By incorporating state-space graph in recent years, driven by rapid technological advancements, embeddings into the LSTM model, we further enrich the evolving customer expectations, and increased model's understanding of the relationships and dependencies competition. As customers demand more personalized and among various features within the dataset, which may convenient services, financial institutions are under pressure lead to improved performance. This combination of LSTM to develop a deeper understanding of their clients' needs and models and state graph embeddings offers a more scalable preferences. This has led to a growing interest in leveraging and efficient solution in predicting customer goals and actions, data-driven approaches to gain insights into customer behavior while maintaining a high level of accuracy and robustness and predict future actions.

Log analysis and monitoring are essential aspects in software maintenance and identifying defects. In particular, the temporal nature and vast size of log data leads to an interesting and important research question: How can logs be summarised and monitored over time? While this has been a fundamental topic of research in the software engineering community, work has typically focused on heuristic-, syntax-, or static-based methods. In this work, we suggest an online semantic-based clustering approach to error logs that dynamically updates the log clusters to enable monitoring code error life-cycles. We also introduce a novel metric to evaluate the performance of temporal log clusters. We test our system and evaluation metric with an industrial dataset and find that our solution outperforms similar systems. We hope that our work encourages further temporal exploration in defect datasets.

This paper presents an ongoing study in the area of Human-Robot Collaboration, more precisely collaborative manipulation tasks between one robot and multiple people. We study how different trajectories influence people’s perception of the robot’s goal. To achieve this, we propose an approach based on Probabilistic Motor Primitives and the notion of legibility and predictability of trajectories to create the movements the robot performs during task execution. In this approach we also propose combining of legible and predictable trajectories depending on the state of the task in order to diminish the drawbacks associated with each type of trajectory.