The Industrial/Military Complex is saturated with Technocrats who have algorithmic solutions for everything, including warfare. WWIII will be fought with AI-driven asymmetric tactics at the speed of light and far beyond human ability to understand what it is doing. The Joint Operations Center inside Fort Meade in Maryland is a cathedral to cyber warfare. Part of a 380,000-square-foot, $520 million complex opened in 2018, the office is the nerve center for both the U.S. Cyber Command and the National Security Agency as they do cyber battle. Clusters of civilians and military troops work behind dozens of computer monitors beneath a bank of small chiclet windows dousing the room in light.Three 20-foot-tall screens are mounted on a wall below the windows.

Deepfakes are indeed scary and have managed to strike a nerve for many, especially the ones being victimised for this sophisticated technology. Not only has it become a worldwide concern for many due to its influential impact on election campaigns but also made people anxious due to the criminal activity associated with it. With easily accessible deepfake making tools available for anybody to use and advancements in GANs has made it relatively easy for notorious minds to create these eerie-looking unreal AI-generated videos and images. Such improvement and accessibility has in turn increased the number of deepfake incidents in recent times. Some of them are so incredibly convincing that they manage to surpass the original videos. This news showcased one of the weirder applications of deep fakes, that used artificial intelligence to manipulate an audio-visual content -- a less heard usage, termed as audio deepfake scam.

The Joint Operations Center inside Fort Meade in Maryland is a cathedral to cyber warfare. Part of a 380,000-square-foot, $520 million complex opened in 2018, the office is the nerve center for both the U.S. Cyber Command and the National Security Agency as they do cyber battle. Clusters of civilians and military troops work behind dozens of computer monitors beneath a bank of small chiclet windows dousing the room in light. Three 20-foot-tall screens are mounted on a wall below the windows. On most days, two of them are spitting out a constant feed from a secretive program known as "Project IKE." The room looks no different than a standard government auditorium, but IKE represents a radical leap forward. If the Joint Operations Center is the physical embodiment of a new era in cyber warfare -- the art of using computer code to attack and defend targets ranging from tanks to email servers -- IKE is the brains. It tracks every keystroke made by the 200 fighters working on computers below the big screens and churns out predictions about the possibility of success on individual cyber missions. It can automatically run strings of programs and adjusts constantly as it absorbs information. IKE is a far cry from the prior decade of cyber operations, a period of manual combat that involved the most mundane of tools.

Malware, phishing and ransomware are constantly keeping security teams on their toes. But there is one risk to data security that cannot be stopped by cybersecurity software: human error. Unlike malicious threat actors, human error doesn't come and go as trends in the cyber landscape change. It is true of data breaches throughout history: indeed, a CybSafe study found that human error caused 90% of cyber data breaches in the UK during 2019. For organisations looking to protect intellectual property, or shield customer data, human error is the most dangerous threat of all.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Iran is allegedly mulling over an attempt to assassinate the United States' ambassador to South Africa as retaliation for the American drone attack earlier this year that killed Qassem Soleimani, the head of the Islamic Revolutionary Guard Corps' elite Quds Force. The news of Tehran's purported plans was first reported by Politico, who spoke with one official familiar with the issue and another official who has seen the intelligence. If Iran does attempt to carry out the assassination, it would severely ratchet up the already tense relations between Washington and Tehran, along with giving the Trump administration impetus to retaliate.

The possible risk that AI systems could promote discrimination by reproducing and enforcing unwanted bias in data has been broadly discussed in research and society. Many current legal standards demand to remove sensitive attributes from data in order to achieve "fairness through unawareness". We argue that this approach is obsolete in the era of big data where large datasets with highly correlated attributes are common. In the contrary, we propose the active use of sensitive attributes with the purpose of observing and controlling any kind of discrimination, and thus leading to fair results. Systematic, unequal treatment of individuals based on their membership of a sensitive group is considered discrimination.

There is increased interest in assisting non-expert audiences to effectively interact with machine learning (ML) tools and understand the complex output such systems produce. Here, we describe user experiments designed to study how individual skills and personality traits predict interpretability, explainability, and knowledge discovery from ML generated model output. Our work relies on Fuzzy Trace Theory, a leading theory of how humans process numerical stimuli, to examine how different end users will interpret the output they receive while interacting with the ML system. While our sample was small, we found that interpretability -- being able to make sense of system output -- and explainability -- understanding how that output was generated -- were distinct aspects of user experience. Additionally, subjects were more able to interpret model output if they possessed individual traits that promote metacognitive monitoring and editing, associated with more detailed, verbatim, processing of ML output. Finally, subjects who are more familiar with ML systems felt better supported by them and more able to discover new patterns in data; however, this did not necessarily translate to meaningful insights. Our work motivates the design of systems that explicitly take users' mental representations into account during the design process to more effectively support end user requirements.

This document describes Pilot, our submission for Human-Agent Negotiation Challenge at IJCAI 2020. Pilot is a virtual human that participates in a sequence of three negotiations with a human partner. Our system is based on the Interactive Arbitration Guide Online (IAGO) negotiation framework. We leverage prior Affective Computing and Psychology research in negotiations to guide various key principles that define the behavior and personality of our agent. Pilot has been selected as one of the finalists for presentation at IJCAI.

Community detection is a fundamental problem in the analysis of complex networks. It is the analogue of clustering in network data mining. Within community detection methods, hierarchical algorithms are popular. However, their iterative nature and the need to recompute the structural properties used to split the network (i.e. edge betweenness in Girvan and Newman's algorithm), make them unsuitable for large network data sets. In this paper, we study how local structural network properties can be used as proxies to improve the efficiency of hierarchical community detection while, at the same time, achieving competitive results in terms of modularity. In particular, we study the potential use of the structural properties commonly used to perform local link prediction, a supervised learning problem where community structure is relevant, as nodes are prone to establish new links with other nodes within their communities. In addition, we check the performance impact of network pruning heuristics as an ancillary tactic to make hierarchical community detection more efficient

Independent sociological polls are forbidden in Belarus. Online polls performed without sound scientific rigour do not yield representative results. Yet, both inside and outside Belarus it is of great importance to obtain precise estimates of the ratings of all candidates. These ratings could function as reliable proxies for the election's outcomes. We conduct an independent poll based on the combination of the data collected via Viber and on the streets of Belarus. The Viber and the street data samples consist of almost 45000 and 1150 unique observations respectively. Bayesian regressions with poststratification were build to estimate ratings of the candidates and rates of early voting turnout for the population as a whole and within various focus subgroups. We show that both the officially announced results of the election and early voting rates are highly improbable. With a probability of at least 95%, Sviatlana Tikhanouskaya's rating lies between 75% and 80%, whereas Aliaksandr Lukashenka's rating lies between 13% and 18% and early voting rate predicted by the method ranges from 9% to 13% of those who took part in the election. These results contradict the officially announced outcomes, which are 10.12%, 80.11%, and 49.54% respectively and lie far outside even the 99.9% credible intervals predicted by our model. The only marginal groups of people where the upper bounds of the 99.9% credible intervals of the rating of Lukashenka are above 50% are people older than 60 and uneducated people. For all other marginal subgroups, including rural residents, even the upper bounds of 99.9% credible intervals for Lukashenka are far below 50%. The same is true for the population as a whole. Thus, with a probability of at least 99.9% Lukashenka could not have had enough electoral support to win the 2020 presidential election in Belarus.