University of Nicosia

About: University of Nicosia is a education organization based out in Nicosia, Cyprus. It is known for research contribution in the topics: Population & Context (language use). The organization has 988 authors who have published 2765 publications receiving 30748 citations.

Testing the predictability of the Cyprus Stock Exchange: the case of an emerging market

Abstract: A systematic investigation of the effect of different neural network architecture alternatives for predicting the future course of stock prices in the Cyprus Stock Exchange (CSE) market is conducted. This market exhibited an abrupt increase in the general index price during the last year, thus forming a very interesting case for research purposes. The influence of various economic and political factors, from both the local and the international scene, have also been examined. The main conclusion drawn is that the CSE market is governed by unique conditions which when properly modeled can yield successful predictions.

A Systematic Review, Meta-Analysis and Meta-Regression on the Effects of Carbohydrates on Sleep.

Abstract: This study aimed to assess the effects of quantity, quality and periodization of carbohydrates consumption on sleep. PubMed, SCOPUS and Cochrane Library were searched through October 2020. Data were pooled using random-effects meta-analysis. Eleven articles were included in the meta-analysis which consisted of 27 separate nutrition trials, resulting in 16 comparison data sets (sleep quantity n = 11; sleep quality n = 5). Compared to high carbohydrate (HCI), low carbohydrate intake (LCI) moderately increased duration and proportion of N3 sleep stage (ES = 0.37; 95% CI = 0.18, 0.56; p < 0.001 and ES = 0.51; 95% CI = 0.33, 0.69; p < 0.001, respectively). HCI prolonged rapid eye movement (REM) stage duration (ES = −0.38; 95% CI = 0.05, −8.05; p < 0.001) and proportion (ES = −0.46; 95% CI = −0.83, −0.01; p < 0.001), compared to LCI. The quality of carbohydrate intake did not affect sleep stages. Meta-regression showed that the effectiveness of carbohydrate quantity and quality in sleep onset latency was significantly explained by alterations of carbohydrate intake as a percentage of daily energy intake (R2 = 25.87, p = 0.018) and alterations in the glycemic load (R2 = 50.8, p = 0.048), respectively. Alterations in glycemic load partially explained the variance of the effectiveness of carbohydrate quality in sleep efficiency (R2 = 89.2, p < 0.001) and wake after sleep onset (R2 = 64.9, p = 0.018). Carbohydrate quantity was shown to affect sleep architecture, and especially N3 and REM sleep stages. Alterations in both quantity and quality of carbohydrate intake showed a significant effect on sleep initiation. Variations in carbohydrate quality significantly affected measures of sleep continuation. Further studies are needed to assess the effect of long-term carbohydrate interventions on sleep.

Hybrid auto encoder network for iris nevus diagnosis considering potential malignancy

Abstract: Iris nevus can be described as a growth commonly found on the iris, or regions surrounding the pupil. This growth is usually pigmented and non-cancerous, and therefore harmless; often requiring little medical attention. However, it has been established that there exists a relatively high risk of transformation of such growths into iris melanoma, which is cancerous or malignant. Furthermore, it has been shown that iris nevus infected patients also run risk of developing secondary glaucoma which requires very crucial medical intervention. Considering the above mentioned severe medical conditions that are associated with iris nevus, its diagnosis hence becomes very important. Generally, the diagnosis of iris nevus is achieved by examining eye images of patients taken by a medical expert. However, diagnosis is not an easily achievable task considering how racial and environmental factors affect the colour of patients' irises and pupils; hence pigmented growths may be concealed from a medical examiner. Also, factors such as stress and fatigue from examiners can lead to erroneous diagnosis. This research presents the use of trained hybrid auto encoders in the intelligent diagnosis of iris nevus. It is suggested that the use of the designed system as described in this work can significantly raise the confidence of medical diagnosis.

Usage Intention of Social Robots for Domestic Purpose: From Security, Privacy, and Legal Perspectives

Abstract: Domestic robots, also known as companion robots or social robots, are physical humanoid robots. They connect with online platforms which enhance their functionalities, such as voice and emotion recognition, human facial recognition, along with other Artificial Intelligence (AI) related capabilities make them special. People’s perceptions of robots are changing rapidly. The market for domestic robots is expected to grow exponentially over the next few years. As the use of domestic robots increases, people perceive different kinds of security, privacy, and legal issues with their usage. Few studies have focused on these issues. This study has attempted to understand users’ intention to use robots for household purposes and focuses on their perceptions about security, privacy, and legal issues. Reviewing the literature and the theories helped us to develop the conceptual model. The model has been validated by PLS-SEM technique. The study finds that privacy and security concerns affect households use of domestic robots. The study also finds that few people are aware of legal implications. Also, the study reveals that people expect to get many benefits from using robots for household purposes.