This paper presents a method for solving the supervised learning problem in which the output is highly nonlinear and discontinuous. It is proposed to solve this problem in three stages: (i) cluster the pairs of input-output data points, resulting in a label for each point; (ii) classify the data, where the corresponding label is the output; and finally (iii) perform one separate regression for each class, where the training data corresponds to the subset of the original input-output pairs which have that label according to the classifier. It has not yet been proposed to combine these 3 fundamental building blocks of machine learning in this simple and powerful fashion. This can be viewed as a form of deep learning, where any of the intermediate layers can itself be deep. The utility and robustness of the methodology is illustrated on some toy problems, including one example problem arising from simulation of plasma fusion in a tokamak.

Bike usage in Smart Cities becomes paramount for sustainable urban development. Cycling provides tremendous opportunities for a more healthy lifestyle, lower energy consumption and carbon emissions as well as reduction of traffic jams. While the number of cyclists increase along with the expansion of bike sharing initiatives and infrastructures, the number of bike accidents rises drastically threatening to jeopardize the bike urban movement. This paper studies cycling risk and discomfort using a diverse spectrum of data sources about geolocated bike accidents and their severity. Empirical continuous spatial risk estimations are calculated via kernel density contours that map safety in a case study of Zurich city. The role of weather, time, accident type and severity are illustrated. Given the predominance of self-caused accidents, an open-source software artifact for personalized route recommendations is introduced. The software is also used to collect open baseline route data that are compared with alternative ones that minimize risk or discomfort. These contributions can provide invaluable insights for urban planners to improve infrastructure. They can also improve the risk awareness of existing cyclists' as well as support new cyclists, such as tourists, to safely explore a new urban environment by bike.

San Francisco supervisors approved a ban on police using facial recognition technology, making it the first city in the U.S. with such a restriction. SAN FRANCISCO – San Francisco supervisors voted Tuesday to ban the use of facial recognition software by police and other city departments, becoming the first U.S. city to outlaw a rapidly developing technology that has alarmed privacy and civil liberties advocates. The ban is part of broader legislation that requires city departments to establish use policies and obtain board approval for surveillance technology they want to purchase or are using at present. Several other local governments require departments to disclose and seek approval for surveillance technology. "This is really about saying: 'We can have security without being a security state. We can have good policing without being a police state.' And part of that is building trust with the community based on good community information, not on Big Brother technology," said Supervisor Aaron Peskin, who championed the legislation.

In this Oct. 31 photo, a man has his face painted to represent efforts to defeat facial recognition. It was during a protest at Amazon headquarters over the company's facial recognition system. In this Oct. 31 photo, a man has his face painted to represent efforts to defeat facial recognition. It was during a protest at Amazon headquarters over the company's facial recognition system. San Francisco has become the first U.S. city to ban the use of facial recognition technology by police and city agencies.

The FAA plans to release its remote identification ruling for UAS in July, UAS Integration Office Executive Director Jay Merkle said in front of Congress last week. The remote ID rules -- often compared to license plates for drones -- would allow the FAA, police officers and other public officials to look up a UAS by a broadcast unique identifier and find out information about the operator. This would go hand-in-hand with registration rules to prevent uncooperative flights around airports or other illegal uses from going unpunished. "We are working currently to ensure that we keep the policy component along with standards and remote id infrastructure all developed and harmonized," Merkle said during a Senate Commerce Committee hearing about integrating new entrants into the National Airspace System. Remote ID has its detractors, who say it exposes too much private information of operators, but the FAA determined that it is necessary since, unlike with a car, the operator is not present, and there needs to be some accountability attached to that anonymity.

A Navy scientist has re-engineered the software that allows long-endurance drones to powerlessly climb into the sky on bubbles of warm air. In a U.S. patent application published on May 2, Aaron Kahn, an engineer working on the Autonomous Locator of Thermals (ALOFT) project at the Naval Research Laboratory, reported that he has extensively tested the new software that detects and estimates the position of thermals, i.e., rising columns of warm air that birds use to stay aloft without flapping their wings. Unlike birds, soaring drones need the benefits of thermal detection and position estimation software as the warm air tends to drift relative to the ground due to winds. Prior systems relied on batch estimation processes that "require storing large arrays of data, which is not ideal for operation on small micro-controllers with limited memory resources." Kahn's new soaring software uses extended Kalman filtering, a kind of algorithm already used by the Navy for navigating submarines and cruise missiles. Now it can help orbit drones like the tiny CICADA glider or long-endurance solar-soaring UAVs that might also have photovoltaic or fuel cells feeding battery-powered propellers.

We demonstrate that it is possible to leverage big data in the form of tweets and linked webpages to find expressions of sentiment that signal "bad behavior" such as cyber attacks. We hypothesize that expressions of "outrage" (high intensity, negative affect sentiment) against an organization in public data may be predictive of cyber attacks for two reasons: 1) threat actors may be motivated to launch an attack based on anger/discontent, and 2) outrage associated with an organization or industry may increase the likelihood of that organization or industry being victimized by threat actors (i.e., as a form of "vigilante justice"). We measure sentiment in online content and determine trends in public emotion and their correlation to trends in cyber attacks, as reported in Hackmageddon. We demonstrate that dimensions of sentiment, as afforded by our use of the Circumplex model of emotion, do yield correlations to reported cyber attacks, but differ dependent upon the domain of the data. Thus the use of this technique requires careful analysis for optimal application.

We synthesize shared control protocols subject to probabilistic temporal logic specifications. More specifically, we develop a framework in which a human and an autonomy protocol can issue commands to carry out a certain task. We blend these commands into a joint input to a robot. We model the interaction between the human and the robot as a Markov decision process (MDP) that represents the shared control scenario. Using inverse reinforcement learning, we obtain an abstraction of the human's behavior and decisions. We use randomized strategies to account for randomness in human's decisions, caused by factors such as complexity of the task specifications or imperfect interfaces. We design the autonomy protocol to ensure that the resulting robot behavior satisfies given safety and performance specifications in probabilistic temporal logic. Additionally, the resulting strategies generate behavior as similar to the behavior induced by the human's commands as possible. We solve the underlying problem efficiently using quasiconvex programming. Case studies involving autonomous wheelchair navigation and unmanned aerial vehicle mission planning showcase the applicability of our approach.

The FLAIRS poster track is designed to promote discussion of emerging ideas and work in order to encourage and help guide researchers — especially new researchers — who are able to present a full poster in the conference poster session and receive that critical work-shaping feedback that helps guide good work into great work. Abstracts of those posters appear here, which we hope to see fully developed into future FLAIRS papers..

Navigating a career constitutes one of life’s most enduring challenges, particularly within a unique organization like the US Navy. While the Navy has numerous resources for guidance, accessing and identifying key information sources across the many existing platforms can be challenging for sailors (e.g., determining the appropriate program or point of contact, developing an accurate understanding of the process, and even recognizing the need for planning itself). Focusing on intermediate goals, evaluations, education, certifications, and training is quite demanding, even before considering their cumulative long-term implications. These are on top of generic personal issues, such as financial difficulties and homesickness when at sea for prolonged periods. We present the preliminary construction of a conversational intelligent agent designed to provide a user-friendly, adaptive environment that recognizes user input pertinent to these issues and provides guidance to appropriate resources within the Navy. User input from “counseling sessions” is linked, using advanced natural language processing techniques, to our framework of Navy training and education standards, promotion protocols, and organizational structure, producing feedback on resources and recommendations sensitive to user history and stated career goals. The proposed innovative technology monitors sailors’ career progress, proactively triggering sessions before major career milestones or when performance drops below Navy expectations, by using a mixed-initiative design. System-triggered sessions involve positive feedback and informative dialogues (using existing Navy career guidance protocols). The intelligent agent also offers counseling for personal problems, triggering targeted dialogues designed to gather more information, offer tailored suggestions, and provide referrals to appropriate resources or to a human counselor when in-depth counseling is warranted. This software, currently in alpha testing, has the potential to serve as a centralized information hub, engaging and encouraging sailors to take ownership of their career paths in the most efficient way possible, benefiting both individuals and the Navy as a whole.