This paper reports a case study of an application of high-resolution agent-based modeling and simulation to pandemic response planning on a university campus. In the summer of 2020, we were tasked with a COVID-19 pandemic response project to create a detailed behavioral simulation model of the entire campus population at Binghamton University. We conceptualized this problem as an agent migration process on a multilayer transportation network, in which each layer represented a different transportation mode. As no direct data were available about people's behaviors on campus, we collected as much indirect information as possible to inform the agents' behavioral rules. Each agent was assumed to move along the shortest path between two locations within each transportation layer and switch layers at a parking lot or a bus stop, along with several other behavioral assumptions. Using this model, we conducted simulations of the whole campus population behaviors on a typical weekday, involving more than 25,000 agents. We measured the frequency of close social contacts at each spatial location and identified several busy locations and corridors on campus that needed substantial behavioral intervention. Moreover, systematic simulations with varying population density revealed that the effect of population density reduction was nonlinear, and that reducing the population density to 40-45% would be optimal and sufficient to suppress disease spreading on campus. These results were reported to the university administration and utilized in the pandemic response planning, which led to successful outcomes.

FAQ documents are commonly used with text documents and websites to provide important information in the form of question answer pairs to either aid in reading comprehension or provide a shortcut to the key ideas. We suppose that salient sentences from a given document serve as a good proxy fro the answers to an aggregated set of FAQs from readers. We propose a system for generating FAQ documents that extract the salient questions and their corresponding answers from sizeable text documents scraped from the Stanford Encyclopedia of Philosophy. We use existing text summarization, sentence ranking via the Text rank algorithm, and question-generation tools to create an initial set of questions and answers. Finally, we apply some heuristics to filter out invalid questions. We use human evaluation to rate the generated questions on grammar, whether the question is meaningful, and whether the question's answerability is present within a summarized context. On average, participants thought 71 percent of the questions were meaningful.

With the recent prevalence of remote education, academic assessments are often conducted online, leading to further concerns surrounding assessment misconducts. This paper investigates the potentials of online assessment misconduct (e-cheating) and proposes practical countermeasures against them. The mechanism for detecting the practices of online cheating is presented in the form of an e-cheating intelligent agent, comprising of an internet protocol (IP) detector and a behavioural monitor. The IP detector is an auxiliary detector which assigns randomised and unique assessment sets as an early procedure to reduce potential misconducts. The behavioural monitor scans for irregularities in assessment responses from the candidates, further reducing any misconduct attempts. This is highlighted through the proposal of the DenseLSTM using a deep learning approach. Additionally, a new PT Behavioural Database is presented and made publicly available. Experiments conducted on this dataset confirm the effectiveness of the DenseLSTM, resulting in classification accuracies of up to 90.7%.

In the art business, perhaps more than in almost any other industry, information is power--and when it comes to the value of artworks, good information is especially hard to come by. An oft-repeated legend has it that Larry Gagosian rose to prominence in part by securing invitations to collectors' homes, memorizing the trophies on display, and using that knowledge to generate third-party offers high enough to literally sell the artworks off their walls. So it's not entirely surprising that many of the stories in this issue investigate how data--a lack of it, an abundance of it, or a new, high-tech version of it--has the potential to shake the art market's very foundation. To examine the dark side of information asymmetry, we spelunk into the murky story of Inigo Philbrick, a former wunderkind dealmaker who capitalized on the market's opacity--specifically the fact that buyers and sellers typically have no idea who is on the opposite end of a deal--to allegedly swindle his clients to the tune of tens of millions of dollars. We also explore how artificial intelligence could rewire the art market as a much more transparent, highly efficient industry--but only, perhaps, if the parties who have historically hoarded information are willing to share it for the good of the whole.

For entrepreneurial MIT students looking to put their skills to work for a greater good, the Media Arts and Sciences class MAS.664 (AI for Impact) has been a destination point. With the onset of the pandemic, that goal came into even sharper focus. Just weeks before the campus shut down in 2020, a team of students from the class launched a project that would make significant strides toward an open-source platform to identify coronavirus exposures without compromising personal privacy. Their work was at the heart of Safe Paths, one of the earliest contact tracing apps in the United States. The students joined with volunteers from other universities, medical centers, and companies to publish their code, alongside a well-received white paper describing the privacy-preserving, decentralized protocol, all while working with organizations wishing to launch the app within their communities.

Pain Points, Needs, and Design Opportunities This paper is a study done on the usage of notebooks for data science. It cover a bunch of the negative impacts of using notebooks for data science. Deployment, setup, collaboration, and reliablity are a few of the examples. Quantifying the Carbon Emissions of Machine Learning Training a neural network can take a lot of computer processing power. This processing power comes at a cost to the environment.

Vecna Robotics, the autonomous mobile robot and workflow orchestration company, announced it has been recognized as the recipient of three industry accolades. The company has been inducted into the Spring 2020 MIT STEX25 startup accelerator cohort, named an RBR50 Robotics Innovations Award winner by Robotics Business Review and recognized by Supply & Demand Chain Executive as one of the 100 Top Supply Chain Projects of 2020 for its work with Milton CAT. These industry awards highlight the most innovative U.S. startups, leading robotics organizations and the most successful real-world supply chain use-cases. "This has been a historic year for Vecna Robotics," said Daniel Theobald, founder and CEO, Vecna Robotics. "We kicked off 2020 with a strong Series B financing round that helped accelerate our vision and product strategy that we'll continue executing on for the remainder of the year and beyond. These awards validate the commitment we've made to building world-class robotic platforms and orchestration software that helps businesses streamline their logistics operations. A special thank you to each organization for their recognition, and to the Vecna Robotics team for their hard work and dedication."

CSRE is providing a total of $300,000 in funding for the projects, with an additional $300,000 in matching and supplemental funding from other colleges, departments, and institutes. "Today's challenges to global, national, and individual security are numerous and complex," said CSRE Director James W. Houck, "and we are delighted to support these innovative and exciting initiatives." CSRE was established in 2017 to promote interdisciplinary research and education to protect people, infrastructure and institutions from the broad range of threats and hazards confronting society today. Contributing units include the Provost and Office of the Senior Vice President for Research, as well as the colleges of Agricultural Sciences, Earth and Mineral Sciences, Engineering, Information Sciences and Technology, and the Liberal Arts; Penn State Law and the School of International Affairs; Penn State Harrisburg; Applied Research Laboratory; Institute for Computational and Data Sciences; Institutes of Energy and the Environment; Huck Institutes of the Life Sciences; and the Social Sciences Research Institute. In its first three years, CSRE has provided over $633,000 in funding, augmented by an additional $581,000 from contributing units, to a total of 39 seed projects and faculty fellowships and hosted a number of guest speakers, workshops and other events.

Sign in to report inappropriate content. Michael Littman is a professor of computer science at Brown University. He is an AAAI Fellow and an ACM Fellow. He's been contributing to the field of AI and Reinforcement Learning since the early days. He's helped a new generation of RL students by creating the Machine Learning and the Reinforcement Learning and Decision Making lectures available online on Udacity for free, which is the same course material used at Georgia Tech's OMSCS program.

Grid Date added (newest) Uploads Reinforcement Learning (Machine Learning TMLS20_T0139 Spring 2020) - Duration: 1 hour, 11 minutes. School of AI January 2020 One shot object detection & YOLOv3 by Ivan Goncharov - Duration: 43 minutes. Knowledge is the power Earthling Ed's talk in the Netherlands - Duration: 43 minutes. Our road trip Zwillbrocker Venn Flamingo watch - Duration: 2 minutes, 14 seconds. Reinforcement Learning a SLAM Based Approach - Duration: 3 minutes, 3 seconds.