Une approche de MDE pour la résolution de problèmes de configuration : Une application à la plate-forme Eclipse

Finding the right configuration is often a challenging task since one needs to deal with many dependencies between plug-ins and most of existing configuration engines are not flexible enough to work in different scenarios. In this paper we propose a MDE-based approach to solve configuration problems, considering them as constraints satisfaction problems. This approach has been applied by an industrial partner to the management of plug-ins in the Eclipse framework, a big issue for all the technolNational audienceLa recherche de la bonne configuration est souvent une tâche complexe nécessitant la gestion des nombreuses dépendances entre plug-ins. D'autant plus que la plupart des moteurs de configuration existants n'ont pas la flexibilité nécessaire permettant de s'adapter à différents scénarios. Dans cet article, nous proposons une approche fondée sur l'IDM permettant la résolution de problèmes de configuration, en les représentant comme des problèmes de satisfaction de contraintes. Un de nos partenaires industriels a utilisé cette approche pour la gestion des plug-ins dans le cadre d'Eclipse. Cette gestion est considérée comme un problème important pour tous les fournisseurs de solutions basées sur Eclipse

Optimizing Lifespan and Energy Consumption by Smart Meters in Green-Cloud-Based Smart Grids

Green clouds optimally use energy resources in large-scale distributed computing environments. Large scale industries such as smart grids are adopting green cloud paradigm to optimize energy needs and to maximize lifespan of smart devices such as smart meters. Both, energy consumption and lifespan of smart meters are critical factors in smart grid applications where performance of these factors decreases with each cycle of grid operation such as record reading and dispatching to the edge nodes. Also, considering large-scale infrastructure of smart grid, replacing out-of-energy and faulty meters is not an economical solution. Therefore, to optimize the energy consumption and lifespan of smart meters, we present a knowledge-based usage strategy for smart meters in this paper. Our proposed scheme is novel and generates custom graph of smart meter tuple datasets and fetches the frequency of lifespan and energy consumption factors. Due to very large-scale dataset graphs, the said factors are fine-grained through R3F filter over modified Hungarian algorithm for smart grid repository. After receiving the exact status of usage, the grid places smart meters in logical partitions according to their utilization frequency. The experimental evaluation shows that the proposed approach enhances lifespan frequency of 100 smart meters by 72% and optimizes energy consumption at an overall percentile of 21% in the green cloud-based smart grid

Binary Pattern for Nested Cardinality Constraints for Software Product Line of IoT-Based Feature Models

Software product line (SPL) is extensively used for reusability of resources in family of products. Feature modeling is an important technique used to manage common and variable features of SPL in applications, such as Internet of Things (IoT). In order to adopt SPL for application development, organizations require information, such as cost, scope, complexity, number of features, total number of products, and combination of features for each product to start the application development. Application development of IoT is varied in different contexts, such as heat sensor indoor and outdoor environment. Variability management of IoT applications enables to find the cost, scope, and complexity. All possible combinations of features make it easy to find the cost of individual application. However, exact number of all possible products and features combination for each product is more valuable information for an organization to adopt product line. In this paper, we have proposed binary pattern for nested cardinality constraints (BPNCC), which is simple and effective approach to calculate the exact number of products with complex relationships between application's feature models. Furthermore, BPNCC approach identifies the feasible features combinations of each IoT application by tracing the constraint relationship from top-to-bottom. BPNCC is an open source and tool-independent approach that does not hide the internal information of selected and non-selected IoT features. The proposed method is validated by implementing it on small and large IoT application feature models with “n” number of constraints, and it is found that the total number of products and all features combinations in each product without any constraint violation

A review of NLIDB with deep learning: findings, challenges and open issues

Relational databases are storage for a massive amount of data. Knowledge of structured query language is a prior requirement to access that data. That is not possible for all non-technical personals, leading to the need for a system that translates text to SQL query itself rather than the user. Text to SQL task is also crucial because of its economic and industrial value. Natural Language Interface to Database (NLIDB) is the system that supports the text-to-SQL task. Developing the NLIDB system is a long-standing problem. Previously they were built based on domain-specific ontologies via pipelining methods. Recently a rising variety of Deep learning ideas and techniques brought this area to the attention again. Now end to end Deep learning models is being proposed for the task. Some publicly available datasets are being used for experimentation of the contributions, making the comparison process convenient. In this paper, we review the current work, summarize the research trends, and highlight challenging issues of NLIDB with Deep learning models. We discussed the importance of datasets, prediction model approaches and open challenges. In addition, methods and techniques are also summarized, along with their influence on the overall structure and performance of NLIDB systems. This paper can help future researchers start having prior knowledge of findings and challenges in NLIDB with Deep learning approaches

Detecting wake lock leaks in Android apps using machine learning

The popularity of Android devices has increased exponentially with an increase in the number of mobile devices. Millions of online apps are used in these devices. Energy consumption of a device is a major concern for end-users, who want a long usage time on a single battery charge. The energy consumed by the app must be optimized by developers, and the available APIs must be used carefully. A wake-lock is used in apps to control the power state of the Android device and often leads to energy leakage. In this study, we detected wake-lock leaks in Android apps using machine learning. We pre-processed apps by extracting wake-lock related APIs to obtain the structural information of wake-lock usage and oversampled the data using the synthetic minority oversampling technique (SMOTE) to balance the dataset. The machine learning algorithms used to detect wake-lock leaks were first optimized using grid search to determine the best parameters. These parameters were then used in training to detect wake-lock leaks in these apps. We employed various machine learning algorithms and divided them into simple and ensemble algorithms to evaluate their efficacy. The support vector machine (SVM) and stochastic gradient boosting (SGB) were the most effective, producing 97 % and 98 % accuracy, respectively

Multi-Objective Optimum Solutions for IoT-Based Feature Models of Software Product Line

A software product line is used for the development of a family of products utilizing the reusability of existing resources with low costs and time to market. Feature Model (FM) is used extensively to manage the common and variable features of a family of products, such as Internet of Things (IoT) applications. In the literature, the binary pattern for nested cardinality constraints (BPNCC) approach has been proposed to compute all possible combinations of development features for IoT applications without violating any relationship constraints. Relationship constraints are a predefined set of rules for the selection of features from an FM. Due to high probability of relationship constraints violations, obtaining optimum features combinations from large IoT-based FMs are a challenging task. Therefore, in order to obtain optimum solutions, in this paper, we have proposed multi-objective optimum-BPNCC that consists of three independent paths (first, second, and third). Furthermore, we applied heuristics on these paths and found that the first path is infeasible due to space and execution time complexity. The second path reduces the space complexity; however, time complexity increases due to the increasing group of features. Among these paths, the performance of the third path is best as it removes optional features that are not required for optimization. In experiments, we calculated the outcomes of all three paths that show the significant improvement of optimum solution without constraint violation occurrence. We theoretically prove that this paper is better than previously proposed optimization algorithms, such as a non-dominated sorting genetic algorithm and an indicator-based evolutionary algorithm

OGLE-2018-BLG-0022: First Prediction of an Astrometric Microlensing Signal from a Photometric Microlensing Event

In this work, we present the analysis of the binary microlensing event OGLE-2018-BLG-0022 that is detected toward the Galactic bulge field. The dense and continuous coverage with the high-quality photometry data from ground-based observations combined with the space-based {\it Spitzer} observations of this long time-scale event enables us to uniquely determine the masses $M_1=0.40 \pm 0.05~M_\odot$ and $M_2=0.13\pm 0.01~M_\odot$ of the individual lens components. Because the lens-source relative parallax and the vector lens-source relative proper motion are unambiguously determined, we can likewise unambiguously predict the astrometric offset between the light centroid of the magnified images (as observed by the {\it Gaia} satellite) and the true position of the source. This prediction can be tested when the individual-epoch {\it Gaia} astrometric measurements are released.Comment: 10 pages, 10 figures, 4 table

KMT-2018-BLG-0029Lb: A Very Low Mass-Ratio Spitzer Microlens Planet

At q = 1.81 ± 0.20 × 10⁻⁵, KMT-2018-BLG-0029Lb has the lowest planet-host mass ratio q of any microlensing planet to date by more than a factor of two. Hence, it is the first planet that probes below the apparent “pile-up” at q = 5–10 ×10⁻⁵. The event was observed by Spitzer, yielding a microlens-parallax π_E measurement. Combined with a measurement of the Einstein radius θ_E from finite-source effects during the caustic crossings, these measurements imply masses of the host M_(host) = 1.14^(+0.10)_(−0.12)M⊙ and planet M_(planet) = 7.59^(+0.75)_(−0.69)M⊕, system distance D_L = 3.38^(+0.22)_(−0.26) 3.38^(+0.22)_(−0.26) kpc and projected separation a⊥ = 4.27^(+0.21)_(−0.23) 4.27^(+0.21)_(−0.23) AU. The blended light, which is substantially brighter than the microlensed source, is plausibly due to the lens and could be observed at high resolution immediately

SpitzerSpitzer Parallax of OGLE-2018-BLG-0596: A Low-mass-ratio Planet around an M-dwarf

We report the discovery of a $Spitzer$ microlensing planet OGLE-2018-BLG-0596Lb, with preferred planet-host mass ratio $q \sim 2\times10^{-4}$. The planetary signal, which is characterized by a short $(\sim 1~{\rm day})$ "bump" on the rising side of the lensing light curve, was densely covered by ground-based surveys. We find that the signal can be explained by a bright source that fully envelops the planetary caustic, i.e., a "Hollywood" geometry. Combined with the source proper motion measured from $Gaia$, the $Spitzer$ satellite parallax measurement makes it possible to precisely constrain the lens physical parameters. The preferred solution, in which the planet perturbs the minor image due to lensing by the host, yields a Uranus-mass planet with a mass of $M_{\rm p} = 13.9\pm1.6~M_{\oplus}$ orbiting a mid M-dwarf with a mass of $M_{\rm h} = 0.23\pm0.03~M_{\odot}$. There is also a second possible solution that is substantially disfavored but cannot be ruled out, for which the planet perturbs the major image. The latter solution yields $M_{\rm p} = 1.2\pm0.2~M_{\oplus}$ and $M_{\rm h} = 0.15\pm0.02~M_{\odot}$. By combining the microlensing and $Gaia$ data together with a Galactic model, we find in either case that the lens lies on the near side of the Galactic bulge at a distance $D_{\rm L} \sim 6\pm1~{\rm kpc}$. Future adaptive optics observations may decisively resolve the major image/minor image degeneracy.Comment: 34 pages, 8 figures, Submitted to AAS journa