担当区分：責任著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

Abu Dhabi, United Arab Emirates   https://abc-research.github.io/2025   2025年04月21日  -  2025年04月25日

The Silent Speech Decoding Challenge, hosted as part of the ABC 2025 conference, was designed to advance the decoding of silent (inner) speech from electroencephalography (EEG) signals, specifically targeting Arabic language processing. Participants leveraged advanced machine learning (ML) and deep learning (DL) techniques to classify EEG data from a uniquely collected dataset. This dataset, gathered by Qatar University's Machine Learning Group, consisted of EEG recordings from ten subjects across multiple sessions, capturing both silent and overt speech for six distinct inner speech commands. Participants were tasked with developing robust ML/DL models to accurately decode these neural signals, addressing challenges such as generalization across subjects and robustness to noise and motion artifacts.

DOI： https://doi.org/10.60401/ijabc.104

DOI： https://doi.org/10.60401/ijabc.104

その他リンク： https://autocare.ai/abc2025

担当区分：責任著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

Abu Dhabi, United Arab Emirates   https://abc-research.github.io/2025   2025年04月21日  -  2025年04月25日

Early detection of depressive episodes is crucial in managing mental health disorders such as Major Depressive Disorder (MDD) and Bipolar Disorder. However, existing methods often necessitate active participation or are confined to clinical settings. While facial expressions have shown promise in laboratory settings for identifying depression, their potential in real-world applications remains largely unexplored. In this challenge we introduce a data collected state of the art facial behavior sensing system, that tracks different facial behavior primitives such as Action Units, Landmarks, Head Pose, Eye Open state and others for task of detecting depressive episode in the wild. The challenge duration was three months, from Dec 12, 2024 to Feb 28, 2025 and 8 teams participated. Among the final teams, Team Persistence (Team ID 160) achieved 0.77 AUROC for universal model and 0.88 AUROC for hybrid model and became the winner.

DOI： https://doi.org/10.60401/ijabc.102

DOI： https://doi.org/10.60401/ijabc.102

その他リンク： https://autocare.ai/abc2025

担当区分：責任著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

Abu Dhabi, United Arab Emirates   https://abc-research.github.io/2025   2025年04月21日  -  2025年04月25日

This paper presents a summary of the virtual data generation for complex industrial activity recognition Challenge, which focused on exploring virtual data generation techniques to improve the performance of human activity recognition (HAR) in complex industrial environments. The challenge utilized the OpenPack dataset, a large-scale multimodal collection of sensor data captured during real-world packaging operations. Participants were tasked with generating synthetic accelerometer data to augment a baseline HAR model.

DOI： https://doi.org/10.60401/ijabc.101

DOI： https://doi.org/10.60401/ijabc.101

その他リンク： https://autocare.ai/abc2025

担当区分：責任著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

Abu Dhabi, United Arab Emirates   https://abc-research.github.io/2025   2025年04月21日  -  2025年04月25日

抄録
In this study, we propose to optimize temporal parameters with pose estimation data of simulated abnormal activities of developmentally disabled individuals by incorporating behavior context to Large Language Models (LLMs). Facilities for the developmentally disabled face the challenge of detecting abnormal behaviors because of limited staff and the difficulty of spotting subtle movements. Traditional methods often struggle to identify these behaviors because abnormal actions are irregular and unpredictable, leading to frequent misses or misclassifications. The main contributions of this work is the creation of a unique dataset with labeled abnormal behaviors and the proposed application of LLMs to this dataset comparing results of Zero-Shot and Few-Shot. Our method leverages the context of the collected abnormal activity data to prompt LLMs to suggest window size, overlap rate, and LSTM model’s length sequence tailored to the specific characteristics of these activities.

DOI： https://doi.org/10.60401/ijabc.39

DOI： https://doi.org/10.60401/ijabc.39

その他リンク： https://autocare.ai/abc2025

担当区分：責任著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

Abu Dhabi, United Arab Emirates   https://abc-research.github.io/2025   2025年04月21日  -  2025年04月25日

抄録
In this paper, we improve nursing activity recognition in gastrostomy tube feeding (GTF) with temporal variations and sequential errors by integrating activity context to Large Language Model (LLM) for guided feature selection and post-processing. GTF is a delicate nursing procedure that allows direct stomach access in children for supplemental feeding or medication, but it is underrepresented in datasets, posing challenges for accurate detection. Leveraging the contextual adaptability of LLMs, we generate new features suggested by the language model, combining them with hand-crafted features to optimize the model. For post-processing, a sliding window smoothing method based on majority voting is applied. To mitigate duration-based discrepancies, a priority handling is incorporated for short-duration activities to pre- serve their recognition accuracy while addressing repeated labels caused by long-duration actions.

DOI： https://doi.org/10.60401/ijabc.38

DOI： https://doi.org/10.60401/ijabc.38

その他リンク： https://autocare.ai/abc2025

開催期間： 2025年04月21日 - 2025年04月25日   記述言語：英語   開催地：Abu Dhabi, United Arab Emirates   国名：アラブ首長国連邦  

In this study, we use Large Language Models (LLMs) for room recognition in nursing facilities, leveraging contextual data to address data limitations. Traditional models struggle with Received Signal Strength Indicator (RSSI) variability and sparse data. LLMs offer a promising alternative by integrating domain knowledge to address signal inconsistencies and improve localization accuracy.

その他リンク： https://abc-research.github.io/2025

受賞国：日本国

Awarded Best Short Paper for the work "LLM-based Indoor Localization in Nursing Care Facility: A Zero-Shot Ablation Study" presented during the 12th International Conference on Informatics, Electronics & Vision (ICIEV) in Japan last May 26-29, 2025.

受賞国：アラブ首長国連邦

Awarded with Outstanding Contribution for actively serving as Challenge Organization Chair co-hosting four coding challenges with various universities while also serving as the Publication Chair.

受賞国：ドイツ連邦共和国

Awarded Excellent Paper for the work "A Relabeling Approach to Signal Patterns for Beacon-based Indoor Localization in Nursing Care Facility" presented in the 5th International Conference on Activity and Behavior Computing held in Kaiserslautern, Germany last September 7th - 9th, 2023.

受賞国：シンガポール共和国

Awarded Best Paper for the work titled "Non-invasive Diabetes Detection using Facial Texture Features Captured in a Less Restrictive Environment" presented during the 22nd World Conference on Applied Science, Engineering and Technology held on September 2019 in Singapore.

受賞国：フィリピン共和国

Bachelor thesis implemented using Synopsis Industry tool grade for design. Frequency Detector with NOR gates and divide-by-64 with pseudo-NMOS divide-by-2 frequency divider is proposed, designed and simulated in TSMC 0.18 um 1P6M CMOS process technology to come up with minimum chip area and achieve fast lock-in time. This PLL design is specifically intended for Continuous-Time Sigma-Delta ADC operating at 640MHz frequency which is an important component of ICs used in electronics and communication devices whose clock rates and timing relationships are vital. This work has a lock-time of around 2.5 us which is a fast lock-in value for the lock-in time of ADC clock generator. T