Abstract
The incorrect requirements elicitation, requirements changes and evolution during the project lifetime are the main causes pointed out for the failure of software projects. The requirements in the context of Software as a Service are in constant change and evolution which makes even more critical the attention given to Requirements Engineering (RE). The dynamic context evolution due to new stakeholders needs brings additional challenges to the RE such as the need to review the prioritization of requirements and manage their changes related to their baseline. It is important to apply methodologies and techniques for requirements change management to allow a flexible development of SaaS and to ensure their timely adaptation to change. However, the existing techniques and solutions can take a long time to be implemented so that they become ineffective. In this work, a new methodology to manage functional requirements is proposed. This new methodology is based on collecting and analysis of information about the usage of the service to extract pages visited, execution traces and functionalities more used. The analysis performed will allow review the existing requirements, propose recommendations based on quality concerns and improve service usability with the ultimate goal of increasing the software lifetime. Copyright © 2014 SCITEPRESS - Science and Technology Publications.

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Abstract
The present study surveys and maps the existing methodological approaches for measuring creative employment. Based on a unique matched employer-employee data-set which encompasses over three million Portuguese workers, we found that the magnitude of the creative class varies considerably between approaches, ranging from 2.5%, using the conventional industry-based taxonomy and 30.8%, using Florida's occupational proposal. The disparities are justified on the basis of the departure definition of what creative employment is and operationalization issues regarding which industries and occupations should be included. Interestingly, when we focus on core creative employment, the figures conveyed by the distinct approaches are strikingly similar (around 6%), suggesting that, at least where core creative employment is concerned, the distinct approaches converge. The diversity of approaches and measurements are not necessarily a bad thing in itself, but has to be adequately acknowledged in order to accomplish adequate public-policy guidance.

Abstract
The conventional assignment-based first/best fit decreasing algorithms (FFD/BFD) are not polynomial in the one-dimensional cutting stock input size in its most common format. Therefore, even for small instances with large demands, it is difficult to compute FFD/BFD solutions. We present pattern-based methods that overcome the main problems of conventional heuristics in cutting stock problems by representing the solution in a much more compact format Using our pattern-based heuristics, FFD/BFD solutions for extremely large cutting stock instances, with billions of items, can be found in a very short amount of time.

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This paper describes the TURTLE project that aim to develop sub-systems with the capability of deep-sea long-term presence. Our motivation is to produce new robotic ascend and descend energy efficient technologies to be incorporated in robotic vehicles used by civil and military stakeholders for underwater operations. TURTLE contribute to the sustainable presence and operations in the sea bottom. Long term presence on sea bottom, increased awareness and operation capabilities in underwater sea and in particular on benthic deeps can only be achieved through the use of advanced technologies, leading to automation of operation, reducing operational costs and increasing efficiency of human activity.

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The growing penetration of solar power technology at low voltage (LV) level introduces new challenges in the distribution grid operation. Across the world, Distribution System Operators (DSO) are implementing the Smart Grid concept and one key function, in this new paradigm, is solar power forecasting. This paper presents a new forecasting framework, based on vector autoregression theory, that combines spatial-temporal data collected by smart meters and distribution transformer controllers to produce six-hour-ahead forecasts at the residential solar photovoltaic (PV) and secondary substation (i.e., MV/LV substation) levels. This framework has been tested for 44 micro-generation units and 10 secondary substations from the Smart Grid pilot in Evora, Portugal (one demonstration site of the EU Project SuSTAINABLE). A comparison was made with the well-known Autoregressive forecasting Model (AR - univariate model) leading to an improvement between 8% and 12% for the first 3 lead-times.