Interoperability-Enhanced Knowledge Management in Law Enforcement: An Integrated Data-Driven Forensic Ontological Approach to Crime Scene Analysis

Nowadays, more and more sciences are involved in strengthening the work of law enforcement authorities. Scientific documentation is evidence highly respected by the courts in administering justice. As the involvement of science in solving crimes increases, so does human subjectivism, which often leads to wrong conclusions and, consequently, to bad judgments. From the above arises the need to create a single information system that will be fed with scientific evidence such as fingerprints, genetic material, digital data, forensic photographs, information from the forensic report, etc., and also investigative data such as information from witnesses’ statements, the apology of the accused, etc., from various crime scenes that will be able, through formal reasoning procedure, to conclude possible perpetrators. The present study examines a proposal for developing an information system that can be a basis for creating a forensic ontology—a semantic representation of the crime scene—through descriptive logic in the owl semantic language. The Interoperability-Enhanced information system to be developed could assist law enforcement authorities in solving crimes. At the same time, it would promote closer cooperation between academia, civil society, and state institutions by fostering a culture of engagement for the common good

An integrated assessment of family history on the risk of developing acute coronary syndromes (CARDIO2000 Study)

Objective - In this work we assessed a risk score for developing a first event of acute coronary syndrome (ACS) based on the family history of the cardiovascular risk factors. Methods and results - The studied population consisted of 848 randomly selected middle-aged patients with first event of ACS and 1078 sex-age-region matched controls admitted to the same hospitals for minor operations and without any clinical suspicion of cardiovascular disease in their life. A Family History Score (FHS) was developed based on the presence of coronary heart disease, hypertension, hypercholesterolaemia and diabetes mellitus, among first-degree relatives of the participants after adjusting for the family size. The evaluation of FHS was based on conditional logistic regression analysis, after controlling for demographic variables as well as for the mutual confounding effects of other risk factors. Family history of CHD, hypercholesterolaemia and diabetes was highly associated with the development of the disease. The introduced FHS was also highly associated with the development of ACS among participants who had no family history of CHD (odds ratio = 10.9, p < 0.001), whereas it was not associated with the development of the disease among participants who had a family history of CHD (odds ratio = 1.41, p = 0.543). Conclusions - The suggested FHS could be a useful tool in the primary prevention of ACS, as well as in detecting and understanding associations between genetic vulnerability and cardiovascular risk factors

Towards a graph theoretical approach to study gender lateralization effect in mathematical thinking

Gender differences in mathematical thinking is a common concern of scientists from different research fields. Both parents and teachers report that males seem to perform better in complex mathematics compared to females. This study comes to shed light in the different organization of the underlying functional networks, in order to investigate the aforementioned observation, without supporting or rejecting this statement. In this sense, it is generally accepted that females use their both hemispheres to accomplish a certain task, while males use mostly the hemisphere which is properly suited. For the purposes of the current analysis, electroencephalographic recordings were collected from 11 males and 11 females, during a difficult mathematical task. Then a previously proposed model was used in order to pass from the sensor level to the cortical one, in order to examine the networks formed among the cortical dipoles. Mutual information was employed to form the graphs represeting the functional connectivity among the different dipoles, while the density, the global and the local efficiencies were further examined. The results suggest that females use their both hemisphere to solve the complex mathematical task while males use mostly their left hemisphere which is the responsible one for the mathematical thinking

Semantic Representation of the Intersection of Criminal Law & Civil Tort

The more complex and globalized social structures become, the greater the need for new ways of exchanging information and knowledge. Legal science is a field that needs to be codified to allow the interoperability between people and states, as well as between humans and machines. The objective of this work is to develop an ontology in order to describe two different pillars of codified law (civil and criminal) and be able to depict the interaction between them. To answer the above question, we examine the Greek Criminal Law as depicted in the Greek Penal Code (ΠΚ) and the way its articles can be analyzed. Then we examine Tort as described in the Greek Civil Code (AΚ) and link the two codifications through the concepts of illegality and damage, both being prerequisites of tortious liability. Following that, through the Protégé application, a legal ontology is created in the OWL semantic language, while finally, four articles of the Penal Code are codified in the ontology and a presentation of their relation to the civil tort is required from a reasoning algorithm

On the classification of emotional biosignals evoked while viewing affective pictures: An integrated data-mining-based approach for healthcare applications

Recent neuroscience findings demonstrate the fundamental role of emotion in the maintenance of physical and mental health. In the present study, a novel architecture is proposed for the robust discrimination of emotional physiological signals evoked upon viewing pictures selected from the International Affective Picture System (IAPS). Biosignals are multichannel recordings from both the central and the autonomic nervous systems. Following the bidirectional emotion theory model, IAPS pictures are rated along two dimensions, namely, their valence and arousal. Following this model, biosignals in this paper are initially differentiated according to their valence dimension by means of a data mining approach, which is the C4.5 decision tree algorithm. Then, the valence and the gender information serve as an input to a Mahalanobis distance classifier, which dissects the data into high and low arousing. Results are described in Extensible Markup Language (XML) format, thereby accounting for platform independency, easy interconnectivity, and information exchange. The average recognition (success) rate was 77.68% for the discrimination of four emotional states, differing both in their arousal and valence dimension. It is, therefore, envisaged that the proposed approach holds promise for the efficient discrimination of negative and positive emotions, and it is hereby discussed how future developments may be steered to serve for affective healthcare applications, such as the monitoring of the elderly or chronically ill people