Empirical studies of pair programming for CS/SE teaching in higher education: a systematic literature review

The objective of this paper is to present the current evidence relative to the effectiveness of pair programming (PP) as a pedagogical tool in higher education CS/SE courses. We performed a systematic literature review (SLR) of empirical studies that investigated factors affecting the effectiveness of PP for CS/SE students and studies that measured the effectiveness of PP for CS/SE students. Seventy-four papers were used in our synthesis of evidence, and 14 compatibility factors that can potentially affect PP's effectiveness as a pedagogical tool were identified. Results showed that students' skill level was the factor that affected PP's effectiveness the most. The most common measure used to gauge PP's effectiveness was time spent on programming. In addition, students' satisfaction when using PP was overall higher than when working solo. Our meta-analyses showed that PP was effective in improving students' grades on assignments. Finally, in the studies that used quality as a measure of effectiveness, the number of test cases succeeded, academic performance, and expert opinion were the quality measures mostly applied. The results of this SLR show two clear gaps in this research field: 1) a lack of studies focusing on pair compatibility factors aimed at making PP an effective pedagogical tool and 2) a lack of studies investigating PP for software design/modeling tasks in conjunction with programming tasks.

A unified mixed-integer programming model for simultaneous fluence weight and aperture optimization in VMAT, tomotherapy, and cyberknife

All rights reserved. In this paper, we propose and study a unified mixed-integer programming model that simultaneously optimizes fluence weights and multi-leaf collimator (MLC) apertures in the treatment planning optimization of VMAT, Tomotherapy, and CyberKnife. The contribution of our model is threefold: (i) Our model optimizes the fluence and MLC apertures simultaneously for a given set of control points. (ii) Our model can incorporate all volume limits or dose upper bounds for organs at risk (OAR) and dose lower bound limits for planning target volumes (PTV) as hard constraints, but it can also relax either of these constraint sets in a Lagrangian fashion and keep the other set as hard constraints. (iii) For faster solutions, we propose several heuristic methods based on the MIP model, as well as a meta-heuristic approach. The meta-heuristic is very efficient in practice, being able to generate dose- and machinery-feasible solutions for problem instances of clinical scale, e.g., obtaining feasible treatment plans to cases with 180 control points, 6750 sample voxels and 18,000 beamlets in 470 seconds, or cases with 72 control points, 8000 sample voxels and 28,800 beamlets in 352 seconds. With discretization and down-sampling of voxels, our method is capable of tackling a treatment field of 8000-64,000cm3, depending on the ratio of critical structure versus unspecified tissues.

Supporting scientists in re-engineering sequential programs to parallel using model-driven engineering

Developing complex computational-intensiveand data-intensive scientific applications requires effectiveutilization of the computational power of the availablecomputing platforms including grids, clouds, clusters, multicoreand many-core processors, and graphical processingunits (GPUs). However, scientists who need to leverage suchplatforms are usually not parallel or distributed programmingexperts. Thus, they face numerous challenges whenimplementing and porting their software-based experimentaltools to such platforms. In this paper, we introduce asequential-to-parallel engineering approach to help scientistsin engineering their scientific applications. Our approach isbased on capturing sequential program details, plannedparallelization aspects, and program deployment details usinga set of domain-specific visual languages (DSVLs). Then, usingcode generation, we generate the corresponding parallelprogram using necessary parallel and distributedprogramming models (MPI, OpenCL, or OpenMP). Wesummarize three case studies (matrix multiplication, N-Bodysimulation, and signal processing) to evaluate our approach.

Assessment of a six-week computer-based remediation program for social cognition in chronic schizophrenia

Background: Programs to remediate cognitive deficits have shown promising results in schizophrenia, but remediation of social cognition deficits is less well understood. Social cognitive deficits may cause more disability than the widely recognized neurocognitive deficits, suggesting that this is an area worthy of further investigation. Aim: Implement and evaluate a brief computerized cognitive remediation program designed to improve memory, attention, and facial affect recognition (FAR) in outpatients with chronic schizophrenia. <br /><br />Methods: Baseline assessments of FAR and of clinical, cognitive, and psychosocial functioning were completed on 20 males with schizophrenia enrolled in an outpatient rehabilitation program at the Shanghai Mental Health Center (the intervention group) and on 20 males with schizophrenia recruited from among regular outpatients at the Center (the control group). Both groups received treatment as usual, but the intervention group also completed an average of 12.7 sessions of a computer-based remediation program for neurocognitive, social, and FAR functioning over a 6-week period. The baseline measures were repeated in both groups at the end of the 6-week trial. <br /><br />Results: There were no statistically significant differences in the changes in clinical symptoms (assessed by the Positive and Negative Syndrome Scale, PANSS) or cognitive measures (assessed using the Hong Kong List Learning Test and the Letter-Number Sequencing Task) between the intervention and control groups over the 6-week trial, but there were modest improvements on the PANSS for the intervention group between baseline and after the intervention. There was a significantly greater improvement in the social functioning measure (the Personal and Social Performance scale, PSP) in the intervention group than in the control group. The pre-post change in the total facial recognition score in the intervention group was statistically significant (paired t-test=-2.60, p=0.018), and there was a statistical trend of a greater improvement in facial recognition in the intervention group than in the control group (F(1,37)=2.93; p=0.092). <br /><br />Conclusions: Integration of FAR training with a short, computer-administrated cognitive remediation program may improve recognition of facial emotions by individuals with schizophrenia, and, thus, improve their social functioning. But more work on developing the FAR training modules and on testing them in larger, more diverse samples will be needed before this can be recommended as a standard part of cognitive remediation programs.

A model for teaching an introductory programming course using ADRI

High failure and drop-out rates from introductory programming courses continue to be of significant concern to computer science disciplines despite extensive research attempting to address the issue. In this study, we include the three entities of the didactic triangle, instructors, students and curriculum, to explore the learning difficulties that students encounter when studying introductory programming. We first explore students&rsquo; perceptions of the barriers and affordances to learning programming. A survey is conducted with introductory programming students to get their feedback on the topics and associated learning resources in the introductory programming course. The instructors&rsquo; perceptions are included by analyzing current teaching materials and assessment tools used in the course. As a result, an ADRI based approach is proposed to address the problems identified in the teaching and learning processes of an introductory programming course.

Prenatal acoustic communication programs offspring for high posthatching temperatures in a songbird.

In many species, embryos can perceive and learn external sounds. Yet, the possibility that parents may use these embryonic capacities to alter their offspring's developmental trajectories has not been considered. Here, we demonstrate that zebra finch parents acoustically signal high ambient temperatures (above 26°C) to their embryos. We show that exposure of embryos to these acoustic cues alone adaptively alters subsequent nestling begging and growth in response to nest temperature and influences individuals' reproductive success and thermal preferences as adults. These findings have implications for our understanding of maternal effects, phenotypic plasticity, developmental programming, and the adaptation of endothermic species to a warming world.

Evolution of classifiers for pitch estimation of piano music using cartesian genetic programming

Pitch Estimation, also known as Fundamental Frequency (F0) estimation, has been a popular research topic for many years, and is still investigated nowadays. The goal of Pitch Estimation is to find the pitch or fundamental frequency of a digital recording of a speech or musical notes. It plays an important role, because it is the key to identify which notes are being played and at what time. Pitch Estimation of real instruments is a very hard task to address. Each instrument has its own physical characteristics, which reflects in different spectral characteristics. Furthermore, the recording conditions can vary from studio to studio and background noises must be considered. This dissertation presents a novel approach to the problem of Pitch Estimation, using Cartesian Genetic Programming (CGP).We take advantage of evolutionary algorithms, in particular CGP, to explore and evolve complex mathematical functions that act as classifiers. These classifiers are used to identify piano notes pitches in an audio signal. To help us with the codification of the problem, we built a highly flexible CGP Toolbox, generic enough to encode different kind of programs. The encoded evolutionary algorithm is the one known as 1 + , and we can choose the value for . The toolbox is very simple to use. Settings such as the mutation probability, number of runs and generations are configurable. The cartesian representation of CGP can take multiple forms and it is able to encode function parameters. It is prepared to handle with different type of fitness functions: minimization of f(x) and maximization of f(x) and has a useful system of callbacks. We trained 61 classifiers corresponding to 61 piano notes. A training set of audio signals was used for each of the classifiers: half were signals with the same pitch as the classifier (true positive signals) and the other half were signals with different pitches (true negative signals). F-measure was used for the fitness function. Signals with the same pitch of the classifier that were correctly identified by the classifier, count as a true positives. Signals with the same pitch of the classifier that were not correctly identified by the classifier, count as a false negatives. Signals with different pitch of the classifier that were not identified by the classifier, count as a true negatives. Signals with different pitch of the classifier that were identified by the classifier, count as a false positives. Our first approach was to evolve classifiers for identifying artifical signals, created by mathematical functions: sine, sawtooth and square waves. Our function set is basically composed by filtering operations on vectors and by arithmetic operations with constants and vectors. All the classifiers correctly identified true positive signals and did not identify true negative signals. We then moved to real audio recordings. For testing the classifiers, we picked different audio signals from the ones used during the training phase. For a first approach, the obtained results were very promising, but could be improved. We have made slight changes to our approach and the number of false positives reduced 33%, compared to the first approach. We then applied the evolved classifiers to polyphonic audio signals, and the results indicate that our approach is a good starting point for addressing the problem of Pitch Estimation.

New Rigorous Decomposition Methods for Mixed-integer Linear and Nonlinear Programming

Process systems design, operation and synthesis problems under uncertainty can readily be formulated as two-stage stochastic mixed-integer linear and nonlinear (nonconvex) programming (MILP and MINLP) problems. These problems, with a scenario based formulation, lead to large-scale MILPs/MINLPs that are well structured. The first part of the thesis proposes a new finitely convergent cross decomposition method (CD), where Benders decomposition (BD) and Dantzig-Wolfe decomposition (DWD) are combined in a unified framework to improve the solution of scenario based two-stage stochastic MILPs. This method alternates between DWD iterations and BD iterations, where DWD restricted master problems and BD primal problems yield a sequence of upper bounds, and BD relaxed master problems yield a sequence of lower bounds. A variant of CD, which includes multiple columns per iteration of DW restricted master problem and multiple cuts per iteration of BD relaxed master problem, called multicolumn-multicut CD is then developed to improve solution time. Finally, an extended cross decomposition method (ECD) for solving two-stage stochastic programs with risk constraints is proposed. In this approach, a CD approach at the first level and DWD at a second level is used to solve the original problem to optimality. ECD has a computational advantage over a bilevel decomposition strategy or solving the monolith problem using an MILP solver. The second part of the thesis develops a joint decomposition approach combining Lagrangian decomposition (LD) and generalized Benders decomposition (GBD), to efficiently solve stochastic mixed-integer nonlinear nonconvex programming problems to global optimality, without the need for explicit branch and bound search. In this approach, LD subproblems and GBD subproblems are systematically solved in a single framework. The relaxed master problem obtained from the reformulation of the original problem, is solved only when necessary. A convexification of the relaxed master problem and a domain reduction procedure are integrated into the decomposition framework to improve solution efficiency. Using case studies taken from renewable resource and fossil-fuel based application in process systems engineering, it can be seen that these novel decomposition approaches have significant benefit over classical decomposition methods and state-of-the-art MILP/MINLP global optimization solvers.

Cálculo de constantes ópticas de películas delgadas de Cu3BiS3 a través del método de Wolfe

Se calculó la obtención de las constantes ópticas usando el método de Wolfe. Dichas contantes: coeficiente de absorción (α), índice de refracción (n) y espesor de una película delgada (d ), son de importancia en el proceso de caracterización óptica del material. Se realizó una comparación del método del Wolfe con el método empleado por R. Swanepoel. Se desarrolló un modelo de programación no lineal con restricciones, de manera que fue posible estimar las constantes ópticas de películas delgadas semiconductoras, a partir únicamente, de datos de transmisión conocidos. Se presentó una solución al modelo de programación no lineal para programación cuadrática. Se demostró la confiabilidad del método propuesto, obteniendo valores de α = 10378.34 cm−1, n = 2.4595, d =989.71 nm y Eg = 1.39 Ev, a través de experimentos numéricos con datos de medidas de transmitancia espectral en películas delgadas de Cu3BiS3.