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.

ISIS is quickly recruiting to supplant existing al-Shabab fighters in Somalia to declare a more entrenched presence in the horn of Africa. Three drone airstrikes on Sunday against the Al Qaeda-linked Islamic terrorist group Al-Shabab in Somalia killed four militants, according to the U.S. military. U.S. Africa Command officials said an initial assessment concluded that two airstrikes killed two militants and destroyed two vehicles in Qunyo Barrow, and the third airstrike killed two militants in Caliyoow Barrow. The precision airstrikes, which were in coordination with the Somali government, came a day after a truck bombing in Somalia's capital killed at least 78 people. People salvaging goods after a car bomb destroyed shops in Mogadishu, Somalia, on Saturday.

Prior work inspired by compression algorithms has described how the Burrows Wheeler Transform can be used to create a distance measure for bioinformatics problems. We describe issues with this approach that were not widely known, and introduce our new Burrows Wheeler Markov Distance (BWMD) as an alternative. The BWMD avoids the shortcomings of earlier efforts, and allows us to tackle problems in variable length DNA sequence clustering. BWMD is also more adaptable to other domains, which we demonstrate on malware classification tasks. Unlike other compression-based distance metrics known to us, BWMD works by embedding sequences into a fixed-length feature vector. This allows us to provide significantly improved clustering performance on larger malware corpora, a weakness of prior methods.

We investigate heterogenous employment effects of Flemish training programmes. Based on administrative individual data, we analyse programme effects at various aggregation levels using Modified Causal Forests (MCF), a causal machine learning estimator for multiple programmes. While all programmes have positive effects after the lock-in period, we find substantial heterogeneity across programmes and types of unemployed. Simulations show that assigning unemployed to programmes that maximise individual gains as identified in our estimation can considerably improve effectiveness. Simplified rules, such as one giving priority to unemployed with low employability, mostly recent migrants, lead to about half of the gains obtained by more sophisticated rules.

We consider a wide family of vehicle routing problem variants with many complex and practical constraints, known as rich vehicle routing problems, which are faced on a daily basis by C.H. Robinson (CHR). Since CHR has many customers, each with distinct requirements, various routing problems with different objectives and constraints should be solved. We propose a set partitioning framework with a number of route generation algorithms, which have shown to be effective in solving a variety of different problems. The proposed algorithms have outperformed the existing technologies at CHR on 10 benchmark instances and since, have been embedded into the company's transportation planning and execution technology platform.