Knowledge Graph semantic enhancement of input data for improving AI

Intelligent systems designed using machine learning algorithms require a large number of labeled data. Background knowledge provides complementary, real world factual information that can augment the limited labeled data to train a machine learning algorithm. The term Knowledge Graph (KG) is in vogue as for many practical applications, it is convenient and useful to organize this background knowledge in the form of a graph. Recent academic research and implemented industrial intelligent systems have shown promising performance for machine learning algorithms that combine training data with a knowledge graph. In this article, we discuss the use of relevant KGs to enhance input data for two applications that use machine learning -- recommendation and community detection. The KG improves both accuracy and explainability

Dawn singing behavior of a tropical bird species, the Pied Bush Chat Saxicola caprata

This paper aims to study the structure and pattern of dawn song in a tropical avian species, the Pied Bush Chat (Saxicola caprata) in Haridwar (290 55’ N, 780 08’ E; Uttarakhand, India) in 2009. Males delivered complex dawn chorus on daily basis during only breeding season (February to July). The dawn song bout was made up of a number of distinct sections called song types. Each song type consisted of a series of similar or dissimilar units referred to as elements. Song type length averaged 1.43±0.23 sec and did not differ significantly among males. Theaverage number and types of elements in a song type were observed 8.15±1.64 and 8.01±1.56, respectively.In more than 80% of observations, song types were delivered with immediate variety and males did not follow any definite sequential pattern of song delivery. Males sang continuously for about 30 min at high rates during dawn. Males performed continuous dawn singing throughout the breeding season and seemed to interact vocally through counter-singing for extended period. Observations suggest that dawn song delivery in Pied Bush Chat plays an important role in maintenance and adjustment of social relationship among neighbouring males

Optimal multi-rate rigid body attitude estimation based on Lagrange-d'Alembert principle

The rigid body attitude estimation problem under multi-rate measurements is treated using the discrete-time Lagrange-d'Alembert principle. Angular velocity measurements are assumed to be sampled at a higher rate compared to the direction vector measurements for attitude. The attitude determination problem from two or more vector measurements in the body-fixed frame is formulated as Wahba's problem. At instants when direction vector measurements are absent, a discrete-time model for attitude kinematics is used to propagate past measurements. A discrete-time Lagrangian is constructed as the difference between a kinetic energy-like term that is quadratic in the angular velocity estimation error and an artificial potential energy-like term obtained from Wahba's cost function. An additional dissipation term is introduced and the discrete-time Lagrange-d'Alembert principle is applied to the Lagrangian with this dissipation to obtain an optimal filtering scheme. A discrete-time Lyapunov analysis is carried out by constructing an appropriate discrete-time Lyapunov function. The analysis shows that the filtering scheme is exponentially stable in the absence of measurement noise and the domain of convergence is almost global. For a realistic evaluation of the scheme, numerical experiments are conducted with inputs corrupted by bounded measurement noise. These numerical simulations exhibit convergence of the estimated states to a bounded neighborhood of the actual states.Comment: arXiv admin note: substantial text overlap with arXiv:2007.0818

Multivariate Calibration of Non-replicated Measurements for Heteroscedastic Errors

The quality of multivariate calibration (MVC) models obtained depends on the effective treatment of errors in spectral data. If errors in different absorbance measurements are correlated and have different variances, then the Maximum Likelihood Principal Component Regression (MLPCR) (Wentzell et al, Anal. Chem. 69 (1997), 2299-2311) is an optimal approach which gives a more accurate MVC model. However, this approach requires either complete knowledge of the error covariances or replicated measurements of all spectra from which an estimate of error covariances can be obtained. We propose a method for developing MVC models from non-replicated measurements when errors in different absorbances are independent, but can have different unknown variances. The core of the proposed approach is an Iterative Principal Component Analysis method which simultaneously estimates the lower dimensional spectral subspace and all the error variances. Application of this approach to simulated and experimental data sets demostrates that the quality of the model obtained using the proposed method is better than that obtained using PCR, and is comparable to accuracy of the model obtained using MLPCR

Influence of integrated nutrient management on physiological parameters of lentil (Lens culinaris Medik.)

During the rabi season of 2021, a field experiment was conducted in the North Western plains of Uttarakhand at Crop Research Centre, School of Agriculture, Uttaranchal University, Dehradun to examine the impact of integrated nutrient management (INM) on lentil growth, yield, and economics (Lens culinaris Medik.). The experiment was laid in Randomized Block Design with seven treatments i.e. T1 (Control, 100% RDF (Recommended Dose of Fertilizers), T2 (75 % NPK (Nitrogen, Phosphorus, Potassium) + 25 % FYM (Farm Yard Manure), T3 (50 % NPK + 50 % FYM), T4 (75 % NPK + 25 % Azotobacter), T5 (50 % NPK + 50 % Azotobacter), T6 (75 % NPK + 25 % (Vermicompost + Azotobacter)) & T7 (50 % NPK+ 50 % (Vermicompost + Azotobacter)). The treatments T7 with the combination of 50 per cent NPK and 50 per cent vermicompost plus Azotobacter showed maximum LAI (Leaf Area Index) (0.25), NAR (Net Assimilation Rate) (0.0020), chlorophyll content (3.05), dry matter (4.44 g), and protein content (26.99 %) in contrast to other six treatments

Results from the Deep-Convective Clouds (DCC) Based Response Versus Scan-Angle (RVS) Characterization for the MODIS Reflective Solar Bands

The Terra and Aqua MODIS scan mirror reflectance is a function of the angle of incidence (AOI) and was characterized prior to launch by the instrument vendor. The relative change of the prelaunch response versus scan-angle (RVS) is tracked and linearly scaled on-orbit using observations at two AOIs of 11.2deg and 50.2deg corresponding to the moon view and solar diffuser, respectively. As the missions continue to operate well beyond their design life of 6 years, the assumption of linear scaling between the two AOIs is known to be inadequate in accurately characterizing the RVS, particularly at short wavelengths. Consequently, an enhanced approach of supplementing the on-board measurements with response trends from desert pseudo-invariant calibration sites (PICS) was formulated in MODIS Collection 6 (C6). An underlying assumption for the continued effectiveness of this approach is the long-term (multi-year) and short-term (month-to-month) stability of the PICS. Previous work has shown that the deep convective clouds (DCC) can also be used to monitor the on-orbit RVS performance with less trend uncertainties than desert sites. In this paper, the raw sensor response to the DCC is used to characterize the on-orbit RVS on a band and mirror side basis. These DCC-based RVS results are compared with the C6 PICS-based RVS, showing an agreement within 2% observed in most cases. The pros and cons of using a DCC-based RVS approach are also discussed in this paper. Although this reaffirms the efficacy of the C6 PICS-based RVS, the DCC-based RVS approach presents itself as an effective alternative for future considerations. Potential applications of this approach to other instruments such as SNPP and JPSS VIIRS are also discussed

Generative Boltzmann Adversarial Network in Manet Attack Detection and QOS Enhancement with Latency

Mobile Ad-Hoc Network (MANET) are considered as self-configured network those does not have any centralized base station for the network monitoring and control. MANET environment does not control architecture for routing for the frequent maintenance of topology. The drastic development of Internet leads to adverse effect of development in MANET for different multimedia application those are sensitive to latency. Upon the effective maintenance of the QoS routing route discovery is performed to calculate queue and contention delay. However, the MANET requirement comprises of the complex procedure to withstand the Quality of Service (QoS) with Artificial Intelligence (AI). In MANET it is necessary to compute the MANET attacks with improved QoS with the reduced latency as existing model leads to higher routing and increased latency.  In this paper proposed a Generative Boltzmann Networking Weighted Graph (GBNWG) model for the QoS improvement in MANET to reduce latency. With GBNWG model the MANET model network performance are computed with the weighted graph model. The developed weighted graph computes the routes in the MANET network for the estimation of the available path in the routing metrices. The proposed GBNWG model is comparatively estimated with the conventional QOD technique. Simulation analysis stated that GBNWG scheme exhibits the improved performance in the QoS parameters. The GBNWG scheme improves the PDR value by 12%, 41% reduced control packets and 45% improved throughput value