Search results for: Papathanasiou Anastasios

Authors: Papathanasiou Anastasios, Liontos George, Liagkou Vasiliki, Glavas Euripides

Abstract:

The purpose of this paper is to describe the growing problem of various cyber fraud schemes that exist on the internet and are currently among the most prevalent. The main focus of this paper is to provide a detailed description of the modus operandi, tools, and techniques utilized in four basic typologies of cyber frauds: Business Email Compromise (BEC) attacks, investment fraud, romance scams, and online sales fraud. The paper aims to shed light on the methods employed by cybercriminals in perpetrating these types of fraud, as well as the strategies they use to deceive and victimize individuals and businesses on the internet. Furthermore, this study outlines defense strategies intended to tackle the issue head-on, with a particular emphasis on the crucial role played by European legislation. European legislation has proactively adapted to the evolving landscape of cyber fraud, striving to enhance cybersecurity awareness, bolster user education, and implement advanced technical controls to mitigate associated risks. The paper evaluates the advantages and innovations brought about by the European legislation while also acknowledging potential flaws that cybercriminals might exploit. As a result, recommendations for refining the legislation are offered in this study in order to better address this pressing issue.

Keywords: Business email compromise, cybercrime, European legislation, investment fraud, Network and Information Security, online sales fraud, romance scams.

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Authors: F. Gekas, C. Pankou, I. Mylonas, E. Ninou, E. Sinapidou, A. Lithourgidis, F. Papathanasiou, J. –K. Petrevska, F. Papadopoulou, P. Zouliamis, G. Tsaprounis, I. Tokatlidis, C. Dordas

Abstract:

The present study aimed to investigate whether chlorophyll meter readings (SPAD) can be used as criterion of singleplant selection in maize breeding. Experimentation was performed at the ultra-low density of 0.74 plants/m2 in order the potential yield per plant to be fully expressed. R-31 honeycomb experiments were conducted in three different areas in Greece (Thessaloniki, Giannitsa and Florina) using 30 inbred lines at well-watered and water-stressed conditions during the 2012 growing season. The chlorophyll meter readings had higher rates at dry conditions, except location of Giannitsa where differences were not significant. Genotypes of highest chlorophyll meter readings were consistent across areas, emphasizing on the character’s stability. A positive correlation between the chlorophyll meter readings and grain yield was strengthening over time and culminated at the physiological maturity stage. There was a clear sign that the chlorophyll meter readings has the potential to be used for the selection of stress-adaptive genotypes and may permit modern maize to be grown at wider range of environments addressing the climate change scenarios.

Keywords: Drought-prone environments, honeycomb breeding, SPAD, Zea mays.

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Authors: Fotios N. Vlachos, Anastasios D. Garetsos, Nikolaos K. Uzunoglu, Efstathios D. Gotsis

Abstract:

A four element prototype phased array surface probe has been designed and constructed to improve clinical human prostate spectroscopic data. The probe consists of two pairs of adjacent rectangular coils with an optimum overlap to reduce the mutual inductance. The two pairs are positioned on the anterior and the posterior pelvic region and two couples of varactors at the input of each coil undertake the procedures of tuning and matching. The probe switches off and on automatically during the consecutive phases of the MR experiment with the use of an analog switch that is triggered by a microcontroller. Experimental tests that were carried out resulted in high levels of tuning accuracy. Also, the switching mechanism functions properly for various applied loads and pulse sequence characteristics, producing only 10 μs of latency.

Keywords: Automatic tuning, prostate imaging, phased array, spectroscopy.

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Authors: Efthymios Gravas, Anastasios Alexandridis, Antonios Stavropoulos

Abstract:

The growing interest in the issue of intangible assets not only in the scientific community but also in some professional bodies internationally can be explained by several points of view. From the business perspective, enterprises are increasingly motivated by external and internal forces to measure and proactively manage their intangibles. With respect to the issue of intangibles, goodwill has been debated in many countries throughout the world. Despite the numerous efforts and the existence of international accounting standards there is not yet a common accepted accounting treatment for goodwill. This study attempts on the one hand to impress the accounting treatment of goodwill internationally, on the other hand analyses the major subjects in relation to the accounting treatment of goodwill in Greece, since 2005, year where the international accounting standards have been in use for the Greek listed companies. The results indicate that the accounting treatment for the goodwill in Greece, despite the effort for accounting harmonization in Europe from 2005, sustains many differences especially for the no listed companies.

Keywords: Intangible Assets, Goodwill, International Accounting Standards, Greek Accounting System and Law

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Authors: Dimitrios Zarpalas, Anastasios Zafeiropoulos, Petros Daras, Nicos Maglaveras

Abstract:

Segmentation techniques based on Active Contour Models have been strongly benefited from the use of prior information during their evolution. Shape prior information is captured from a training set and is introduced in the optimization procedure to restrict the evolution into allowable shapes. In this way, the evolution converges onto regions even with weak boundaries. Although significant effort has been devoted on different ways of capturing and analyzing prior information, very little thought has been devoted on the way of combining image information with prior information. This paper focuses on a more natural way of incorporating the prior information in the level set framework. For proof of concept the method is applied on hippocampus segmentation in T1-MR images. Hippocampus segmentation is a very challenging task, due to the multivariate surrounding region and the missing boundary with the neighboring amygdala, whose intensities are identical. The proposed method, mimics the human segmentation way and thus shows enhancements in the segmentation accuracy.

Keywords: Medical imaging & processing, Brain MRI segmentation, hippocampus segmentation, hippocampus-amygdala missingboundary, weak boundary segmentation, region based segmentation, prior information, local weighting scheme in level sets, spatialdistribution of labels, gradient distribution on boundary.

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Authors: Despina Galani, Anastasios Alexandridis, Antonios Stavropoulos

Abstract:

The main thrust of this paper is to assess the level of disclosure in the annual reports of non-financial Greek firms and to empirically investigate the hypothesized impact of several firm characteristics on the extent of mandatory disclosure. A disclosure checklist consisting of 100 mandatory items was developed to assess the level of disclosure in the 2009 annual reports of 43 Greek companies listed at the Athens stock exchange. The association between the level of disclosure and some firm characteristics was examined using multiple linear regression analysis. The study reveals that Greek companies on general have responded adequately to the mandatory disclosure requirements of the regulatory bodies. The findings also indicate that firm size was significant positively associated with the level of disclosure. The remaining variables such as age, profitability, liquidity, and board composition were found to be insignificant in explaining the variation of mandatory disclosures. The outcome of this study is undoubtedly of great concern to the investment community at large to assist in evaluating the extent of mandatory disclosure by Greek firms and explaining the variation of disclosure in light of firm-specific characteristics.

Keywords: Mandatory disclosure, Annual report, Disclosure index

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Authors: Kottaridi Klimentia, Demopoulos Vasilis, Sidiropoulos Anastasios, Ihara Diego, Nikolaidis Vasileios, Antonopoulos Dimitrios

Abstract:

Aflatoxins are highly poisonous and carcinogenic compounds produced by species of the genus Aspergillus spp. that can infect a variety of agricultural foods, including dried figs. Biological and environmental factors, such as population, pathogenicity and aflatoxinogenic capacity of the strains, topography, soil and climate parameters of the fig orchards are believed to have a strong effect on aflatoxin levels. Existing methods for aflatoxin detection and measurement, such as high-performance liquid chromatography (HPLC), and enzyme-linked immunosorbent assay (ELISA), can provide accurate results, but the procedures are usually time-consuming, sample-destructive and expensive. Predicting aflatoxin levels prior to crop harvest is useful for minimizing the health and financial impact of a contaminated crop. Consequently, there is interest in developing a tool that predicts aflatoxin levels based on topography and soil analysis data of fig orchards. This paper describes the development of a risk assessment tool for the contamination of aflatoxin on dried figs, based on the location and altitude of the fig orchards, the population of the fungus Aspergillus spp. in the soil, and soil parameters such as pH, saturation percentage (SP), electrical conductivity (EC), organic matter, particle size analysis (sand, silt, clay), concentration of the exchangeable cations (Ca, Mg, K, Na), extractable P and trace of elements (B, Fe, Mn, Zn and Cu), by employing machine learning methods. In particular, our proposed method integrates three machine learning techniques i.e., dimensionality reduction on the original dataset (Principal Component Analysis), metric learning (Mahalanobis Metric for Clustering) and K-nearest Neighbors learning algorithm (KNN), into an enhanced model, with mean performance equal to 85% by terms of the Pearson Correlation Coefficient (PCC) between observed and predicted values.

Keywords: aflatoxins, Aspergillus spp., dried figs, k-nearest neighbors, machine learning, prediction

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