Система с обратной связью для оценки состояния человека по его манере работы на клавиатуре
Аннотация
В данной работе предложена модель оценки эмоционального состояния человека, основанная на данных о его взаимодействии с компьютерной клавиатурой. Модель интегрирует механизм обратной связи, что значительно повышает точность прогнозирования эмоциональных состояний человека. Путем системного анализа динамики изменения параметров использования клавиатуры пользователем и наблюдения за индивидуальными факторами окружающей среды, предлагаемый подход позволяет не только предсказывать состояние пользователя клавиатуры, но и адаптируется к изменениям в его поведении. Результаты тестирования модели с помощью специально разработанного программного обеспечения подтвердили ее эффективность. Системы на основе моделей с обратной связью позволяют улучшить взаимодействие человека с интеллектуальными системами, способствуют развитию интеллектуальных систем и человека. Полученные результаты подтверждают необходимость и направление для улучшения человеко-машинного интерфейса и являются основой для будущей их интеграции в более сложные системы оценки психофизиологического состояния человека.
Литература
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, et al., “Language Models are Few-Shot Learners,” in Proc. of 34th Conference on Neural Information Processing Systems (NeurIPS 2020), pp. 1–75, 2020.
V. Sanh, A.Webson, C. Raffel, S. H. Bach, “Multitask Prompted Training Enables Zero-Shot Task Generalization,” in Proc. ICLR 2022 Conference, pp. 1–161, 2022.
J. Huang and K. C. Chang, “Towards Reasoning in Large Language Models: A Survey,” in Proc. Findings of the Association for Computational Linguistics: ACL 2023, pp. 1049–1065, 2023.
K. Harrigian, “Geocoding Without Geotags: A Text-based Approach for reddit,” in Proc. of the 2018 EMNLPWorkshop W-NUT: The 4thWorkshop on Noisy User-generated Text, pp. 17–27, 2018.
D. S. Shah, G. K. Siddiqi, S. He, and R. Bansal, “Local Life: Stay Informed Around You, A Scalable Geoparsing and Geotagging Approach to Serve Local NewsWorldwide,” in arXiv, 2023. [Online]. Available: https://arxiv.org/abs/2305.07168
M.-H. Tsou, Q. Zhang, J. Xu, A. Nara, and M. Gawron, “Building Dynamic Ontological Models for Place using Social Media Data from Twitter and SinaWeibo,” in arXiv, 2023. [Online]. Available: https://arxiv.org/abs/2303.00877
R. Priedhorsky, A. Culotta, and S. Y. Del Valle, “Inferring the Origin Locations of Tweets with Quantitative Confidence,” in Proc. of CSCW ’14: Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing, pp. 1523-1536, 2014.
W. Li, P. Serdyukov, A. P. de Vries, C. Eickhoff, and M. Larson, “The Where in the Tweet,” in Proc. of the 20th ACM Conference on Information and Knowledge Management, pp. 2473–2476, 2011; doi:10.1145/2063576.2063995
S. Chandra, L. Khan, and F. B. Muhaya, “Estimating Twitter User Location Using Social Interactions–A Content Based Approach,” in Proc. of 2011 IEEE International Conference on Privacy, Security, Risk, and Trust, and IEEE International Conference on Social Computing, pp. 838–843, 2011; doi:10.1109/passat/socialcom.2011.120
Z. Cheng, J. Caverlee, and K. Lee, “You are where you Tweet: A content-based approach to geo-locating Twitter users,” in Proc. of the 19th ACM Conference on Information and Knowledge Management, pp. 759–768, 2010; doi:10.1145/1871437.1871535
C. Li and A. Sun, “Extracting fine-grained location with temporal awareness in tweets: A two-stage approach,” Journal of the Association for Information Science and Technology, vol. 68, no. 7, pp. 1652-1670, 2017;
oi:10.1002/asi.23816
Y. Ikawa, M. Enoki, and M. Tatsubori, “Location inference using microblog messages,” in Proc. of the 21st International Conference onWorld WideWeb, pp. 687–690, 2012.
J. Gelernter and N. Mushegian, “Geo-parsing Messages from Microtext,” Transactions in GIS, vol. 15, no. 6, pp. 753-773, 2011.
K. M. Ryoo and S. Moon, “Inferring Twitter user locations with 10 km accuracy,” in Proc. of the 23rd International Conference onWorld WideWeb, pp. 643–648, 2014.
J. Eisenstein, B. O’Connor, N. A. Smith, and E. P. Xing, “A latent variable model for geographic lexical variation,” in Proc. of the 2010 Conference on Empirical Methods in Natural Language Processing, pp. 1277–1287, 2010.
J. Mahmud, J. Nichols, and C. Drews, “Where Is This Tweet From? Inferring Home Locations of Twitter Users,” in Proceedings of the International AAAI Conference on Web and Social Media, vol. 6, no. 1, pp. 511–514, 2021; doi:10.1609/icwsm.v6i1.1429
T. Sakaki, M. Okazaki, and Y. Matsuo, “Earthquake shakes Twitter users: real-time event detection by social sensors,” in Proc. of the 19th international conference onWorld wide web, pp. 851–860, 2010.
R. Li, K. H. Lei, R. Khadiwala, and K. C-C. Chang, “TEDAS: A Twitter-based Event Detection and Analysis System,” in Proc. of 2012 IEEE 28th International Conference on Data Engineering, pp. 1273–1276, 2012.
M. Sasaki, S. Okura, and S. Ono, “A Simple Text-based Relevant Location Prediction Method using Knowledge Base” in Proc. of the 12th Language Resources and Evaluation Conference, pp. 116–121, 2020.
M. A. Radke, N. Gautam, A. Tambi, U. A. Deshpande, and Z. Syed, “Geotagging Text Data on theWeb—A Geometrical Approach,” IEEE Access, vol. 6, pp. 22045–22060, 2018.
T. Louf, B. Gonc¸alves, J. J. Ramasco, D. Saˊ nchez, and J. Grieve, “American cultural regions mapped through the lexical analysis of social media,” Humanities and Social Sciences Communications, vol. 10, no. 1, pp. 133(1–11), 2023; doi:10.1057/s41599-023-01611-3
T. Kew, A. Shaitarova, I. Meraner, J. Goldzycher, S. Clematide, and M. Volk, “Geotagging a Diachronic Corpus of Alpine Texts: Comparing Distinct Approaches to ToponymRecognition,” in Proc. of theWorkshop on Language Technology for Digital Historical Archives, pp. 11–18, 2019.
B. Hecht, L. Hong, B. Suh, and E. H. Chi, “Tweets from Justin Bieber’s Heart: The Dynamics of the "Location" Field in User Profiles,” in Proc. of. CHI ’11: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 237–246, 2011.
H. Chang, D. Lee, M. Eltaher, and J. Lee, “@Phillies Tweeting from Philly? Predicting Twitter User Locations with SpatialWord Usage,” in Proc. of 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 111–118, 2012; doi:10.1109/ASONAM.2012.29
S. A. Reddy and M. Ramchander, “Location Prediction For Tweets Content Using Machine Learning Algorithms,” IJCSPUB, vol. 12, no. 4, pp. 398–402, 2022.
M. Alsaqer, S. Alelyani, M. Mohana, K. Alreemy, and A. Alqahtani, “Predicting Location of Tweets Using Machine Learning Approaches,” Applied Sciences, vol. 13, no. 5, p. 3025, 2023.
K. Indira, E. Brumancia, P. S. Kumar, and S. P. T. Reddy, “Location prediction on Twitter using machine learning Techniques,” in Proc. of 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), pp. 700–703, 2019, doi:10.1109/icoei.2019.8862768
S. Brunsting, H. De Sterck, R. Dolman, and T. van Sprundel, “GeoTextTagger: High-Precision Location Tagging of Textual Documents using a Natural Language Processing Approach,” in arXiv, 2016. [Online]. Available: https://arxiv.org/abs/1601.05893
S. Kinsella, V. Murdock, and N. O’Hare, “’I’m Eating a Sandwich in Glasgow’: Modeling Locations with Tweets,” in SMUC ’11: Proc. of the 3rd international workshop on Search and mining user-generated contents, pp. 61–68, 2011
P. Mishra, “Geolocation of Tweets with a BiLSTM Regression Model,” in Proc. of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects, pp. 283–289, 2020.
B. Han, P. Cook, and T. Baldwin, “Text-Based Twitter User Geolocation Prediction,” Journal of Artificial Intelligence Research, vol. 49, pp. 451–500, 2014; doi:10.1613/jair.4200
K. Lutsai and C. H. Lampert, “Predicting the Geolocation of Tweets Using transformer models on Customized Data,” The Journal of Spatial Information Science , vol. 29, pp. 69–99, 2024; doi:10.5311/josis.2024.29.295
L. F. Simanjuntak, R. Mahendra, and E. Yulianti, “We Know You Are Living in Bali: Location Prediction of Twitter Users Using BERT Language Model,” Big Data Cogn. Comput., vol. 6, no. 3, p. 77, 2022.
C.-Y. Huang, H. Tong, J. He, and R. Maciejewski, “Location Prediction for Tweets,” Frontiers in Big Data, vol. 2, pp. 1–12, 2019; doi:10.3389/fdata.2019.00005
A. Ritter, S. Clark, Mausam, and O. Etzioni, “Named Entity Recognition in Tweets: An Experimental Study,” in EMNLP ’11: Proc. of the Conference on Empirical Methods in Natural Language Processing, pp. 1524–1534, 2011.
J. Lingad, S. Karimi, and J. Yin, “Location Extraction From Disaster-Related Microblogs,” in WWW’13 Companion: Proc. of the 22nd International Conference onWorld WideWeb, pp. 1017–1020, 2013.
E. Belliardo, K. Kalimeri, and Y. Mejova, “Leave no Place Behind: Improved Geolocation in Humanitarian Documents,” in Proc. of GoodIT ’23: ACM International Conference on Information Technology for Social Good, pp. 1–9, 2023.
R. Lamsal, A. Harwood, and M. Rodriguez Read, “Where did you tweet from? Inferring the origin locations of tweets based on contextual information,” in arXiv, 2022. [Online]. Available: https://arxiv.org/abs/2211.16506
L. S. Snyder, M. Karimzadeh, R. Chen, and D. S. Ebert, “City-level Geolocation of Tweets for Real-time Visual Analytics,” in Proc. of GeoAI ’19: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, pp. 85–88, 2019.
H. N. Serere, B. Resch, and C. R. Havas, “Enhanced geocoding precision for location inference of tweet text using spaCy, Nominatim and Google Maps. A comparative analysis of the influence of data selection,” PLoS One, vol. 18, no. 3, p. e0282942, 2023; doi:10.1371/journal.pone.0282942
O. Ajao, “Content-aware Location Inference and Misinformation in Online Social Networks,” Sheffield Hallam University Research Archive (SHURA), 2019; doi:10.7190/shu-thesis-00252
S. Hasni and S. Faiz, “Word embeddings and deep learning for location prediction: tracking Coronavirus from British and American tweets,” Social Network Analysis and Mining, vol. 11, p. 66, 2021.
D. Dogan, B. Altun, M. S. Zengin, M. Kutlu, and T. Elsayed, “Catch Me If You Can: Deceiving Stance Detection and Geotagging Models to Protect Privacy of Individuals on Twitter,” in Proc. of the International AAAI Conference onWeb and Social Media, vol. 17, pp. 173–184, 2023.
Материал публикуется под лицензией:
