Employee management is of paramount importance for enterprises as employees play a pivotal role in a company's success. The human resource department in a company uses an employee management system which has proved to be more efficient than the manual processes. Employee management is a set of practices and techniques for efficient and effective management of employees and other staff related operations. Employee management solution provides a system that encompasses all procedures in a digital format which allows smooth and accurate operation of applications. Apart from the technical aspect employee management system helps to identify the underperforming employees.

Abstract-- Characterizing the dynamic interactive patterns of complex systems helps gain in-depth understanding of how components interrelate with each other while performing certain functions as a whole. In this study, we present a novel multimodal data fusion approach to construct a complex network, which models the interactions of biological subsystems in the human body under emotional states through physiological responses. Joint recurrence plot and temporal network metrics are employed to integrate the multimodal information at the signal level. A benchmark public dataset of is used for evaluating our model. I. INTRODUCTION Daily activities of human body are performed through the joint functioning of biological subsystems, including nervous, muscular, respiratory, etc. Extensive attention has been devoted into developing methods for utilizing the rich information collected from human body via multiple sources, while each source of information is referred to as a modality.

For the company that popularized CRM -- and indeed uses that abbreviation as its stock symbol -- Salesforce faces a potential identity crisis in 2019 as competitors such as Oracle, SAP, Zendesk and others pivot from CRM to customer experience management. Salesforce CX-enabling tools will likely be one focus of the San Francisco-based cloud software vendor in the coming year, as will Salesforce integrations that make customer data move more fluidly between its clouds. Large companies, such as Nike and Mastercard, that sell both to businesses and consumers -- or B2B2C in marketing parlance -- expect their platforms to support CX improvement initiatives that focus on customer retention and buttressing retail store experiences with online alternatives. Salesforce CX-facing features ripe for upgrading include AI-based personalization tools, as well as those that can measure campaign effectiveness against advanced segments and metrics, said Chris Jacob, director of product marketing for Salesforce Marketing Cloud. These tools must become more sophisticated by design, because Salesforce customers pour more data into their marketing and sales efforts every year.

The sharp and recent increase in the availability of data captured by different sensors combined with their considerably heterogeneous natures poses a serious challenge for the effective and efficient processing of remotely sensed data. Such an increase in remote sensing and ancillary datasets, however, opens up the possibility of utilizing multimodal datasets in a joint manner to further improve the performance of the processing approaches with respect to the application at hand. Multisource data fusion has, therefore, received enormous attention from researchers worldwide for a wide variety of applications. Moreover, thanks to the revisit capability of several spaceborne sensors, the integration of the temporal information with the spatial and/or spectral/backscattering information of the remotely sensed data is possible and helps to move from a representation of 2D/3D data to 4D data structures, where the time variable adds new information as well as challenges for the information extraction algorithms. There are a huge number of research works dedicated to multisource and multitemporal data fusion, but the methods for the fusion of different modalities have expanded in different paths according to each research community. This paper brings together the advances of multisource and multitemporal data fusion approaches with respect to different research communities and provides a thorough and discipline-specific starting point for researchers at different levels (i.e., students, researchers, and senior researchers) willing to conduct novel investigations on this challenging topic by supplying sufficient detail and references.

In statistical modeling with Gaussian Process regression, it has been shown that combining (few) high-fidelity data with (many) low-fidelity data can enhance prediction accuracy, compared to prediction based on the few high-fidelity data only. Such information fusion techniques for multifidelity data commonly approach the high-fidelity model $f_h(t)$ as a function of two variables $(t,y)$, and then using $f_l(t)$ as the $y$ data. More generally, the high-fidelity model can be written as a function of several variables $(t,y_1,y_2....)$; the low-fidelity model $f_l$ and, say, some of its derivatives, can then be substituted for these variables. In this paper, we will explore mathematical algorithms for multifidelity information fusion that use such an approach towards improving the representation of the high-fidelity function with only a few training data points. Given that $f_h$ may not be a simple function -- and sometimes not even a function -- of $f_l$, we demonstrate that using additional functions of $t$, such as derivatives or shifts of $f_l$, can drastically improve the approximation of $f_h$ through Gaussian Processes. We also point out a connection with "embedology" techniques from topology and dynamical systems.

Data is changing the world, but today's enterprises remain inundated with an ever-rising tide of data generated by products, customers, partners, and business ecosystems. Successful organisations in this digital age are those that have found a way to harness it in order to drive operational efficiencies, better serve customers, tap new market opportunities, and gain competitive advantage. Despite this, not all business functions have progressed swiftly in their usage of data. HR is a case in point. Depending on how digitally mature a company is, data-driven decision-making can be adopted by HR for great results.

Humans make accurate decisions by interpreting complex data from multiple sources. Medical diagnostics, in particular, often hinge on human interpretation of multi-modal information. In order for artificial intelligence to make progress in automated, objective, and accurate diagnosis and prognosis, methods to fuse information from multiple medical imaging modalities are required. However, combining information from multiple data sources has several challenges, as current deep learning architectures lack the ability to extract useful representations from multimodal information, and often simple concatenation is used to fuse such information. In this work, we propose Multimodal DenseNet, a novel architecture for fusing multimodal data. Instead of focusing on concatenation or early and late fusion, our proposed architectures fuses information over several layers and gives the model flexibility in how it combines information from multiple sources. We apply this architecture to the challenge of polyp characterization and landmark identification in endoscopy. Features from white light images are fused with features from narrow band imaging or depth maps. This study demonstrates that Multimodal DenseNet outperforms monomodal classification as well as other multimodal fusion techniques by a significant margin on two different datasets.

Humanoid robots have apparently similar body structure like human beings. Due to their technical design, they are sharing the same workspace with humans. They are placed to clean things, to assist old age people, to entertain us and most importantly to serve us. To be acceptable in the household, they must have higher level of intelligence than industrial robots and they must be social and capable of interacting people around it, who are not supposed to be robot specialist. All these come under the field of human robot interaction (HRI). There are various modes like speech, gesture, behavior etc. through which human can interact with robots. To solve all these challenges, a multimodel technique has been introduced where gesture as well as speech is used as a mode of interaction.

The starting point in Part 1 of this series was the fragmented "knowledge landscape" of most big companies. Information is everywhere but it mostly lives in autonomous silos, in different formats and suffers from "semantic incoherency". This is a huge problem for extending the use of AI in business, beyond the many "narrow" (i.e. To meet this challenge, I argued in Part 2 that we need to "connect up" different forms of enterprise knowledge, with the help of semantic technologies - such as ontologies and knowledge graphs - from the "Symbolic AI" tradition where meaning and reasoning take center stage. This concluding post will focus on business outcomes – the benefits that leading-edge companies around the world are already beginning to achieve, leveraging semantic graph technologies to integrate enterprise knowledge and transform knowledge work.

Over-the-air computation (AirComp) shows great promise to support fast data fusion in Internet-of-Things (IoT) networks. AirComp typically computes desired functions of distributed sensing data by exploiting superposed data transmission in multiple access channels. To overcome its reliance on channel station information (CSI), this work proposes a novel blind over-the-air computation (BlairComp) without requiring CSI access, particularly for low complexity and low latency IoT networks. To solve the resulting non-convex optimization problem without the initialization dependency exhibited by the solutions of a number of recently proposed efficient algorithms, we develop a Wirtinger flow solution to the BlairComp problem based on random initialization. To analyze the resulting efficiency, we prove its statistical optimality and global convergence guarantee. Specifically, in the first stage of the algorithm, the iteration of randomly initialized Wirtinger flow given sufficient data samples can enter a local region that enjoys strong convexity and strong smoothness within a few iterations. We also prove the estimation error of BlairComp in the local region to be sufficiently small. We show that, at the second stage of the algorithm, its estimation error decays exponentially at a linear convergence rate.