Abstract
This paper presents an overview and main features of Enki, a web-based learning environment for computer science languages. Enki was designed to be a sort of entry level IDE, aggregating tools for navigating and viewing course materials, for solving exercises and receiving automated feedback, as well as promoting the learning process. Enki uses services from several other systems, namely for content sequencing and recommendation, exercise assessment, and gamification.

Abstract
There are two main types of semantic measures (SM): similarity and relatedness. There are also two main types of datasets, those intended for similarity evaluations and those intended for relatedness. Although they are clearly distinct, they are similar enough to generate some misconceptions. Is there a confusion between similarity and relatedness among the semantic measure community, both the designers of SMs and the creators of benchmarks? This is the question that the research presented in this paper tries to answer. Authors performed a survey of both the SMs and datasets and executed a cross evaluation of those measures and datasets. The results show different consistency of measures with datasets of the same type. This research enabled us to conclude not only that there is indeed some confusion but also to pinpoint the SMs and benchmarks less consistent with their intended type.

Abstract
The goal of the semantic measures is to compare pairs of concepts, words, sentences or named entities. Their categorization depends on what they measure. If a measure only considers taxonomy relationships is a similarity measure; if it considers all type of relationships it is a relatedness measure. The evaluation process of these measures usually relies on semantic gold standards. These datasets, with several pairs of words with a rating assigned by persons, are used to assess how well a semantic measure performs. There are a few frameworks that provide tools to compute and analyze several well-known measures. This paper presents a novel tool - SMComp - a testbed designed for path-based semantic measures. At its current state, it is a domain-specific tool using three different versions of WordNet. SMComp has two views: one to compute semantic measures of a pair of words and another to assess a semantic measure using a dataset. On the first view, it offers several measures described in the literature as well as the possibility of creating a new measure, by introducing Java code snippets on the GUI. The other view offers a large set of semantic benchmarks to use in the assessment process. It also offers the possibility of uploading a custom dataset to be used in the assessment. © Teresa Costa and José Paulo Leal;licensed under Creative Commons License CC-BY.

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Abstract
Wireless sensor networks are notoriously difficult to program and debug. This fact not only stems from the nature of the hardware, but also from the current approaches for developing programming languages and runtime systems for these platforms. In particular, current systems do not place enough stress on providing formal descriptions of the language and its runtime system, and on proving static properties, like type-safety and soundness. In this paper, we present the design, specification, and implementation of a programming language and a runtime system for wireless sensor networks that are safe by design. We say this in the sense that we can statically detect a large set of would-be runtime errors, and that the runtime system will not incorrectly execute an application, once the latter is deployed. We have a full prototype implementation of the system that supports SunSPOT devices, the simulation tool VisualSense, and local computer networks for fast deployment and testing of applications. Development is supported by an IDE implemented on top of the Eclipse tool that embeds both the compiler and the virtual machine seamlessly, and is used to produce software releases.