Auxiliary Database Research Articles

Uncovering the Significance of Transformational Leadership in Islamic-Based Institutions: A Systematic Exploration

Studies on transformational leadership have shown to be beneficial for employees and organizations, particularly those with an Islamic foundation. Islamic elements present within Islamic institutions seem to have different viewpoints regarding the numerous implications of transformational leadership. To have a clear understanding of the transformational leadership presence in Islamic-based institutions, it is crucial to compile prior literature. Then will ensure the efficiency on effectiveness of transformational leadership in Islamic-based institutions for future reference. Regrettably, there has yet to be enough research that thoroughly reviews the body of knowledge on transformational leadership implications for employees in Islamic-based institutions. Therefore, a thorough literature assessment on the impact of transformational leadership on employees in Islamic-based institutions was conducted in this project. This study used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) review approach to guarantee that the results were of high quality. Two eminent databases, Scopus and Web of Science, as well as one auxiliary database, Google Scholar, were used to retrieve the articles. Two key topics were found through thematic analysis, including 1) employee performance and effectiveness, and 2) employee ethical behavior. Six sub-themes were then created by combining these two primary themes. This research will add to the body of knowledge currently available in the fields of transformational leadership and practical application in institutions with an Islamic foundation. The following are the study's key contributions: 1) pinpointing the beneficial effects of transformational leadership towards employees in Islamic-based organizations, and 2) formulating recommendations for more research in this area.

Emission-Based Machine Learning Approach for Large-Scale Estimates of Black Carbon in China

Tremendous efforts have been made to construct large-scale estimates of aerosol components. However, Black Carbon (BC) estimates over large spatiotemporal scales are still limited. We proposed a novel approach utilizing machine-learning techniques to estimate BC on a large scale. We leveraged a comprehensive gridded BC emission database and auxiliary variables as inputs to train various machine learning (ML) models, specifically a Random Forest (RF) algorithm, to estimate high spatiotemporal BC concentration over China. Different ML algorithms have been applied to a large number of potential datasets and detailed variable importance and sensitivity analysis have also been carried out to explore the physical relevance of variables on the BC estimation model. RF algorithm showed the best performance compared with other ML models. Good predictive performance was observed for the training cases (R2 = 0.78, RMSE = 1.37 μgm−3) and test case databases (R2 = 0.77, RMSE = 1.35 μgm−3) on a daily time scale, illustrating a significant improvement compared to previous studies with remote sensing and chemical transport models. The seasonal variation of BC distributions was also evaluated, with the best performance observed in spring and summer (R2 ≈ 0.7–0.76, RMSE ≈ 0.98–1.26 μgm−3), followed by autumn and winter (R2 ≈ 0.7–0.72, RMSE ≈ 1.37–1.63 μgm−3). Variable importance and sensitivity analysis illustrated that the BC emission inventories and meteorology showed the highest importance in estimating BC concentration (R2 = 0.73, RMSE = 1.88 μgm−3). At the same time, albedo data and some land cover type variables were also helpful in improving the model performance. We demonstrated that the emission-based ML model with an appropriate auxiliary database (e.g., satellite and reanalysis datasets) could effectively estimate the spatiotemporal BC concentrations at a large scale. In addition, the promising results obtained through this approach highlight its potential to be utilized for the assessment of other primary pollutants in the future.

Validation of SMAP Soil Moisture Products Using Ground-Based Observations for the Paddy Dominated Tropical Region of India

The Soil Moisture Active Passive (SMAP) mission currently provides three surface soil moisture products based solely on instrument measurements. The three soil moisture products are: 1) the radiometer-only 36 km gridded; 2) a radiometer-only enhanced product gridded at 9 km; and 3) a high-resolution (3 km) SMAP-Sentinel active–passive product. It is important to validate these released SMAP soil moisture products over various land covers and hydroclimatic domains before they are routinely used in scientific research and applications. This paper evaluates SMAP-based soil moisture products for typical Indian conditions of extreme seasonal variability that leads to changes from very wet to dry soil, especially for the paddy dominated region. The assessment metrics indicate that the enhanced passive-only soil moisture product meets the SMAP accuracy requirement of 0.04 m3/m3 during the nongrowing season (NGS) with unbiased root-mean-square error (ubRMSE) values ranging between 0.025 and 0.036 m3/m3. However, this product underperformed during the paddy growing season (GS) with ubRMSE values ranging between 0.063 and 0.097 m3/m3. In addition, the SMAP-Sentinel active–passive soil moisture product shows satisfactory performance during the NGS (ubRMSE, 0.017–0.051 m3/m3), but during the GS, ubRMSE ranged between 0.089 and 0.104 m3/m3. Use of the vegetation water content climatology and low clay fraction in SMAP baseline algorithm (auxiliary database) that mismatched with the actual values may be the possible source of errors and biases in the SMAP soil moisture products. The reported study provides guidelines for the application of enhanced SMAP soil moisture products in India, especially for the tropical region, and provides information that can be used to improve the retrieval algorithm.

Low-Resolution Face Recognition with Single Sample per Person via Domain Adaptation

Numerous low-resolution (LR) face images are captured by a growing number of surveillance cameras nowadays. In some particular applications, such as suspect identification, it is required to recognize an LR face image captured by the surveillance camera using only one high-resolution (HR) profile face image on the ID card. This leads to LR face recognition with single sample per person (SSPP), which is more challenging than conventional LR face recognition or SSPP face recognition. To address this tough problem, we propose a Boosted Coupled Marginal Fisher Analysis (CMFA) approach, which unites domain adaptation and coupled mappings. An auxiliary database containing multiple HR and LR samples is introduced to explore more discriminative information, and locality preserving domain adaption (LPDA) is designed to realize good domain adaptation between SSPP training set (target domain) and auxiliary database (source domain). We perform LPDA on HR and LR images in both domains, then in the domain adaptation space we apply CMFA to learn the discriminative coupled mappings for classification. The learned coupled mappings embed knowledge from the auxiliary dataset, thus their discriminative ability is superior. We extensively evaluate the proposed method on FERET, LFW and SCface database, the promising results demonstrate its effectiveness on LR face recognition with SSPP.

SEMI-AUTOMATIC ROAD NETWORK EXTRACTION FROM DIGITAL IMAGES USING OBJECT-BASED CLASSIFICATION AND MORPHOLOGICAL OPERATORS

Abstract: The demand for geospatial data concerning road network is constant, due to the wide variety of application which needs this type of data. It stands out the importance of this data in cartography update cycles, that can be obtained using automated processes of feature extraction in digital images, which are more accurate, fast and less costly than the traditional methods. In this sense, this work aimed the road network extraction from RapidEye satellite imagery, by developing a hybrid methodology using techniques of object-based image classification and morphological operators. The methodology was tested in three different sites, with images acquired in distinct dates, and the extraction process was evaluated through metrics obtained from the linear matching procedure. By the proposed extraction process, were achieved in terms of correctness and completeness the values of 92.23% and 85.15% for test site 1, the values of 79.16% and 81.06% for test site 2, and the values of 82.05% and 92.22% for test site 3, respectively. The results shown that the proposed methodology presented a good performance for semi-automatic road network extraction from Rapideye images, representing an alternative to auxiliary road network database acquisition and updating.

Bidirectional feature selection with global and local structure preservation for small size samples

Collecting samples is a challenging task for face recognition, especially for some real-world applications such as law enhancement and ID card identification, where there is usually single sample per person (SSPS) used to train a face recognition system. To extract discriminative features from the small size samples, in this paper we propose virtual samples via bidirectional feature selection with global and local structure preservation (VS-BFS-GL) to augment the number of training samples. In VS-BFS-GL, bidirectional feature selection is developed, which introduces L2,1 norm to explore the face variations from both horizontal and vertical directions. Further, to include more variations in the virtual images, the global structure information and sample-specified local structure information of the SSPP training set are considered. By integrating bidirectional feature selection, global and local structure, the limited training samples are fully utilized and more knowledge are mined. To further improve the effectiveness of VS-BFS-GL, an auxiliary database containing different face variations can be used to explore the local structure information. We extensively evaluated the proposed approach on AR and FERET database. The promising recognition results demonstrate that VS-BFS-GL is robust to expression, pose and partial occlusion variations in the faces.

Missing Modality Transfer Learning via Latent Low-Rank Constraint.

Transfer learning is usually exploited to leverage previously well-learned source domain for evaluating the unknown target domain; however, it may fail if no target data are available in the training stage. This problem arises when the data are multi-modal. For example, the target domain is in one modality, while the source domain is in another. To overcome this, we first borrow an auxiliary database with complete modalities, then consider knowledge transfer across databases and across modalities within databases simultaneously in a unified framework. The contributions are threefold: 1) a latent factor is introduced to uncover the underlying structure of the missing modality from the known data; 2) transfer learning in two directions allows the data alignment between both modalities and databases, giving rise to a very promising recovery; and 3) an efficient solution with theoretical guarantees to the proposed latent low-rank transfer learning algorithm. Comprehensive experiments on multi-modal knowledge transfer with missing target modality verify that our method can successfully inherit knowledge from both auxiliary database and source modality, and therefore significantly improve the recognition performance even when test modality is inaccessible in the training stage.

Modelling a Human-Like Bot in a First Person Shooter Game

Autonomous agents in videogames, usually called bots, have tried to behave as human players from their emergence more than 20 years ago. They normally try to model a part of a human expert player's knowledge with respect to the game, trying to become a competitive opponent or a good partner for other players. This paper presents a deep description of the design of a bot for playing 1 vs. 1 Death Match mode in the first person shooter Unreal Tournament™ 2004 (UT2K4). This bot uses a state-based Artificial Intelligence model which emulates a big part of the behavior/knowledge (actions and tricks) of an expert human player in this mode. This player has participated in international UT2K4 championships. The behavioral engine considers primary and secondary actions, and uses a memory approach. It is based in an auxiliary database for learning about the fighting arena, so it stores weapons and items locations once the bot has discovered them, as a human player would do. This so-called Expert Bot has yielded excellent results, beating the game default bots even in the hardest difficulty, and being a very hard opponent for medium-level human players.

Latent Low-Rank Transfer Subspace Learning for Missing Modality Recognition

We consider an interesting problem in this paper that uses transfer learning in two directions to compensate missing knowledge from the target domain. Transfer learning tends to be exploited as a powerful tool that mitigates the discrepancy between different databases used for knowledge transfer. It can also be used for knowledge transfer between different modalities within one database. However, in either case, transfer learning will fail if the target data are missing. To overcome this, we consider knowledge transfer between different databases and modalities simultaneously in a single framework, where missing target data from one database are recovered to facilitate recognition task. We referred to this framework as Latent Low-rank Transfer Subspace Learning method (L2TSL). We first propose to use a low-rank constraint as well as dictionary learning in a learned subspace to guide the knowledge transfer between and within different databases. We then introduce a latent factor to uncover the underlying structure of the missing target data. Next, transfer learning in two directions is proposed to integrate auxiliary database for transfer learning with missing target data. Experimental results of multi-modalities knowledge transfer with missing target data demonstrate that our method can successfully inherit knowledge from the auxiliary database to complete the target domain, and therefore enhance the performance when recognizing data from the modality without any training data.

Online Prediction of Machining Distortion of Aeronautical Parts Caused by Re-Equilibration of Residual Stresses

This paper is concerned with the prediction of part distortion induced by residual stress relief during machining. Aeronautical manufacturing requires nowadays the production of large monolithic parts from workpieces obtained by forging or rolling operations. Residual stresses induced during these forming steps are often consequent and represent a significant obstacle for manufacturers. Re-equilibration of initial residual stresses induced by material removal causes the part to deform.The method presented in this paper provides a tool for predicting changes in the geometry of the part occurring during machining and after unclamping. This prediction is based on an on-line data acquisition. From the predicted shape, the manufacturer is able to anticipate extra conforming steps eventually required afterwards and an updated toolpath accounting for geometrical changes happening during the on-going operation can be derived.The impact of the unknown initial residual stresses on the part deformation during machining is predicted on-line by comparing measured deformations to a reduced basis of mechanically admissible evolutions for the part geometry induced by material removal. This reduced basis is extracted from a larger database constructed off-line thanks to machining simulations and observations of previously machined similar parts.An auxiliary database for post-machining distortion is constructed similarly and makes possible a quick and reliable on-line prediction of the final shape of the part (i.e. after unclamping).

Semiparametric efficiency in GMM models with auxiliary data

We study semiparametric efficiency bounds and efficient estimation of parameters defined through general moment restrictions with missing data. Identification relies on auxiliary data containing information about the distribution of the missing variables conditional on proxy variables that are observed in both the primary and the auxiliary database, when such distribution is common to the two data sets. The auxiliary sample can be independent of the primary sample, or can be a subset of it. For both cases, we derive bounds when the probability of missing data given the proxy variables is unknown, or known, or belongs to a correctly specified parametric family. We find that the conditional probability is not ancillary when the two samples are independent. For all cases, we discuss efficient semiparametric estimators. An estimator based on a conditional expectation projection is shown to require milder regularity conditions than one based on inverse probability weighting.

A Global Simulation of Microwave Emission: Error Structures Based on Output From ECMWF's Operational Integrated Forecast System

The European Centre for Medium-range Weather Forecasts (ECMWF) brightness will use temperatures from the soil moisture and ocean salinity mission to analyze root zone soil moisture through a variational data assimilation system. The first guess is obtained from numerical weather prediction (NWP) model fields, an auxiliary database, and a land surface microwave emission model. In this paper, we present the community microwave emission model and research the first-guess errors in L-band brightness temperatures. An error propagation study is performed on errors introduced through: (1) uncertainties in the parameterizations of the radiative transfer model; (2) auxiliary geophysical quantities for the radiative transfer computations; and (3) an imperfect NWP model. It is found that the vegetation and dielectric models introduce uncertainties with a difference of up to 25 K between models. However, the biggest error in brightness temperature is likely related to the use of an auxiliary vegetation database, which results in differences of -20 to +20 K in our simulations. These potential errors are in many regions higher than the variance in brightness temperatures related to an imperfect NWP model.

GOBASE--a database of mitochondrial and chloroplast information.

GOBASE is a relational database containing integrated sequence, RNA secondary structure and biochemical and taxonomic information about organelles. GOBASE release 6 (summer 2002) contains over 130 000 mitochondrial sequences, an increase of 37% over the previous release, and more than 30 000 chloroplast sequences in a new auxiliary database. To handle this flood of new data, we have designed and implemented GOpop, a Java system for population and verification of the database. We have also implemented a more powerful and flexible user interface using the PHP programming language. http://megasun.bch.umontreal.ca/gobase/gobase.html.

MPW: the Metabolic Pathways Database.

The Metabolic Pathwasy Database (MPW) (www.biobase.com/emphome.html/homepage. html.pags/pathways.html) a derivative of EMP (www.biobase.com/EMP) plays a fundamental role in the technology of metabolic reconstructions from sequenced genomes under the PUMA (www.mcs.anl.gov/home/compbio/PUMA/Production/ ReconstructedMetabolism/reconstruction.html), WIT (www.mcs.anl.gov/home/compbio/WIT/wit.html ) and WIT2 (beauty.isdn.msc.anl.gov/WIT2.pub/CGI/user.cgi) systems. In October 1997, it included some 2800 pathway diagrams covering primary and secondary metabolism, membrane transport, signal transduction pathways, intracellular traffic, translation and transcription. In the current public release of MPW (beauty.isdn.mcs.anl.gov/MPW), the encoding is based on the logical structure of the pathways and is represented by the objects commonly used in electronic circuit design. This facilitates drawing and editing the diagrams and makes possible automation of the basic simulation operations such as deriving stoichiometric matrices, rate laws, and, ultimately, dynamic models of metabolic pathways. Individual pathway diagrams, automatically derived from the original ASCII records, are stored as SGML instances supplemented by relational indices. An auxiliary database of compound names and structures, encoded in the SMILES format, is maintained to unambiguously connect the pathways to the chemical structures of their intermediates.

Page-query compaction of secondary memory auxiliary databases

Prestoring redundant data in secondary memory auxiliary databases is an idea that can often yield improved retrieval performance through better clustering of related data. The clusters can be based on either whole query results or, as this paper indicates, on more specialized units called page-queries. The deliberate redundancy introduced by the designer is typically accompanied by much unnecessary redundancy among the elements of the auxiliary database. This paper presents algorithms for efficiently removing unwanted redundancy in auxiliary databases organized into page-query units. The algorithms presented here extend prior work done for secondary memory compaction in two respects: First, since it is generally not possible to remove all unwanted redundancies, the paper shows how can the compaction be done to remove the most undesirable redundancy from a system performance point-of-view. For example, among the factors considered in determining the worst redundancies are the update behavior and the effects of a particular compaction scheme on memory utilization. Second, unlike traditional approaches for database compaction which aim merely at reducing the storage space, this paper considers the paging characteristics in deciding on an optimal compaction scheme. This is done through the use of page-queries. Simulation results are presented and indicate that page-query compaction results in less storage requirements and more time savings than could be obtained by standard non-page-query compaction.

Page-queries as a tool for organizing secondary memory auxiliary databases. I. Algorithmic aspects and architectural framework of query processing

In this paper, a new approach to organizing secondary memory redundancy in centralized databases is investigated. The approach is based on creating and maintaining a second derived database in secondary storage, called the auxiliary database. The auxiliary database stores data derived from the main database based on the usage pattern. Unlike traditional full materialization approaches, the new approach does not attempt to fully materialize all anticipated queries. Instead, a partial materialization scheme is employed in which only the best possible partial materializations are selected. In order to achieve meaningful and useful partial materializations, new atomic packets of storage called page-queries are defined and used as the only units of storage in the auxiliary database. A page-query roughly consists of one page's contribution to the result of one stored query. It is shown that using page-queries to organize the auxiliary database allows exploiting the irregularities in both the distribution of data values in the database and in its access patterns in a way that could not be achieved by full materialization. Simulation results are presented to show the auspiciousness of using the concept of page-queries to organize secondary memory derived databases. Finally, the approach allows a uniform treatment of the problems of optimal partial materialization and optimal selection of partial indexes.

Page-queries as a tool for organizing secondary memory auxiliary databases. II. Optimal selection of SADB contents

In this paper, a specific aspect of a new approach to organizing secondary memory redundancy in centralized databases is investigated. The approach is based on optimal management of a special type of redundant temporaries, collectively called the auxiliary database. The specific aspect investigated is the problem of optimal selection of the contents of the auxilary database. The new approach is based on exploiting the irregularities in both the distribution of data values in the database and in its access patterns. In order to exploit the nonuniformity in the distribution of both queries and data, special atomic data constructs, called page-queries are used as the smallest indivisible information units in the auxiliary database. A page-query is roughly defined as the result of one query when it is processed against only one memory page. It is shown that use of these atomic data packets as the only building blocks in secondary memory auxilary databases allows us to realize optimization potential that could otherwise not be achieved. The problem of maintaining the auxiliary database in response to database evolution is approached in a self-adaptive fashion by periodically reorganizing its contents to suit the common usage and data distribution patterns. A mathematical programming formulation of the problem is given for which a fast solution algorithm is known. The approach taken to handle updates is to keep the main database always up to date and to adaptively select the contents of the temporaries based on their update history. Measurements obtained from a real implementation are presented and provide preliminary verification of the proposed approach.

POTRE: A Computer Code for the Assessment of Dose from Ingestion

The paper discribes the computer code POTRE and the auxiliary database system which allow to assess the radiation exposure from ingestion of foodstuffs contaminated by radionuclides released from a nuclear facility during normal operation into the atmosphere.

Using Microcomputer CAD Packages in Planning

Abstract This article reviews three microcomputer based computer aided design and drafting programs (CAD)—AutoCAD, CADKEY, and Generic CADD—for their usefulness for planning applications. It gives particular attention to spatial data input techniques, environmental analysis, plan preparation, analysis, and presentation capabilities. The article concludes that microcomputer based CAD is capable of many professional planning tasks. This capability is enhanced when a variety of flexible design functions are available, and it is possible to easily use the graphic output components with auxiliary data base and analysis programs.

Using R:BASE to Construct an Auxiliary Database

In March 1987 the Capital District Library Council, which acts as a resource consortium for 47 libraries in a ten‐county area of upstate New York, became one of two sites (the other is Rochester Library Council) in a pilot project to find, catalog, and preserve local collections of New York state newspapers as part of the United States Newspaper Program (USNP), a nationwide effort funded by a grant from the National Endowment for the Humanities.