Abstract
Extracting the semantic relatedness of terms is an important topic in several areas, including data mining, information retrieval and web recommendation. This paper presents an approach for computing the semantic relatedness of terns in RDF graphs based on the notion of proximity. It proposes a formal definition of proximity in terms of the set paths connecting two concept nodes, and an algorithm for finding this set and computing proximity with a given error margin. This algorithm was implemented on a tool called Shakti that extracts relevant ontological data for a given domain from DBpedia - a community effort to extract structured data from the Wikipedia. To validate the proposed approach Shakti was used to recommend web pages on a Portuguese social site related to alternative music and the results of that experiment are also reported.

Abstract
A single decomposition strategy cannot capture all aspects of a concept, so we need to extend Object Oriented Decomposition (today most used strategy). We propose roles as a way to compose classes that provides a modular way of capturing and reusing those concerns that fall outside a concept's main purpose, while being a natural extension of the OO paradigm. Roles have been used successfully to model the different views a concept provides and we want to bring that experience to the programming level. We discuss how to make roles modular and reusable. We also show how to compose classes with roles using JavaStage, our role supporting language. To validate our approach we developed generic and reusable roles for the Gang of Four patterns. We developed reusable roles for 10 out of 23 patterns. We also were able to use some of these roles in JHotDraw framework.

Abstract
NoSQL databases manage the bulk of data produced by modern Web applications such as social networks. This stems from their ability to partition and spread data to all available nodes, allowing NoSQL systems to scale. Unfortunately, current solutions' scale out is oblivious to the underlying data access patterns, resulting in both highly skewed load across nodes and suboptimal node configurations. In this paper, we first show that judicious placement of HBase partitions taking into account data access patterns can improve overall throughput by 35%. Next, we go beyond current state of the art elastic systems limited to uninformed replica addition and removal by: i) reconfiguring existing replicas according to access patterns and ii) adding replicas specifically configured to the expected access pattern. MeT is a prototype for a Cloud-enabled framework that can be used alone or in conjunction with OpenStack for the automatic and heterogeneous reconfiguration of a HBase deployment. Our evaluation, conducted using the YCSB workload generator and a TPC-C workload, shows that MeT is able to i) autonomously achieve the performance of a manual configured cluster and ii) quickly reconfigure the cluster according to unpredicted workload changes. © 2013 ACM.

Abstract
Experiments on cognitive processes require a detailed analysis of the contribution of many participants. In the case of cognitive processes in writing, these experiments require special software tools to collect gestures performed with a pen or a stylus, and recorded with special hardware. These tools produce different kinds of data files in binary and proprietary formats that need to be managed on a workstation file system for further processing with generic tools, such as spreadsheets and statistical analysis software. The lack of common formats and open repositories hinders the possibility of distributing the workload among researchers within the research group, of re-processing the collected data with software developed by other research groups, and of sharing results with the rest of the cognitive processes research community. This paper describes the development of Hand Spy, a collaborative environment for managing experiments in the cognitive processes in writing. This environment was designed to cover all the stages of the experiment, from the definition of tasks to be performed by participants, to the synthesis of results. Collaboration in Hand Spy is enabled by a rich web interface. To decouple the environment from existing hardware devices for collecting written production, namely digitizing tablets and smart pens, Hand Spy is based on the InkML standard, an XML data format for representing digital ink. This design choice shaped many of the features in Hand Spy, such as the use of an XML database for managing application data and the use of XML transformations. XML transformations convert between persistent data representations used for storage and transient data representations required by the widgets on the user interface. Despite being a system independent from a specific collecting device, for the system validation, a framework for data collection was created. This framework has also been highlighted in the paper due to the important role it took in a data collection process, of a scientific project to study the cognitive processes involved in writing.

Abstract

Abstract
This paper presents an extension to a hardware/software system architecture in which repetitive instruction traces, called Megablocks, are accelerated by a Reconfigurable Processing Unit (RPU). This scheme is supported by a custom toolchain able to automatically generate a RPU tailored for the execution of one or more Megablocks detected offline. Switching between hardware and software execution is done transparently, without modifications to source code or executable binaries. Our approach has been evaluated using an architecture with a MicroBlaze General Purpose Processor (GPP) softcore. By using a memory sharing mechanism, the RPU can access the GPP's data memory, allowing the acceleration of Megablocks with load/store operations. For a set of 21 embedded benchmarks, an average speedup of 1.43x is achieved, and a potential speedup of 2.09x is predicted for an implementation using a low overhead interface for communication between GPP and RPU.