RES-Q: Self-evolving Post-disaster Response Plans Utilizing Semantics

During the last decade, communities at local and national level are implementing actions geared towards improving disaster resilience. In this context, the importance of ICT in disaster risk management is rapidly increasing globally, especially nowadays amidst the climate crisis and the covid-19 pandemic. However, disaster risk management operations require contributions and collaboration of different type of actors and infrastructures with different functions, rules, protocols and datasets, forming complex contexts in decision making and event coordination. Hence, semantic interoperability between the various stakeholders is one of the challenges to be confronted. In this paper, we present the RES-Q (RESCUE) approach that proposes an information technology solution concerning the real-time recommendation and orchestration of post-disaster response plans. The implemented RES-Q prototype comprises an expert system and a workflow execution engine based on an ontological infrastructure for modeling the response actions for each type of disaster. The ontological model is designed using a multi-layer approach encapsulating the required knowledge streams and a semantic rule repository. During the execution of a post-disaster plan, the system reasons over the rules and composes the next steps of the corresponding response processes. The rule repository is able to infer new knowledge as each plan progresses, which can update the RES-Q ontology accordingly. © 2022, National Technical University of Athens. All rights reserved.

RES-Q: Toward Semantic Interoperability for Risk and Disaster Management in Smart Cities

The COVID-19 pandemic has imposed new challenges in preserving the goal of developing smart and sustainable cities worldwide while improving urban resilience. In the smart city domain, disaster or crisis management operations require contributions and collaboration from different types of entities with various functions, rules, and protocols, forming complex contexts in decision-making or event coordination. The management of the corresponding information usually coming from multiple heterogeneous sources and sometimes with attributes revealing semantic inconsistencies constitutes an emerging challenge. Furthermore, the demand for interoperability between the various services and IoT devices at local and national level is imperative. Yet, existing literature highlights that the conceptualization of a holistic reference schema that covers all the dimensions of the smart city disaster/crisis management domain and allows the exchange of information through different agents has not been fully addressed so far. We present the RES-Q (RESCUE) semantic model, which includes the needed domain knowledge streams for the smart city crisis management domain. This model aims for data consolidation and linkage in order to be further utilized for the implementation of a common knowledge repository and advanced analysis. In this context, semantic web technologies are proposed as a promising solution for providing semantic interoperability in crisis and/or disaster management in the smart city discourse. Finally, data consolidation and harmonization methodology is presented, which is used for the integration of different data sources, according to the RES-Q model. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Comparative Study of Deep Learning Techniques for Financial Indices Prediction

Automated trading is an approach to investing whereby market predictions are combined with algorithmic decision-making strategies for the purpose of generating high returns while minimizing downsides and risk. Recent advancements in Machine and Deep learning algorithms has led to new and sophisticated models to improve this functionality. In this paper, a comparative analysis is conducted concerning eight studies which focus on the American and the European stock markets. The simple method of Golden Cross trading strategy is being utilized for the assessment of models in real-world trading scenarios. Backtesting was performed in two indices, the S&P 500 and the EUROSTOXX 50, resulting in relative good performance, aside from the significant downfall in global markets due to COVID-19 outbreak, which appeared to affect all models. © 2021, IFIP International Federation for Information Processing.