An Adaptive Locally Connected Neuron Model: Focusing Neuron

This paper presents a new artificial neuron model capable of learning its receptive field in the topological domain of inputs. The model provides adaptive and differentiable local connectivity (plasticity) applicable to any domain. It requires no other tool than the backpropagation algorithm to learn its parameters which control the receptive field locations and apertures. This research explores whether this ability makes the neuron focus on informative inputs and yields any advantage over fully connected neurons. The experiments include tests of focusing neuron networks of one or two hidden layers on synthetic and well-known image recognition data sets. The results demonstrated that the focusing neurons can move their receptive fields towards more informative inputs. In the simple two-hidden layer networks, the focusing layers outperformed the dense layers in the classification of the 2D spatial data sets. Moreover, the focusing networks performed better than the dense networks even when 70$\%$ of the weights were pruned. The tests on convolutional networks revealed that using focusing layers instead of dense layers for the classification of convolutional features may work better in some data sets.Comment: 45 pages, a national patent filed, submitted to Turkish Patent Office, No: -2017/17601, Date: 09.11.201

Implicit Theories and Self-efficacy in an Introductory Programming Course

Contribution: This study examined student effort and performance in an introductory programming course with respect to student-held implicit theories and self-efficacy. Background: Implicit theories and self-efficacy shed a light into understanding academic success, which must be considered when developing effective learning strategies for programming. Research Questions: Are implicit theories of intelligence and programming, and programming-efficacy related to each other and student success in programming? Is it possible to predict student course performance using a subset of these constructs? Methodology: Two consecutive surveys (N=100 and N=81) were administered to non-CS engineering students in I\c{s}{\i}k University. Findings: Implicit theories and self-beliefs are interrelated and correlated with effort, performance, and previous failures in the course and students explain failure in programming course with "programming-aptitude is fixed" theory, and also that programming is a difficult task for themselves.Comment: Programming Education. 8 page

A Survey on Automated Diagnosis of Alzheimer's Disease Using Optical Coherence Tomography and Angiography

Retinal optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) are promising tools for the (early) diagnosis of Alzheimer's disease (AD). These non-invasive imaging techniques are cost-effective and more accessible than alternative neuroimaging tools. However, interpreting and classifying multi-slice scans produced by OCT devices is time-consuming and challenging even for trained practitioners. There are surveys on machine learning and deep learning approaches concerning the automated analysis of OCT scans for various diseases such as glaucoma. However, the current literature lacks an extensive survey on the diagnosis of Alzheimer's disease or cognitive impairment using OCT or OCTA. This has motivated us to do a comprehensive survey aimed at machine/deep learning scientists or practitioners who require an introduction to the problem. The paper contains 1) an introduction to the medical background of Alzheimer's Disease and Cognitive Impairment and their diagnosis using OCT and OCTA imaging modalities, 2) a review of various technical proposals for the problem and the sub-problems from an automated analysis perspective, 3) a systematic review of the recent deep learning studies and available OCT/OCTA datasets directly aimed at the diagnosis of Alzheimer's Disease and Cognitive Impairment. For the latter, we used Publish or Perish Software to search for the relevant studies from various sources such as Scopus, PubMed, and Web of Science. We followed the PRISMA approach to screen an initial pool of 3073 references and determined ten relevant studies (N=10, out of 3073) that directly targeted AD diagnosis. We identified the lack of open OCT/OCTA datasets (about Alzheimer's disease) as the main issue that is impeding the progress in the field.Comment: Submitted to Computerized Medical Imaging and Graphics. Concept, methodology, invest, data curation, and writing org.draft by Yasemin Turkan. Concept, method, writing review editing, and supervision by F. Boray Te

True-false lumen segmentation of aortic dissection using multi-scale wavelet analysis and generative-discriminative model matching

Computer aided diagnosis in the medical image domain requires sophisticated probabilistic models to formulate quantitative behavior in image space. In the diagnostic process detailed knowledge of model performance with respect to accuracy, variability, and uncertainty is crucial. This challenge has lead to the fusion of two successful learning schools namely generative and discriminative learning. In this paper, we propose a generative-discriminative learning approach to predict object boundaries in medical image datasets. In our approach, we perform probabilistic model matching of both modeling domains to fuse into the prediction step appearance and structural information of the object of interest while exploiting the strength of both learning paradigms. In particular, we apply our method to the task of true-false lumen segmentation of aortic dissections an acute disease that requires automated quantification for assisted medical diagnosis. We report empirical results for true-false lumen discrimination of aortic dissection segmentation showing superior behavior of the hybrid generative-discriminative approach over their non hybrid generative counterpart

Adaptive Convolution Kernel for Artificial Neural Networks

Many deep neural networks are built by using stacked convolutional layers of fixed and single size (often 3$\times$3) kernels. This paper describes a method for training the size of convolutional kernels to provide varying size kernels in a single layer. The method utilizes a differentiable, and therefore backpropagation-trainable Gaussian envelope which can grow or shrink in a base grid. Our experiments compared the proposed adaptive layers to ordinary convolution layers in a simple two-layer network, a deeper residual network, and a U-Net architecture. The results in the popular image classification datasets such as MNIST, MNIST-CLUTTERED, CIFAR-10, Fashion, and ``Faces in the Wild'' showed that the adaptive kernels can provide statistically significant improvements on ordinary convolution kernels. A segmentation experiment in the Oxford-Pets dataset demonstrated that replacing a single ordinary convolution layer in a U-shaped network with a single 7$\times$7 adaptive layer can improve its learning performance and ability to generalize.Comment: 25 page

Examining the relationship between employees’ perceptions on competency training and affective commitment: The moderating influence of volition

The purpose of this study is to examine the relationship between employees’ perceptions on competency training and affective commitment and the moderating effect of employees’ volition on this relationship. Data were collected from a field survey of 159 technicians from selected institutes in Sarawak. Regression analysis indicated that there was a positive relationship between employee’s perceived training comprehensiveness and affective commitment. In addition, the results indicated that the relationship between employee’s perceptions on competency training and affective commitment will be more positive for those who take on the competency training by own volition. Implications for researchers and practitioners are proposed based on the findings

Computerised diagnosis of malaria

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Effects of the topical hemostatic agent Ankaferd Blood Stopper on the incidence of alveolar osteitis after surgical removal of an impacted mandibular third molar

Background: Alveolar osteitis (AO) is a commonly seen post‑operative complication during the wound‑healing period after permanent tooth extraction or surgical removal of impacted third molar teeth.Objectives: The aim of this clinical study was to evaluate the effects of administration of the topical hemostatic agent Ankaferd Blood Stopper (ABS) into the socket on AO formation after impacted mandibular third molar extraction.Patients and Methods: Bilaterally, 100 half‑impacted mandibular third molars were extracted in 50 patients. Then, 1.0 mL ABS was administered to achieve hemostasis in one half of the sockets and as a control, the other half was irrigated with 1.0 mL physiological serum after surgery.Results: There was no statistically significant difference in terms of AO formation (P > 0.05) between the extraction sites. However, the postoperative pain in ABS administration sites was higher than in the other sites for the first 2 days after surgery (P < 0.05).Conclusions: The results showed that ABS administration did not increase the incidence of AO formation. Thus, ABS can be used safely for hemostasis after impacted mandibular third molar surgery.Key words: Alveolar osteitis, Ankaferd Blood Stopper, hemostasis, third mola