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The transition from primary to secondary school is more successful when students’ learning is consistent. Students are also more likely to enjoy school, engage with learning, and have a high academic achievement in secondary school when they feel motivated. This is a critical aspect, especially in cases in which global pandemic situations allow only online schooling opportunities. Students that are away from school lack the traditional sources of motivation and self-regulated learning skills; thus, research is needed to identify other important factors that can be developed in remote settings. The aim of this study was to find out how students perceive their experience with the transition from primary to secondary school and how such a transition influences students’ self-regulated learning (SRL) and motivation. Self-reported data were collected during the COVID-19 breakout from a total of n = 80 sixth and seventh grade students aged 12–14 years old. The results showed that students had a successful transition, especially when they were supported by their parents and teachers. Next, bivariate Pearson correlation analysis indicated that students’ perceptions about their experience with the transition from primary to secondary school, their self-regulated learning, and their motivation were significantly correlated. No gender differences were found among any of the main study variables. Teachers can foster students’ SRL skills by implementing effective teaching methods and by guiding them towards SRL-enhancing techniques.
Ana Uka; Arban Uka. The Effect of Students’ Experience with the Transition from Primary to Secondary School on Self-Regulated Learning and Motivation. Sustainability 2020, 12, 8519 .
AMA StyleAna Uka, Arban Uka. The Effect of Students’ Experience with the Transition from Primary to Secondary School on Self-Regulated Learning and Motivation. Sustainability. 2020; 12 (20):8519.
Chicago/Turabian StyleAna Uka; Arban Uka. 2020. "The Effect of Students’ Experience with the Transition from Primary to Secondary School on Self-Regulated Learning and Motivation." Sustainability 12, no. 20: 8519.
Transition from primary to secondary school is more successful when students’ learning is consistent. Students are also more likely to enjoy the school, engage with learning, and have a high academic achievement in the secondary school when they feel motivated. This is a critical aspect especially in cases when global pandemics situations allow only the online schooling opportunity. Students that are away from school lack the traditional sources of motivation and self-regulated learning skills, thus research is needed to identify other important factors that can be developed in remote settings. The aim of this study was to find out how students perceive their experience with the transition from primary to secondary school and how such a transition influences students’ self-regulated learning (SRL) and motivation. Self-reported data were collected during the COVID-19 breakout from a total of N=80, 6th and 7th grade students aged 12-14 years old. Results showed that students had a successful transition, especially when they are supported by their parents and teachers. Next, Bivariate Pearson Correlation analysis indicated that students’ perceptions about their experience with the transition from primary to secondary school and their self-regulated learning and motivation are significantly correlated. No gender differences were found among all main study variables.
Ana Uka; Arban Uka. The Effect of Students’ Experience with the Transition from Primary to Secondary School on Self-Regulated Learning and Motivation. 2020, 1 .
AMA StyleAna Uka, Arban Uka. The Effect of Students’ Experience with the Transition from Primary to Secondary School on Self-Regulated Learning and Motivation. . 2020; ():1.
Chicago/Turabian StyleAna Uka; Arban Uka. 2020. "The Effect of Students’ Experience with the Transition from Primary to Secondary School on Self-Regulated Learning and Motivation." , no. : 1.
A biometric system is presented using the human iris to help determine the authenticity of an individual. The system extracts the unique features of the iris that are recorded in templates. These templates are then compared with other irides utilising Daugman’s method. This follows a strict procedure (including segmentation, normalization, encoding and matching) over which a user has complete control. Often the recognition phase is crucial in nonoptimal or noncooperative conditions. In this work, a comparison is made of the relative accuracy of utilizing noisy iris datasets. The performance is analysed for a different number of iris images per person, for different number of individuals, for different noise levels using three different segmentations and three different encoding schemes. Adjustment of the Gabor filters’ bandwidth used in the encoding stage proves to be decisive in improving the accuracy for higher noise levels.
Oktay Koc; Arban Uka; Maaruf Ali; Klevis Muda; Orges Balla; Albana Roci. Iris Recognition Performance Analysis for Noncooperative Conditions. 2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE) 2020, 172 -175.
AMA StyleOktay Koc, Arban Uka, Maaruf Ali, Klevis Muda, Orges Balla, Albana Roci. Iris Recognition Performance Analysis for Noncooperative Conditions. 2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE). 2020; ():172-175.
Chicago/Turabian StyleOktay Koc; Arban Uka; Maaruf Ali; Klevis Muda; Orges Balla; Albana Roci. 2020. "Iris Recognition Performance Analysis for Noncooperative Conditions." 2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE) , no. : 172-175.
Medical field depends heavily on understanding and analyzing microscopy images of cells to better diagnose diseases, to evaluate the effectiveness of various medical treatments and to determine their health under stress. The amount of data that needs to be analyzed has increased and computer assisted analysis has become crucial as it would be very labor intensive for the medical practitioners otherwise. Many of the images are acquired using brightfield microscopy with no staining in order to avoid all the side effects. The unstained images have some associating challenges as they suffer from random nonuniform illumination, low contrast, relatively high transparency of the cytoplasm. The initial challenge of the large amount of data calls for the use of deep learning algorithms, whereas the other structural challenges call for the need to carefully train the convolutional neural networks in order to have a reliable system of evaluation. We have prepared a dataset of 20.000 images and we have tested the trained models on datasets with different number of images (N=300-8000). Here is this work we present classification of the cell health using convolutional neural networks and monitor the effect of the preprocessing steps on the overall accuracy.
Arban Uka; Xhoena Polisi; Julien Barthes; Albana Ndreu Halili; Florenc Skuka; Nihal Engin Vrana. Effect of Preprocessing on Performance of Neural Networks for Microscopy Image Classification. 2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE) 2020, 162 -165.
AMA StyleArban Uka, Xhoena Polisi, Julien Barthes, Albana Ndreu Halili, Florenc Skuka, Nihal Engin Vrana. Effect of Preprocessing on Performance of Neural Networks for Microscopy Image Classification. 2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE). 2020; ():162-165.
Chicago/Turabian StyleArban Uka; Xhoena Polisi; Julien Barthes; Albana Ndreu Halili; Florenc Skuka; Nihal Engin Vrana. 2020. "Effect of Preprocessing on Performance of Neural Networks for Microscopy Image Classification." 2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE) , no. : 162-165.
Computer assisted techniques could enable the use of morphological characteristics of hepatic spheroids as surrogate for their response to various stimuli. The aim of this work is to develop an automatic analysis procedure able to correctly acquire all the important morphological parameters of the hepatic spheroids under static and after stimulation conditions. Within the datasets, several issues can occur related to the non-uniform illumination background and inherent limitation in counting the exact object number when two or more are adjacent. Some of the images include patterns with intensity comparable to the spheroid intensity, such as extended grooves, and they are filtered out based on their eccentricity values. Traditional methods such as Otsu threshold does not segment the spheroid images truthfully due to their energy minimization based approach. To circumvent this limitation initially background removal is applied as a preprocessing step. Filters applied for this depend on the relative size of the spheroids in order not to diminish the image quality. Therefore, we propose a guided automatic threshold value that can discriminate between the background and the spheroids more accurately by finding the critical peak on the pixel intensity histogram. Pixel intensity histograms are composed of three modes and the local minimum after the peak at the lowest values is the threshold value. After applying the new guided thresholding technique, watershed algorithm is used in order to determine the separating nodes between objects that are contiguous to each-other. These two techniques are compared with Gabor filters based methods that are shape based filters. Employing the three methods spheroid parameters including the number, area and the perimeter were determined and their performance and robustness are discussed.
Xhoena Polisi; Albana Halili; Constantin Edi Tanase; Arban Uka; Nihal Engin Vrana; Amir Ghaemmaghami. Computer Assisted Analysis of the Hepatic Spheroid Formation. Learning and Analytics in Intelligent Systems 2020, 117 -126.
AMA StyleXhoena Polisi, Albana Halili, Constantin Edi Tanase, Arban Uka, Nihal Engin Vrana, Amir Ghaemmaghami. Computer Assisted Analysis of the Hepatic Spheroid Formation. Learning and Analytics in Intelligent Systems. 2020; ():117-126.
Chicago/Turabian StyleXhoena Polisi; Albana Halili; Constantin Edi Tanase; Arban Uka; Nihal Engin Vrana; Amir Ghaemmaghami. 2020. "Computer Assisted Analysis of the Hepatic Spheroid Formation." Learning and Analytics in Intelligent Systems , no. : 117-126.
Iris recognition is a well-known biometric identification system which distinguishes authentic and imposter individuals based on the features of their irides. It employs stringent statistical analyses of the features of irides due to the fact that each person has a unique iris, just like a fingerprint. In this work, the approach adopted towards the iris recognition problem is through an exhaustive and careful analysis of the statistical properties of the iris images and the randomness of spurious noise effects. The ability to differentiate two different templates from each other improves with the increase in the number of the degrees of freedom (DOF). The DOF depends on the encoding schemes utilized and moreover, it is hypothesized that the encoding schemes used in themselves could influence the recognition performance. The CASIA (Chinese Academy of Sciences Institute of Automation) version 1 database of iris images used in this study has been modified by the addition of artificial noise in order to simulate practical real life in situ noisy iris capture environments. The classical and state-of-the-art segmentation techniques have been compared, determining whether they are superior to the others under several conditions. The 1D, 2D Gabor filters and the short window implementation were all tested. The conclusion was that the 2D Gabor Filters produce a lower equal error rate (EER), higher accuracy and decidability than by using the one-dimensional log Gabor filter. After modifying the one-dimensional log Gabor filters, a lower EER and higher accuracy was found as the noise level increased. This makes the modified 1D log Gabor Filters a better proposition in noisy conditions. The generated iris templates have a predetermined theoretical value of DOF and from the statistical analysis, an experimental value can be determined. The relation between these values can be used as a metric to compare different databases.
Oktay Koc; Loredana Tosku; Julian Hoxha; Ali Osman Topal; Maaruf Ali; Arban Uka. Detailed Analysis of IRIS Recognition Performance. 2019 International Conference on Computing, Electronics & Communications Engineering (iCCECE) 2019, 253 -258.
AMA StyleOktay Koc, Loredana Tosku, Julian Hoxha, Ali Osman Topal, Maaruf Ali, Arban Uka. Detailed Analysis of IRIS Recognition Performance. 2019 International Conference on Computing, Electronics & Communications Engineering (iCCECE). 2019; ():253-258.
Chicago/Turabian StyleOktay Koc; Loredana Tosku; Julian Hoxha; Ali Osman Topal; Maaruf Ali; Arban Uka. 2019. "Detailed Analysis of IRIS Recognition Performance." 2019 International Conference on Computing, Electronics & Communications Engineering (iCCECE) , no. : 253-258.
Iris recognition is a biometric authentication system proving vital for ensuring security and has been employed as an important case to test the algorithms developed in pattern recognition. The unique circular shape of the iris and its time invariance makes it a versatile technique that has an accuracy that can be mathematically proven. Here in this work we propose a new segmentation technique and two new encoding schemes. The newly proposed techniques are tested on the best, the worst and on all the irises of two widely known databases (CASIA and IIT Delhi database) and the results are compared with the classical segmentation and classical encoding schemes. The segmentation is improved and as a result the accuracy and equal error rate also. In CASIA database the use of the new segmentation improves the EER from 3.14% to 0.82% (on all 756 images of the dataset). When tested on the whole IIT iris database, the EER is improved from 3.88% to 0.34%; and on the worst images of IIT EER is improved from 13.30% to 1.00%.
Arban Uka; Albana Roci; Oktay Koc. Improved segmentation algorithm and further optimization for iris recognition. IEEE EUROCON 2017 -17th International Conference on Smart Technologies 2017, 85 -88.
AMA StyleArban Uka, Albana Roci, Oktay Koc. Improved segmentation algorithm and further optimization for iris recognition. IEEE EUROCON 2017 -17th International Conference on Smart Technologies. 2017; ():85-88.
Chicago/Turabian StyleArban Uka; Albana Roci; Oktay Koc. 2017. "Improved segmentation algorithm and further optimization for iris recognition." IEEE EUROCON 2017 -17th International Conference on Smart Technologies , no. : 85-88.
Detection of underground objects remains an important task today, particularly when attempting to recover landmines. A Ground Penetrating Radar (GPR) sends short pulses in time domain to detect underground objects by recording the reflected signal and analyzing its properties. GPR traces acquired at different positions in space are combined together to form B-scan images. The presence of objects produce hyperbolic shape fluctuations in B-scan images that depend on the shape, dielectric properties, and depth of the sensed object, and also on the properties of the medium. Dielectric properties of some objects, like plastic objects, create very small fluctuations in GPR traces. Efficient algorithms to analyze recorded patterns need to be developed and improved based on the inherent variations of the overall conditions. This paper compares the results of three algorithms on three different scenarios for detecting underground objects under noise using B-scan images: Histograms of Oriented Gradients (HOG), 3-row Average Subtraction (3RAS) and Min-max normalization. According to the results, HOG and 3RAS algorithms increase Object Detection Ratio (ODR) from 88% to 93% while decreasing the False Alarm Rate (FAR) considerably. The accuracy is also tested for different image sizes. And for certain algorithms, lower resolution images result in higher accuracy.
Ibrahim Mesecan; Arban Uka; Endri Stoja; Betim Cico. Comparison of histograms of oriented gradients and 3-row Average Subtraction (3RAS) using GprMax. 2017 6th Mediterranean Conference on Embedded Computing (MECO) 2017, 1 -5.
AMA StyleIbrahim Mesecan, Arban Uka, Endri Stoja, Betim Cico. Comparison of histograms of oriented gradients and 3-row Average Subtraction (3RAS) using GprMax. 2017 6th Mediterranean Conference on Embedded Computing (MECO). 2017; ():1-5.
Chicago/Turabian StyleIbrahim Mesecan; Arban Uka; Endri Stoja; Betim Cico. 2017. "Comparison of histograms of oriented gradients and 3-row Average Subtraction (3RAS) using GprMax." 2017 6th Mediterranean Conference on Embedded Computing (MECO) , no. : 1-5.
The temperature dependencies (10‐300 K) of seven Raman‐active mode frequencies in layered semiconductor gallium telluride have been measured in the frequency range from 25 to 300 cm‐1. Softening and broadening of the optical phonon lines are observed with increasing temperature. Comparison between the experimental data and theories of the shift of the phonon lines during heating of the crystal showed that the experimental dependencies can be explained by contributions from thermal expansion and lattice anharmonicity. Lattice anharmonicity is determined to be due to threephonon processes.
A. Aydinli; N.M. Gasanly; Arban Uka; H. Efeoğlu. Anharmonicity in GaTe layered crystals. Crystal Research and Technology 2002, 37, 1303 -1309.
AMA StyleA. Aydinli, N.M. Gasanly, Arban Uka, H. Efeoğlu. Anharmonicity in GaTe layered crystals. Crystal Research and Technology. 2002; 37 (12):1303-1309.
Chicago/Turabian StyleA. Aydinli; N.M. Gasanly; Arban Uka; H. Efeoğlu. 2002. "Anharmonicity in GaTe layered crystals." Crystal Research and Technology 37, no. 12: 1303-1309.