Abstract
The original version of this article unfortunately contained amistake. The presentation of Table 3 was incorrect. The corrected (Table 3) is given below. © 2017, Springer Science+Business Media New York.

Abstract
With an advancement towards the paradigm of Internet of Things (IoT), in which every device will be interconnected and communicating with each other, the field of wireless sensor networks has helped to resolve an ever-growing demand in meeting deadlines and reducing power consumption. Among several standards that provide support for IoT, the recently published IEEE 802.15.4e protocol is specifically designed to meet the QoS requirements of industrial applications. IEEE 802.15.4e provides five Medium-Access Control (MAC) behaviors, including three that target time-critical applications: Deterministic and Synchronous Multichannel Extension (DSME); Time Slotted Channel Hopping (TSCH) and Low Latency Deterministic Network (LLDN). However, the standard and the literature do not provide any worst-case bound analysis of these behaviors, thus it is not possible to effectively predict their timing performance in an application and accurately devise a network in accordance to such constraints. This paper fills this gap by contributing network models for the three time-critical MAC behaviors using Network Calculus. These models allow deriving the worst-case performance of the MAC behaviors in terms of delay and buffering requirements. We then complement these results by carrying out a thorough performance analysis of these MAC behaviors by observing the impact of different parameters. © 2017 IEEE.

Abstract
Many user information needs are strongly influenced by time. Some of these intents are expressed by users in queries issued indistinctively over time. Others follow a seasonal pattern. Examples of the latter are the queries "Golden Globe Award", "September 11th" or "Halloween", which refer to seasonal events that occur or have occurred at a specific occasion and for which, people often search in a planned and cyclic manner. Understanding this seasonal behavior, may help search engines to provide better ranking approaches and to respond with temporally relevant results leading into user's satisfaction. Detecting the diverse types of seasonal queries is therefore a key step for any search engine looking to present accurate results. In this paper, we categorize web search queries by their seasonality into 4 different categories: Non-Seasonal (NS, e.g., "Secure passwords"), Seasonal-related to ongoing events (SOE, "Golden Globe Award"), Seasonal-related to historical events (SHE, e.g., "September 11th") and Seasonal-related to special days and traditions (SSD, e.g., "Halloween"). To classify a given query we extract both time series (using the document publish date) and content features from its relevant documents. A Random Forest classifier is then used to classify web queries by their seasonality. Our experimental results show that they can be categorized with high accuracy. © 2017 Copyright held by the owner/author(s).

Abstract
Neste artigo, é apresentada uma pesquisa-ação com o objetivo de motivar os alunos a desenvolverem suas aprendizagens de programação de computadores no ensino superior, particularmente na transição da programação de nível iniciante para a programação avançada. Para alcançar este objetivo, foi desenvolvida uma abordagem motivacional denominada SimProgramming. A partir das reflexões sobre o processo desta pesquisa, conclui-se que SimProgramming em sua aplicação ao ensino de programação de computadores em turmas intermediárias é promissor e ainda apresenta potencial para ser usado em outros contextos educacionais.;In this paper, an action research is presented to motivate students to develop their learning of computer programming in higher education, particularly in the transition from beginner to advanced programming. To achieve this goal, a motivational approach was developed called SimProgramming. From the reflections on the process of this research, it is concluded that SimProgramming in its application to the teaching of computer programming in intermediate classes is promising and still presents potential to be used in other educational contexts.

Abstract

Abstract
Hash tries are a trie-based data structure with nearly ideal characteristics for the implementation of hash maps. In this paper, we present a novel, simple and scalable hash trie map design that fully supports the concurrent search, insert and remove operations on hash maps. To the best of our knowledge, our proposal is the first concurrent hash map design that puts together the following characteristics: (i) be lock-free; (ii) use fixed size data structures; and (iii) maintain the access to all internal data structures as persistent memory references. Experimental results show that our proposal is quite competitive when compared against other state-of-the-art proposals implemented in Java. Its design is modular enough to allow different types of configurations aimed for different performances in memory usage and execution time. © 2017 IEEE.