Traditional quantitative research methods of data collection in programming, such as questionnaires and interviews, are the most common approaches for researchers in this field. However, in recent years, eye-tracking has been on the rise as a new method of collecting evidence of visual attention and the cognitive process of programmers. Eyetracking has been used by researchers in the field of programming to analyze and understand a variety of tasks such as comprehension and debugging. In this paper, we will focus on reporting how experiments that used eye-trackers in programming research are conducted, and the information that can be collected from these experiments. In this mapping study, we identify and report on 63 studies, published between 1990 and June 2017, collected and gathered via manual search on digital libraries and databases related to computer science and computer engineering. Among the five main areas of research interest are program comprehension and debugging, which received an increased interest in recent years, non-code comprehension, collaborative programming and requirements traceability research, which had the fewest number of publications due to possible limitations of the eye-tracking technology in this type of experiments. We find that most of the participants in these studies were students and faculty members from institutions of higher learning, and while they performed programming tasks on a range of programming languages and programming representations, we find Java language and UML representation to be the most used materials. We also report on a range of eye-trackers and attention tracking tools that have been utilized, and find Tobii eye-trackers to be the most used devices by researchers.

Kidney image enhancement is challenging due to the unpredictable quality of MRI images, as well as the nature of kidney diseases. The focus of this work is on kidney images enhancement by proposing a new Local Fractional Entropy (LFE)-based model. The proposed model estimates the probability of pixels that represent edges based on the entropy of the neighboring pixels, which results in local fractional entropy. When there is a small change in the intensity values (indicating the presence of edge in the image), the local fractional entropy gives fine image details. Similarly, when no change in intensity values is present (indicating smooth texture), the LFE does not provide fine details, based on the fact that there is no edge information. Tests were conducted on a large dataset of different, poor-quality kidney images to show that the proposed model is useful and effective. A comparative study with the classical methods, coupled with the latest enhancement methods, shows that the proposed model outperforms the existing methods.

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Cancer is the general name for a group of more than 100 diseases. Although cancer includes different types of diseases, they all start because abnormal cells grow out of control. Without treatment, cancer can cause serious health problems and even loss of life. Early detection of cancer may reduce mortality and morbidity. This paper presents a review of the detection methods for lung, breast, and brain cancers. These methods used for diagnosis include artificial intelligence techniques, such as support vector machine neural network, artificial neural network, fuzzy logic, and adaptive neuro-fuzzy inference system, with medical imaging like X-ray, ultrasound, magnetic resonance imaging, and computed tomography scan images. Imaging techniques are the most important approach for precise diagnosis of human cancer. We investigated all these techniques to identify a method that can provide superior accuracy and determine the best medical images for use in each type of cancer.

The study investigated the effects of chunking and perceptual patterns that guide the drawings of Rey complex figure. Ten adult participants (M age = 22.2 yr., SD = 4.1) reproduced a single stimulus in four drawing modes including delayed recall, tracing, copying, and immediate recall across 10 sessions producing a total of 400 trials. It was hypothesized that the effect of chunking is most obvious in the free recall tasks than in the tracing or copying tasks. Measures such as pauses, patterns of drawings, and transitions among patterns of drawings suggested that participants used chunking to aid rapid learning of the diagram. The analysis of the participants' sequence of chunk production further revealed that they used a spatial schema to organize the chunks. Findings from this study provide additional evidence to support prior studies that claim graphical information is hierarchically organized.

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