Edificios inteligentes y computación afectiva para mejorar la interacción humano robot

Descripción:

Smart buildings provide a variety of sensor-based services to support and enhance the quality of human activities. Advanced technologies such as robotics are increasingly added to smart buildings’ ecosystems, creating a need to incorporate affective computing techniques to augment the quality of human-building, and human-robot interactions. To better understand user’s needs and expectations about human-building interactions, we conducted a pilot study using a mixed methods approach combining short surveys and controlled laboratory activities. We recruited 66 participants and collected several data elements characterizing their perceptions and expectations about smart building services. This paper presents preliminary evidence showing acceptance of specific human-building interaction methods based on ambient-sensors information such as in-context voice, behavior, and emotion, recognition. We also identified a need for educational activities to promote the understanding of smart building concepts and their impact in modern society. These results can be leveraged to assist the design of future services that include human-building and human-robot interactions.

Tipo de publicación: Book Chapter

Publicado en: Proceedings of the 15th International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2023)

Descripción:

Malicious URLs are constantly used for phishing, malware distribution and other illegal activities. Because benign URLs are needed for the Internet to function, malicious URLs are hard to block. While several works have focused on offline classification of malicious URLs, real-time detection still needs to be investigated. This paper evaluates the performance of real-time malicious URL detection using two techniques: blacklist methods and machine learning methods, deployed in both local and cloud environments. The study highlights significant differences in latency and connection failure rates under various load conditions, providing insights into the strengths and limitations of each approach. The blacklist method consistently demonstrates lower latency, making it suitable for scenarios requiring quick response times, though its stability may be compromised under high loads in a local setup. In contrast, the machine learning method offers advanced detection capabilities but exhibits higher latency, particularly in local environments, due to its resource-intensive nature. The cloud environment mitigates some latency issues but still lags behind the blacklist method in terms of speed. The findings emphasize that most latency stems from the verification process, with the local environment requiring significant optimization to reduce delays. The study concludes that implementing a proxy for real-time URL detection is viable, especially in cloud environments, where resource management can better handle increased demand.

Tipo de publicación: Conference Paper

Publicado en: 2024 IEEE Latin-American Conference on Communications (LATINCOM)