Abstract
Internet-of-Things systems are assemblies of highly-distributed and heterogeneous parts that, in orchestration, work to provide valuable services to end-users in many scenarios. These systems depend on the correct operation of sensors, actuators, and third-party services, and the failure of a single one can hinder the proper functioning of the whole system, making error detection and recovery of paramount importance, but often overlooked. By drawing inspiration from other research areas, such as cloud, embedded, and mission-critical systems, we present a set of patterns for self-healing IoT systems. We discuss how their implementation can improve system reliability by providing error detection, error recovery, and health mechanisms maintenance. © 2020 ACM.

Abstract

Abstract
As entity-oriented search takes the lead in modern search, the need for increasingly flexible tools, capable of motivating innovation in information retrieval research, also becomes more evident. Army ANT is an open source framework that takes a step forward in generalizing information retrieval research, so that modern approaches can be easily integrated in a shared evaluation environment. We present an overview on the system architecture of Army ANT, which has four main abstractions: (i) readers, to iterate over text collections, potentially containing associated entities and triples; (ii) engines, that implement indexing and searching approaches, supporting different retrieval tasks and ranking functions; (iii) databases, to store additional document metadata; and (iv) evaluators, to assess retrieval performance for specific tasks and test collections. We also introduce the command line interface and the web interface, presenting a learn mode as a way to explore, analyze and understand representation and retrieval models, through tracing, score component visualization and documentation. © Springer Nature Switzerland AG 2020.

Abstract
The hypergraph-of-entity is a joint representation model for terms, entities and their relations, used as an indexing approach in entity-oriented search. In this work, we characterize the structure of the hypergraph, from a microscopic and macroscopic scale, as well as over time with an increasing number of documents. We use a random walk based approach to estimate shortest distances and node sampling to estimate clustering coefficients. We also propose the calculation of a general mixed hypergraph density measure based on the corresponding bipartite mixed graph. We analyze these statistics for the hypergraph-of-entity, finding that hyperedge-based node degrees are distributed as a power law, while node-based node degrees and hyperedge cardinalities are log-normally distributed. We also find that most statistics tend to converge after an initial period of accentuated growth in the number of documents. We then repeat the analysis over three extensions-materialized through synonym, context, and tf_bin hyperedges-in order to assess their structural impact in the hypergraph. Finally, we focus on the application-specific aspects of the hypergraph-of-entity, in the domain of information retrieval. We analyze the correlation between the retrieval effectiveness and the structural features of the representation model, proposing ranking and anomaly indicators, as useful guides for modifying or extending the hypergraph-of-entity.

Abstract

Abstract
Directive-driven programming models, such as OpenMP, are one solution for exploring the potential parallelism when targeting multicore architectures. Although these approaches significantly help developers, code parallelization is still a non-trivial and time-consuming process, requiring parallel programming skills. Thus, many efforts have been made toward automatic parallelization of the existing sequential code. This article presents AutoPar-Clava, an OpenMP-based automatic parallelization compiler which: (1) statically detects parallelizable loops in C applications; (2) classifies variables used inside the target loop based on their access pattern; (3) supportsreductionclauses on scalar and array variables whenever it is applicable; and (4) generates a C OpenMP parallel code from the input sequential version. The effectiveness of AutoPar-Clava is evaluated by using the NAS and Polyhedral Benchmark suites and targeting a x86-based computing platform. The achieved results are very promising and compare favorably with closely related auto-parallelization compilers, such as Intel C/C++ Compiler (icc), ROSE, TRACO and CETUS.