This paper introduces a systematic framework for the identification of memory statements in human-AI interactions. We present a novel benchmark dataset of 4000 labeled statements spanning five types of memory (episodic, semantic, spatial, emotional, associative) and a non-memory category, generated through a controlled GPT-4o and Faker-based pipeline with thematic randomization. The dataset was balanced across memory categories to ensure equitable class representation, while domain contexts (e.g., medical, educational, personal) were grouped by span identifiers to maintain contextual consistency without affecting label stratification. A representative, categorybalanced subset was reviewed and validated by board-certified psychiatrists to confirm semantic and cognitive plausibility, enhancing dataset reliability. On this corpus, we fine-tuned a RoBERTa-based transformer backbone, resulting in a specialized model we term MemoryBERT, which achieved perfect accuracy across all classes. Unlike prior studies that presuppose reliable memory categorization, our contribution establishes the first dedicated backbone for systematic memory identification in AI systems. As this work represents a feasibility study and preliminary stage of a broader research effort, future work will extend expert validation, expand the dataset with humanauthored and adversarial samples, and benchmark additional transformer architectures on the same classification task.

We present a decentralized reinforcement learning framework for autonomous UAV navigation and person tracking in GNSS-denied environments. The system integrates RGB-thermal fusion, geometric projection for relative localization, and a Proximal Policy Optimization (PPO) controller trained to balance tracking accuracy with real-time obstacle avoidance. Each UAV independently refines its policy using local observations and transmits compressed updates using a bandwidth-efficient federated averaging strategy. To ensure reliable deployment, our framework employs sim-to-real transfer methods, including domain randomization and sensor noise injection. Evaluations in simulated urban and agricultural environments show that our approach outperforms centralized and rule-based baselines across tracking accuracy, energy efficiency, and robustness metrics-validating its scalability and applicability in real-world UAV missions without reliance on GNSS or constant connectivity.

The increasing integration of renewable sources introduces volatility and uncertainty into energy systems, particularly under extremely low production and/or extremely high consumption events. To address this, we present BASTION.ev (Bayesian Simulation & Forecasting for Extreme Events), a modular and scalable framework for intelligent energy management, which combines Extreme Value Analysis, Machine Learningbased forecasting, and simulation-driven optimization. We opt for grid search instead of Bayesian Optimization to simulate and optimize control strategies due to its lower implementation complexity and transparent evaluation across parameter combinations. Specifically, our proposed framework ingests time-series energy data to engineer temporal and contextual features to support predictive modeling. Extreme Value Analysis is used to define critical thresholds using generalized Pareto distributions, allowing the classification of operational states into extreme low, normal, and extreme high imbalance scenarios. These states are forecasted using tree-based multi-class classifiers across multiple time horizons (e.g., 1 to 72 hours ahead). The simulation of battery behavior is then evaluated under forecasted imbalance conditions, with control strategies (charge/discharge rates) optimized through a Bayesian Optimization to finetune parameters in order to maximize energy efficiency and, subsequently, economic return. Although based on mock-up data, simulations reveal that high feed-in profits are achieved with aggressive charging and minimal discharging. As a use case in this work, we focused our simulation on a 72-hour ahead forecasting window, where, despite class imbalance, the model achieved a macro F1-score of 0.63 and an overall accuracy of 91 %. The predicted imbalance states drive a simulation engine that evaluates energy system responses across control strategies. To optimize performance, we employ grid search as a baseline, over manually predefined charge/discharge configurations. Using mock-up data, the top configuration (charge$=15 \text{kWh}$, discharge$=1 \text{kWh}$) yielded a simulated profit of € 420.3, with only 66 kWh imported and 4335 kWh fed into the grid. Then, we use Bayesian Optimization for parameter finetuning and results improve greatly, Yielding € 584.00 simulated profit with 0 kWh imported energy for a fully self-sufficient PV production and 5840 kWh surplus fed into the grid with a charge rate of 20 kWh and a discharge rate of 0.1 kWh.

This paper introduces Energy-VERITIES, a framework for modeling epistemic risk and trust towards the integration of generative AI and large language models (LLMs) within the energy digital spine ecosystem. It defines epistemic risk zones using a set-theoretic Venn model over AI outputs ($\boldsymbol{A}$), expert ($E$) and non-expert ($N$) beliefs, internet sources ($D \_I$), and verified domain facts ($D$), with a critical risk zone formalized as (D_I$\cap A \cap E \cap N$) - D, representing hallucinated outputs that appear credible due to overlapping AI, expert, and stakeholder beliefs but diverge from verified domain facts. To assess energy stakeholderAI interaction, we propose a set of Trust Performance Indicators (TPIs) that quantify epistemic outcomes from the user's perspective, extending the confusion matrix and the previously devised VIRTSI model, which characterizes dynamic human trust states through a finite state automaton informed by user validation behavior. These include, for example, MJO-HRAR (Misconceptually Justified Overtrust), where users accept false outputs after flawed validation, and BOV-HRAR (Blind Overtrust), where no validation occurs. An empirical study of ChatGPT's energy advice in Greece illustrates the framework's diagnostic value, revealing that 39 % of validated responses were still false, over 30 % of hallucinations were accepted without validation, and correct answers were often dismissed. These findings indicate the need for improvements in interface-level explainability, validation support, and targeted AI literacy for energy stakeholders. The Energy-VERITIES framework offers a foundation for identifying epistemic risks and guiding the trustworthy integration of generative AI and LLMs in high-stakes domains, like energy.

We present COGNET-MD-X-REQ: a COGnitive NETwork Evaluation Toolkit that identifies precise fine-tuning needs for Large Language Models (LLMs) in multimodal medical diagnosis. COGNET-MD-X-REQ is a novel, two-step evaluation framework, inspired by Objective Structured Clinical Examinations (OSCEs), that enhances LLM applicability and precision. Our framework integrates IoT-driven data retrieval with structured interaction evaluation and domain-specific analysis. Leveraging Image-Metadata Analysis, Named Entity Recognition, and Knowledge Graphs, COGNET-MD-X-REQ identifies weak performance areas by analyzing multimodal model outputs across medical subdomains. We selected GPT-4V to use in the evaluation of our proposed framework on a set of publicly available image-based MCQs in General Pathology. The model achieved 84% accuracy, with notable weaknesses in cardiovascular conditions like atherosclerosis. The framework pinpointed domain-specific deficiencies, enabling targeted fine-tuning. The evaluation leads to the conclusion that our proposed COGNET-MD-X-REQ framework introduces a precision-focused, iterative approach to fine-tuning LLMs by dynamically identifying under-performing areas or knowledge domains that can be related to organs or/and specific conditions and health states. This method reduces reliance on broad retraining, making it suitable for resource-sensitive and safety-critical domains such as medical diagnostics.

In an era of information overload, distinguishing fact from misinformation is a critical challenge. We introduce AlethaNet, a multilayered linguistic framework for automated fact-checking that integrates syntactic, semantic, and pragmatic analysis with contextual metadata. Our approach combines feature extraction, machine learning (XGBoost), and SHAP-based interpretability with a dynamic credibility scoring system. This score is computed using a weighted formula incorporating historical truthfulness, speaker metadata (e.g., job title, party, platform), and AI-inferred reasoning via DeepSeek R1—a generative LLM shown to approximate expert evaluations on the LIAR dataset. Unlike static, binary classification models, AlethaNet frames the problem as truthful vs. misleading, offering a more nuanced detection of misinformation. Experiments demonstrate improved accuracy (75.65%) when incorporating credibility scores. DeepSeek R1 extends the framework’s ability to assess previously unseen sources, while SHAP ensures transparency by highlighting the most influential features. AlethaNet thus presents a scalable, explainable, and autonomous method for adaptive credibility assessment, advancing AI-driven misinformation detection.

Artificial Intelligence (AI) has been growing significantly recently, based on machine learning, deep learning and the latest Generative AI and Large Language Models (LLMs), like ChatGPT. All AI tools are promising to improve decision-making in many domains, including complex ones, such as energy. As such, there have been energy-specific AI systems that incorporate domain expertise, in addition to general-purpose LLMs that integrate knowledge on a plethora of domains, including energy, based on their training from the internet. However, AI systems can produce errors due to their probabilistic nature, and many users, like energy stakeholders, lack the training to use them effectively. Which AI do we mean and how well is it trusted. It is worth exploring differences in AI systems.By employing the VIRTSI model, this paper compares trust states of energy stakeholders on domain specific AI systems versus general-purpose generative AI. For the purposes of the comparison, we use a domain specific AI-based Energy system entitled AI-ENERGIA-SYS that has been previously developed versus ChatGPT 4.0 by OpenAI. VIRTSI is a rigorous computational model for the dynamics of human trust states, spanning from overtrust to distrust, through user modelling and quantifies the efficiency of the interaction in VIRTSI-adapted confusion matrices. The findings reveal that ChatGPT is persuasive and user-friendly to stakeholders, but its lack of domain awareness and explainability leads to overtrust, risking decision quality. In contrast, AI-ENERGIA-SYS, though more complex, supports better trust calibration, meaning that, when trusted, it is usually correct, but it is not trusted so frequently as ChatGPT. The results suggest that future combinations of energy-specific AI systems with general-purpose generative AI could provide improved trust dynamics and more effective decision support.

In this paper, we introduce AIBARS (Adaptive Immune Boundary-Aware Recognition System), a novel learning algorithm designed to improve upon the traditional Artificial Immune Recognition System (AIRS). AIBARS addresses key limitations of AIRS by integrating a boundary-aware stimulation measure, an antigen-specific stopping criterion, and a more directed mutation approach. These innovations enable AIBARS to effectively navigate complex decision boundaries and reduce time complexity, particularly in high-dimensional datasets. Experimental results show that AIBARS achieves a 5-10% improvement in classification accuracy compared to AIRS. Additionally, AIBARS reduces memory cells by 20-30%, leading to more efficient data representation and a 15-25% reduction in computational costs. The algorithm also reduces processing time by 10-20%, making it a more efficient approach overall. Evaluations were conducted using a variety of datasets, including public domain datasets from the UCI Machine Learning Repository and synthetic datasets. The results indicate that AIBARS offers significant performance and efficiency gains over AIRS, while substantially reducing computational expenses. • AIBARS improves accuracy, enhances decision boundary detection, and reduces memory cells by 20-30% for efficiency. • AIBARS outperforms AIRS, cuts costs by 15-25%, reduces time by 10-20%, and is validated on UCI and synthetic datasets. • AIBARS adapts to complex tasks, with efficient stimulation and mutation strategies for handling high-dimensional datasets. • AIBARS enhances generalization, reducing overfitting and ensuring reliable performance from training to real-world data. • AIBARS: Adaptive immune-inspired system improving data classification by 5-10% over AIRS with robust, adaptive features.

Objective: Large language models constitute a breakthrough state-of-the-art Artificial Intelligence technology which is rapidly evolving and promises to aid in medical diagnosis. In this study, we propose a novel evaluation framework for extraction of fine-tuning requirements based on the Objective Structured Clinical Examinations (OSCE) that can increase LLM potential and applicability. Methods: We developed an OSCE based evaluation meta-framework leveraging IoT-based data retrieval with a two-step approach designed to analyze and guide improvement of LLMs in multimodal medical diagnosis: (1) structured interaction evaluation and (2) domain-specific analysis of extracted data. Using Image-Metadata Analysis (IMA), Named Entity Recognition (NER), and Knowledge Graphs (KG), this framework identifies image domains, extracts relevant entities, and assesses connections in KGs. These methods collectively reveal areas for improvement, guiding fine-tuning to enhance diagnostic accuracy and contextual understanding in medical applications. Results: Using this paradigm, (1) we evaluate the correctness and accuracy of generated medical diagnosis with publicly available multimodal-multiple-choice-questions in the vast domain of General Pathology and (2) proceed to the domain-specific analysis. We identify and visualize the model performance across specific organs, diseases, and pathological themes, detecting areas of lower accuracy, such as in cardiovascular conditions like atherosclerosis. This targeted approach enables precision-focused fine-tuning, applying additional data to specific weaknesses rather than a broad, generalized tuning across all pathology. Contributions: Our framework’s primary contribution is its OSCE-inspired ability to dynamically identify and target under-performing areas within a broad domain, enhancing fine-tuning efficiency and diagnostic accuracy in a resource-effective and iterative manner removing the dependency on bulk adjustments, making it particularly suitable for sensitive applications where precision and resource efficiency are essential, such as in medical diagnostics.

In this paper, we explore memory and Schema through the lens of mathematical representation. By defining themes related to cognitive processes, we propose a compression algorithm that measures and retains important information of previous (historic) user-Generative AI interaction. Time and memory are interconnected via a decay mechanism, where context gets more abstract as time passes. At the same time, memories with thematic resemblance can be structured into Schemas using set theory, based on rules influenced by a maturity threshold. This threshold is determined by factors such as criticality, emotional-user interaction, and mass, which is the measure of the summation of related memories. Our approach reconstructs memories into reusable parameters and formulates Schemas, offering a foundation for more personalized and cost-effective human-GAI interactions.

In Large Language Models (LLMs), such as GPT, BERT, Mistral or others, “reducing the domain space” for text generation involves limiting the range of content that can be utilized for generating responses. This approach aims at enhancing the relevance and precision of the text produced. While various strategies exist to achieve this, this work explores Sequential Language Model Integration (SLMI), which mirrors the organization and distribution of knowledge across different fields of expertise. More specifically, SLMI is the technique of linking multiple LLMs (LLM-Chains) in a systematic manner. In this paper, we refer to a process of choosing, linking and connecting LLMs with other services (often to complete a generative task, invoke external functions and machine learning services, or tackle problems) as “Large Language Models as a Service”. We outline the development and evaluation process of an SLMI methodology to refine response accuracy. Focusing on the medical field, we also establish a framework for knowledge reduction based on “knowledge paths”, analogous to the distinct specializations within medicine. We apply this framework to a dermatology case study and utilize our evaluation pipeline to assess the results. Reducing the knowledge domain from medicine in general down to dermatology, we tested our methodology and found gains regarding accuracy and diagnostic improvement, as well as a reduction in costs regarding total tokens generated.

This paper introduces Morpheas, an AI-empowered sleep evaluation and calibration system that leverages state-of-the-art technologies, like Large Language Models (LLMs) and Named Entity Recognition (NER). Morpheas integrates Sequential Language Model Integration (SLMI) workflows to simulate the initial steps of sleep disorder diagnosis, utilizing the GPT-4 engine enhanced with Rules of Conduct. Using SLMI and medical and psychological diagnostic tools, we propose a novel multi-step personalisation methodology for creating a gradation system for the improvement of patient-AI interactions. To test and showcase this personalisation approach, we simulate keeping a sleep diary for diagnosing insomnia using a trait-based personalised LLM aimed at addressing sleep concerns. For this purpose, we apply the Big Five Personality Traits (BFPT) where LLMs are again used to extract the responses and facilitate the patient throughout the process, providing guidance and explanation. We then extract the entities from these interactions with NER, in order to identify patterns, provide an explainability basis for patients and effectively customize Cognitive Behavioral Therapy for Insomnia (CBTi), whether through one-on-one sessions or via digital platforms.

The rapid integration of intelligent processes and methods into information systems in the Artificial Intelligence (AI) era has led to a substantial shift towards autonomous software decision-making. This evolution necessitates robust human oversight, especially in critical domains like Healthcare, Education, and Energy. Human trust in AI plays a vital role in influencing decision-making processes of users interacting with AI. This paper presents VIRTSI (Variability and Impact of Reciprocal Trust States towards Intelligent systems), a novel rigorous computational model for human-AI Interaction. VIRTSI simulates human trust states, spanning from overtrust to distrust, through user modelling. It comprises: 1. A trust dynamics representational model based on Deterministic Finite State Automata (DFAs), illustrating transitions among cognitive trust states in response to AI-generated replies. 2. A trust evaluation model based on Confusion Matrices, originating from machine learning and Accuracy Metrics, providing a quantitative framework for analysing human trust dynamics. As a result, this is the first time that trust dynamics have been thoroughly traced in a representational model and a method has been developed to assess the impact of possibly harmful states like overtrust and distrust. An empirical study on the recently launched Large Language Model of generative AI, ChatGPT (version 3.5), provides a radical underexplored AI-generated platform for evaluating the human-AI interaction through VIRTSI. The study involved 1200 interactions of real users as well as AI experts together with experts in two very different domains of evaluation, namely software engineering and poetry. This study traces trust dynamics and the emerging human-AI interaction, in concrete examples of real user synergies with generative AI. The research reveals the vital role of maintaining normal trust states for optimal human-AI interaction and that both AI and human users need further steps towards this goal. The real-world implications of this research can guide the creation and evaluation of user interfaces with AI and the incorporation of functionalities in the development of generative AI chatbots in terms of trust by providing a new rigorous DFA representational method of trust dynamics and a corresponding new perspective of confusion matrix evaluation method of the dynamics’ impact in the efficiency of human-AI dialogues.

In this paper, we evaluate the validity, accuracy, usefulness, and specificity of medical diagnoses related to lung cancer and its staging provided by ChatGPT based on symptoms described by humans. The evaluation is grounded on three main pillars: the validity and accuracy of answers in relation to context and associated references. The specificity and usefulness of the information for both doctors and patients. The economic value added to the healthcare system, determined by several weighted factors derived from the provided answers. The system’s responses are expected to return proposed diagnoses and diagnostic steps, ranked by probability and importance. A specialist conducts the review process.

In this paper, we explore multiple theoretical frameworks to understand and predict user behavior concerning the adoption of innovative, AI-empowered technologies in healthcare. Specifically, our research centers on evaluating the potential adoption rate of AI-empowered medical applications among physicians. To provide empirical support for our investigation, we carried out a comprehensive study employing questionnaires that were disseminated to a practicing medical doctors and medical students. Our methodological framework incorporates two key theories: the Technology Acceptance Model (TAM) and the Diffusion of Innovation Theory (DOI). Utilizing these theories allows us to examine critical factors that influence physicians' willingness to adopt new technologies, such as perceived ease of use and perceived usefulness. Through a nuanced understanding of doctors' perceptions and attitudes toward AI, our research aims to craft targeted strategies that could enhance the rate of adoption for these cutting-edge medical technologies. The overarching goal is to accelerate the integration of AI applications into clinical practice, thereby improving healthcare outcomes and operational efficiencies.

In this paper, we investigate the enhancement of Artificial Intelligence (AI) technologies in healthcare and the better understanding of medical literature with the use of Large Language Models (LLMs) and Natural Language Processing (NLP). Specifically, we introduce a rule-augmented AI-empowered system which incorporates a rule-based decision system, the ChatGPT application programming interface (API), and other external machine learning and analytical APIs to offer diagnostic suggestions to patients. The complexities of patient healthcare experiences, including doctor-patient interactions, understanding levels, treatment procedures, and preventive care, are considered. We illustrate how a diagnostic process typically integrates various strategies depending on various factors. To digitize the greatest portion of the process, we propose and illustrate the use of LLMs for humanizing the communication process and investigating ways to reduce burdens and costs in primary healthcare. We also outline a theoretical decision model for evaluating the use of technological components from external sources versus building them from scratch. The paper is structured into sections detailing background theories and context, our proposed and implemented rule-augmented AI-empowered system, as well as a system test in a corresponding use case. Finally, the paper key findings are presented, which contribute valuable insights for future work in this field.

The great momentum of Artificial Intelligence-empowered applications makes the requirement for detailed and tailored explainability frameworks more crucial. This is particularly evident in the medical domain, where validation of methodologies and outcomes is very important to the adoption of such systems. The depth and the level of understanding of Artificial Intelligence-related concepts is a significant design parameter and necessitates a systemic approach to ensure that a proper level of transparency is incorporated in an Artificial Intelligence-empowered application. In this paper, we propose a novel and generalised approach for the analysis of user requirements and abilities in relation to Artificial Intelligence-empowered applications. Specifically, we use the Technology Acceptance Model (TAM), as a technical methodology to measure the user perception of usefulness and usability of a technology and, subsequently, identify the corresponding depths of explainablity requirements. As a result, we design a layered and personalised explainability framework that may increase adoption rates of domain-specific Artificial Intelligence-empowered technologies.

The Rational Unified Process (R.U.P.) is an iterative Software Engineering Process, that ensures alignment between engineers and stakeholders through optimized and detailed partinionalised steps within predefined constraints [1]. The independently deployable services that are components of distributed systems are called microservices. They are part of applications and are easier to manage and scale. Each microservice serve different purpose and has a unique responsibility making it easier to understand manage and collaborate on. Medical applications are created to assist in treatment, disease prevention and health optimization. However patients' needs and abilities vary and patients' requirements and definitions on the usability aspect of an application are different and should be acknowledged for the application to be successful and for the patients/users to benefit from it. In this study the development of an A.I.-infused medical application, is outlined through the R.U.P. methodology and built using microservices, where the patients' needs and requirements are at the center of continuous development process focused on improvements.

In the digital era, disease diagnosis and patient management is taking a decisive leap forward to dynamically link personalized medical decision making, disease recognition and management with the massive individuality and uniqueness of the human body. Individual needs are recognised and associated with unique individual demands and tastes via automated processes and the pre-processing power of complex and accurate recom-mender systems. The gigantic pool of biometrics and biomarkers are used to predict outcomes and identify patterns in patients in an individualized manner. In this paper, we continue and improve upon recent previous work of ours [1] and develop software systems for personalized nutrition based on biomarkers and deep learning algorithms. Evaluation on real data demonstrates the high performance of our approach.

The digitization of intangible cultural heritage has given rise to new needs but also great opportunities for the preservation, analysis, education and propagation of cultural items that often constitute vast data bases of sophisticated works of art. This is particular the case of songs that comprise a combination of lyrics and music, each constituting an important domain of art. The preservation and propagation of this kind of art around the world becomes yet harder by taking into account the different languages that this art was originally created in, e.g. English, French, Portuguese Greek, Ancient Greek etc. The aim of this specific research is to develop a software tool that will support computer-aided functions of musical and lyrical analysis. The provision of these particular functions is realized through the integration in the proposed software tool of advanced natural language processing algorithms, text mining and thematic modeling. Therefore, it is an application that proposes the utilization of innovative computational methods of the broader object of Machine Learning and Pattern Recognition for the automated processing, clustering and semantic analysis of textual information. As a case of observation, the automated analysis of a part of the poetic activity of a Greek lyrics-writer is presented with the ultimate goal of promoting the cultural heritage.

Sentiment analysis is a rather intriguing subject that modern ICT tools enable us to explore and analyze. In this work we perform, to the best of our knowledge, the most wide analysis of sentiment mapping to geographic locations and time through smartphones, in an attempt to both visualize them and also reveal possible correlations and patterns. Our vast dataset consisting of more than 56.000 samples, from 100 individuals, for a time period of nine months, revealed patterns, both in space and time, that are directly linked to geographic locations of users and provide an aggregated real-time insight on how people feel, allowing for a wide range of applications.

In this paper, we present our research work towards building a visual-facial system to automatically detect and recognize the affective state of a group of individuals who are linked to each other based on the same space and at the same time. More specifically, we present the process of collecting sets of visualfacial samples in various groups of students and integrating them into corresponding databases. The results are discussed, conclusions are drawn and future work is outlined.

This paper decomposes the problem of recommendation into a two level cascade recommendation scheme which benefits from both content-based and collaborative filtering methodologies. The first level utilizes the content-based features of items in order to incorporate the individualized (subjective) user preferences within the recommendation process. This is achieved through the exploitation of the one-class classification paradigm which provides the means in order to filter out user specific undesirable items. The second level, on the other hand, serves the purpose of assigning particular rating degrees to the user-specific desirable items identified by the first level. The combination of two approaches in a cascade form, mimics the social process when someone has selected some items according to his preferences and asks for opinions about these by others, in order to achieve the best selection. Our experimentation provides significant evidence on the recommendation efficiency of the adapted hybrid approach which outperforms pure content-based and pure collaborative techniques.

Towards building a multimodal affect recognition system, we have built a facial expression recognition system and a audio-lingual affect recognition system. In this paper, we present and discuss the development and evaluation process of the two subsystems, concerning the recognition of emotions from audio-lingual and visual-facial modalities. Many researchers agree that these modalities are complementary to each other and that the combination of the two can improve the accuracy in affective user models. Therefore in this paper we present a combination of two modes using multi-criteria decision making theories. The resulted system takes advantage of the strengths of each mode and is more accurate in emotion recognition.

In this paper, we present a framework that we have developed for evaluating middleware systems of mobile multimedia services. In this framework, we combine subjective evaluation criteria relating to users' responses and objective evaluation criteria relating to software parameters. In order to implement this criteria combination, we developed an evaluation framework consisting of three stages. In the first stage, we conduct an empirical study to evaluate various aspects of the usefulness of the mobile multimedia service. In the second stage, we collect qualitative and quantitative software parameters of the middleware system. Finally, in the third stage we examine the overall assessment of the system. For illustration purposes, the evaluation framework is applied to a middleware system that we have developed to facilitate mobile access to digital music libraries.

The plethora and variety of medical data (text, diagrams, images, videos, and other types of clinical values), together with the fact that they are stored in worldwide distributed databases, creates new challenges when attempting to retrieve relevant information. Furthermore, this information is usually attached to time. The aim of this work is to present Information Systempsilas Architecture for content-based multimedia and information retrieval in medical information systems using intelligent techniques and to discuss some of the open research issues in this area.

In this paper, we investigate the possibility of improving the accuracy of visual-facial emotion recognition through use of additional (complementary) information. The investigation is based on three empirical studies that we have conducted involving human subjects and human observers. The studies were concerned with the recognition of emotions from a visual-facial modality, audio-lingual and keyboard-stroke information, respectively. They were inspired by the relative shortage of such previous research in empirical work concerning the strengths and weaknesses of each modality so that the extent can be determined to which the keyboard-stroke and audio-lingual information complements and improves the emotion recognition accuracy of the visual-facial modality. Specifically, our research focused on the recognition of six basic emotion states, namely happiness, sadness, surprise, anger and disgust as well as the emotionless state which we refer to as neutral. We have found that the visual-facial modality may allow the recognition of certain states, such as neutral and surprise, with sufficient accuracy. However, its accuracy in recognizing anger and disgust can be improved significantly if assisted by keyboard-stroke information.

This paper presents a user model server based on Web Services. User model servers are very important because they allow reusability of user modeling reasoning mechanisms which are typically very complex and difficult to construct from scratch. In this paper we show how the potential of interoperability, reusability and component sharing offered by the technology of Web Services have been exploited in the design of a user model server that performs decision making. The reasoning of the user modeling is based on a multi-criteria decision making theory and has been implemented as a Web Service to provide intelligent assistance to users over the Web. Reusability has been shown through the successful application of the user model server into two different applications: an e-mail and a file manager application.

We present a middleware system that facilitates Internet users' access to Web-based digital music libraries and allows them to manipulate audio meta-information taking into consideration content and semantic information of music data. Useful relations in the data are automatically extracted through semantic networks (constructed and maintained in the library). Our system is complemented with a query-by-example retrieval subsystem, user relevance feedback facilities, and a new approach for musical genre classification based on the features extracted from signals that correspond to distinct musical instrument sources, as these sources have been identified by a source separation process. The system operation is illustrated in detail.

Nonlinear tomographic reconstruction algorithms are developed for inversion of data measured in scattering experiments in which the scattered wavefields are modeled by an arbitrarily large number of terms in the Born series. The algorithms attain the form of a Volterra series of nonlinear operators, with the usual filtered backpropagation algorithm of diffraction tomography as the leading linear term.

Nonlinear tomographic reconstruction algorithms are developed for inversion of traveltime measurements in scattering experiments in which data models are derived from an arbitrarily large (possibly infinite) number of terms in the perturbation solution to the ray or eikonal equations. The algorithms attain the form of a Volterra series of nonlinear operators, with the usual linear reconstruction algorithm of traveltime tomography as the leading term. A computer simulation study is included to illustrate the performance of the algorithms for the case of scattering objects with cylindrical symmetry.

The usual propagation transform of diffraction tomography is generalized into higher-order (nonlinear) propagation transforms via use of the Born series as the data-generating model in scattering experiments. Nonlinear tomographic reconstruction algorithms are developed for inversion of scattered field data modeled up to an arbitrarily large (possibly infinite) number of terms in the Born series. A computer simulation study is included to illustrate the performance of the algorithms for the case of scattering objects with cylindrical symmetry.

Nonlinear tomographic reconstruction algorithms are developed for inversion of data measured in scattering experiments in which the complex phase of the wavefields is modeled by an arbitrarily large (possibly infinite) number of terms in the Rytov series. The algorithms attain the form of a Volterra series of nonlinear operators, with the usual filtered backpropagation algorithm of diffraction tomography as the leading linear term. A computer simulation study is included to illustrate the performance of the algorithms for the case of scattering objects with cylindrical symmetry.

We present a time domain algorithm for computation of the maximum likelihood estimate of the location of a known scattering object from wide-band scattering data acquired in a suite of scattering experiments. The algorithm consists of a three-step procedure: (1) data filtering, (2) time-domain backpropagation, and (3) coherent summation and is implemented via a number of forward and inverse Radon transforms integrated into a tomographic scheme. A computer simulation is included for illustration purposes.

We present an efficient algorithm for computation of the maximum likelihood estimate of the location of a known target from short pulse scatter data. The algorithm constitutes a three step procedure: (i) data filtering, (ii) time-domain backpropagation, and (iii) coherent summation and consists of a number of projection and backprojection operations integrated in a tomographic scheme. A computer simulation is included for illustration purposes and relevant applications in radar target identification and buried object detection are discussed.

We previously formulated a new approach for computing invariant features from infrared (IR) images. That approach is unique in the field since it considers not just surface reflection and surface geometry in the specification of invariant features, but it also takes into account internal object composition and thermal state that affect images sensed in the nonvisible spectrum. In this paper, we extend the thermophysical algebraic invariance (TAI) formulation for the interpretation of uncalibrated infrared imagery and further reduce the information that is required to be known about the environment. Features are defined such that they are functions of only the thermophysical properties of the imaged objects. In addition, we show that the distribution of the TAI features can be accurately modeled by symmetric alpha-stable models. This approach is shown to yield robust classifier performance. Results on ground truth data and real infrared imagery are presented. The application of this scheme for site change detection is discussed.

We address the problem of estimation of the parameters of the symmetric, alpha-stable model for impulsive interference. We propose new estimators based on asymptotic extreme value theory, order statistics, and fractional lower-order moments, which can be computed fast and are, therefore, suitable for the design of real-time signal processing algorithms. The performance of the new estimators is evaluated theoretically and via Monte-Carlo simulation.

Derives the exact likelihood function for identification of the parameter of a MA(1) system from observation of its output when the input is an i.i.d. Cauchy sequence of unknown dispersion. Computer simulation illustrates the procedure.

Impulsive noise bursts in communication systems are traditionally handled by incorporating in the receiver a limiter which clips the received signal before integration. An empirical justification for this procedure is that it generally causes the signal-to-noise ratio to increase. Recently, very accurate models of impulsive noise were presented, based on the theory of symmetric /spl alpha/-stable probability density functions. We examine the performance of optimum receivers, designed to detect signals embedded in impulsive noise which is modeled as an infinite variance symmetric /spl alpha/-stable process, and compare it against the performance of several suboptimum receivers. As a measure of receiver performance, we compute an asymptotic expression for the probability of error for each receiver and compare it to the probability of error calculated by extensive Monte-Carlo simulation.>

A recently proposed approach to the inverse problem of detecting the presence and estimating the location of a known object from data collected in a set of diffraction tomographic experiments is evaluated. Experimental data are used to validate of the filtered backpropagation algorithms used, and their robustness to modeling errors and to severe limitations in the angular coverage of the tomographic data is demonstrated. A potential application to medical imaging of soft tissue is illustrated.