This paper presents DriveCoach, an intelligent driving assistance and coaching system designed to strengthen safe driving skills through structured, learning-oriented intervention. The system combines risk assessment and adaptive assistance with a coaching-centered improvement cycle in which risk driving skills are diagnosed, addressed through real-time feedback, and reinforced via tailored post-drive coaching. Using the CARLA driving simulator, we conducted a mixed-method user study to evaluate DriveCoach across four representative driving skills: maintaining safe distance, responding to oncoming vehicles, handling adjacent vehicles, and negotiating intersections. Quantitative analyses demonstrated significant reductions in risk-related events when drivers received real-time assistance and notable improvements in post-coaching performance, indicating short-term skill retention and transfer. Complementary qualitative results revealed strong user acceptance and positive perceptions of the system’s usability and coaching effectiveness. These findings highlight DriveCoach as a human-centered AI system that fosters safer, more reflective driving, contributing to the design of co-adaptive driver support systems that integrate behavioral assessment with personalized coaching.

The paper aims to present a collaboration between the industry and government sectors, focusing on creating a benchmarking platform for predicting urban risk transportation through the utilization of multimodal data. In this collaboration, the industry partner contributes datasets and customer preference surveys obtained from its business operations. On the other hand, government partners curate open datasets sourced from non-profit organizations in both private and public domains. Furthermore, the government provides an accessible platform that allows individuals to conveniently access and leverage resources for the purpose of advancing application development and engaging in research endeavors. Throughout the collaborative effort, a variety of techniques have been under development for forecasting urban risk transportation through the analysis of weather patterns, congestion levels, and people flow data. The core objective of this partnership is to formulate two foundational prediction methods. These methods are intended to serve as benchmarks, offering future users a dependable means to assess the performance of their own approaches in terms of both time-series and datapoints analytics methodologies.

Spatial High Utility Itemset Mining (SHUIM) is an important knowledge discovery technique with many real-world applications. It involves discovering all itemsets that satisfy the user-specified m inimum u tility (minUtil) i n a q uantitative spatiotemporal database. The popular adoption and the successful industrial application of this technique have been hindered by the following two limitations: (i) Since the rationale of SHUIM is to find all itemsets that satisfy the minUtil constraint, it often produces too many patterns, most of which may be redundant or uninteresting to the user. (ii) Specifying a right minUtil value is an open research problem in SHUIM. This paper tackles these two problems by proposing a novel model of top-k spatial high utility itemsets that may exist in a database. A new constraint, called dynamic minimum utility (dMinUtil), was explored to reduce the search space effectively. This constraint is based on a greedy search, where we raise its value through five thresholdraising strategies. An efficient single scan algorithm that employs depth-first search to find all top-k spatial high utility itemsets was also presented in this paper. Experimental results demonstrate that our algorithm is memory and runtime efficient. We will also demonstrate the usefulness of our algorithm with two real-world case studies.

Finding Spatial High Utility Itemsets (SHUIs) in a spatiotemporal database is a challenging problem of great importance in many real-world applications. Most previous works focused on the sequential discovery of SHUIs in a database running on a single machine. Consequently, these works are not suitable for big data (or cloud-based) applications as they suffer from the scalability and fault tolerant problems. This paper proposes several novel pruning techniques to reduce the search space and present a more flexible distributed algorithm to find all desired itemsets from the database using Spark in-memory computing architecture. Our algorithm inherits several advantages of Spark, including low communication cost, fault tolerance, and high scalability. Experimental results demonstrate that the proposed algorithm has good scalability and performance on very large databases. Finally, we present a real-world navigation application in which SHUIs generated from the traffic congestion data have been employed to recommend alternative routes to the users.

In our daily life, the interactive roles of leaders, followers, and coordinators tend to emerge from multiparty collaboration. The primary purpose of this study is to automatically predict the leading role in multiparty interaction by ubiquitous computing techniques. Even though the leading role has been predicted for an entire task, there has been little focus on evaluating how roles are reorganized during a task. To find the verbal and nonverbal cues that might predict roles, we asked neutral third parties to select the participant playing the leading role in an assembly task. We examined the correlation between behavioral data gathered during a task and third-party evaluations of the leading role player in terms of temporal alterations. The preliminary results suggest that task-oriented utterances and verification behaviors regarding progress status contribute to the prediction of the emerging and reorganized leader. Moreover, we discuss the implications of our findings for the design of applications that can enhance multiparty collaboration.

We describe awareness support for a text-based conferencing system. As a disincentive factor of socially-oriented communications on the text-based conferencing system, the lack of visual and auditory cues concerning conversational situations is pointed out. We have developed an application called the "Conversational Awareness Supporting Environment (COASE)", which supports awareness in conversational situations by extracting awareness information from the messaging history and by visualizing conversational situations for users. Through our evaluation, we learnt that COASE makes it easier to recognize the conversational situation and facilitates socially-oriented communication.