Métricas para verificación de autoría y emulación de procesos cognitivos
Resumen
Se proponen dos medidas cuantitativas, el parámetro de Hurst y la legibilidad, para realizar la verificación de autor. Se determinan las cantidades mencionadas para seis diferentes autores considerando seis obras de cada uno de ellos. Con estos valores se construye un espacio de dos dimensiones donde cada punto corresponde a un único autor; midiendo la distancia entre dos puntos de dicho espacio es posible decidir si un texto es atribuible a un autor o no. Adicionalmente dichas medidas proporcionan una interpretación cualitativa, es decir, en términos como la facilidad al leer un texto y si existen palabras, asociados, a pensamientos, que persisten en un texto.
Descargas
Citas
Abbasi, A. and Chen, H. (2005). Applying authorship analysis to extremist- group web forum messages. IEEE Intelligent Systems, 20(5):67–75.
Agazzi, E. (2015). Bioethics as a paradigm of an ethics for a technological society. Bioethics Update, 1(1):3–21.
Ahmad, N., George, R. P., and Jahan, R. (2019). Emerging trends in iot for categorized health care. In 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), volume 1, pages 1438–1441.
Ahmed, H. (2018). The role of linguistic feature categories in authorship verification. Procedia Computer Science, 142:214–221. Arabic Computational Linguistics.
Alhijawi, B., Hriez, S., and Awajan, A. (2018). Text-based authorship identification - a survey. In 2018 Fifth International Symposium on Innovation in Information and Communication Technology (ISIICT), pages 1–7.
Benzebouchi, N. E., Azizi, N., Aldwairi, M., and Farah, N. (2018). Multi- classifier system for authorship verification task using word embeddings. In 2018 2nd International Conference on Natural Language and Speech Processing (ICNLSP), pages 1–6.
Boenninghoff, B., Hessler, S., Kolossa, D., and Nickel, R. M. (2019). Explainable authorship verification in social media via attention-based similarity learning. In 2019 IEEE International Conference on Big Data (Big Data), pages 36–45.
Boenninghoff, B., Rupp, J., Nickel, R. M., and Kolossa, D. (2020). Deep bayes factor scoring for authorship verification.
Brocardo, M. L., Traore, I., and Woungang, I. (2015). Authorship verification of e-mail and tweet messages applied for continuous authentication. Journal of Computer and System Sciences, 81(8):1429–1440.
Bui, Q. and Slepaczuk, R. (2022). Applying hurst exponent in pair trading strategies on nasdaq 100 index. Physica A: Statistical Mechanics and its Applications, 592:126784.
Eduardo, M. (2017). Threats of the internet of things in a techno-regulated society: A new legal challenge of the information revolution. The ORBIT Journal, 1(1):1–17.
Fenoy, M., Mun˜oz-Ferna´ndez, G., Pareja Monturiol, J., and Sepu´lveda, J. S. (2021). Healthy versus congestive heart failure patients. an approach via the hurst parameter. Communications in Nonlinear Science and Numerical Simulation, 103:106004.
Haeussler, C. and Sauermann, H. (2013). Credit where credit is due? the impact of project contributions and social factors on authorship and inventorship. Research Policy, 42(3):688–703.
Illes, M., Wilson, P., and Bruce, C. (2020). Forensic epistemology: A need for research and pedagogy. Forensic Science International: Synergy, 2:51–59.
Issac, K., Pranay, G., Bharanidharan, N., and Rajaguru, H. (2020). A study on real world implementation and future trends of internet of things. In 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), pages 357–361.
Kaushik, N. and Bagga, T. (2021). Smart cities using iot. In 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), pages 1–6.
Khezr, P. and Mohan, V. (2022). The vexing but persistent problem of authorship misconduct in research. Research Policy, 51(3):104466.
Llargues Asensio, J. M., Peralta, J., Arrabales, R., Bedia, M. G., Cortez, P., and Pen˜a, A. L. (2014). Artificial intelligence approaches for the generation and assessment of believable human-like behaviour in virtual characters. Expert Systems with Applications, 41(16):7281–7290.
Lopez-Ortega, O., Perez-Cortés, O., Castillejos-Fernandez, H., Castro- Espinoza, F.-A., and Gonzalez-Mendoza, M. (2020). Written documents analyzed as nature-inspired processes: Persistence, anti-persistence, and random walks-we remember, as along came writing-t. holopainen. Applied Sciences, 10(18).
Lopez-Ortega, O., Perez-Cortés, O., Castro-Espinoza, F., and Montes y Gómez, M. (2018). An agent-based system to assess legibility and cognitive depth of scientific texts. In Advances in Computational Intelligence, pages 69–81, Cham. Springer International Publishing.
Mioara, M. S. (2012). The impact of technological and communication innovation in the knowledge-based society. Procedia - Social and Behavioral Sciences, 51:263–267. The World Conference on Design, Arts and Education (DAE-2012), May 1-3 2012, Antalya, Turkey.
Mnushka, O. and Savchenko, V. (2020). Security model of iot-based systems. In 2020 IEEE 15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET), pages 398–401.
Negoita, O. D., Purcarea, A. A., and Popescu, M. A.-M. (2019). Research on online promoting methods used in a technological society. Procedia Manufacturing, 32:1043–1050. 12th International Conference Interdisciplinarity in Engineering, INTER-ENG 2018, 4?5 October 2018, Tirgu Mures, Romania.
Nirkhi Smita, R. V. Dharaskar, V. M. T. (2016). Authorship verification of online e messages for foorensic investigation. Procedia Computer Science, 78:640–645.
Nurmohamed, F. R. H., Voigt, I., Awadpersad, P., Matawlie, R. H., and Gadjradj, P. S. (2021). Authorship decision-making in the field of orthopedic surgery and sports medicine. Journal of Clinical Orthopaedics and Trauma, 21:101531.
Okuno, S., Asai, H., and Yamana, H. (2014). A challenge of authorship identification for ten-thousand-scale microblog users. In 2014 IEEE International Conference on Big Data (Big Data), pages 52–54.
Omidian, T., Siyanova-Chanturia, A., and Biber, D. (2021). A new multidimensional model of writing for research publication: An analysis of disciplinarity, intra-textual variation, and l1 versus lx expert writing. Journal of English for Academic Purposes, 53:101020.
Pelau, C., Dabija, D.-C., and Ene, I. (2021). What makes an ai device human- like? the role of interaction quality, empathy and perceived psychological anthropomorphic characteristics in the acceptance of artificial intelligence in the service industry. Computers in Human Behavior, 122:106855.
Sboev, A., Litvinova, T., Gudovskikh, D., Rybka, R., and Moloshnikov, I. (2016). Machine learning models of text categorization by author gender using topic-independent features. Procedia Computer Science, 101:135– 142. 5th International Young Scientist Conference on Computational Science, YSC 2016, 26-28 October 2016, Krakow, Poland.
Song, M., Xing, X., Duan, Y., Cohen, J., and Mou, J. (2022). Will artificial intelligence replace human customer service? the impact of communication quality and privacy risks on adoption intention. Journal of Retailing and Consumer Services, 66:102900.
Stanisz, T., Kwapie, J., and Drod, S. (2019). Linguistic data mining with complex networks: A stylometric-oriented approach. Information Sciences, 482:301–320.
Theophilo, A., Giot, R., and Rocha, A. (2021). Authorship attribution of social media messages. IEEE Transactions on Computational Social Systems, pages 1–14.
Tsionas, M. G. (2021). Bayesian analysis of static and dynamic hurst parameters under stochastic volatility. Physica A: Statistical Mechanics and its Applications, 567:125647.
Vivitha Vijayan, S. G. (2019). A survey on author profiling techniques. International Journal of Computer Sciences and Engineering, 7:1065–1069. White, R. and Sprague, N. (2021). Deep metric learning for code authorship attribution and verification. In 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), pages 1089–1093.
Yang, M., Chen, X., Tu, W., Lu, Z., Zhu, J., and Qu, Q. (2018). A topic drift model for authorship attribution. Neurocomputing, 273:133–140.
Zeinali, N. and Pourdarvish, A. (2022). An entropy-based estimator of the hurst exponent in fractional brownian motion. Physica A: Statistical Mechanics and its Applications, 591:126690.
Zraick, R. I., Azios, M., Handley, M. M., Bellon-Harn, M. L., and Manchaiah, V. (2021). Quality and readability of internet information about stuttering. Journal of Fluency Disorders, 67:105824.
