Federated learning is a machine learning paradigm that emerges as a solution to the privacy-preservation demands in artificial intelligence. As machine learning, federated learning is threatened by adversarial attacks against the integrity of the learning model and the privacy of data via a distributed approach to tackle local and global learning. This weak point is exacerbated by the inaccessibility of data in federated learning, which makes harder the protection against adversarial attacks and evidences the need to furtherance the research on defence methods to make federated learning a real solution for safeguarding data privacy. In this paper, we present an extensive review of the threats of federated learning, as well as as their corresponding countermeasures, attacks versus defences. This survey provides a taxonomy of adversarial attacks and a taxonomy of defence methods that depict a general picture of this vulnerability of federated learning and how to overcome it. Likewise, we expound guidelines for selecting the most adequate defence method according to the category of the adversarial attack. Besides, we carry out an extensive experimental study from which we draw further conclusions about the behaviour of attacks and defences and the guidelines for selecting the most adequate defence method according to the category of the adversarial attack. This study is finished leading to meditated learned lessons and challenges.

Due to extensive spread of fake news on social and news media it became an emerging research topic now a days that gained attention. In the news media and social media the information is spread highspeed but without accuracy and hence detection mechanism should be able to predict news fast enough to tackle the dissemination of fake news. It has the potential for negative impacts on individuals and society. Therefore, detecting fake news on social media is important and also a technically challenging problem these days. We knew that Machine learning is helpful for building Artificial intelligence systems based on tacit knowledge because it can help us to solve complex problems due to real word data. On the other side we knew that Knowledge engineering is helpful for representing experts knowledge which people aware of that knowledge. Due to this we proposed that integration of Machine learning and knowledge engineering can be helpful in detection of fake news. In this paper we present what is fake news, importance of fake news, overall impact of fake news on different areas, different ways to detect fake news on social media, existing detections algorithms that can help us to overcome the issue, similar application areas and at the end we proposed combination of data driven and engineered knowledge to combat fake news. We studied and compared three different modules text classifiers, stance detection applications and fact checking existing techniques that can help to detect fake news. Furthermore, we investigated the impact of fake news on society. Experimental evaluation of publically available datasets and our proposed fake news detection combination can serve better in detection of fake news.

Conjoint analysis is a popular experimental design used to measure multidimensional preferences. Researchers examine how varying a factor of interest, while controlling for other relevant factors, influences decision-making. Currently, there exist two methodological approaches to analyzing data from a conjoint experiment. The first focuses on estimating the average marginal effects of each factor while averaging over the other factors. Although this allows for straightforward design-based estimation, the results critically depend on the distribution of other factors and how interaction effects are aggregated. An alternative model-based approach can compute various quantities of interest, but requires researchers to correctly specify the model, a challenging task for conjoint analysis with many factors and possible interactions. In addition, a commonly used logistic regression has poor statistical properties even with a moderate number of factors when incorporating interactions. We propose a new hypothesis testing approach based on the conditional randomization test to answer the most fundamental question of conjoint analysis: Does a factor of interest matter in any way given the other factors? Our methodology is solely based on the randomization of factors, and hence is free from assumptions. Yet, it allows researchers to use any test statistic, including those based on complex machine learning algorithms. As a result, we are able to combine the strengths of the existing design-based and model-based approaches. We illustrate the proposed methodology through conjoint analysis of immigration preferences and political candidate evaluation. We also extend the proposed approach to test for regularity assumptions commonly used in conjoint analysis.

On Dec. 29, 2019, a Honda Civic pulled up to the intersection of Artesia Boulevard and Vermont Avenue in Gardena. It was just after midnight. The traffic light was green. As the car proceeded through the intersection, a 2016 Tesla Model S on Autopilot exited a freeway, ran through a red light and crashed into the Civic. The Civic's driver, Gilberto Alcazar Lopez, and his passenger, Maria Guadalupe Nieves-Lopez, were killed instantly.

A Tesla owner is facing the first felony charges filed against someone using a partially automated driving system in the US, according to AP. The defendant, Kevin George Aziz Riad, was driving a Model S when he ran a red light and crashed into a Honda Civic at a California intersection in 2019. It ended up killing the Civic's two passengers, while Riad and his companion sustained non-life threatening injuries. California prosecutors filed two counts of vehicular manslaughter against Riad in October last year. However, the National Highway Traffic Safety Administration (NHTSA), which has been investigating the incident over the past couple of years, recently confirmed that it was switched on at the time of the crash.

Introduction- Most of the Artificial Intelligent (Ai) Systems are developed as black boxes, especially Machine Learning and Deep Learning-based systems. Nowadays, these Machine and Deep Learning-based systems make decisions for our daily life, and should be explainable and should not be taken for granted to the end-users. The implication of such systems is rarely explored for the efficiency in the public usage (i.e., usage in -- Agriculture, Air Combat, Military Training, Education, Finance, Health Care, Human Resources, Customer Service, Autonomous Vehicles, Social Media, and several others[1]-[9]). Not only these, but the future might also be relying on Ai based system that will do our laundry, mow our lawn, fight wars [9]. Thus, there is so much room to improve the transparency of the systems along with fairness and accountability. There are some works that already stated the necessity of guidelines and governance of the Ai based systems, but more exposure is required in each area of application.

Major international airlines rushed on Tuesday to rejig or cancel flights to the United States on the eve of a 5G wireless rollout that triggered safety concerns, despite two wireless carriers saying they will delay parts of the deployment. The Federal Aviation Administration has warned that potential 5G interference could affect height readings that play a key role in bad-weather landings on some jets and airlines say the Boeing 777 is among models initially in the spotlight. Despite an announcement by AT&T and Verizon that they would delay turning on some 5G towers near airports, several airlines still canceled flights. Others said more cancellations were likely unless the FAA issued new formal guidance in the wake of the wireless announcements. The world's largest operator of the Boeing 777, Dubai's Emirates, said it would suspend flights to nine U.S. destinations from Jan. 19, the planned date for the deployment of 5G wireless services.

Regulating AI is complex, and it is very likely errors will be made. Pablo Molina, CISO at Drexel University says that this won´t keep regulators from adopting rules in 2022.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The White House said it "strongly condemns" a deadly attack from Yemen's Houthi rebels on an Abu Dhabi oil facility on Monday that killed three people and sparked a fire at a nearby airport. "Our commitment to the security of the UAE is unwavering and we stand beside our Emirati partners against all threats to their territory," National Security Advisor Jake Sullivan said in a statement Monday afternoon. The Associated Press contributed to this report.

Topic taxonomies, which represent the latent topic (or category) structure of document collections, provide valuable knowledge of contents in many applications such as web search and information filtering. Recently, several unsupervised methods have been developed to automatically construct the topic taxonomy from a text corpus, but it is challenging to generate the desired taxonomy without any prior knowledge. In this paper, we study how to leverage the partial (or incomplete) information about the topic structure as guidance to find out the complete topic taxonomy. We propose a novel framework for topic taxonomy completion, named TaxoCom, which recursively expands the topic taxonomy by discovering novel sub-topic clusters of terms and documents. To effectively identify novel topics within a hierarchical topic structure, TaxoCom devises its embedding and clustering techniques to be closely-linked with each other: (i) locally discriminative embedding optimizes the text embedding space to be discriminative among known (i.e., given) sub-topics, and (ii) novelty adaptive clustering assigns terms into either one of the known sub-topics or novel sub-topics. Our comprehensive experiments on two real-world datasets demonstrate that TaxoCom not only generates the high-quality topic taxonomy in terms of term coherency and topic coverage but also outperforms all other baselines for a downstream task.