Question Driven Introductory Programming Instruction: A Pilot Study

For most beginners, learning computer programming is a complex undertaking. Demotivation and learned helplessness have been widely reported. In addition to the subject’s complexity, low in-class involvement has been linked to poor student performance. This work introduces a novel instructional technique called Student-Driven Probe Instruction (SDPI) to address the low levels of in-class involvement in basic programming courses. The concept was straightforward: rather than the teacher lecturing/explaining material to the class and requesting questions, the students were shown a piece of code or other relevant material and given the opportunity to ask questions first. Explanations followed only after the questions had been asked, not before. Participation was tracked through two metrics: the number of questions asked in class and emails/Slack contacts with the instructor. Significant improvements were recorded for in-class participation. Average quiz scores also improved meaningfully. According to a course evaluation survey, students favored SDPI over the conventional lecture format since it piqued their interest in the material and gave them the confidence to ask questions in class

Analysis of ovarian tumor pathology by Fourier Transform Infrared Spectroscopy

<p>Abstract</p> <p>Background</p> <p>Ovarian cancer is the second most common cancer among women and the leading cause of death among gynecologic malignancies. In recent years, infrared (IR) spectroscopy has gained attention as a simple and inexpensive method for the biomedical study of several diseases. In the present study infrared spectra of normal and malignant ovarian tissues were recorded in the 650 cm^-1to 4000 cm^-1region.</p> <p>Methods</p> <p>Post surgical tissue samples were taken from the normal and tumor sections of the tissue. Fourier Transform Infrared (FTIR) data on twelve cases of ovarian cancer with different grades of malignancy from patients of different age groups were analyzed.</p> <p>Results</p> <p>Significant spectral differences between the normal and the ovarian cancerous tissues were observed. In particular changes in frequency and intensity in the spectral region of protein, nucleic acid and lipid vibrational modes were observed. It was evident that the sample-to-sample or patient-to-patient variations were small and the spectral differences between normal and diseased tissues were reproducible.</p> <p>Conclusion</p> <p>The measured spectroscopic features, which are the spectroscopic fingerprints of the tissues, provided the important differentiating information about the malignant and normal tissues. The findings of this study demonstrate the possible use of infrared spectroscopy in differentiating normal and malignant ovarian tissues.</p

Adaptive Differential Evolution and its Application to Machine Vision

Over recent years, Evolutionary Algorithms (EA) have emerged as a practical approach for solving hard optimization problems ubiquitously presented in real life. The inherent advantage of EA over other types of numerical optimization methods lies in the fact that they require much less or no prior knowledge of the objective function. Differential Evolution (DE) has emerged as a highly competitive and powerful real parameter optimizer in the diverse community of evolutionary algorithms. The study of this dissertation is focused on two main approaches. The first approach focuses on studying and improving DE by creating its variants that aim at altering/adapting its control parameters and mutation strategies during the course of the search. The performance of DE depends largely upon the mutation strategy used, its control parameters namely the scale factor F, the crossover rate Cr, and the population size NP, and is quite sensitive to their appropriate settings. A simple and effective technique that alters F in stages, first through random perturbations and then through the application of an annealing schedule, is proposed. After that, the impact and efficacy of adapting mutation strategies with or without adapting the control parameters is investigated. The second approach is concerned with the application side of DE which is used as an optimizer either as the primary algorithm or as a surrogate to improve the performance of the overall system. The focus area is video based vehicle classification. A DE based vehicle classification system is proposed. The system in its essence, aims to classify a vehicle, based on the number of circles (axles) in an image using Hough Transform which is a popular parameter based feature detection method. Differential Evolution (DE) is coupled with Hough Transform to improve the overall accuracy of the classification system. DE is further employed as an optimizer in an extension of the previous vehicle detector and classifier. This system has a novel appearance based model utilizing pixel color information and is capable of classifying multi-lane moving vehicles into seven different classes. Five different variants of DE on varied videos are tested, and a performance profile of all the variants is provided

Real Parameter Optimization Using Differential Evolution

Over recent years, Evolutionary Algorithms (EA) have emerged as a practical approach to solve hard optimization problems presented in real life. The inherent advantage of EA over other types of numerical optimization methods lies in the fact that they require very little or no prior knowledge of the objective function. Information like differentiability or continuity is not necessary. The inspiration to learn from evolutionary processes and emulate them on a computer comes from varied directions, the most pertinent of which is the field of optimization. This paper presents one such Evolutionary Algorithm known as Differential Evolution (DE) and tests its performance on benchmark problems. Different variants of basic DE are discussed and their advantages and disadvantages are listed. This paper, through exhaustive experimentation, proposes an acceptable set of control parameters which may be applied to most of the benchmark functions to achieve good performance