Thermal preferences vary from person to person and may change over time. The main objective of this paper is to sequentially pose intelligent queries to occupants in order to optimally learn the indoor air temperature values which maximize their satisfaction. Our central hypothesis is that an occupant's preference relation over indoor air temperature can be described using a scalar function of these temperatures, which we call the "occupant's thermal utility function". Information about an occupant's preference over these temperatures is available to us through their response to thermal preference queries : "prefer warmer," "prefer cooler" and "satisfied" which we interpret as statements about the derivative of their utility function, i.e. the utility function is "increasing", "decreasing" and "constant" respectively. We model this hidden utility function using a Gaussian process prior with built-in unimodality constraint, i.e., the utility function has a unique maximum, and we train this model using Bayesian inference. This permits an expected improvement based selection of next preference query to pose to the occupant, which takes into account both exploration (sampling from areas of high uncertainty) and exploitation (sampling from areas which are likely to offer an improvement over current best observation). We use this framework to sequentially design experiments and illustrate its benefits by showing that it requires drastically fewer observations to learn the maximally preferred temperature values as compared to other methods. This framework is an important step towards the development of intelligent HVAC systems which would be able to respond to occupants' personalized thermal comfort needs. In order to encourage the use of our PE framework and ensure reproducibility in results, we publish an implementation of our work named GPPrefElicit as an open-source package in Python.

To be prepared against cyberattacks, most organizations resort to security information and event management systems to monitor their infrastructures. These systems depend on the timeliness and relevance of the latest updates, patches and threats provided by cyberthreat intelligence feeds. Open source intelligence platforms, namely social media networks such as Twitter, are capable of aggregating a vast amount of cybersecurity-related sources. To process such information streams, we require scalable and efficient tools capable of identifying and summarizing relevant information for specified assets. This paper presents the processing pipeline of a novel tool that uses deep neural networks to process cybersecurity information received from Twitter. A convolutional neural network identifies tweets containing security-related information relevant to assets in an IT infrastructure. Then, a bidirectional long short-term memory network extracts named entities from these tweets to form a security alert or to fill an indicator of compromise. The proposed pipeline achieves an average 94% true positive rate and 91% true negative rate for the classification task and an average F1-score of 92% for the named entity recognition task, across three case study infrastructures.

Synthetic aperture sonar (SAS) imagery can generate high resolution images of the seafloor. Thus, segmentation algorithms can be used to partition the images into different seafloor environments. In this paper, we compare two possibilistic segmentation approaches. Possibilistic approaches allow for the ability to detect novel or outlier environments as well as well known classes. The Possibilistic Fuzzy Local Information C-Means (PFLICM) algorithm has been previously applied to segment SAS imagery. Additionally, the Possibilistic K-Nearest Neighbors (PKNN) algorithm has been used in other domains such as landmine detection and hyperspectral imagery. In this paper, we compare the segmentation performance of a semi-supervised approach using PFLICM and a supervised method using Possibilistic K-NN. We include final segmentation results on multiple SAS images and a quantitative assessment of each algorithm.

Deep learning has been successfully used in numerous applications because of its outstanding performance and the ability to avoid manual feature engineering. One such application is electroencephalogram (EEG) based brain-computer interface (BCI), where multiple convolutional neural network (CNN) models have been proposed for EEG classification. However, it has been found that deep learning models can be easily fooled with adversarial examples, which are normal examples with small deliberate perturbations. This paper proposes an unsupervised fast gradient sign method (UFGSM) to attack three popular CNN classifiers in BCIs, and demonstrates its effectiveness. We also verify the transferability of adversarial examples in BCIs, which means we can perform attacks even without knowing the architecture and parameters of the target models, or the datasets they were trained on. To our knowledge, this is the first study on the vulnerability of CNN classifiers in EEG-based BCIs, and hopefully will trigger more attention on the security of BCI systems.

In recent months the science fiction world has grown increasingly political, with dozens of writers contributing stories to anthologies such as Resist: Tales from a Future Worth Fighting Against and If This Goes On. Another prominent example is A People's Future of the United States, edited by Victor LaValle and John Joseph Adams. "I wanted to use my position as an editor to try to help magnify the voices of the people that we invited to participate in this anthology," Adams says in Episode 354 of the Geek's Guide to the Galaxy podcast. "To sort of shout back at the Trump administration, and also to try to imagine some new futures that might help us figure out how to get back to normal from here." The book draws inspiration (and its title) from Howard Zinn's counterculture classic A People's History of the United States, and like that earlier work, A People's Future of the United States tries to present a wide variety of marginalized perspectives.

It's not every day that an Air Force captain can give a four-star general an earful. Gen. Stephen Wilson, the vice chief of staff, invites input from his very junior colleague because Kanaan's expertise is artificial intelligence. Wilson says he believes AI's ability to sort mountains of data to find targets like terrorists is a way to change the nature of war. The Air Force needs to lean on Kanaan and other young, tech-savvy airmen, Wilson says, to help transform the way it uses data. "It's pretty unusual," Wilson says of his relationship with Kanaan.

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. NASA is sending a small helicopter to Mars in 2020, and it managed to get airborne in a simulated Martian atmosphere without crashing or exploding. I really want to get excited about this thing, and from a technology perspective, I am.

The HTA Robotics Team seeks help raising funds for our trip to the Vex IQ Robotics World Championships in Louisville, KY. Our students have worked tirelessly this season, we now need your help to reach our goal of attending the World Championships. In order to meet this goal, we are asking for donations or sponsorships from local businesses. By giving, you will be helping our students represent HTA and our great State of Hawaii as they compete with the best teams from around the world in this prestigious international competition.

The UK government is among a group of countries that are attempting to thwart plans to formulate and impose a pre-emptive ban on killer robots. Delegates have been meeting at the UN in Geneva all week to discuss potential restrictions under international law to so-called lethal autonomous weapons systems, which use artificial intelligence to help decide when and who to kill. Most states taking part – and particularly those from the global south – support either a total ban or strict legal regulation governing their development and deployment, a position backed by the UN secretary general, António Guterres, who has described machines empowered to kill as "morally repugnant". But the UK is among a group of states – including Australia, Israel, Russia and the US – speaking forcefully against legal regulation. As discussions operate on a consensus basis, their objections are preventing any progress on regulation.

When Stanford announced a new artificial intelligence institute, the university said the "designers of AI must be broadly representative of humanity" and unveiled 120 faculty and tech leaders partnering on the initiative. Some were quick to notice that not a single member of this "representative" group appeared to be black. The backlash was swift, sparking discussion on the severe lack of diversity across the AI field. But the problems surrounding representation extend far beyond exclusion and prejudice in academia. Major tech corporations have launched AI "ethics" boards that not only lack diversity, but sometimes include powerful people with interests that don't align with the ethics mission.