The possibility of recognizing diverse aspects of human behavior and environmental context from passively captured data motivates its use for mental health assessment. In this paper, we analyze the contribution of different passively collected sensor data types (WiFi, GPS, Social interaction, Phone Log, Physical Activity, Audio, and Academic features) to predict daily self-report stress and PHQ-9 depression score. First, we compute 125 mid-level features from the original raw data. These 125 features include groups of features from the different sensor data types. Then, we evaluate the contribution of each feature type by comparing the performance of Neural Network models trained with all features against Neural Network models trained with specific feature groups. Our results show that WiFi features (which encode mobility patterns) and Phone Log features (which encode information correlated with sleep patterns), provide significative information for stress and depression prediction.

The possibility of recognizing diverse aspects of human behavior and environmental context from passively captured data motivates its use for mental health assessment. In this paper, we analyze the contribution of different passively collected sensor data types (WiFi, GPS, Social interaction, Phone Log, Physical Activity, Audio, and Academic features) to predict daily selfreport stress and PHQ-9 depression score. First, we compute 125 mid-level features from the original raw data. These 125 features include groups of features from the different sensor data types. Then, we evaluate the contribution of each feature type by comparing the performance of Neural Network models trained with all features against Neural Network models trained with specific feature groups. Our results show that WiFi features (which encode mobility patterns) and Phone Log features (which encode information correlated with sleep patterns), provide significative information for stress and depression prediction.

The increasing complexity and heterogeneity of Internet of Things (IoT) systems pose significant challenges for systematic security and vulnerability assessment. From a knowledge-centric perspective, IoT security analysis requires transforming heterogeneous asset information into structured and interpretable security knowledge. In this paper, we propose a structured methodology for vulnerability analysis that models the attack surface of an IoT system by explicitly linking asset characteristics to known vulnerabilities, security controls, and countermeasures. The approach starts with a visual representation of the system architecture, where hardware, software, and communication components are identified and described through their technical characteristics. These characteristics are automatically mapped to relevant vulnerabilities, security controls, and countermeasures using a dedicated software tool called AVCA (Asset Vulnerabilities and Countermeasures Analyzer). The tool generates graph-based analytical representations that model vulnerabilities–countermeasures relationships in compliance with the Cloud Security Alliance (CSA) IoT Security Framework. From these graphs, attack–countermeasure trees are derived to provide a clear and interpretable representation of potential threats and mitigation strategies. The proposed methodology was evaluated through a case study involving a representative IoT system and an exploratory applicability experiment with participants with different levels of experience in IoT and cybersecurity. The results suggest that the approach is feasible and practically applicable for supporting security analysts in the systematic assessment of IoT attack surfaces, vulnerability identification, and selection of appropriate countermeasures under the evaluated conditions. This work highlights the role of structured and interpretable knowledge extraction as a foundation for knowledge-centric and interpretable IoT security analysis.