Alzheimer’s disease (AD) remains a significant challenge in neurological disorders, necessitating advanced diagnostic techniques for early detection and intervention. This study presents a novel approach for AD classification utilizing a combination of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals. The distinctive characteristics of the cognitive tasks employed in data acquisition underscore the need for task-specific feature extraction insights. To this end, we propose an innovative event-specific feature extraction method that adapts to the unique attributes of each task and signal. By tailoring feature extraction to the inherent characteristics of each task, we achieve maximal information extraction, thereby elevating classification performance. Our methodology employs Recursive Feature Elimination with Cross-Validation, which progressively deletes features with low importance from the model. This iterative process generates the essential features after the feature extraction. The EEG-fNIRS feature fusion capitalizes on their complementary nature, enhancing the discriminatory power of the classification model. Also, in addition to the resting state data usually used for AD classification, we used data collected through three cognitive tasks to identify rich features of AD patients. Our method demonstrates significant promise in effective diagnosis with varying cognitive statuses - healthy controls, mild cognitive impairment, and AD patients.

Aspect-based sentiment analysis (ABSA) is a task of identifying fine-grained sentiment entities in a given sentence, which is generally formulated as a sequence labeling problem. Recently, advancements in large pre-trained language models (PLMs) led to generative ABSA, where the task is treated as text-to-text transition resolved by fine-tuning PLMs. Although the generative methods are designed to capture sentence-level semantic information, they are inappropriate for explicit comprehension of sentiment structure. In order to address this issue, we propose sentiment element named entity recognition (SENER) for ABSA. SENER integrates the concepts of named entity recognition (NER) and generative ABSA to retrieve the sentiment entities with predefined sentiment element names, leading to better semantic and sentiment structure understanding. Experimental results on several ABSA tasks show that the proposed SENER significantly outperforms previous works on ASQP and ASTE.

We present a challenging dataset, ChangeSim, aimed at online scene change detection (SCD) and more. The data is collected in photo-realistic simulation environments with the presence of environmental non-targeted variations, such as air turbidity and light condition changes, as well as targeted object changes in industrial indoor environments. By collecting data in simulations, multi-modal sensor data and precise ground truth labels are obtainable such as the RGB image, depth image, semantic segmentation, change segmentation, camera poses, and 3D reconstructions. While the previous online SCD datasets evaluate models given well-aligned image pairs, ChangeSim also provides raw unpaired sequences that present an opportunity to develop an online SCD model in an end-to-end manner, considering both pairing and detection. Experiments show that even the latest pair-based SCD models suffer from the bottleneck of the pairing process, and it gets worse when the environment contains the non-targeted variations. Our dataset is available at https://sammica.github.io/ChangeSim/.

Text entry takes an important role of effectively delivering the intention of users to computers, where physical and soft keyboards have been widely used. However, with the recent trends of developing technologies like augmented reality and increasing contactless services due to COVID-19, a more advanced type of text entry is required. To tackle this issue, we propose Air-Text which is an intuitive system to write in the air using fingertips as a pen. Unlike previously suggested air-writing systems, Air-Text provides various functionalities by the seamless integration of air-writing and text-recognition modules. Specifically, the air-writing module takes a sequence of RGB images as input and tracks both the location of fingertips (5.33 pixel error in 640x480 image) and current hand gesture class (98.29% classification accuracy) frame by frame. Users can easily perform writing operations such as writing or deleting a text by changing hand gestures, and tracked fingertip locations can be stored as a binary image. Then the text-recognition module, which is compatible with any pre-trained recognition models, predicts a written text in the binary image. In this paper, examples of single digit recognition with MNIST classifier (96.0% accuracy) and word-level recognition with text recognition model (79.36% character recognition rate) are provided.