An Indian textile baron has revealed that he was duped out of 70 million rupees ( 833,000) by online scammers impersonating federal investigators and even the Supreme Court chief justice. The fraudsters posing as officers from India's Central Bureau of Investigation (CBI) called SP Oswal, chairman and managing director of the textile manufacturer Vardhman, on August 28 and accused him of money laundering. For the next two days, Oswal was under digital surveillance as he was ordered to keep Skype open on his phone 24/7 during which he was interrogated and threatened with arrest. The fraudsters also conducted a fake virtual court hearing with a digital impersonation of Chief Justice of India DY Chandrachud as the judge. Oswal paid the amount after the court verdict via Skype without realising that he was the latest victim of an online scam using a new modus operandi, called "digital arrest".

During the 2016 U.S. presidential race, a Russian "troll-farm" calling itself the Internet Research Agency sought to harm Hillary Clinton's election chances and help Donald Trump reach the White House by using Twitter to spread false news stories and other disinformation, according to a 2020 report from the Senate Intelligence Committee. Most of that content apparently was produced by human beings, a supposition supported by the fact that activity dropped off on Russian holidays. Soon, though, if not already, such propaganda will be produced automatically by artificial intelligence (AI) systems such as ChatGPT, a chatbot capable of creating human-sounding text. "Imagine a scenario where you have ChatGPT generating these tweets. The number of fake accounts you could manage for the same price would be much larger," says V.S. Subrahmanian, a professor of computer science at Northwestern University, whose research focuses on the intersection of AI and security problems.

On August 27, 2020, Amazon introduced its Amazon Halo: a technology comprised of AI software and a wristband that monitors body indicators including voice to detect problems, suggests a behavioral change, or other actions to potentially improve our health.a One day later, Elon Musk and his team presented their Neuralink technology--AI software and a skull chip implant that receives and sends signals to our brain to compensate for brain malfunctioning, aiming to solve various brain-related health problems. These announcements seem like great news amid the health crisis that engulfs many of us, with technology coming to our rescue to confront some of the most critical diseases of humankind. Yet risks remain, and once the genie is out of the bottle, they are often difficult to manage and contain--they range from unintended consequences and side effects to threats to privacy and loss or misdirection of control. Endless devices surrounding us include processors that compute and monitor our abundant but wasteful lifestyle, with generations of products getting faster, cheaper, and "better."

The recent emergence of U.S. Navy videos of UFOs--and the fact that the government is addressing them seriously--will no doubt generate larger than average buzz around "Roswell: The Final Verdict," although the title suggests something like "Final Destination 6": Will the question of intergalactic life ever really be resolved until extraterrestrials can walk comfortably among us? "Final Verdict" is hooked to the 74th anniversary of the incidents at Roswell. It's safe to expect similar celebrations next year. Meanwhile, this Discovery production is an ambitious if somewhat overheated summing-up of what happened near the New Mexico city in 1947, the stuff of both scientific speculation and folklore: Did the government cover up the crash landing of an alien spaceship, replete with otherworldly visitors? Or did the "witnesses" who claimed that it all happened construct an elaborate hoax?

During World War II, British intelligence agents planted false documents on a corpse to fool Nazi Germany into preparing for an assault on Greece. "Operation Mincemeat" was a success, and covered the actual Allied invasion of Sicily. The "canary trap" technique in espionage spreads multiple versions of false documents to conceal a secret. Canary traps can be used to sniff out information leaks, or as in WWII, to create distractions that hide valuable information. WE-FORGE, a new data protection system designed in the Department of Computer Science, uses artificial intelligence to build on the canary trap concept.

But over the last, I would say 15 years, what's become increasingly clear is that AI, artificial intelligence, is going to play a huge role both on the defensive side and on the offensive side. So, over the next few years, we're going to see people increasingly use AI to attack systems

A number of organizations ranging from terrorist groups such as ISIS to politicians and nation states reportedly conduct explicit campaigns to influence opinion on social media, posing a risk to democratic processes. There is thus a growing need to identify and eliminate "influence bots" - realistic, automated identities that illicitly shape discussion on sites like Twitter and Facebook - before they get too influential. Spurred by such events, DARPA held a 4-week competition in February/March 2015 in which multiple teams supported by the DARPA Social Media in Strategic Communications program competed to identify a set of previously identified "influence bots" serving as ground truth on a specific topic within Twitter. Past work regarding influence bots often has difficulty supporting claims about accuracy, since there is limited ground truth (though some exceptions do exist [3,7]). However, with the exception of [3], no past work has looked specifically at identifying influence bots on a specific topic. This paper describes the DARPA Challenge and describes the methods used by the three top-ranked teams.

We present a unified logical framework for representing and reasoning about both quantitative and qualitative preferences in fuzzy answer set programming [Saad, 2010; Saad, 2009; Subrahmanian, 1994], called fuzzy answer set optimization programs. The proposed framework is vital to allow defining quantitative preferences over the possible outcomes of qualitative preferences. We show the application of fuzzy answer set optimization programs to the course scheduling with fuzzy preferences problem described in [Saad, 2010]. To the best of our knowledge, this development is the first to consider a logical framework for reasoning about quantitative preferences, in general, and reasoning about both quantitative and qualitative preferences in particular.

Hybrid Probabilistic Programs (HPPs) are logic programs that allow the programmer to explicitly encode his knowledge of the dependencies between events being described in the program. In this paper, we classify HPPs into three classes called HPP_1,HPP_2 and HPP_r,r>= 3. For these classes, we provide three types of results for HPPs. First, we develop algorithms to compute the set of all ground consequences of an HPP. Then we provide algorithms and complexity results for the problems of entailment ("Given an HPP P and a query Q as input, is Q a logical consequence of P?") and consistency ("Given an HPP P as input, is P consistent?"). Our results provide a fine characterization of when polynomial algorithms exist for the above problems, and when these problems become intractable.

Recently, geospatial abduction was introduced by the authors in [Shakarian et. al. 2010] as a way to infer unobserved geographic phenomena from a set of known observations and constraints between the two. In this paper, we introduce the SCARE-S2 software tool which applies geospatial abduction to the environment of Afghanistan. Unlike previous work, where we looked for small weapon caches supporting local attacks, here we look for insurgent high-value targets (HVT's), supporting insurgent operations in two provinces. These HVT's include the locations of insurgent leaders and major supply depots. Applying this method of inference to Afghanistan introduces several practical issues not addressed in previous work. Namely, we are conducting inference in a much larger area (24,940 sq km as compared to 675 sq km in previous work), on more varied terrain, and must consider the influence of many local tribes. We address all of these problems and evaluate our software on 6 months of real-world counter-insurgency data. We show that we are able to abduce regions of a relatively small area (on average, under 100 sq km and each containing, on average, 4.8 villages) that are more dense with HVT's (35 X more than the overall area considered).