Industry Report "Cognitive Computing Market" provides a clear picture of the Current Market Scenario which includes past and estimated future size with respect to Value and Volume, Technological Advancement, Macro Economical and Governing Factors in the Cognitive Computing market. Cognitive Computing is defined as the technology based on the principle of artificial intelligence, signal processing, machine learning, and natural language processing (NLP) among others technology. It brings human like intelligence for a many business applications which will include big data. Cognitive Computing is a well-known technology basically specialized for processing and analyzing large and unstructured datasets. The major drivers of the cognitive computing market are the advancements in computing platforms like cloud, mobile, and big data analytics which will drive the growth of the market in the forecast period.

Today's largest manufacturing country headed the output league table nearly two centuries ago before being ousted by Britain in the first industrial revolution. China accounts for 20 per cent of global output, followed by the United States with 18 per cent, Japan 10 per cent, Germany 7 per cent and South Korea with 4 per cent, according to the most recent (2015) data from the United Nations Conference on Trade and Development. The UK is ninth with 2 per cent. In the intervening centuries there have been sizeable shifts. China reclaimed its crown after 150 years by overtaking America during the past decade.

Key Questions Answered in this Report: • What is the estimated global artificial intelligence in agriculture market size in terms of value during the period 2018-2024? Global Artificial Intelligence (AI) in Agriculture Market Forecast, 2019-2024 The Global Artificial Intelligence (AI) in Agriculture Market Analysis projects the market to grow at a significant CAGR of 28.38% during the forecast period from 2019 to 2024. The reported growth in the market is expected to be driven by the increasing need to optimize farm operation planning, growing demand to derive insights from emerging complexities of data-driven farming, and rising development of autonomous equipment in agriculture. Artificial intelligence has emerged to be a strong driving force behind the growth of data-driven farming.Regions and countries where agriculture is the major source of livelihood and sustenance, the artificial intelligence technology has led to greater profitability in the farms of those economies. The reduction in expenditure and resultant positive RoI with AI's integration in farm equipment and operations has even reached above 30% in a few countries.

In February 2019, CB Insights announced our third annual AI 100 -- a list of the 100 most promising AI startups across the globe. We take a look at where these companies are now. In 2019, companies from 3 continents and 18 industries made it to the CB Insights AI 100. They were selected from a pool of 3K companies based on a range of criteria, including patent activity, investor profile, news sentiment analysis, market potential, partnerships, competitive landscape, team strength, tech novelty, and more. Since announcing our list, 7 of these startups have been snapped up by major corporations, 4 went on to become unicorns, and several have entered into partnerships with corporations like Microsoft, Oracle, HSBC, and General Electric.

In this paper, we investigate a bitopological duality for algebras of Fitting's multi-valued logic. We also extend the natural duality theory for ISP I( L) by developing a duality for ISP(L), where L is a finite algebra in which underlying lattice is bounded distributive. Keywords: Bitopology, Fitting's logic, Natural duality theory. 1 Introduction Stone's pioneering work in the mid 1930 [19] on the dual equivalence between the category of Boolean algebras and homomorphism, and the category of Stone spaces(compact zero-dimensional Hausdorff spaces) and continuous maps, is being considered as the origin of duality theory. Stone further developed a general work [12] for the category of bounded distributive lattices in 1937. Priestley in 1970 [18] investigate another duality for the category of bounded distributive lattices with the help of ordered Stone spaces(known as Priesley spaces), which overcome difficulties in Stone's work [12].

Sexual harassment in academia is often a hidden problem because victims are usually reluctant to report their experiences. Recently, a web survey was developed to provide an opportunity to share thousands of sexual harassment experiences in academia. Using an efficient approach, this study collected and investigated more than 2,000 sexual harassment experiences to better understand these unwanted advances in higher education. This paper utilized text mining to disclose hidden topics and explore their weight across three variables: harasser gender, institution type, and victim's field of study. We mapped the topics on five themes drawn from the sexual harassment literature and found that more than 50% of the topics were assigned to the unwanted sexual attention theme. Fourteen percent of the topics were in the gender harassment theme, in which insulting, sexist, or degrading comments or behavior was directed towards women. Five percent of the topics involved sexual coercion (a benefit is offered in exchange for sexual favors), 5% involved sex discrimination, and 7% of the topics discussed retaliation against the victim for reporting the harassment, or for simply not complying with the harasser. Findings highlight the power differential between faculty and students, and the toll on students when professors abuse their power. While some topics did differ based on type of institution, there were no differences between the topics based on gender of harasser or field of study. This research can be beneficial to researchers in further investigation of this paper's dataset, and to policymakers in improving existing policies to create a safe and supportive environment in academia.

In mixed multi-view data, multiple sets of diverse features are measured on the same set of samples. By integrating all available data sources, we seek to discover common group structure among the samples that may be hidden in individualistic cluster analyses of a single data-view. While several techniques for such integrative clustering have been explored, we propose and develop a convex formalization that will inherit the strong statistical, mathematical and empirical properties of increasingly popular convex clustering methods. Specifically, our Integrative Generalized Convex Clustering Optimization (iGecco) method employs different convex distances, losses, or divergences for each of the different data views with a joint convex fusion penalty that leads to common groups. Additionally, integrating mixed multi-view data is often challenging when each data source is high-dimensional. To perform feature selection in such scenarios, we develop an adaptive shifted group-lasso penalty that selects features by shrinking them towards their loss-specific centers. Our so-called iGecco+ approach selects features from each data-view that are best for determining the groups, often leading to improved integrative clustering. To fit our model, we develop a new type of generalized multi-block ADMM algorithm using sub-problem approximations that more efficiently fits our model for big data sets. Through a series of numerical experiments and real data examples on text mining and genomics, we show that iGecco+ achieves superior empirical performance for high-dimensional mixed multi-view data.

In this paper, we consider the tensor completion problem representing the solution in the tensor train (TT) format. It is assumed that tensor is high-dimensional, and tensor values are generated by an unknown smooth function. The assumption allows us to develop an efficient initialization scheme based on Gaussian Process Regression and TT-cross approximation technique. The proposed approach can be used in conjunction with any optimization algorithm that is usually utilized in tensor completion problems. We empirically justify that in this case the reconstruction error improves compared to the tensor completion with random initialization. As an additional benefit, our technique automatically selects rank thanks to using the TT-cross approximation technique.

Recently, the binary expansion testing framework was introduced to test the independence of two continuous random variables by utilizing symmetry statistics that are complete sufficient statistics for dependence. We develop a new test by an ensemble method that uses the sum of squared symmetry statistics and distance correlation. Simulation studies suggest that this method improves the power while preserving the clear interpretation of the binary expansion testing. We extend this method to tests of independence of random vectors in arbitrary dimension. By random projections, the proposed binary expansion randomized ensemble test transforms the multivariate independence testing problem into a univariate problem. Simulation studies and data example analyses show that the proposed method provides relatively robust performance compared with existing methods.

Consider a dynamic task allocation problem, where tasks are unknowingly distributed over an environment . This paper considers ea ch task comprised of two sequential subtasks: detection and completion, where e ach subtask can only be carried out by a certain type of agent . We address th is problem using a novel natur e - inspired approach called "hunter and gathere r" . Th e proposed method employs two complementary teams of agents: one agile in detecting (hunters) and another dexterous in completing (gathere r s) the tasks . To minimize the collective cost of task accomplishments in a distributed manner, a game - theor etic solution is introduced to couple agents from complementary teams . We utiliz e market - based negotiation models to develop incentive - based decision - making algorithms rely ing on innovative notions of " certainty and uncertainty profit margins " . The simulation results demonstrate that employing two complementary teams of hunters and gatherers can effectually improve the number of tasks completed by agents compared to conventional methods, while the collec tive cost of accomplishments is minimized . In addition, t he stability and efficacy of the proposed solutions are studied using Nash equilibrium analysis and statistical analysis respectively . It is also numerically show n that the proposed solution s function fairly, i.e. for each type of agent, the overall w orkload is distributed equally . Index Terms -- Distributed multiagent system, dynamic task allocation, game theory, negotiation. Multirobot systems are expected to undertake imperative roles in a wide variety of fields such as urban search and rescue (USAR) [1, 2], agricultural field operations [3], security patrols [4, 5], environmental monitoring [6], and industrial procedures [7] . Studies have shown that multi - robot systems have advantage over single - robot systems by offering more reliability, redundancy, and time efficiency when the nature of the tasks is inherently dist ributed [8] . Nonetheless, the problem of multi - robot task - allocation (MRTA) poses many critical challenges that has called for investigation in the past two decades [9 - 11] . In this regards, t he complexity of MRTA problems increases significantly in a dynamic environment, where the number and location of tasks are unknown for agents [12, 13] . Thus, robot s need to explore the environment to find tasks before accomplishing them.