Accurate short-term load forecasting is essential for the efficient operation of the power sector. Forecasting load at a fine granularity such as hourly loads of individual households is challenging due to higher volatility and inherent stochasticity. At the aggregate levels, such as monthly load at a grid, the uncertainties and fluctuations are averaged out; hence predicting load is more straightforward. This paper proposes a method called Forecasting using Matrix Factorization (fmf) for short-term load forecasting (stlf). fmf only utilizes historical data from consumers’ smart meters to forecast future loads (does not use any non-calendar attributes, consumers’ demographics or activity patterns information, etc.) and can be applied to any locality. A prominent feature of fmf is that it works at any level of user-specified granularity, both in the temporal (from a single hour to days) and spatial dimensions (a single household to groups of consumers). We empirically evaluate fmf on three benchmark datasets and demonstrate that it significantly outperforms the state-of-the-art methods in terms of load forecasting. The computational complexity of fmf is also substantially less than known methods for stlf such as long short-term memory neural networks, random forest, support vector machines, and regression trees.

Teaching refactoring effectively while making students realize the importance and benefits of refactoring is a challenge. In this direction, an experiment was carried out while conducting the course project for the Refactoring and Design Patterns course. This paper discusses the results of the experiment that involved two different project schemes to carry out refactoring activities on the same code base. One scheme was post-enhancement refactoring and the other was pre-enhancement refactoring. The aim of the experiment was to decide which scheme was beneficial in terms of better understanding, appreciation, and implementation of refactoring.

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Dynamically adaptive systems (DAS), such as smart grids, cloud computing applications, sensor networks and P2P networks tend to change their structure at runtime. Therefore, design-time modeling for such systems are sometimes not enough for self-management. To this end, we have developed a dynamic mathematical modeling framework for runtime modeling for DAS. In this paper, we describe how our system engineers a linear programming model for self-optimization by using a smart-grid application for power distribution as a case-study. At runtime whenever, an optimization is desired this modeling framework captures the state of the system, converts it into an appropriate linear programming model, plan the changes using mathematical manipulations and apply the changes to the actual system. Our initial simulation results show that this framework is able to capture accurate runtime models of large power systems and is able to adapt itself with the change in the size or structure of the system by constructing a succinct model which is faster and more efficient than a design time model.

In this paper, we propose and evaluate a self-optimization strategy for a clustering-based tag recommendation system. For tag recommendation, we use an efficient discriminative clustering approach. To develop our self-optimization strategy for this tag recommendation approach, we empirically investigate when and how to update the tag recommender with minimum human intervention. We present a nonlinear optimization model whose solution yields the clustering parameters that maximize the recommendation accuracy within an administrator specified time window. Evaluation on "BibSonomy'' data produces promising results. For example, by using our self-optimization strategy a 6% increase in average F1 score is achieved when the administrator allows up to 2% drop in average F1 score in the last one thousand recommendations.

Dynamically adaptive systems (DAS) such as smart grids, cloud computing applications, sensor networks and P2P networks tend to change their structure at runtime. Therefore, design-time modeling for such systems are sometimes not enough to incorporate self-* properties. To this end, we have developed a dynamic mathematical modeling framework for runtime optimizations for DAS. In this paper, we describe how our system engineers a linear programming model by using a smart-grid application for power distribution as a case-study. At runtime whenever an optimization is desired this modeling framework captures the state of the system, converts it into an appropriate linear programming model, plan the changes using mathematical manipulations and apply the changes to the actual system. Our results show that this framework is able to capture accurate runtime models of large power systems and is able to adapt itself with the change in the size or structure of the system.

Many studies have shown that students often face difficulty in applying programming concepts to design a program that solves a given task. To impart better problem solving skills a number of pedagogical approaches have been presented in the literature. However, most of these approaches provide a general strategy of problem solving. But in reality problem solving is a skill that is developed with experience over a period of time. In this paper, we present a pedagogical approach to teach problem solving using think-alouds. In a think-aloud problem solving approach students learn the skill of problem solving by closely observing an 'experienced programmer. We used this approach in a CS2 class and our evaluation results show that think-aloud problem solving is an extremely effective pedagogical technique, particularly for female students.

Initial deployment and subsequent dynamic reconfiguration of a software system is difficult because of the interplay of many interdependent factors, including cost, time, application state, and system resources. As the size and complexity of software systems increases, procedures (manual or automated) that assume a static software architecture and environment are becoming untenable. We have developed a novel technique for carrying out the deployment and reconfiguration planning processes that leverages recent advances in the field of temporal planning. We describe a tool called Planit, which manages the deployment and reconfiguration of a software system utilizing a temporal planner. Given a model of the structure of a software system, the network upon which the system should be hosted, and a goal configuration, Planit will use the temporal planner to devise possible deployments of the system. Given information about changes in the state of the system, network and a revised goal, Planit will use the temporal planner to devise possible reconfigurations of the system. We present the results of a case study in which Planit is applied to a system consisting of various components that communicate across an application-level overlay network.