Timo Aho

CoRED: browser-based Collaborative Real-time Editor for Java web applications

While the users of completed applications are heavily moving from desktop to the web browser, the majority of developers are still working with desktop IDEs such as Eclipse or Visual Studio. In contrast to professional installable IDEs, current web-based code editors are simple text editors with extra features. They usually understand lexical syntax and can do highlighting and indenting, but lack many of the features seen in modern desktop editors. In this paper, we present CoRED, a browser-based collaborative real-time code editor for Java applications. CoRED is a complete Java editor with error checking and automatic code generation capabilities, extended with some features commonly associated with social media. As a proof of the concept, we have extended CoRED to support Java based Vaadin framework for web applications. Moreover, CoRED can be used either as a stand-alone version or as a component of any other software. It is already used as a part of browser based Arvue IDE.

Rule Ensembles for Multi-target Regression

Methods for learning decision rules are being successfully applied to many problem domains, especially where understanding and interpretation of the learned model is necessary. In many real life problems, we would like to predict multiple related (nominal or numeric) target attributes simultaneously. Methods for learning rules that predict multiple targets at once already exist, but are unfortunately based on the covering algorithm, which is not very well suited for regression problems. A better solution for regression problems may be a rule ensemble approach that transcribes an ensemble of decision trees into a large collection of rules. An optimization procedure is then used for selecting the best (and much smaller) subset of these rules, and to determine their weights. Using the rule ensembles approach we have developed a new system for learning rule ensembles for multi-target regression problems. The newly developed method was extensively evaluated and the results show that the accuracy of multi-target regression rule ensembles is better than the accuracy of multi-target regression trees, but somewhat worse than the accuracy of multi-target random forests. The rules are significantly more concise than random forests, and it is also possible to create very small rule sets that are still comparable in accuracy to single regression trees.

A Walk from 2-Norm SVM to 1-Norm SVM

This paper studies how useful the standard 2-norm regularized SVM is in approximating the 1-norm SVM problem. To this end, we examine a general method that is based on iteratively re-weighting the features and solving a 2-norm optimization problem. The convergence rate of this method is unknown. Previous work indicates that it might require an excessive number of iterations. We study how well we can do with just a small number of iterations. In theory the convergence rate is fast, except for coordinates of the current solution that are close to zero. Our empirical experiments confirm this. In many problems with irrelevant features, already one iteration is often enough to produce accuracy as good as or better than that of the 1-norm SVM. Hence, it seems that in these problems we do not need to converge to the 1-norm SVM solution near zero values. The benefit of this approach is that we can build something similar to the 1-norm regularized solver based on any 2-norm regularized solver. This is quick to implement and the solution inherits the good qualities of the solver such as scalability and stability.

Knowledge transfer in collaborative teams: experiences from a two-week code camp

Software engineering has both technological and social dimensions. As development teams spanning across the globe are increasingly the norm and while the web enables massive online collaboration, there is a growing need for effective collaboration tools. In this paper, we describe experiences on collaborative programming as a tool for learning software development. To investigate the nature of collaboration in software engineering education, we arranged a two-week-long course experiment where students used a collaborative online integrated development environment to create different kinds of web services. We present lessons learned from the experiment and discuss how collaboration can act as a tool for knowledge transfer among learners.

Temperature coefficients for GaInP/GaAs/GaInNAsSb solar cells

We report the temperature coefficients for MBE-grown GaInP/GaAs/GaInNAsSb multijunction solar cells and the corresponding single junction sub-cells. Temperature-dependent current-voltage measurements were carried out using a solar simulator equipped with a 1000 W Xenon lamp and a three-band AM1.5D simulator. The triple-junction cell exhibited an efficiency of 31% at AM1.5G illumination and an efficiency of 37–39% at 70x real sun concentration. The external quantum efficiency was also measured at different temperatures. The temperature coefficients up to 80°C, for the open circuit voltage, the short circuit current density, and the conversion efficiency were determined to be −7.5 mV/°C, 0.040 mA/cm2/°C, and −0.09%/°C, respectively.

On the role of gamification and localization in an open online learning environment: javala experiences

Massive Open Online Courses (MOOCs) have rapidly become an important tool for educational institutes in teaching programming. Nevertheless, high drop-out rates have always been a problem in online learning. As MOOCs have become an important part of modern education, reducing the drop-out rate has become a more and more relevant research problem. This work studies a nine-year-long period of maintaining an open, online learning environment of programming. The aim is to find out how the implementation of the learning environment could engage the students to learning and this way affect the drop-out rate. We provide an insight to experiences stemming from nine years of data collected with Javala, an online system created to help shifting from C++ to Java programming. The paper also discusses two key properties of Javala, gamification, and localization, together with data to assess their significance.

Unsupervised Classifier Selection Based on Two-Sample Test

We propose a well-founded method of ranking a pool of mtrained classifiers by their suitability for the current input of ninstances. It can be used when dynamically selecting a single classifier as well as in weighting the base classifiers in an ensemble. No classifiers are executed during the process. Thus, the ninstances, based on which we select the classifier, can as well be unlabeled. This is rare in previous work. The method works by comparing the training distributions of classifiers with the input distribution. Hence, the feasibility for unsupervised classification comes with a price of maintaining a small sample of the training data for each classifier in the pool. In the general case our method takes time and space , where tis the size of the stored sample from the training distribution for each classifier. However, for commonly used Gaussian and polynomial kernel functions we can execute the method more efficiently. In our experiments the proposed method was found to be accurate.

Reducing splaying by taking advantage of working sets

Access requests to keys stored into a data structure often exhibit locality of reference in practice. Such a regularity can be modeled, e.g., by working sets. In this paper we study to what extent can the existence of working sets be taken advantage of in splay trees. In order to reduce the number of costly splay operations we monitor for information on the current working set and its change. We introduce a simple algorithm which attempts to splay only when necessary. Under worst-case analysis the algorithm guarantees an amortized logarithmic bound. In empirical experiments it is 5% more efficient than randomized splay trees and at most 10% more efficient than the original splay tree. We also briefly analyze the usefulness of the commonly-used Zipfs distribution as a general model of locality of reference.

GaInNAs solar cell with back surface reflector

We report the effects of back surface reflectors on quantum efficiency of single-junction 1 eV GaInNAs solar cells. For solar cells with Au back surface reflector, an average external quantum efficiency of 72% was achieved between 920 nm and 1250 nm. The average internal quantum efficiency of the cell was over 90% for the same wavelength range. The highest short-circuit current density calculated from the external quantum efficiency of a solar cell with Au back surface reflector was 12.8 mA/cm2, which is 17% higher than what was obtained using conventional Ti/Au metallization. This would enable fabrication of GaInP/GaAs/GaInNAs solar cell with an efficiency of at least 30% at AM1.5G.

Comparison of metal/polymer back reflectors with half-sphere, blazed, and pyramid gratings for light trapping in III-V solar cells

We report on the fabrication of diffraction gratings for application as back contact reflectors. The gratings are designed for thin-film solar cells incorporating absorbers with bandgap slightly lower than GaAs, i.e. InAs quantum dot or GaInNAs solar cells. Light trapping in the solar cells enables the increase of the absorption leading to higher short circuit current densities and higher efficiencies. We study metal/polymer back reflectors with half-sphere, blazed, and pyramid gratings, which were fabricated either by photolithography or by nanoimprint lithography. The gratings are compared in terms of the total and the specular reflectance, which determine their diffraction capabilities, i.e. the feature responsible for increasing the absorption. The pyramid grating showed the highest diffuse reflection of light compared to the half-sphere structure and the blazed grating. The diffraction efficiency measurements were in agreement with the numerical simulations. The validated model enables designing such metal/polymer back reflectors for other type of solar cells by refining the optimal dimensions of the gratings for different wavelength ranges.