Panteleimon Rodis

Panteleimon Rodis

This paper introduces a framework specifically designed for sparse and irregular time series {risk estimation}. It is based on a Transformer Autoencoder with local attention, which leverages the powerful pattern identification capabilities of transformers complemented by traditional data cleaning and normalization methods. It efficiently captures relevant patterns within irregular sequences suffering from sparse data collection, benefiting from the discriminative ability of the local attention mechanism. The proposed framework is applied to a real-world case study, on the risk estimation of non-technical losses in electrical power systems in a wide area in Greece. Non-technical losses in electrical power systems, primarily stemming from electricity theft, pose significant economic and operational challenges. Detecting these anomalies is particularly challenging due to the inherent sparse and irregular nature of real-world data collection practices. Traditional risk estimation methods struggle with effectively capturing long-range dependencies and robustly handling such data characteristics. We demonstrate that our approach effectively yields highly discriminative latent features, which results in more consistent risk estimation compared with existing state-of-the-art and widely used methods. It achieves high recall and precision, meeting the critical objectives of the problem. As such, our solution offers a robust and effective tool for risk detection in irregular time series datasets.

Panteleimon Rodis

Purpose - This paper presents a methodology for defining and modeling context-awareness and describing efficiently the interactions between systems, applications and their context. Also the relation of modern context-aware systems with distributed computation is investigated. Design/methodology/approach - On this purpose, definitions of context and context-awareness are developed based on the theory of computation and especially on a computational model for interactive computation which extends the classical Turing Machine model. The computational model proposed here, encloses interaction and networking capabilities for computational machines. Findings - The definition of context presented here develop a mathematical framework for working with context. Also the modeling approach of distributed computing enables us to build robust, scalable and detailed models for systems and application with context-aware capabilities. Also enables us to map the procedures that support context-aware operations providing detailed descriptions about the interactions of applications with their context as well as with other external sources. Practical implications - A case study of a cloud based context-aware application is examined using the modeling methodology described in the paper so as to demonstrate the practical usage of the theoretical framework that is presented Originality/value - The originality on the framework presented here relies on the connection of context-awareness with the theory of computation and distributed computing.

Panteleimon Rodis

Large Language Models (LLMs) are increasingly integrated into critical decision-making pipelines, a trend that raises the demand for robust and automated data analysis. Current approaches to dataset risk analysis are limited to manual auditing methods which involve time-consuming and complex tasks, whereas fully automated analysis based on Artificial Intelligence (AI) suffers from hallucinations and issues stemming from AI alignment. To this end, this work proposes a framework for dataset risk estimation that integrates Generative AI under human guidance and supervision, aiming to set the foundations for a future automated risk analysis paradigm. Our approach utilizes LLMs to identify semantic and structural properties in database schemata, subsequently propose clustering techniques, generate the code for them and finally interpret the produced results. The human supervisor guides the model on the desired analysis and ensures process integrity and alignment with the task's objectives. A proof of concept is presented to demonstrate the feasibility of the framework's utility in producing meaningful results in risk assessment tasks.

Panteleimon Rodis

According to E.T. Jaynes and E.P. Wigner, entropy is an anthropomorphic concept in the sense that in a physical system correspond many thermodynamic systems. The physical system can be examined from many points of view each time examining different variables and calculating entropy differently. In this paper we discuss how this concept may be applied in information entropy; how Shannon's definition of entropy can fit in Jayne's and Wigner's statement. This is achieved by generalizing Shannon's notion of information entropy and this is the main contribution of the paper. Then we discuss how entropy under these considerations may be used for the comparison of password complexity and as a measure of diversity useful in the analysis of the behavior of genetic algorithms.