We propose a technique to develop (and localize in) topological maps from light detection and ranging (Lidar) data. Localizing an autonomous vehicle with respect to a reference map in real-time is crucial for its safe operation. Owing to the rich information provided by Lidar sensors, these are emerging as a promising choice for this task. However, since a Lidar outputs a large amount of data every fraction of a second, it is progressively harder to process the information in real-time. Consequently, current systems have migrated towards faster alternatives at the expense of accuracy. To overcome this inherent trade-off between latency and accuracy, we propose a technique to develop topological maps from Lidar data using the orthogonal Tucker3 tensor decomposition. Our experimental evaluations demonstrate that in addition to achieving a high compression ratio as compared to full data, the proposed technique, $\textit{TensorMap}$, also accurately detects the position of the vehicle in a graph-based representation of a map. We also analyze the robustness of the proposed technique to Gaussian and translational noise, thus initiating explorations into potential applications of tensor decompositions in Lidar data analysis.

Global enterprise software giant Oracle will up the delivery of emerging technologies in the South African market as most local businesses are proceeding with their digital transformation initiatives. So says Arun Khehar, Oracle's senior vice-president for applications sales for the Eastern Central Europe, Middle East and Africa region. In an e-mail interview with ITWeb, Khehar said the world of business is changing at an unimaginable rate. Emerging technologies are changing the way companies do business at every level, across every function, he notes. These technologies have the ability to learn and adapt and change, giving the organisation the space it needs to focus on its core business and growth." According to Khehar, the adoption of cloud technology has come around far quicker than anyone anticipated. "In South Africa, we are seeing the transformation at CEO level in favour of cloud adoption, in particular hybrid solutions.

Multilateral solutions remain the best way to address threats to international peace and security. This is true when we talk about the situation in Syria, Libya, Yemen or the Middle East peace process.

Big data and artificial intelligence offer new ways to solve age-old problems, such as how to feed a growing population. Under our Grand Challenge program, RTI is funding research and analysis based on these technologies to promote agricultural resilience and food security in Rwanda. Most of Rwanda's crop production comes from smallholder farms. The country's agriculture officials have historically had insufficient data on where crops are cultivated or how much yield to expect--a hindrance when planning for the future of their growing country. The government is looking to technology for a solution. Building on our previous work with emerging technologies, machine learning, economics, and agriculture, we are developing a new decision-making tool that can be applied in Rwanda and beyond.

ONCE THOUGHT OF AS A NICHE TOY for early adopters, drones can now be found buzzing over parks, in select cities, and are even being increasingly used for video production as the popularity of aerial photography soars. However, drones aren't only for fun and entertainment, and the high-pitched hum of their spinning propellers could replace the wail of ambulance sirens for global citizens as drones are put to work for humanitarian purposes. In March of 2017, DJI, the manufacturers of the most popular commercial drones, published a report about drones' life-saving capabilities, citing cases in which drones manned by volunteers or bystanders were used in emergency situations like floods and avalanches, resulting in 59 life-saving rescues in China, Canada, the U.S., and Turkey. Given that it takes 25 people 35 hours to search one square mile for missing persons, compared to the 30 minutes it takes a drone to cover the same area, regardless of treacherous conditions on the ground, drones are uniquely suited for search and rescue, even when piloted by hobbyists. Based on the increasing trend of drone use in the last 10 months covered by the report, DJI estimated that drones would be directly responsible for saving at least one person per week in the future.

Global Artificial Intelligence In Transportation Market Research Report 2019 to 2025 provides a unique tool for evaluating the market, highlighting opportunities, and supporting strategic and tactical decision-making. This report recognizes that in this rapidly-evolving and competitive environment, up-to-date marketing information is essential to monitor performance and make critical decisions for growth and profitability. Global Artificial Intelligence In Transportation market size will increase to Million US$ by 2025, from Million US$ in 2018, at a CAGR of during the forecast period. In this study, 2018 has been considered as the base year and 2019 to 2025 as the forecast period to estimate the market size for Artificial Intelligence In Transportation. This report studies the global market size of Artificial Intelligence In Transportation in key regions like North America, Europe, Asia Pacific, Central & South America and Middle East & Africa, focuses on the consumption of Artificial Intelligence In Transportation in these regions.

In a study that used artificial intelligence to analyze design elements, such as artwork and wall colors, in pictures of living rooms posted to Airbnb, a popular home rental website, the researchers found that people tended to follow cultural trends when they decorated their interiors. In the United States, where the researchers had economic data from the U.S. Census, they also found that people across socioeconomic lines put similar efforts into interior decoration. "We were interested in seeing how other cultures decorated," said Clio Andris, assistant professor of geography, Penn State and an Institute for CyberScience associate. "We see maps of the world and wonder, 'What's it like living there,' but we don't really know what it's like to be in people's living rooms and in their houses. This was like people around the world inviting us into their homes."

The science of service centers has advanced with hold-time estimates, call-back options and voice-recognition technologies. Yet once the customer reaches an agent, odds are high that the agent will not be able to solve the problem in one go. Unsolved problems lead to more complaints, greater customer churn and wasted time of employees trying to calm upset customers. Artificial intelligence (AI) promises to substantially improve the experience. Early efforts are helping companies improve the overall customer experience, while reducing costs--in staff time, service escalations such as field technician visits, and defecting customers--in the bargain.

WHEN the first printed books with illustrations started to appear in the 1470s in the German city of Augsburg, wood engravers rose up in protest. Worried about their jobs, they literally stopped the presses. In fact, their skills turned out to be in higher demand than before: somebody had to illustrate the growing number of books. Fears about the impact of technology on jobs have resurfaced periodically ever since. The latest bout of anxiety concerns the arrival of artificial intelligence (AI).

Accordingly, a prime step in text mining applications is to extract interesting patterns and features, from this supply of unstructured data. Feature extraction can be considered as the core of social media mining tasks such as sentiment analysis, event detection, and news recommendation [2]. In the literature, sentiment analysis tends to be used to refer to the task of classifying the polarity of a given piece of text at the document, sentence, feature, or aspect level [23]. There are various applications on a variety of domains which utilize sentiment analysis, in this regard one can mention applying the sentiment analysis for political reviews to estimate the general viewpoint of the parties [43], predicting stock market prices based on sentiment analysis by utilizing the different financial news data [5], and making use of the sentiment analysis to recognize the current medical and psychological status for a community [23]. Machine learning algorithms and statistical learning techniques have been rising in a variety of scientific fields [9, 10]. A number of machine learning techniques have been proposed to perform the task of sentiment analysis. As one of the powerful sub-domains of machine learning in recent years, deep learning models are emerging as a persuasive computational tool, they have affected many research areas and can be traced in many applications. With respect to the deep learning, textual deep representation models attempt to discover and present intricate syntactic and semantic representations of texts, automatically from data without any handmade feature engineering.