This paper introduces CWGPT, a ChatGPT-4-based tool designed for Cognitive Walkthrough (CW) inspired evaluations of web interfaces. The primary goal is to assist users, particularly students and inexperienced designers, in evaluating web interfaces. Our tool, operating as a conversational agent, provides detailed evaluations of a user-specified task by intelligently guessing the subtasks and actions required to accomplish them, answering the standard CW questions, and providing helpful feedback and practical suggestions to improve the usability of the analyzed interface. For our study, we selected a group of web applications designed by students from a Web and Software Architecture course. We compare the outcome of the CWs we executed on ten web apps against the corresponding CWGPT analyses. We then describe the study we conducted involving five author-students to assess the tool’s efficacy in helping them recognize and solve usability issues. In addition to introducing a novel adaptation of ChatGPT, the outcomes of the described experience underscore the promising potential of AI in usability evaluations.

State-of-the-art Earthquake Early Warning systems rely on a network of sensors connected to a fusion center in a client–server paradigm. The fusion center runs different algorithms on the whole data set to detect earthquakes. Instead, we propose moving computation to the edge, with detector nodes that probe the environment and process information from nearby probes to detect earthquakes locally. Our approach tolerates multiple node faults and partial network disruption and keeps all data locally, enhancing privacy. This paper describes our proposal’s rationale and explains its architecture. We then present an implementation that uses Raspberry, NodeMCU, and the Crowdquake machine learning model.

In this work, we analyze the different contexts in which one chooses to integrate artificial intelligence into an interface and the implications of this choice in managing user interaction. While AI in systems can provide significant benefits, it is not infallible and can make errors that seriously affect users. We aim to understand how to design more robust human-AI systems so that these initial AI errors do not lead to more catastrophic failures. To prevent failures, it is essential to detect errors as early as possible and have clear mechanisms to repair them. However, detecting errors in AI systems can be challenging. Therefore, we examine various approaches to error detection and repair, including post-hoc estimation, the use of traces and ambiguity, and multiple sensor layers.

Cruising-for-parking in an urban area is a time-consuming and frustrating activity. We present four machine learning-based models to predict the parking availability of street segments in an urban area on a three-level scale, which navigator and smart-parking apps can exploit to ease and reduce the cruising phase. The models were trained with data generated by a cruising-for-parking simulator that we developed, replicating four parking behavior types (workers, residents, buyers, and visitors). The generated data is comparable to that collectible with smartphones’ sensors. We simulated 40 users moving for 200 weeks in the city area of San Giovanni in Rome. We collected information about users’ parking, unparking, and cruising actions over considered road segments at different time slots. Once a significant amount of trips were collected, we extracted ten features for each road segment at a given time slot. With the obtained dataset, which contained 761 samples, we trained and compared four supervised machine learning models that receive the history of a segment and, in return, classify the Parking Availability Level of the segment as Green, Yellow or Red. The four models were further evaluated in a different city area, San Lorenzo, and obtained very accurate results. We can predict parking availability with an accuracy above 97% for all the street segments where we collected 30 or more user actions, confirming the robustness of the simulator in generating synthetic cruising-for-parking data and the suitability of designing a Parking Availability Classifier (PAC) based on data collectible by smartphones.