Abstract
Programing paradigms define how to think and design while creating software. Object-Oriented and Functional paradigms are two of the most adopted for synthesizing it. Modern languages, attempting to provide higher abstractions, are increasingly supporting native multi-paradigm programming styles. The Object-functional approach still uses classes for information and high-level structure, but allows algorithms to be implemented functionally. New challenges now exist and there is a general lack of knowledge on best practices for adopting this paradigm. This research proposes the systematic usage of software patterns to capture these new recurring problems and their solutions, though not discarding the identification of new algorithms and designs. We will use Scala as a base language, and will attempt to validate our hypothesis through multiple methodologies, including quasi-experiments and case studies. We expect to provide a basis for improvement for programming languages (through pattern absorption) and for software engineering professionals.

Abstract

Abstract
This paper describes the development, deployment and trial results from 9 volunteers using the eCAALYX system. The eCAALYX system is an ambient assisted living telemonitoring system aimed at older adults suffering with co-morbidity. Described is a raw account of the challenges that exist and results in bringing a Telemedicine system from laboratory to real-world implementation and results for usability, functionality and reliability.

Abstract
With an aging global population, Ambient Assisted Living (AAL) attempts to improve life expectancy and quality of life through the remote monitoring of various health signals using personal and home-based sensors. Possible medical conditions can be early ascertained by observable patterns over the patients' health data. However, aggregating multiple raw signals and matching against medical protocols can be computational and bandwidth intensive. Moreover, adding new protocols requires non-trivial expertise to define necessary rules. This paper describes a lightweight, scalable, and composable mechanism that captures, processes and infers possible health problems from raw data obtained from multiple sensors.

Abstract
The large amount of pages in Websites is a problem for users who waste time looking for the information they really want. Knowledge about users' previous visits may provide patterns that allow the customization of the Website. This concept is known as Adaptive Website: a Website that adapts itself for the purpose of improving the user's experience. Some Web Mining algorithms have been proposed for adapting a Website. In this paper, a recommender system using agents with two different algorithms (associative rules and collaborative filtering) is described. Both algorithms are incremental and work with binary data. Results show that this multi-agent approach combining different algorithms is capable of improving user's satisfaction.

Abstract
Dynamic partitioning is a promising technique where computations are transparently moved from a General Purpose Processor (GPP) to a coprocessor during application execution. To be effective, the mapping of computations to the coprocessor needs to consider aggressive optimizations. One of the mapping optimizations is loop pipelining, a technique extensively studied and known to allow substantial performance improvements. This paper describes a technique for pipelining Megablocks, a type of runtime loop developed for dynamic partitioning. The technique transforms the body of Megablocks into an acyclic dataflow graph which can be fully pipelined and is based on the atomic execution of loop iterations. For a set of 9 benchmarks without memory operations, we generated pipelined hardware versions of the loops and estimate that the presented loop pipelining technique increases the average speedup of non-pipelined coprocessor accelerated designs from 1.6x to 2.2x. For a larger set of 61 benchmarks which include memory operations, the technique achieves a speedup increase from 2.5x to 5.6x. ©2012 IEEE.