Our paper introduces a novel middleware designed to enhance the user experience of novice users of video event retrieval systems. The middleware simplifies the search process while maintaining advanced semantic information-seeking features. It achieves this by refining sentence representation, rewriting the original query based on user intent and dataset metadata knowledge, reducing ambiguities, and increasing relevance with domain-specific terms. Our approach allows users to provide simplified descriptions, while the system automates query refinement and parameterizes these for different search functions. This reduces the difficulty of navigating complex user interfaces and selecting from available functionalities. Additionally, the multilingual feature supports non-native English speakers by helping them describe their queries more effectively. In general, our system is suitable for people who are not experienced in advanced retrieval tasks, specifically focusing on its applicability in the special session Interactive Video Retrieval for Beginners (IVR4B).

The advent of large language models (LLMs) has energised research in Question-Answering (QA) tasks, enabling responses across varied domains like economics and mathematics. Despite their capabilities, LLMs often lack explainability due to their complex parameter embeddings. Additionally, integrating multimedia data into QA systems introduces challenges in processing and interpreting diverse data types such as text, images, audio, and video. This necessitates sophisticated algorithms for accurate information retrieval across media while ensuring the reliability of the data and responses remains a significant challenge. The AIQAM workshop aims to bring together researchers and practitioners to address these challenges and enhance QA systems with multimedia data. The focus is on promoting innovations that improve the accuracy, explainability, and trustworthiness of QA systems, contributing to the development of the field.

Lifelogging was introduced as the process of passively capturing personal daily events via wearable devices. It ultimately creates a visual diary encoding every aspect of one's life with the aim of future sharing or recollecting. In this paper, we present LifeSeeker, a lifelog image retrieval system participating in the Lifelog Search Challenge (LSC) for 3 years, since 2019. Our objective is to support users to seek specific life moments using a combination of textual descriptions, spatial relationships, location information, and image similarities. In addition to the LSC challenge results, a further experiment was conducted in order to evaluate the power retrieval of our system on both expert and novice users. This experiment informed us about the effectiveness of the user's interaction with the system when involving non-experts.

Identifying stress level can provide valuable data for mental health analytics as well as labels for annotation systems. Although much research has been conducted into stress detection models using heart rate variability at a higher cost of data collection, there is a lack of research on the potential of using low-resolution Electrodermal Activity (EDA) signals from consumer-grade wearable devices to identify stress patterns. In this paper, we concentrate on performing statistical analyses on the stress detection capability of two popular approaches of training stress detection models with stress-related biometric signals: user-dependent and user-independent models. Our research manages to show that user-dependent models are statistically more accurate for stress detection. In terms of effectiveness assessment, the balanced accuracy (BA) metric is employed to evaluate the capability of distinguishing stress and non-stress conditions of the models trained on either low-resolution or high-resolution Electrodermal Activity (EDA) signals. The results from the experiment show that training the model with (comparatively low-cost) low-resolution EDA signal does not affect the stress detection accuracy of the model significantly compared to using a high-resolution EDA signal. Our research results demonstrate the potential of attaching the user-dependent stress detection model trained on personal low-resolution EDA signal recorded to collect data in daily life to provide users with personal stress level insight and analysis.

The Lifelog Search Challenge (LSC) is an annual comparative benchmarking activity for comparing approaches to interactive retrieval from multi-modal lifelogs. LSC'20, the third such challenge, attracts fourteen participants with their interactive lifelog retrieval systems. These systems are comparatively evaluated in front of a live-audience at the LSC workshop at ACM ICMR'20 in Dublin, Ireland. This overview motivates the challenge, presents the dataset and system configuration used in the challenge, and briefly presents the participating teams.