Applied Intelligence manuscript No. (will be inserted by the editor) Abstract The widespread adoption of smartphones dramaticallygenerated by our attacks models when used to harden increases the risk of attacks and the spread the developed anti-malware system improves the detection of mobile malware, especially on the Android platform. Machine learning based solutions have been already Keywords Adversarial machine learning · malware used as a tool to supersede signature based anti-malware detection · poison attacks · adversarial example · systems. However, malware authors leverage attributes jacobian algorithm. Hence, to evaluate the vulnerability of machine 1 Introduction learning algorithms in malware detection, we propose five different attack scenarios to perturb malicious applications Nowadays using the Android application is very popular (apps). Every Android application inappropriately fits discriminant function on has a Jar-like APK format and is an archive file which the set of data points, eventually yielding a higher misclassification contains Android manifest and Classes.dex Further, to distinguish the adversarial manifest file holds information about the application examples from benign samples, we propose two defense structure and each part responsible for certain actions. To validate our For instance, the requested permissions must be accepted attacks and solutions, we test our model on three different by the users for successful installation of applications. We also test our methods The manifest file contains a list of hardware using various classifier algorithms and compare them components and permissions required by each application. Promising results show that generated the manifest file that are useful for running applications. Additionally, evasive variants is saved as the classes.dex In a nutshell, the by presenting some adversary-aware approaches?generated malware sample is statistically identical to a Do we require retraining of the current ML model to designbenign sample. To do so, adversaries adopt adversarial adversary-aware learning algorithms? How to properlymachine learning algorithms (AML) to design an example test and validate the countermeasure solutions inset called poison data which is used to fool machine a real-world network? The goal of this paper is to shedlearning models.

Predictive analysis in business process monitoring aims at forecasting the future information of a running business process. The prediction is typically made based on the model extracted from historical process execution logs (event logs). In practice, different business domains might require different kinds of predictions. Hence, it is important to have a means for properly specifying the desired prediction tasks, and a mechanism to deal with these various prediction tasks. Although there have been many studies in this area, they mostly focus on a specific prediction task. This work introduces a language for specifying the desired prediction tasks, and this language allows us to express various kinds of prediction tasks. This work also presents a mechanism for automatically creating the corresponding prediction model based on the given specification. Differently from previous studies, instead of focusing on a particular prediction task, we present an approach to deal with various prediction tasks based on the given specification of the desired prediction tasks. We also provide an implementation of the approach which is used to conduct experiments using real-life event logs.

By any criteria, 5G is a significant advance on the previous 4G standards. The first 5G networks already offer download speeds of up to 600 megabits per second and have the potential to get significantly faster. By contrast, 4G generally operates at up to 28 Mbits/s--and most mobile-phone users will have experienced that rate grinding to zero from time to time, for reasons that aren't always clear.

As Uber finally closes in on its IPO, its self-driving car unit is getting a big cash infusion and some independence. The company announced tonight that Toyota, Denso and Softbank are investing a total of $1 billion in its Advanced Technologies Group (Uber ATG), in a deal that values that part of the company at $7.25 billion. This adds onto Toyota's $500 million investment last year, which the two said would lead to the creation of an autonomous fleet based on Toyota's Sienna minivan. So far, many of the big car companies are teaming up to develop autonomous tech combined with ridesharing angles as it's expected to be a huge market in the next few years. According to Uber CEO Dara Khosrowshahi, "The development of automated driving technology will transform transportation as we know it, making our streets safer and our cities more livable. Today's announcement, along with our ongoing OEM and supplier relationships, will help maintain Uber's position at the forefront of that transformation."

When Uber publicly filed for an initial public offering last week, it cemented its reputation as a technology behemoth with more than a few liabilities. One particularly weighty albatross: its Autonomous Technology Group, which since 2015 has poured hundreds of millions into building self-driving car tech it has yet to commercialize. Make that at least $1 billion: According to the filing, Uber spent $457 million in 2018 on research and development for autonomous vehicles (and its other tech moonshots, like "flying taxis")--a figure up 19 percent from 2017. So it was good news for Uber--not to mention the potential shareholders circling its IPO--when it announced a major investment into its Autonomous Technology Group from a Japanese consortium on Thursday. The $1 billion infusion comes from Toyota, the automotive supplier Denso, and the Softbank Vision Fund, which is aggressively bankrolling ambitious transportation technology companies.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. It only takes 10 Spotpower (SP) to haul a truck across the Boston Dynamics parking lot ( 1 degree uphill, truck in neutral). These Spot robots are coming off the production line now and will be available for a range of applications soon.

A natural gas fired turbine, manufactured by Caterpillar Inc. subsidiary Solar Turbines Inc., runs a compressor at a Williston Basin Interstate Pipeline Co., a subsidiary of MDU Resources Group Inc., natural gas compression station in Bismarck, North Dakota. In the world of oil and natural gas, engineers, geologists, and drilling and production departments tend to get the lion's share of the credit when good things happen, and most of the blame when they don't. That's fair, given the crucial roles these groups of employees play within the thousands of companies that make up the U.S. oil and gas industry. But in recent years, as overall domestic production has risen at a pace no one could have foreseen even five years ago, the credit has begun to shift. These human resources remain indispensable to the success of any company, but the deployment of a raft of advancing technologies has played an ever-advancing role over time in enabling companies to maximize recoveries and profits.

Routine tasks kill productivity and creativity. It's an obvious fact, but if you need proof, there are stats showing that people spend up to 80 percent of their average workday on activities of little to zero value. How can organizations solve the problem? Incorporating chatbots is one of the most innovative and effective answers. When Merge Queue, or MQ for short, joined one of our projects, we all felt relieved.

Before the A.C.A., data brokers bought data from pharmacies and sold it to insurance companies, which would then deny coverage based on prescription histories. Future uses of data in insurance will not be so straightforward. As machine learning works its way into more and more decisions about who gets coverage and what it costs, discrimination becomes harder to spot. Part of the problem is the automatic deference that society has so often given to technology, as though artificial intelligence is unerring. But the other problem is that artificial intelligence is known to reproduce biases that aren't explicitly coded into it.

Artificial intelligence systems can – if properly used – help make government more effective and responsive, improving the lives of citizens. Improperly used, however, the dystopian visions of George Orwell's "1984" become more realistic. On their own and urged by a new presidential executive order, governments across the U.S., including state and federal agencies, are exploring ways to use AI technologies. As an AI researcher for more than 40 years, who has been a consultant or participant in many government projects, I believe it's worth noting that sometimes they've done it well – and other times not quite so well. The potential harms and benefits are significant.