Abstract
Demand Response (DR) is known as an effective solution for many power grid problems such as high operating cost as well as high peak demand. In order to achieve full potential of DR programs, DR must be implemented optimally. On this basis, determining optimal DR location, appropriate DR program and efficient penetration rate for each DR program is of great practical interest due to the fact that it guides the system operators to choose proper DR strategies. This paper presents a novel linear framework for DR optimization problem incorporated into the network-constrained unit commitment with the aim of determining optimal location, type and penetration rate of DR programs considering several practical limitations. To this end, a number of tariff-based and incentive-based DR programs have been taken into account according to the customer's benefit function based on the price elasticity concept. The IEEE 24 bus Reliability Test System (RTS 24-bus) is used to demonstrate the applicability of the proposed model. Finally, DR optimization is analyzed with regards to different customer's elasticity values and also different number of candidate load buses which reveal the applicability and effectiveness of the proposed methodology.

Abstract
In today’s software industry, systems are constantly changing. To maintain their quality and to prevent failures at controlled costs is a challenge. One way to foster quality is through thorough and systematic testing. Therefore, the definition of adequate tests is crucial for saving time, cost and effort. This paper presents a framework that generates software test cases automatically based on user interaction data. We propose a data-driven software test generation solution that combines the use of frequent sequence mining and Markov chain modeling. We assess the quality of the generated test cases by empirically evaluating their coverage with respect to observed user interactions and code. We also measure the plausibility of the distribution of the events in the generated test sets using the Kullback-Leibler divergence.

Abstract
Literature is the noblest of all the arts. Music dies on the air, or at best exists only in memory; oratory ceases with the effort; the painter's colors fade and the canvas rots; the marble is dragged from its pedestal and is broken into fragments.

Abstract
There are countless reasons cited in scientific studies to explain the difficulties in programming learning. The reasons range from the subject's complexity, the ineffective teaching and study methods, to psychological aspects such as demotivation. Still, learning programming often boils down to practice on exercise solving. Hence, it is essential to understand that the nature of a programming exercise is an important factor for the success and consistent learning. This paper explores different approaches on the creation of a programming exercise, starting with realizing how it is currently formalized, presented and evaluated. From there, authors suggest variations that seek to broaden the way an exercise is solved and, with this diversity, increase student engagement and learning outcome. The several types of exercises presented can use gamification techniques fostering student motivation. To contextualize the student with his peers, we finish presenting metrics that can be obtained by existing automatic assessment tools. 2012 ACM Subject Classification Applied computing ! Education.

Abstract
In recent years, virtual power plants (VPPs) rose as an effective framework to aggregate the collective potential of distributed energy resources (DERs), including distributed generation (DG) and energy storage systems (ESS), through demand response (DR) program implementation. In this work, the operation of two indispensable DER assets, electric vehicles (EVs) and photovoltaic-equipped parking lots (PVPLs), is coordinated in an optimal energy management framework, in order to study their possible aggregation as a VPP. The proposed energy management system (EMS) was developed using the optimization and simulation tools, namely GAMS and MATLAB, and is intended for use by grid operators to coordinate the operation of PVPLs and home energy management systems (HEMSs) in the context of smart cities. The developed model was validated and tested by considering real-life case studies in the city of Porto, Portugal.

Abstract
Node mobility in multi-hop communication environments is an important feature of Wireless Sensor Network (WSN)-based monitoring systems. It allows nodes to have freedom of movement, without being restricted to a single-hop communication range. In IEEE 802.15.4 WSNs, nodes are only able to transfer data messages after completing a connection with a coordinator through an association mechanism. Within this context, a handover procedure needs to be executed by a mobile node whenever there is a disconnection from a coordinator and the establishment of a connection to another one. Many applications, such as those found in health monitoring systems, strongly need support for node mobility without loss of data during the handover. However, it has been observed that the time required to execute the handover procedure is one of the main reasons why IEEE 802.15.4 cannot fully support mobility. This paper proposes an improvement to this procedure using a set of combined strategies, such as anticipation of both the handover mechanism and the scan phase enhancement. Simulations show that it is possible to reduce latency during the association and re-association processes, making it feasible to develop WSN-based distributed monitoring systems with mobile nodes and stringent time constraints.