The global artificial intelligence in fashion market accounted for US$ 270.0 Mn in 2018 and is expected to grow at a CAGR of 36.9% over the forecast period 2019-2027, to account for US$ 4,391.7 Mn in 2027. Driving factors such as availability of massive amount of data due to increasing proliferation of digital services across the globe, and real time consumer behavior insights and increased operational efficiency are driving the adoption of AI in fashion industry will drive the market during the forecast period and have a high impact in the short term. However, factors such as concerns related to data privacy and security is anticipated to hinder the market growth in the coming years. AI integration in fashion plays a crucial role in sales, marketing, and customer-focused purposes.Initial adopters point toward the key impacts of technology in improving customer experience and decent growth in company revenue. Elevated customer experience helps the retailer to crack entirely new tactics of customer engagement and communication.With AI integration, the retailers can precisely spot the customers' expected needs at precise times and offer the appropriate product to gain a competitive advantage. Some of the past initiatives taken in the fashion industry sector which has revolutionize the use of AI in the sector are North Face leveraging IBM Watson's ML technology to recommend more personalized apparel to the customers.Further, eBay's AI integration helps their sellers sell more by better inventory management and pricing recommendations.

We asked 9 industry experts to contribute their thoughts on the year ahead, and a common theme was the need for these technologies to mature, with the genuinely useful implementations finally getting to market. Expanding on last year, we have chosen seven areas of interest to focus on in 2019. Each represents a vital area of innovation in the financial industry, and has a particular relevance to Luxembourg's thriving financial technology ecosystem. Each week we will be choosing one of the topics to focus on, both in the content we share on social media, but also in a dedicated newsletter looking at the top five stories from that week. First, let's introduce the topics with some of our favourite summaries for the uninitiated: "Regtech growth will explode in 2019 because regulators worldwide will start truly driving it. Multiple countries will hold a joint hackathon at midyear, aiming to use technology to remove one of the biggest regtech blockers: how to share data widely to find risk patterns and fight financial crime, while fully protecting privacy and cybersecurity. Solutions will solve myriad regulatory problems. Even more importantly, the shared experience will move regulatory bodies into a new era of active innovation and collaboration with each other, industry, and academia. Anti-money laundering will continue to be a leading use case, because the current system is so broken and costly and there's so much low-hanging fruit to harvest through technology. We'll also see AI and blockchain solving more problems, from digital identity and financial fairness and inclusion to API-based regulatory reporting, machine-readable regulations, and even machine-executable compliance. These regulatory breakthroughs are not just nice-to-have. They are essential, if fintech innovation is to flourish. The regulations are the rules of the road we're all traveling."

Buildings often have impressive facades but hidden flaws can bring expensive disasters. Densely developed cities have strong demand for "infrastructure-building doctors" which use artificial intelligence (AI) technologies and robotics to find structural flaws – demand that Harris Sun, Chief Executive Officer and Founder of RaSpect, is eager to meet. The start-up improves on existing building inspections by using AI and cloud-based data analysis to build up models of the structures. This permits remote detection which saves costs and time. Being among the winners of a competition held as part of the Hong Kong Trade Development Council's (HKTDC) Start-up Express 2019 development programme, Mr Sun is looking forward to using the HKTDC's business-matching activities to expand overseas.

Smart grid is an alternative solution of the conventional power grid which harnesses the power of the information technology to save the energy and meet today's environment requirements. Due to the inherent vulnerabilities in the information technology, the smart grid is exposed to a wide variety of threats that could be translated into cyber-attacks. In this paper, we develop a deep learning-based intrusion detection system to defend against cyber-attacks in the advanced metering infrastructure network. The proposed machine learning approach is trained and tested extensively on an empirical industrial dataset which is composed of several attack categories including the scanning, buffer overflow, and denial of service attacks. Then, an experimental comparison in terms of detection accuracy is conducted to evaluate the performance of the proposed approach with Naive Bayes, Support Vector Machine, and Random Forest. The obtained results suggest that the proposed approaches produce optimal results comparing to the other algorithms. Finally, we propose a network architecture to deploy the proposed anomaly-based intrusion detection system across the Advanced Metering Infrastructure network. In addition, we propose a network security architecture composed of two types of Intrusion detection system types, Host and Network-based, deployed across the Advanced Metering Infrastructure network to inspect the traffic and detect the malicious one at all the levels.

Background. Recently, In Italy the vaccination coverage for key immunizations, as MMR, has been declining, with measles outbreaks. In 2017, the Italian Government expanded the number of mandatory immunizations establishing penalties for families of unvaccinated children. During the 2018 elections campaign, immunization policy entered the political debate, with the government accusing oppositions of fuelling vaccine scepticism. A new government established in 2018 temporarily relaxed penalties and announced the introduction of flexibility. Objectives and Methods. By a sentiment analysis on tweets posted in Italian during 2018, we aimed at (i) characterising the temporal flow of communication on vaccines, (ii) evaluating the usefulness of Twitter data for estimating vaccination parameters, and (iii) investigating whether the ambiguous political communication might have originated disorientation among the public. Results. The population appeared to be mostly composed by "serial twitterers" tweeting about everything including vaccines. Tweets favourable to vaccination accounted for 75% of retained tweets, undecided for 14% and unfavourable for 11%. Twitter activity of the Italian public health institutions was negligible. After smoothing the temporal pattern, an up-and-down trend in the favourable proportion emerged, synchronized with the switch between governments, providing clear evidence of disorientation. Conclusion. The reported evidence of disorientation documents that critical health topics, as immunization, should never be used for political consensus. This is especially true given the increasing role of online social media as information source, which might yield to social pressures eventually harmful for vaccine uptake, and is worsened by the lack of institutional presence on Twitter. This calls for efforts to contrast misinformation and the ensuing spread of hesitancy.

Smart grid is an emerging and promising technology. It uses the power of information technologies to deliver intelligently the electrical power to customers, and it allows the integration of the green technology to meet the environmental requirements. Unfortunately, information technologies have its inherent vulnerabilities and weaknesses that expose the smart grid to a wide variety of security risks. The Intrusion detection system (IDS) plays an important role in securing smart grid networks and detecting malicious activity, yet it suffers from several limitations. Many research papers have been published to address these issues using several algorithms and techniques. Therefore, a detailed comparison between these algorithms is needed. This paper presents an overview of four data mining algorithms used by IDS in Smart Grid. An evaluation of performance of these algorithms is conducted based on several metrics including the probability of detection, probability of false alarm, probability of miss detection, efficiency, and processing time. Results show that Random Forest outperforms the other three algorithms in detecting attacks with higher probability of detection, lower probability of false alarm, lower probability of miss detection, and higher accuracy.

We propose an algorithm combining calibrated prediction and generalization bounds from learning theory to construct confidence sets for deep neural networks with PAC guarantees---i.e., the confidence set for a given input contains the true label with high probability. We demonstrate how our approach can be used to construct PAC confidence sets on ResNet for ImageNet, a visual object tracking model, and a dynamics model the half-cheetah reinforcement learning problem.

A data leak by smart home device manufacturer Wyze left the personal details of 2.4 million users exposed on the internet for more than three weeks. Among the compromised information was user email addresses, WiFi network names, smart device details and the health statistics of a limited number of users. Founded by former Amazon employees, the Seattle, Washington-based firm specialises in inexpensive smart cameras, light bulbs, plugs and security devices. Wyze has now secured the database and forced users to reset their account passwords, as well as their connections with other services like Amazon's Alexa or Google assistant. A data leak by smart home device manufacturer Wyze left the personal details of 2.4 million users exposed on the internet for more than three weeks.

LONDON – Helicopter cash, climate crises, smart cities and the space economy -- investors have all those possibilities ahead as they enter the third decade of the 21st century. They go into the new decade with a spring in their step after watching world stocks add over $25 trillion in value in the past 10 years and a bond rally put $13 trillion worth of bond yields below zero. They also saw internet-based firms transform the way humans work, shop and relax. Now investors are positioning for the tech revolution's next 10 years. Could we see a repeat of the Roaring '20, as the 1920s were known -- years of prosperity, technological innovation and such social developments as women winning the right to vote?

New York, December 30, 2019: The global Artificial Intelligence Platform market is segregated on the basis of Component as Tools and Services. Based on Deployment the global Artificial Intelligence Platform market is segmented in Cloud and On-Premises. Based on End-User Industry the global Artificial Intelligence Platform market is segmented in Manufacturing, Healthcare, BFSI, Research and Academia, Transportation, Retail and Ecommerce, and Others. The global Artificial Intelligence Platform market is expected to exceed more than US$ 10.8 Billion by 2024, at a CAGR of more than 28% in the given forecast period. The global Artificial Intelligence Platform market report provides geographic analysis covering regions, such as North America, Europe, Asia-Pacific, and Rest of the World.