Abstract
We propose a coding scheme based on the combination of interleaving with systematic channel codes for secrecy. The basic idea consists of generating a random interleaving key that is used to shuffle/interleave information at the source. The message and the interleaving key are then both encoded with a systematic code and the part related to the interleaving key is removed/punctured before being sent to the channel, hence operating as a hidden key for any receiver (legitimate or not) that needs to deinterleave the message. Successfully obtaining the original message then depends on determining the interleaving key, which can only be done through the parity bits that result from jointly encoding the interleaving key and the message. We provide a method to determine the necessary signal-to-noise ratio difference that enables successful reception at the legitimate receiver without the eavesdropper having access to the message. In addition, we provide evidence that this scheme may also be used to turn a realistic channel into a discrete memoryless channel, thus providing a first practical implementation of an abstract channel that can be employed with a wiretap code to provide information-theoretic security guarantees.

Abstract
The linear inverted pendulum model has been used predominantly to generate balanced humanoid walking. This model assumes that the hip height is fixed during the walk. In this paper, generating a fast walk is studied with the main focus on the effect of hip height movement. Our approach is based on modeling the hip height movement and learning its parameters in order to generate a fast walk. The hip height trajectory is generated using Fourier basis functions. The generated trajectory is the input to programmable Central Pattern Generators (CPGs) in order to modulate generated trajectories smoothly. The inverted pendulum model is utilized to model a balanced walking. A numerical approach is presented to control inverted pendulum dynamics. Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is employed to search for appropriate hip height trajectory and walking parameters that optimize walking speed. This approach has been tested not only to obtain fast forward walk but also a fast side walk. Experiments are conducted on both simulated and real NAO robots. The results show that the change from the learned forward walk to learned side walk is performed stably, which confirm the important role of using CPGs. The comparison of the results of the proposed gait model (and development approach) with those obtained using fixed hip height also shows that fixed height walking is slower than variable height walking.

Abstract
We show various hardness results for knapsack and related problems; in particular we will show that unless the Exponential-Time Hypothesis is false, subset-sum cannot be approximated any better than with an FPTAS. We also provide new unconditional lower bounds for approximating knapsack in Ketan Mulmuley’s parallel PRAM model. Furthermore, we give a simple new algorithm for approximating knapsack and subset-sum, that can be adapted to work for small space, or in small parallel time. © 2014, Springer Science+Business Media New York.

Abstract
This study proposes a model for the prediction of smart household load demand influenced by a dynamic pricing demand response (DR) program. Price-based DR programs have a considerable impact on household demand pattern due to the expected choice of customers or their home energy management systems (HEMSs) to use more energy in low price periods in order to reduce their electricity procurement cost. Many studies in the literature have dealt with power prediction, but the authors are prior in the field attempting to include the impact of different DR strategies on load demand prediction of smart households. The proposed methodology is expected to be valuable for utilities, retailers, aggregators, etc., in order to evaluate the success of their price-based DR strategies and predict adverse effects such as power peaks in normally off-peak periods and stress of infrastructure.

Abstract
The Fluvial Ecology Laboratory at the University of Tras-os-Montes and Alto Douro (LEF-CITAB) uses the River Habitat Survey (RHS) methodology a Water Framework Directive accepted method for assessing the character and habitat quality of rivers, which involves the use of a paper questionnaire, GPS and photographic camera for the collection of data in the field, which can be very cumbersome. In order to make this a more efficient and rapid process LEF-CITAB suggested the creation of a mobile application to record field data. This paper outlines the development of the proposed mobile application - Mobile RHS. (C) 2015 The Authors. Published by Elsevier B.V.

Abstract
This paper presents a Quantum Particle Swarm Optimization (QPSO) applied to hour-ahead scheduling in Smart Grid (SG). The unforeseen events not considered in day-ahead scheduling, must be overcome when approaching intraday operation. This implies new constraints in hour-ahead formulation. The developed methodology aims to complement the day-ahead scheduling tools already available on the literature. The unforeseen events in the hour-ahead can include change of forecasted load demand, market prices and availability of renewable generation. The QPSO solves the problem in adequate execution time under the hour-ahead time scale. This is demonstrated using a scenario with high penetration of distributed generation and gridable vehicles. Furthermore, a comparison with GAMS is presented.