Semantic Management of Urban Traffic Congestion

Urban traffic congestion is a problem which affects the world and is related to the massive urbanization and excessive number of cars on our streets. This causes a variety of problems, from economical/financial and health-related, to environmental warnings caused by high CO2 and NO2 emissions. This paper proposes a novel software engineering solution, which generates a software application aimed at individual drivers on urban roads, in order to help and ease overall congestion. The novelty is twofold. We target individual drivers in order to motivate them to re-think the purpose and goals of each journey they take. Consequently, the proposed software application enables reasoning upon various options an individual driver may have and helps in choosing the best possible solution for an individual. Our software application utilizes reasoning with SWRL enabled OWL ontologies, which can be hosted by any software application we run in our cars, ready to assist in driving, and implemented in Android / iOS environments

Software Architectures for Smart Applications in the Management of Chronic Diseases: A Study of Reversibility of Diabetes 2

The paper proposes a software architecture for applications which use the reasoning upon SWRL enabled OWL ontologies and SQL like retrievals as core computational models. The application assists patients affected by diabetes 2, to personalize the reversibility of the condition through the diet and life style changes. The novelty is in (a) the deployment of SWRL enabled OWL ontologies in the management of data related to the personalisation of reversibility of diabetes 2 and in (b) the proposed software architecture, which contains and manipulates the SWRL enabled OWL ontology and SQL databases at the same time and transparently. The application, which has been implemented within the Java environment and NetBeans, is reusable in any other problem domain when the personalization of healthcare delivery is required. The proposed architecture also generates applications within Android environments without changing its style and the computational models

Semantic Framework for Practicing Data Science in Public Health Organizations during the Covid-19 Pandemics

This paper proposes a semantic framework based on software architectures for accommodating data science practices to the needs of Public Health Organizations (PHO), during the COVID-19 pandemics. The goal is to create an environment suitable for deploying data science on an ad-hoc basis, upon the selection of data generated by governments, non-government organizations, public databases and social media, but guided by PHO own needs and expertise. It is important to run predictions, through learning technologies, which may depend on circumstances and situations relevant for PHO in the particular moment and thus enable better decision making in the time of the pandemic. The proposed software architecture relies on its deployment within integrated development environments and plug-ins/APIs towards software tools, and libraries for (a) data gathering and preprocessing, (b) predictions with learning technologies (c) reasoning with semantic technologies and (d) including human intervention to aid in understanding the situation in which PHO questions may be answered. The illustration of the proposal uses the sentiment analysis of Twitter data relevant to COVID-19 and classification of tweets with machine learnin

Software Tool for Semantic Resources Allocation in Humanitarian Crises

Resource Allocation (RAlloc) is one of the most important tasks in organizing humanitarian response to humanitarian crises. It is not only that adequate and efficient RAlloc save lives and reduce damages caused by humanitarian crises, but RAlloc must be fast and efficient to save time and resources. Given that RAlloc is a type of a decision making process, it is expected that decision on RAlloc are based on accurate and relevant information generated at various stages of humanitarian response. In this paper we promote Semantic Resource Allocation (SemRAlloc) tool which a) collects and interprets the semantics of an environment where RAlloc is required and b) the reasons upon the semantics of that environment in order to make appropriate RAlloc. The tool is built with computations based on SWRL enabled OWL ontologies. The prototype has been implemented as a desk-top application which can also run in mobile/wireless environments, including Android smart phones

Personalized Drug Administration to Patients with Parkinson’s Disease: Manipulating Sensor Generated Data in Android Environments

This paper illustrates the application of mobile and wireless technologies for estimating the severity of Parkinson Disease symptoms, and performing a personalized drug administration to PD patients. The measurements of patient finger pressures on the screen of a smart phone, translated into analogue voltage and digital bits, are taken by an Android App. The computations performed through Fast Fourier Transformations (FFT) and Reaction and Movement time, enable the calculation of the severity of the PD symptoms, which results in an appropriate drug administration for that patient, at the moment when the measurement of patient finger pressures is taken. The novelty of this research is twofold. It allows a high level of personalization in PD treatment and uses modern technologies to bring new solutions in the field of drug administration to PD patients

Creating Intelligent Computational Edge through Semantic Mediation

This research proposes semantic mediation based on reasoning and the first order logic for mediating the best possible configuration of Computational Edge, relevant for software applications which may benefit for running computations with proximity to their data sources. The mediation considers the context in which these applications exist and exploits the semantic of that context for decision making on where computational elements should reside and which data they should use. The application of semantic mediation could address the initiative to accommodate algorithms from predictive and learning technologies, push AI towards computational edges and potentially contribute towards creating a computing continuum

Experiences of Revalidating the Undergraduate and Postgraduate Courses Within the Information Systems Curricula at University of Westminster, UK

Information Systems (IS) is probably the most frequently used name for a variety of academic programs focusing on applied information technology, whose curricula is available from a range of schools and university departments. For more than a decade we have successfully run at the University of Westminster, UK, BSc and MSc courses in IS, within our IS department of the Cavendish School of Computer Science. The major developments of curriculum design related to subject content, construction of courses and teaching/learning strategies, has triggered changes in our IS programs which were implemented through the IS course reviews in 2002. This paper addresses the purpose of course reviews within the UK Higher Education (HE) environment, gives a rationale for our curriculum changes, describes the revalidated IS courses at both BSc and MSc levels including our teaching and assessment strategies, and comments on our progress to date