Data is at the core of every business irrespective of the field of activity it is engaged in. Business success depends on how effectively it uses its multiple kinds and copious amounts of data to interact with each individual constituent from employees and customers to vendors, business associates, influencers etc. Sharing and exchanging data efficiently at minimum cost is absolutely critical to your path to competitive advantage. Snowflake is a globally recognised expert across industries, small, medium and large in establishing data exchange and managing complex data sharing in a governed and secure way with minimal risk, cost, headache and delay that have plagued traditional methods. Thus your organisation will have the most modern data sharing ability to easily and quickly forge one-to-one, one-to-many, and many-to-many relationships to share data in new and imaginative ways reducing time to a level never before possible. Data exchange is the process of sending and receiving data in a manner that the information, content or meaning assigned to the data is not altered during the transmission.

In this paper, we consider data acquired by multimodal sensors capturing complementary aspects and features of a measured phenomenon. We focus on a scenario in which the measurements share mutual sources of variability but might also be contaminated by other measurement-specific sources such as interferences or noise. Our approach combines manifold learning, which is a class of nonlinear data-driven dimension reduction methods, with the well-known Riemannian geometry of symmetric and positive-definite (SPD) matrices. Manifold learning typically includes the spectral analysis of a kernel built from the measurements. Here, we take a different approach, utilizing the Riemannian geometry of the kernels. In particular, we study the way the spectrum of the kernels changes along geodesic paths on the manifold of SPD matrices. We show that this change enables us, in a purely unsupervised manner, to derive a compact, yet informative, description of the relations between the measurements, in terms of their underlying components. Based on this result, we present new algorithms for extracting the common latent components and for identifying common and measurement-specific components.

Check out their product page … link Get the Chemometrics and Spectroscopy News in real time on Twitter @ CalibModel and follow us. Near-Infrared Spectroscopy (NIRS) "NIR Spectroscopic Techniques for Quality and Process Control in the Meat Industry" LINK "Estimating coefficient of linear extensibility using Vis–NIR reflectance spectral data: Comparison of model validation approaches" LINK "NIR spectroscopy and chemometric tools to identify high content of deoxynivalenol in barley" LINK "Combining multivariate method and spectral variable selection for soil total nitrogen estimation by Vis–NIR spectroscopy" LINK "Multi-task deep learning of near infrared spectra for improved grain quality trait predictions" LINK "Multi-factor Fusion Models for Soluble Solid Content Detection in Pear (Pyrus bretschneideri'Ya') Using Vis/NIR Online Half-transmittance Technique" LINK "Determining regression equations for predicting the metabolic energy values of barley-producing cultivars in Iran and ...

Recently, tremendous interest has been devoted to develop data fusion strategies for energy efficiency in buildings, where various kinds of information can be processed. However, applying the appropriate data fusion strategy to design an efficient energy efficiency system is not straightforward; it requires a priori knowledge of existing fusion strategies, their applications and their properties. To this regard, seeking to provide the energy research community with a better understanding of data fusion strategies in building energy saving systems, their principles, advantages, and potential applications, this paper proposes an extensive survey of existing data fusion mechanisms deployed to reduce excessive consumption and promote sustainability. We investigate their conceptualizations, advantages, challenges and drawbacks, as well as performing a taxonomy of existing data fusion strategies and other contributing factors. Following, a comprehensive comparison of the state-of-the-art data fusion based energy efficiency frameworks is conducted using various parameters, including data fusion level, data fusion techniques, behavioral change influencer, behavioral change incentive, recorded data, platform architecture, IoT technology and application scenario. Moreover, a novel method for electrical appliance identification is proposed based on the fusion of 2D local texture descriptors, where 1D power signals are transformed into 2D space and treated as images. The empirical evaluation, conducted on three real datasets, shows promising performance, in which up to 99.68% accuracy and 99.52% F1 score have been attained. In addition, various open research challenges and future orientations to improve data fusion based energy efficiency ecosystems are explored.

Business intelligence (BI) brings a varied collection of strategies that uncover the hidden insights beneath the data sources and convert raw data into intelligent information for business decision making. To stay competitive, businesses must rediscover and use the data they have generated, this makes BI so important. Business intelligence, lets organisations to extract insights from a pool of accessible data to deliver exact, significant, and nearly real-time inputs for decision making. This specialization is offered in collaboration with Tableau, and is aimed for newcomers to data visualization with no prior experience using Tableau. In this course, you will view examples from real world business cases and journalistic examples from leading media companies.

We present a novel method for inferring ground-truth signal from multiple degraded signals, affected by different amounts of sensor "exposure". The algorithm learns a multiplicative degradation effect by performing iterative corrections of two signals solely from the ratio between them. The degradation function d should be continuous, satisfy monotonicity, and d(0) 1. We use smoothed monotonic regression method, where we easily incorporate the aforementioned criteria to the fitting part. We include theoretical analysis and prove convergence to the ground-truth signal for the noiseless measurement model. Lastly, we present an approach to fuse the noisy corrected signals using Gaussian processes. We use sparse Gaussian processes that can be utilized for a large number of measurements together with a specialized kernel that enables the estimation of noise values of all sensors. The data fusion framework naturally handles data gaps and provides a simple and powerful method for observing the signal trends on multiple timescales (long-term and short-term signal properties). The viability of correction method is evaluated on a synthetic dataset with known ground-truth signal.

The infamous data science workflow with interconnected circles of data acquisition, wrangling, analysis, and reporting understates the multi-connectivity and non-linearity of these components. The same is true for machine learning and deep learning workflows. I understand the need for oversimplification is expedient in presentations and executive summaries. However, it may paint unrealistic pictures, hide the intricacies of ML development and conceal the realities of the mess. This brings me to the tools of the trade or more commonly referred as the infrastructure of artificial intelligence which is the vehicle under which all libraries, experimentations, designs and creative minds meet.

Joining a geology team, the Geoscience Data Specialist will play a vital role in the continual development of our digital transformation tools and our geoscience analytic techniques. You will be working alongside our geoscience experts to analyse geological data workflows and develop the database and dynamic data-driven improvements. You will be responsible for maintaining consistency and integrity of the corporate database and for the development of shared resources for workflows. The successful candidate will be responsible for supporting the development and implementation of technologies and efficient workflows to extract, transform, load, manipulate, explore, analyse, report, and visualise data for operational and legacy projects. You will work on projects to assist with data identification, inventory, extraction, transformation and database loading and will also develop, manage and apply ETL tools, scripts and database systems for geoscience data.

We propose a method for combining probabilistic outputs of classifiers to make a single consensus class prediction when no further information about the individual classifiers is available, beyond that they have been trained for the same task. The lack of relevant prior information rules out typical applications of Bayesian or Dempster-Shafer methods, and the default approach here would be methods based on the principle of indifference, such as the sum or product rule, which essentially weight all classifiers equally. In contrast, our approach considers the diversity between the outputs of the various classifiers, iteratively updating predictions based on their correspondence with other predictions until the predictions converge to a consensus decision. The intuition behind this approach is that classifiers trained for the same task should typically exhibit regularities in their outputs on a new task; the predictions of classifiers which differ significantly from those of others are thus given less credence using our approach. The approach implicitly assumes a symmetric loss function, in that the relative cost of various prediction errors are not taken into account. Performance of the model is demonstrated on different benchmark datasets. Our proposed method works well in situations where accuracy is the performance metric; however, it does not output calibrated probabilities, so it is not suitable in situations where such probabilities are required for further processing.

Telarus, LLC, the leading master agent of business cloud infrastructure, cybersecurity, and contact center services, and Mobile Solutions, a leading provider of full lifecycle Managed Mobility Services, have partnered to offer Telarus partners, and their customers, access to Mobile Solutions' comprehensive SaaS solutions and services Telarus, LLC, the leading master agent of business cloud infrastructure, cybersecurity, and contact center services, and Mobile Solutions, a leading provider of full lifecycle Managed Mobility Services, have partnered to offer Telarus partners, and their customers, access to Mobile Solutions' comprehensive SaaS solutions and services. Telarus is the only master agent that reinvests in developing tools for the main purpose of making their partners' lives easier, being the tech expert their clients want them to be, and helping them differentiate themselves from the competition. Telarus has been named the top master agent by the Telecom Association for the past three years – the first and only company in the carrier channel to accomplish this feat. "With the recent focus on mobility and endpoint security, we are pleased to offer our partners access to Mobile Solutions, which offers a comprehensive portfolio of services backed by true mobility experts," said Roger Blohm, Telarus EVP. As technology continues to alter the business landscape, the need for key solutions has grown.