The market size of the military robot market will rise considerably in the coming years owing to improving border surveillance and patrolling. The growth of inefficient border research and growing ISR functions has led to military robot industry propulsion. The robots are for defensive drive purposes and are remotely controlled which can perform dangerous activities making them more efficient than the other tools. The wider market will be influenced by a growing need for advanced monitoring, targeting and information-gathering systems in the military due to the presence of challenging and life-risking activities. Furthermore, the rising need for isolated processes for a long period and technological developments in unused systems worldwide is another factor that significantly increases the growth of the market.

The major driver of global warming has been identified as the anthropogenic release of greenhouse gas (GHG) emissions from industrial activities. The quantitative monitoring of these emissions is mandatory to fully understand their effect on the Earth's climate and to enforce emission regulations on a large scale. In this work, we investigate the possibility to detect and quantify industrial smoke plumes from globally and freely available multi-band image data from ESA's Sentinel-2 satellites. Using a modified ResNet-50, we can detect smoke plumes of different sizes with an accuracy of 94.3%. The model correctly ignores natural clouds and focuses on those imaging channels that are related to the spectral absorption from aerosols and water vapor, enabling the localization of smoke. We exploit this localization ability and train a U-Net segmentation model on a labeled sub-sample of our data, resulting in an Intersection-over-Union (IoU) metric of 0.608 and an overall accuracy for the detection of any smoke plume of 94.0%; on average, our model can reproduce the area covered by smoke in an image to within 5.6%. The performance of our model is mostly limited by occasional confusion with surface objects, the inability to identify semi-transparent smoke, and human limitations to properly identify smoke based on RGB-only images. Nevertheless, our results enable us to reliably detect and qualitatively estimate the level of smoke activity in order to monitor activity in industrial plants across the globe. Our data set and code base are publicly available.

We present a Python implementation for RS-HDMR-GPR (Random Sampling High Dimensional Model Representation Gaussian Process Regression). The method builds representations of multivariate functions with lower-dimensional terms, either as an expansion over orders of coupling or using terms of only a given dimensionality. This facilitates, in particular, recovering functional dependence from sparse data. The code also allows imputation of missing values of the variables. The capabilities of this regression tool are demonstrated on test cases involving synthetic analytic functions, the potential energy surface of the water molecule, and financial market data.

One of the most commonly used methods for forming confidence intervals for statistical inference is the empirical bootstrap, which is especially expedient when the limiting distribution of the estimator is unknown. However, despite its ubiquitous role, its theoretical properties are still not well understood for non-asymptotically normal estimators. In this paper, under stability conditions, we establish the limiting distribution of the empirical bootstrap estimator, derive tight conditions for it to be asymptotically consistent, and quantify the speed of convergence. Moreover, we propose three alternative ways to use the bootstrap method to build confidence intervals with coverage guarantees. Finally, we illustrate the generality and tightness of our results by a series of examples, including uniform confidence bands, two-sample kernel tests, minmax stochastic programs and the empirical risk of stacked estimators.

We also review Notal Vision's ForseeHome device and the company's Home OCT AI-enable platform for monitoring AMD. Kester Nahen, PhD, is the chief executive officer at Notal Vision. We'd love to hear from you! Send your comments/questions to Dr. Mali at eyecareinsider@healio.com.

The content based image retrieval aims to find the similar images from a large scale dataset against a query image. Generally, the similarity between the representative features of the query image and dataset images is used to rank the images for retrieval. In early days, various hand designed feature descriptors have been investigated based on the visual cues such as color, texture, shape, etc. that represent the images. However, the deep learning has emerged as a dominating alternative of hand-designed feature engineering from a decade. It learns the features automatically from the data. This paper presents a comprehensive survey of deep learning based developments in the past decade for content based image retrieval. The categorization of existing state-of-the-art methods from different perspectives is also performed for greater understanding of the progress. The taxonomy used in this survey covers different supervision, different networks, different descriptor type and different retrieval type. A performance analysis is also performed using the state-of-the-art methods. The insights are also presented for the benefit of the researchers to observe the progress and to make the best choices. The survey presented in this paper will help in further research progress in image retrieval using deep learning.

Word embeddings are a core component of modern natural language processing systems, making the ability to thoroughly evaluate them a vital task. We describe DiaLex, a benchmark for intrinsic evaluation of dialectal Arabic word embedding. DiaLex covers five important Arabic dialects: Algerian, Egyptian, Lebanese, Syrian, and Tunisian. Across these dialects, DiaLex provides a testbank for six syntactic and semantic relations, namely male to female, singular to dual, singular to plural, antonym, comparative, and genitive to past tense. DiaLex thus consists of a collection of word pairs representing each of the six relations in each of the five dialects. To demonstrate the utility of DiaLex, we use it to evaluate a set of existing and new Arabic word embeddings that we developed. Our benchmark, evaluation code, and new word embedding models will be publicly available.

In Asia Pacific (APAC), adoption of artificial intelligence (AI) and machine learning (ML) in financial markets is accelerating. Though organizations in the Americas still lead in terms of AI/ML maturity and investment levels, those in APAC follow closely behind, according to a new research by Refinitiv, a leading provider of financial market data and infrastructure. Refinitiv, which surveyed more than 420 data scientists, quants, technology and data decision-makers, found that 69% of respondents in APAC view AI/ML as a core component of their business strategy, and 78% are making significant investment in AI/ML. COVID-19 is expected to further push adoption of AI/ML. According to the study, 31% of respondents in Asia said that AI/ML has become more important in their organization as a result of the pandemic, and 35% anticipate increased investment in AI/ML amid the public health crisis.

CallMiner, the leading provider of speech and customer interaction analytics, announced that African Bank has selected the CallMiner Eureka platform to better extract and operationalize insights from voice-based customer interactions. "With CallMiner, we will be able to create an even stronger customer experience by analyzing what our customers need, how they feel, and what we need to do, to deliver exceptional value and service," said Basani Maluleke, CEO of African Bank. "African Bank is fast becoming a pioneering data-driven South African organization and our partnership with CallMiner is another part of our journey to make our organization synonymous with data excellence and to enhance our digital transformation. At the core of our data strategy are our customers – to create an inclusive, personalized banking experience for them and we believe CallMiner provides another tool to bring us one step closer to achieving this." CallMiner Eureka leverages artificial intelligence and machine learning to analyze every customer interaction, across all channels, to automatically uncover actionable insights and intelligence that organizations can use to improve business decisions.

Having been privileged to witness the evolution of the data science and artificial intelligence (AI) scene in the Middle East for the past 10 years and having spoken at one of the first big data events in Dubai back in 2013, it is clear to me that there are considerable opportunities for AI in this vibrant region. Recently, I got the chance to present on the top 10 AI challenges of companies in the Gulf Cooperation Council (GCC) region, at Virtual Executive Boardroom: Key Insights on Becoming a Data-Driven Enterprise, which took place at DigiConnect (UAE) and was delivered to top C-level executives and senior data managers from the most relevant companies in the GCC region. In this post, I will not get into each of these ten challenges. However, I will focus on a common issue that came up as a top priority for them in a quick live poll during the session: The issue of trusting decisions made by AI. Interpreting deep learning networks takes place in a tough playground, so making AI interpretable serves one specific goal, and that is to trust the decisions made by AI models.