Mental illness is a widespread and debilitating condition with substantial societal and personal costs. Traditional diagnostic and treatment approaches, such as self-reported questionnaires and psychotherapy sessions, often impose significant burdens on both patients and clinicians, limiting accessibility and efficiency. Recent advances in Artificial Intelligence (AI), particularly in Natural Language Processing and multimodal techniques, hold great potential for recognizing and addressing conditions such as depression, anxiety, bipolar disorder, schizophrenia, and post-traumatic stress disorder. However, privacy concerns, including the risk of sensitive data leakage from datasets and trained models, remain a critical barrier to deploying these AI systems in real-world clinical settings. These challenges are amplified in multimodal methods, where personal identifiers such as voice and facial data can be misused. This paper presents a critical and comprehensive study of the privacy challenges associated with developing and deploying AI models for mental health. We further prescribe potential solutions, including data anonymization, synthetic data generation, and privacy-preserving model training, to strengthen privacy safeguards in practical applications. Additionally, we discuss evaluation frameworks to assess the privacy-utility trade-offs in these approaches. By addressing these challenges, our work aims to advance the development of reliable, privacy-aware AI tools to support clinical decision-making and improve mental health outcomes.

We investigate the role of attention and memory in complex reasoning tasks. We analyze Transformer-based self-attention as a model and extend it with memory. By studying a synthetic visual reasoning test, we refine the taxonomy of reasoning tasks. Incorporating self-attention with ResNet50, we enhance feature maps using feature-based and spatial attention, achieving efficient solving of challenging visual reasoning tasks. Our findings contribute to understanding the attentional needs of SVRT tasks. Additionally, we propose GAMR, a cognitive architecture combining attention and memory, inspired by active vision theory. GAMR outperforms other architectures in sample efficiency, robustness, and compositionality, and shows zero-shot generalization on new reasoning tasks.

Real-life mobile phone data may contain noisy instances, which is a fundamental issue for building a prediction model with many potential negative consequences. The complexity of the inferred model may increase, may arise overfitting problem, and thereby the overall prediction accuracy of the model may decrease. In this paper, we address these issues and present a robust prediction model for real-life mobile phone data of individual users, in order to improve the prediction accuracy of the model. In our robust model, we first effectively identify and eliminate the noisy instances from the training dataset by determining a dynamic noise threshold using naive Bayes classifier and laplace estimator, which may differ from user-to-user according to their unique behavioral patterns. After that, we employ the most popular rule-based machine learning classification technique, i.e., decision tree, on the noise-free quality dataset to build the prediction model. Experimental results on the real-life mobile phone datasets (e.g., phone call log) of individual mobile phone users, show the effectiveness of our robust model in terms of precision, recall and f-measure.

This paper presents a technical approach to robot learning of motor skills which combines active intrinsically motivated learning with imitation learning. Our architecture, called SGIM-D, allows efficient learning of high-dimensional continuous sensorimotor inverse models in robots, and in particular learns distributions of parameterised motor policies that solve a corresponding distribution of parameterised goals/tasks. This is made possible by the technical integration of imitation learning techniques within an algorithm for learning inverse models that relies on active goal babbling. After reviewing social learning and intrinsic motivation approaches to action learning, we describe the general framework of our algorithm, before detailing its architecture. In an experiment where a robot arm has to learn to use a flexible fishing line , we illustrate that SGIM-D efficiently combines the advantages of social learning and intrinsic motivation and benefits from human demonstration properties to learn how to produce varied outcomes in the environment, while developing more precise control policies in large spaces.