Andreas L. Symeonidis

Automated classification of municipal solid waste remains challenging due to high variability in object appearance, deformation, contamination, and inconsistent consumer disposal behavior, creating a need for representative multimodal datasets that enable robust machine-learning models. In this work, we developed a custom edge-node reverse-vending-machine (RVM) data-collection station equipped with multimodal sensors (RGB-D and microphone array) to co-record RGB-D video streams and impact audio on a single local compute node within the same acquisition window. Strict data acquisition protocols were designed to ensure a consistent environment, controlled illumination, and uniform background conditions while capturing realistic variations associated with everyday Greek consumer waste. The resulting dataset comprises 3300 multimodal samples corresponding to 721 distinct household waste items. Each sample is fully annotated across six categories addressing features critical for recycling: Material (18 classes), Hue (16 classes), Top/Cap, Label, Contamination, and Deformation. In addition, Common Objects in Context (COCO) annotations were added to support segmentation and object detection problems. This dataset is intended to support research on multimodal waste recognition, sensor fusion, and resource-efficient classification, and to serve as a foundation for evaluating models targeting edge-deployed recycling systems.

Background and Objectives: Functional decline is a major determinant of disability, caregiver burden, and loss of independence in behavioral variant frontotemporal dementia (bvFTD). Although bvFTD is characterized by early and rapidly progressive deterioration in everyday functioning, the multimodal contributors associated with longitudinal functional change remain insufficiently understood. The present study aimed to identify the strongest cognitive, behavioral, personality, and neuroimaging correlates of longitudinal functional deterioration in bvFTD using an integrated multimodal framework over a 12-month follow-up period. Methods: Twenty-seven patients diagnosed with bvFTD were recruited from the 2nd Neurology Clinic of the “AHEPA” University Hospital in Thessaloniki, Greece, and underwent comprehensive face-to-face neuropsychological assessment for the evaluation of a wide range of cognitive domains, alongside caregiver-based evaluations of behavioral disturbances, personality changes, and functional abilities, at baseline, 6 months, and 12 months. Brain perfusion single-photon emission computed tomography (SPECT) was acquired only at baseline, with regional cerebral blood flow (rCBF) quantified using a Brodmann area (BA)-based approach. Functional status was assessed using the Disability Assessment for Dementia (DAD) as the primary outcome, while the Frontotemporal Dementia Rating Scale (FRS) served as a secondary measure. Repeated-measures analysis of variance (ANOVA) was used to characterize longitudinal changes across cognitive, behavioral, personality, and functional domains, while linear mixed-effects (LME) models were applied to identify longitudinal correlates of functional decline and sources of inter-individual variability in functional outcomes. Results: Significant progressive decline in functional abilities was observed over the 1-year follow-up period, consistent with an aggressive and rapidly deteriorating clinical course in bvFTD. Reductions in functional performance were evident across both basic and instrumental activities of daily living, with deterioration being more pronounced in instrumental activities. Based on the final LME model, greater apathy-related (negative) behavioral symptoms, global cognitive impairment, attentional and processing speed deficits, and impaired inhibitory control were independently associated with poorer longitudinal functional outcomes (p < 0.001). At the neuroimaging level, reduced baseline perfusion in the right BA 24 within the anterior cingulate cortex was also significantly associated with greater loss of functional independence over time (p < 0.001). Conclusions: Longitudinal functional decline in bvFTD reflects the combined disruption of behavioral regulation, global cognition, executive control, and frontal–cingulate network integrity. The findings provide a preliminary foundation for future research investigating factors associated with accelerated functional decline. Further validation in larger, multicenter longitudinal cohorts is needed to determine the prognostic relevance of these factors and their potential applicability to patient stratification and individualized care planning.

Background and Objectives: Functional decline is a major determinant of disability, caregiver burden, and loss of independence in behavioral variant frontotemporal dementia (bvFTD). Although bvFTD is characterized by early and rapidly progressive deterioration in everyday functioning, longitudinal multimodal predictors of this decline remain insufficiently understood. The present study aimed to identify cognitive, behavioral, personality, and neuroimaging predictors of longitudinal functional deterioration in bvFTD using an integrated multimodal framework over a 12-month follow-up period. Methods: Twenty-seven patients diagnosed with bvFTD were recruited from the 2nd Neurology Clinic at the “AHEPA” University Hospital in Thessaloniki, Greece, and underwent comprehensive face-to-face neuropsychological assessment for the evaluation of a wide range of cognitive domains, alongside caregiver-based informant evaluations of behavioral disturbances, personality changes, and functional abilities, at baseline, 6 months, and 12 months. Functional status was assessed using the Disability Assessment for Dementia (DAD) as the primary outcome, while the Frontotemporal Dementia Rating Scale (FRS) served as a secondary measure. Brain perfusion single-photon emission computed tomography (SPECT) was acquired only at baseline, with regional cerebral blood flow quantified using a Brodmann area (BA)–based approach. Repeated-measures analysis of variance (ANOVA) was used to characterize longitudinal changes across cognitive, behavioral, personality, and functional domains, while linear mixed-effects (LME) models were applied to identify predictors of functional trajectories and sources of inter-individual variability in functional outcomes. Results: Significant progressive decline in functional abilities was observed over the 1-year follow-up period, consistent with an aggressive and rapidly deteriorating clinical course in bvFTD. Functional impairment affected both basic and instrumental activities of daily living, with a more pronounced decline in instrumental activities. Based on the final LME model, greater apathy-related (negative) behavioral symptoms, global cognitive impairment, attentional and processing speed deficits, and impaired inhibitory control were independently associated with poorer longitudinal functional outcomes. At the neuroimaging level, reduced perfusion in the right BA 24 within the anterior cingulate cortex was also significantly associated with greater loss of functional independence over time. Conclusions: Longitudinal functional decline in bvFTD reflects the combined disruption of behavioral regulation, global cognition, executive control, and frontal–cingulate network integrity. These findings support a multidimensional model of functional deterioration and highlight clinically actionable markers for prognosis, stratification, and personalized care in bvFTD.

Precise 3D positioning in GPS-denied environments is a critical enabler of autonomous robotics, industrial automation, and smart logistics within the emerging cyber-physical landscape. This paper presents a distributed and reconfigurable architecture designed to benchmark and provide unified multimodal indoor localization for mobile edge nodes. Unlike rigid commercial solutions, our architecture employs a distributed, reconfigurable framework that allows the rapid interchange of Absolute Localization Methods (UWB, External RGB-D Vision) and Relative Localization Methods (Inertial Odometry, Visual Odometry). We evaluate these modalities individually and in hybrid configurations using a custom low-cost mobile edge node. Experimental results in a controlled environment demonstrate that while all-optical systems offer high precision, a cost-effective fusion of Ultra-Wideband (UWB) and Inertial Measurement Unit (IMU) data provides a robust balance of accuracy and reliability. Conversely, we identify significant limitations in monocular visual odometry within feature-poor indoor spaces. The developed platform serves as a reproducible foundation for researchers to prototype hybrid localization algorithms and assess the trade-offs between hardware cost and operational accuracy within complex cyber-physical ecosystems.

The rapid growth of Cloud Computing and Internet of Things (IoT) has significantly increased the interconnection of computational resources, creating an environment where malicious software (malware) can spread rapidly. To address this challenge, researchers are increasingly utilizing Machine Learning approaches to identify malware through behavioral (i.e. dynamic) cues. However, current approaches are limited by their reliance on large labeled datasets, fixed model training, and the assumption that a trained model remains effective over time-disregarding the ever-evolving sophistication of malware. As a result, they often fail to detect evolving malware attacks that adapt over time. This paper proposes an online learning approach for dynamic malware detection, that overcomes these limitations by incorporating temporal information to continuously update its models using behavioral features, specifically process resource utilization metrics. By doing so, the proposed models can incrementally adapt to emerging threats and detect zero-day malware effectively. Upon evaluating our approach against traditional batch algorithms, we find it effective in detecting zero-day malware. Moreover, we demonstrate its efficacy in scenarios with limited data availability, where traditional batch-based approaches often struggle to perform reliably.

A common problem in the development of Internet-of-Things (IoT) and cyber–physical system (CPS) applications is the complexity of these domains, due to their hybrid and distributed nature at multiple layers (hardware, network, communication, frameworks, etc.). This complexity often leads to implementation errors, some of which result in undesired states of the application and/or the system. The current work focuses on low-code development of behavior verification processes for IoT and CPS applications, in order to raise productivity, minimize risks (due to errors) and enable access to a wider range of end-users to create and verify applications for state-of-the-art domains, such as smart home and smart industry. Model-Driven Development (MDD) approaches are employed for the implementation of a Domain-Specific Language (DSL) that enables the evaluation of IoT and CPS applications, among others. The proposed methodology automates the development of behavior verification processes, allowing domain experts to focus on the real problem, instead of struggling with technical and technological breaches. Through comparative scenario-based analysis and 43 detailed use cases, we illustrate how the proposed methodology automates the development of behavior verification processes, allowing end-users to focus on the verification definition, instead of technical and technological intricacies.

Background/Objectives: Behavioral variant frontotemporal dementia (bvFTD), the most prevalent clinical subtype within the frontotemporal lobar degeneration spectrum disorders, is characterized by early and prominent changes that significantly disrupt everyday functioning. This study aims to identify the key correlates of functional status in bvFTD by investigating the relative contributions of cognitive deficits, behavioral disturbances, personality changes, and brain perfusion abnormalities. Additionally, it seeks to develop a theoretical framework to elucidate how these factors may interconnect and shape unique functional profiles. Methods: A total of 26 individuals diagnosed with bvFTD were recruited from the 2nd Neurology Clinic of “AHEPA” University Hospital in Thessaloniki, Greece, and underwent a comprehensive neuropsychological assessment to evaluate their cognitive functions. Behavioral disturbances, personality traits, and functional status were rated using informant-based measures. Regional cerebral blood flow was assessed using Single Photon Emission Computed Tomography (SPECT) imaging to evaluate brain perfusion patterns. Penalized Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was performed to identify the most robust correlates of functional impairment, followed by path analyses using structural equation modeling to explore how these factors may interrelate and contribute to functional disability. Results: The severity of negative behavioral symptoms (e.g., apathy), conscientiousness levels, and performance on neuropsychological measures of semantic verbal fluency, visual attention, visuomotor speed, and global cognition were identified as the strongest correlates of performance in activities of daily living. Neuroimaging analysis revealed hypoperfusion in the right prefrontal (Brodmann area 8) and inferior parietal (Brodmann area 40) cortices as statistically significant neural correlates of functional impairment in bvFTD. Path analyses indicated that reduced brain perfusion was associated with attentional and processing speed deficits, which were further linked to more severe negative behavioral symptoms. These behavioral disturbances were subsequently correlated with declines in global cognition and conscientiousness, which were ultimately associated with poorer daily functioning. Conclusions: Hypoperfusion in key prefrontal and parietal regions, along with the subsequent cognitive and neuropsychiatric manifestations, appears to be associated with the pronounced functional limitations observed in individuals with bvFTD, even in early stages. Understanding the key determinants of the disease can inform the development of more targeted, personalized treatment strategies aimed at mitigating functional deterioration and enhancing the quality of life for affected individuals.