An application of machine learning to the organization of institutional software repositories

Software reuse has become a major goal in the development of space systems, as a recent NASA-wide workshop on the subject made clear. The Data Systems Technology Division of Goddard Space Flight Center has been working on tools and techniques for promoting reuse, in particular in the development of satellite ground support software. One of these tools is the Experiment in Libraries via Incremental Schemata and Cobweb (ElvisC). ElvisC applies machine learning to the problem of organizing a reusable software component library for efficient and reliable retrieval. In this paper we describe the background factors that have motivated this work, present the design of the system, and evaluate the results of its application

How Environmental Change Will Impact Mosquito-Borne Diseases

Mosquitos, the most lethal species throughout human history, are the most prevalent source of vector-borne diseases and therefore a major global health burden. Mosquito-borne disease incidence is expected to shift with environmental change. These changes can be predicted using species distribution models. With the wide variety of methods used for models, consensus for improving accuracy and comparability is needed. A comparative analysis of three recent modeling approaches revealed that integrating modeling techniques compensates for trade-offs associated with a singular approach. An area that represents a critical gap in our ability to predict mosquito behavior in response to changing climate factors, such as temperature, is evolutionary adaptive potential. Evolutionary studies for mosquitos have documented rapid evolutionary change in photoperiodic traits. Further research on evolutionary adaptive potential for mosquito thermal tolerances using longitudinal studies in conjunction with genomic approaches will allow for more realistic parameterization of mosquito biological processes. One of the primary factors driving disease patterns is urbanization. Urban areas are already highly impacted by climate-related health issues and offer a wide variety of potential aquatic habitats for breeding, thereby presenting vulnerable targets for mosquito populations. Mosquito-borne diseases have been historically underrepresented in urban health planning, and with projected increases in habitat suitability for temperate areas such as the U.S., promoting awareness of this issue constitutes a major health priority for the future. Integrating mosquito control policies into urban planning and design, such as concomitant strategies for elimination in green space development, will be highly beneficial in mitigating adverse health outcomes

Enhancing Big Data Feature Selection Using a Hybrid Correlation-Based Feature Selection

This study proposes an alternate data extraction method that combines three well-known feature selection methods for handling large and problematic datasets: the correlation-based feature selection (CFS), best first search (BFS), and dominance-based rough set approach (DRSA) methods. This study aims to enhance the classifier’s performance in decision analysis by eliminating uncorrelated and inconsistent data values. The proposed method, named CFS-DRSA, comprises several phases executed in sequence, with the main phases incorporating two crucial feature extraction tasks. Data reduction is first, which implements a CFS method with a BFS algorithm. Secondly, a data selection process applies a DRSA to generate the optimized dataset. Therefore, this study aims to solve the computational time complexity and increase the classification accuracy. Several datasets with various characteristics and volumes were used in the experimental process to evaluate the proposed method’s credibility. The method’s performance was validated using standard evaluation measures and benchmarked with other established methods such as deep learning (DL). Overall, the proposed work proved that it could assist the classifier in returning a significant result, with an accuracy rate of 82.1% for the neural network (NN) classifier, compared to the support vector machine (SVM), which returned 66.5% and 49.96% for DL. The one-way analysis of variance (ANOVA) statistical result indicates that the proposed method is an alternative extraction tool for those with difficulties acquiring expensive big data analysis tools and those who are new to the data analysis field.Ministry of Higher Education under the Fundamental Research Grant Scheme (FRGS/1/2018/ICT04/UTM/01/1)Universiti Teknologi Malaysia (UTM) under Research University Grant Vot-20H04, Malaysia Research University Network (MRUN) Vot 4L876SPEV project, University of Hradec Kralove, Faculty of Informatics and Management, Czech Republic (ID: 2102–2021), “Smart Solutions in Ubiquitous Computing Environments

Multistage feature selection methods for data classification

In data analysis process, a good decision can be made with the assistance of several sub-processes and methods. The most common processes are feature selection and classification processes. Various methods and processes have been proposed to solve many issues such as low classification accuracy, and long processing time faced by the decision-makers. The analysis process becomes more complicated especially when dealing with complex datasets that consist of large and problematic datasets. One of the solutions that can be used is by employing an effective feature selection method to reduce the data processing time, decrease the used memory space, and increase the accuracy of decisions. However, not all the existing methods are capable of dealing with these issues. The aim of this research was to assist the classifier in giving a better performance when dealing with problematic datasets by generating optimised attribute set. The proposed method comprised two stages of feature selection processes, that employed correlation-based feature selection method using a best first search algorithm (CFS-BFS) and as well as a soft set and rough set parameter selection method (SSRS). CFS-BFS is used to eliminate uncorrelated attributes in a dataset meanwhile SSRS was utilized to manage any problematic values such as uncertainty in a dataset. Several bench-marking feature selection methods such as classifier subset evaluation (CSE) and principle component analysis (PCA) and different classifiers such as support vector machine (SVM) and neural network (NN) were used to validate the obtained results. ANOVA and T-test were also conducted to verify the obtained results. The obtained averages for two experimentalworks have proven that the proposed method equally matched the performance of other benchmarking methods in terms of assisting the classifier in achieving high classification performance for complex datasets. The obtained average for another experimental work has shown that the proposed work has outperformed the other benchmarking methods. In conclusion, the proposed method is significant to be used as an alternative feature selection method and able to assist the classifiers in achieving better accuracy in the classification process especially when dealing with problematic datasets

Experimental protocol for sea level projections from ISMIP6 stand-alone ice sheet models

Projection of the contribution of ice sheets to sea level change as part of the Coupled Model Intercomparison Project Phase 6 (CMIP6) takes the form of simulations from coupled ice sheet–climate models and stand-alone ice sheet models, overseen by the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6). This paper describes the experimental setup for process-based sea level change projections to be performed with stand-alone Greenland and Antarctic ice sheet models in the context of ISMIP6. The ISMIP6 protocol relies on a suite of polar atmospheric and oceanic CMIP-based forcing for ice sheet models, in order to explore the uncertainty in projected sea level change due to future emissions scenarios, CMIP models, ice sheet models, and parameterizations for ice–ocean interactions. We describe here the approach taken for defining the suite of ISMIP6 stand-alone ice sheet simulations, document the experimental framework and implementation, and present an overview of the ISMIP6 forcing to be used by participating ice sheet modeling groups

Estratégias de encaminhamento para recolha oportunística de informação em redes móveis de internet das coisas

High vehicular mobility in urban scenarios originates inter-vehicles communication discontinuities, a highly important factor when designing a forwarding strategy for vehicular networks. Store, carry and forward mechanisms enable the usage of vehicular networks in a large set of applications, such as sensor data collection in IoT, contributing to smart city platforms. This work focuses on two main topics to enhance the forwarding decision: i) forwarding strategies that make use of location-aware and social-based to perform neighborhood selection, ii) and packet selection mechanisms to provide Quality of Service (QoS). The neighborhood selection is performed through multiple metrics, resulting in three forwarding strategies: (1) Gateway Location Awareness (GLA), a location-aware ranking classification making use of velocity, heading angle and distance to the gateway, to select the vehicles with higher chance to deliver the information in a shorter period of time, thus differentiating nodes through their movement patterns; (2) Aging Social-Aware Ranking (ASAR) that exploits the social behaviours of each vehicle, where nodes are ranked based on a historical contact table, differentiating vehicles with a high number of contacts from those who barely contact with other vehicles; (3) and to merge both location and social aforementioned algorithms, a hybrid approach emerges, thus generating a more intelligent mechanism. Allied to the forwarding criteria, two packet selection mechanisms are proposed to address distinct network functionalities, namely: Distributed Packet Selection, that focuses primarily on data type prioritization and secondly, on packet network lifetime; and Equalized Packet Selection, which uses network metrics to calculate a storage packet ranking. To do so, the packet number of hops, the packet type and packet network lifetime are used. In order to perform the evaluation of the proposed mechanisms, both real and emulation experiments were performed. For each forwarding strategy, it is evaluated the influence of several parameters in the network's performance, as well as comparatively evaluate the strategies in different scenarios. Experiment results, obtained with real traces of both mobility and vehicular connectivity from a real city-scale urban vehicular network, are used to evaluate the performance of GLA, ASAR and HYBRID schemes, and their results are compared to lower- and upper-bounds. Later, these strategies' viability is also validated in a real scenario. The obtained results show that these strategies are a good tradeoff to maximize data delivery ratio and minimize network overhead, while making use of moving networks as a smart city network infrastructure. To evaluate the proposed packet selection mechanisms, a First In First Out packet selection technique is used as ground rule, thus contrasting with the more objective driven proposed techniques. The results show that the proposed mechanisms are capable of provide distinct network functionalities, from prioritizing a packet type to enhancing the network's performance.A elevada mobilidade em cenários veiculares urbanos origina descontinuidades de comunicação entre veículos, um fator altamente importante quando se desenha uma estratégia de encaminhamento para redes veiculares. Mecanismos de store, carry and forward (guardar, carregar e entregar) possibilitam a recolha de dados de sensores em aplicações da Internet das coisas, contribuindo para plataformas de cidades inteligentes. Este trabalho é focado em dois tópicos principais de forma a melhorar a decisão de encaminhamento: i) estratégias de encaminhamento que fazem uso de métricas sociais e de localização para efetuar a seleção de vizinhos, ii) e mecanismos de seleção de pacotes que qualificam a rede com qualidade de serviço. A seleção de vizinhos é feita através de múltiplas métricas, resultando em três estratégias de encaminhamento: Gateway Location Awareness (GLA), uma classificação baseada em localização que faz uso de velocidade, ângulo de direção e distância até uma gateway, para selecionar os veículos com maior probabilidade de entregar a informação num menor período temporal, distinguindo os veículos através dos seus padrões de movimento. Aging Social-Aware Ranking (ASAR) explora os comportamentos sociais de cada veículo, onde é atribuída uma classificação aos veículos com base num histórico de contactos, diferenciando veículos com um alto número de contactos de outros com menos. Por fim, por forma a tirar partido das distintas características de cada uma das destas estratégias, é proposta uma abordagem híbrida, Hybrid between GLA and ASAR (HYBRID). Aliado ao critério de encaminhamento, são propostos dois mecanismos de seleção de pacotes que focam distintas funcionalidades na rede, sendo estes: Distributed Packet Selection, que foca em primeiro lugar na prioritização de determinados tipos de pacotes e em segundo lugar, no tempo de vida que resta ao pacote na rede; e Equalized Packet Selection, que usa métricas da rede para calcular a classificação de cada pacote em memória. Para tal, é usado o numero de saltos do pacote, o tipo de dados do pacote e o tempo de vida que resta ao pacote na rede. De forma a avaliar os mecanismos propostos, foram realizadas experiências em emulador e em cenário real. Para cada estratégia de encaminhamento, e avaliada a influência de vários parâmetros de configuração no desempenho da rede. Para além disso, é feita uma avaliação comparativa entre as várias estratégias em diferentes cenários. Resultados experimentais, obtidos usando traços reais de mobilidade e conetividade de uma rede veicular urbana, são utilizados para avaliar a performance dos esquemas GLA, ASAR e HYRID. Posteriormente, a viabilidade destas estratégias é também validada em cenário real. Os resultados obtidos mostram que estas estratégias são um bom tradeoff para maximizar a taxa de entrega de dados e minimizar a sobrecarga de dados na rede. Para avaliar os mecanismos de seleção de pacotes, um simples mecanismo First In First Out é utilizado como base, contrapondo com as técnicas propostas mais orientadas a objectivos concretos. Os resultados obtidos mostram que os mecanismos propostos são capazes de proporcionar à rede diferentes funcionalidades, desde prioritização de determinado tipos de dados a melhoramentos no desempenho da rede.Agradeço à Fundação Portuguesa para a Ciência e Tecnologia pelo suporte financeiro através de fundos nacionais e quando aplicável cofi nanciado pelo FEDER, no âmbito do Acordo de Parceria PT2020 pelo projecto MobiWise através do programa Operacional Competitividade e Internacionalização (COMPETE 2020) do Portugal 2020 (POCI-01-0145-FEDER-016426).Mestrado em Engenharia Eletrónica e Telecomunicaçõe