. Calculating the minimum block number for facility deployment in a city is similar to the maximum clique problem which belongs to the NP-hard problems. In this paper, we propose an algorithm to find the near minimum block number of geographic map based on road coverage in the urban environment. In the algorithm, we first exploit the Google map as the geographic information to calculate the road coverage of a city and then partitioning the map into numerous partitions according to a given lights (road coverage). We also conduct some experiments to evaluate the performance and accuracy to demonstrate the feasibility of the system. The result shows that the proposed algorithm is effective and efficient.
Optimizing Social-Topic Engagement on Social Network and Knowledge Graph
(2019-01-01)
Teng, Ya-Wen
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Shi, Yishuo
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Tsai, Jui-Yi
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Shuai, Hong-Han
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Tai, Chih-Hua
Existing research on social networks manifests two crucial criteria to improve activity engagement of users: (1) user interests in the activity topics and (2) opportunities of making new friends with some acquaintances. However, current online platforms still involve massive manual selection for activity attendees and contents without proper recommendations. In this paper, therefore, we formulate a new activity organization problem, named Social Knowledge Group Query (SKGQ), to recommend attendees and topic-related contents simultaneously. We prove that SKGQ is NP-hard and design an approximation algorithm, named Social cOntent Knowledge Exploration (SOKE), to jointly choose the activity attendees and topic-related contents according to social-oriented and topic-oriented strategies. Simulation results manifest that the solution acquired by SOKE is close to the optimal solution and outperforms various baselines.