Catálogo de publicaciones - revistas

<jats:p>Online judges are systems designed for the reliable evaluation of algorithm source code submitted by users, which is next compiled and tested in a homogeneous environment. Online judges are becoming popular in various applications. Thus, we would like to review the state of the art for these systems. We classify them according to their principal objectives into systems supporting organization of competitive programming contests, enhancing education and recruitment processes, facilitating the solving of data mining challenges, online compilers and development platforms integrated as components of other custom systems. Moreover, we introduce a formal definition of an online judge system and summarize the common evaluation methodology supported by such systems. Finally, we briefly discuss an Optil.io platform as an example of an online judge system, which has been proposed for the solving of complex optimization problems. We also analyze the competition results conducted using this platform. The competition proved that online judge systems, strengthened by crowdsourcing concepts, can be successfully applied to accurately and efficiently solve complex industrial- and science-driven challenges.</jats:p>

<jats:p>The need to process and store massive amounts of data—Big Data—is a reality. In areas such as scientific experiments, social networks management, credit card fraud detection, targeted advertisement, and financial analysis, massive amounts of information are generated and processed daily to extract valuable, summarized information. Due to its fast development cycle (i.e., less expensive to develop), mainly because of automatic memory management, and rich community resources, managed object-oriented programming languages (e.g., Java) are the first choice to develop Big Data platforms (e.g., Cassandra, Spark) on which such Big Data applications are executed.</jats:p> <jats:p>However, automatic memory management comes at a cost. This cost is introduced by the garbage collector, which is responsible for collecting objects that are no longer being used. Although current (classic) garbage collection algorithms may be applicable to small-scale applications, these algorithms are not appropriate for large-scale Big Data environments, as they do not scale in terms of throughput and pause times.</jats:p> <jats:p>In this work, current Big Data platforms and their memory profiles are studied to understand why classic algorithms (which are still the most commonly used) are not appropriate, and also to analyze recently proposed and relevant memory management algorithms, targeted to Big Data environments. The scalability of recent memory management algorithms is characterized in terms of throughput (improves the throughput of the application) and pause time (reduces the latency of the application) when compared to classic algorithms. The study is concluded by presenting a taxonomy of the described works and some open problems, with regard to Big Data memory management, that could be addressed in future works.</jats:p>

<jats:p>Online social networks (OSNs) are structures that help users to interact, exchange, and propagate new ideas. The identification of the influential users in OSNs is a significant process for accelerating the propagation of information that includes marketing applications or hindering the dissemination of unwanted contents, such as viruses, negative online behaviors, and rumors. This article presents a detailed survey of influential users’ identification algorithms and their performance evaluation approaches in OSNs. The survey covers recent techniques, applications, and open research issues on analysis of OSN connections for identification of influential users.</jats:p>

<jats:p>The last decades have seen a growing interest and demand for community-centered collaborative systems and platforms. These systems and platforms aim to provide an environment in which users can collaboratively create, share, and manage resources. While offering attractive opportunities for online collaboration and information sharing, they also open several security and privacy issues. This has attracted several research efforts toward the design and implementation of novel access control solutions that can handle the complexity introduced by collaboration. Despite these efforts, transition to practice has been hindered by the lack of maturity of the proposed solutions. The access control mechanisms typically adopted by commercial collaborative systems like online social network websites and collaborative editing platforms, are still rather rudimentary and do not provide users with a sufficient control over their resources. This survey examines the growing literature on access control for collaborative systems centered on communities, and identifies the main challenges to be addressed in order to facilitate the adoption of collaborative access control solutions in real-life settings. Based on the literature study, we delineate a roadmap for future research in the area of access control for community-centered collaborative systems.</jats:p>

<jats:p>Modern cloud computing environments support a relatively high degree of automation in service provisioning, which allows cloud service customers (CSCs) to dynamically acquire services required for deploying cloud applications. Cloud modeling languages (CMLs) have been proposed to address the diversity of features provided by cloud computing environments and support different application scenarios, such as migrating existing applications to the cloud, developing new cloud applications, or optimizing them. There is, however, still much debate in the research community on what a CML is, and what aspects of a cloud application and its target cloud computing environment should be modeled by a CML. Furthermore, the distinction between CMLs on a fine-grain level exposing their modeling concepts is rarely made. In this article, we investigate the diverse features currently provided by existing CMLs. We classify and compare them according to a common framework with the goal to support CSCs in selecting the CML that fits the needs of their application scenario and setting. As a result, not only features of existing CMLs are pointed out for which extensive support is already provided but also in which existing CMLs are deficient, thereby suggesting a research agenda.</jats:p>

<jats:p>Crowd-centric research is receiving increasingly more attention as datasets on crowd behavior are becoming readily available. We have come to a point where many of the models on pedestrian analytics introduced in the last decade, which have mostly not been validated, can now be tested using real-world datasets. In this survey, we concentrate exclusively on automatically gathering such datasets, which we refer to as sensing the behavior of pedestrians. We roughly distinguish two approaches: one that requires users to explicitly use local applications and wearables, and one that scans the presence of handheld devices such as smartphones. We come to the conclusion that despite the numerous reports in popular media, relatively few groups have been looking into practical solutions for sensing pedestrian behavior. Moreover, we find that much work is still needed, in particular when it comes to combining privacy, transparency, scalability, and ease of deployment. We report on over 90 relevant articles and discuss and compare in detail 30 reports on sensing pedestrian behavior.</jats:p>

<jats:p>Traditional quantitative research methods of data collection in programming, such as questionnaires and interviews, are the most common approaches for researchers in this field. However, in recent years, eye-tracking has been on the rise as a new method of collecting evidence of visual attention and the cognitive process of programmers. Eye-tracking has been used by researchers in the field of programming to analyze and understand a variety of tasks such as comprehension and debugging. In this article, we will focus on reporting how experiments that used eye-trackers in programming research are conducted, and the information that can be collected from these experiments. In this mapping study, we identify and report on 63 studies, published between 1990 and June 2017, collected and gathered via manual search on digital libraries and databases related to computer science and computer engineering. Among the five main areas of research interest are program comprehension and debugging, which received an increased interest in recent years, non-code comprehension, collaborative programming, and requirements traceability research, which had the fewest number of publications due to possible limitations of the eye-tracking technology in this type of experiments. We find that most of the participants in these studies were students and faculty members from institutions of higher learning, and while they performed programming tasks on a range of programming languages and programming representations, we find Java language and Unified Modeling Language (UML) representation to be the most used materials. We also report on a range of eye-trackers and attention tracking tools that have been utilized, and find Tobii eye-trackers to be the most used devices by researchers.</jats:p>

<jats:p> <jats:italic>Background</jats:italic> —The proliferation of cloud services has opened a space for cloud brokerage services. Brokers intermediate between cloud customers and providers to assist the customer in selecting the most suitable service, helping to manage the dimensionality, heterogeneity, and uncertainty associated with cloud services. <jats:italic>Objective</jats:italic> —Unlike other surveys, this survey focuses on the customer perspective. The survey systematically analyses the literature to identify and classify approaches to realise cloud brokerage, presenting an understanding of the state-of-the-art and a novel taxonomy to characterise cloud brokers. <jats:italic>Method</jats:italic> —A systematic literature survey was conducted to compile studies related to cloud brokerage and explore how cloud brokers are engineered. These studies are then analysed from multiple perspectives, such as motivation, functionality, engineering approach, and evaluation methodology. <jats:italic>Results</jats:italic> —The survey resulted in a knowledge base of current proposals for realising cloud brokers. The survey identified differences between the studies’ implementations, with engineering efforts directed at combinations of market-based solutions, middlewares, toolkits, algorithms, semantic frameworks, and conceptual frameworks. <jats:italic>Conclusion</jats:italic> —Our comprehensive meta-analysis shows that cloud brokerage is still a formative field. Although significant progress has been achieved in this field, considerable challenges remain to be addressed, which are also identified in this survey. </jats:p>

<jats:p>Cartesian Genetic Programming (CGP) is a variant of Genetic Programming with several advantages. During the last one and a half decades, CGP has been further extended to several other forms with lots of promising advantages and applications. This article formally discusses the classical form of CGP and its six different variants proposed so far, which include Embedded CGP, Self-Modifying CGP, Recurrent CGP, Mixed-Type CGP, Balanced CGP, and Differential CGP. Also, this article makes a comparison among these variants in terms of population representations, various constraints in representation, operators and functions applied, and algorithms used. Further, future work directions and open problems in the area have been discussed.</jats:p>

<jats:p>The fast increment in the number of IoT (Internet of Things) devices is accelerating the research on new solutions to make cloud services scalable. In this context, the novel concept of fog computing as well as the combined fog-to-cloud computing paradigm is becoming essential to decentralize the cloud, while bringing the services closer to the end-system. This article surveys e application layer communication protocols to fulfill the IoT communication requirements, and their potential for implementation in fog- and cloud-based IoT systems. To this end, the article first briefly presents potential protocol candidates, including request-reply and publish-subscribe protocols. After that, the article surveys these protocols based on their main characteristics, as well as the main performance issues, including latency, energy consumption, and network throughput. These findings are thereafter used to place the protocols in each segment of the system (IoT, fog, cloud), and thus opens up the discussion on their choice, interoperability, and wider system integration. The survey is expected to be useful to system architects and protocol designers when choosing the communication protocols in an integrated IoT-to-fog-to-cloud system architecture.</jats:p>