The advancement of social media contributes to the growing amount of content they share frequently. This framework provides a sophisticated place for people to report various real-life events. Detecting these events with the help of natural language processing has received researchers' attention, and various algorithms have been developed for this goal. In this paper, we propose a Semantic Modular Model (SMM) consisting of 5 different modules, namely Distributional Denoising Autoencoder, Incremental Clustering, Semantic Denoising, Defragmentation, and Ranking and Processing. The proposed model aims to (1) cluster various documents and ignore the documents that might not contribute to the identification of events, (2) identify more important and descriptive keywords. Compared to the state-of-the-art methods, the results show that the proposed model has a higher performance in identifying events with lower ranks and extracting keywords for more important events in three English Twitter datasets: FACup, SuperTuesday, and USElection. The proposed method outperformed the best reported results in the mean keyword-precision metric by 7.9\%.

This paper proposes a novel methodology for addressing the simulation-reality gap for multi-robot swarm systems. Rather than immediately try to shrink or `bridge the gap' anytime a real-world experiment failed that worked in simulation, we characterize conditions under which this is actually necessary. When these conditions are not satisfied, we show how very simple simulators can still be used to both (i) design new multi-robot systems, and (ii) guide real-world swarming experiments towards certain emergent behaviors when the gap is very large. The key ideas are an iterative simulator-in-the-design-loop in which real-world experiments, simulator modifications, and simulated experiments are intimately coupled in a way that minds the gap without needing to shrink it, as well as the use of minimally viable phase diagrams to guide real world experiments. We demonstrate the usefulness of our methods on deploying a real multi-robot swarm system to successfully exhibit an emergent milling behavior.

Perception, localization, planning, and control, high-level functions often organized in a so-called pipeline, are amongst the core building blocks of modern autonomous (ground, air, and underwater) vehicle architectures. These functions are increasingly being implemented using learning-enabled components (LECs), i.e., (software) components leveraging knowledge acquisition and learning processes such as deep learning. Providing quantified component-level assurance as part of a wider (dynamic) assurance case can be useful in supporting both pre-operational approval of LECs (e.g., by regulators), and runtime hazard mitigation, e.g., using assurance-based failover configurations. This paper develops a notion of assurance for LECs based on i) identifying the relevant dependability attributes, and ii) quantifying those attributes and the associated uncertainty, using probabilistic techniques. We give a practical grounding for our work using an example from the aviation domain: an autonomous taxiing capability for an unmanned aircraft system (UAS), focusing on the application of LECs as sensors in the perception function. We identify the applicable quantitative measures of assurance, and characterize the associated uncertainty using a non-parametric Bayesian approach, namely Gaussian process regression. We additionally discuss the relevance and contribution of LEC assurance to system-level assurance, the generalizability of our approach, and the associated challenges.

School children are using free online artificial intelligence (AI) software to write essays and poetry for them, as well as university applications and even art projects, a teacher told Parliament. Lord Hampton, who is a working teacher in a north London state school, said an A-level product design student of his even generated degree-level designs in minutes using AI. He warned peers in the House of Lords that conversations around AI dominated by issues of plagiarism and intellectual property miss the fact that the curriculum needs to catch up with a changing world. The independent crossbench peer said: "There is a lot of anecdotal evidence, at the moment, that suggests that students are using AI for everything from essays and poetry to university applications and, rather more surprisingly, in the visual arts subjects. Just before Christmas, one of my product design A-level students came up to me and showed me some designs he'd done. "He'd taken a cardboard model, photographed it, put it into a free piece of software, put in three different parameters and had received, within minutes, 20 high-resolution designs, all original, that were degree level - they weren't A-level, they were degree level.

In the upcoming book "Four Battlegrounds: Power in the Age of Artificial Intelligence" by Paul Scharre -- an excerpt of which was posted on Twitter by The Economist defense editor Shashank Joshi -- the author relays an anecdote about a time when the U.S. military used soldiers to refine an AI system's ability to detect people. After six days of training the algorithm by having soldiers walk around, writes Scharre, the engineers on the project flipped the script, pitting the soldiers against the AI by placing the robot in the middle of a traffic circle and tasking the soldiers with approaching it undetected.

A drone attack hit a US-led coalition base in southern Syria, the US military's Central Command has said. "Three one-way attack drones attacked the al-Tanf Garrison in Syria," a CENTCOM statement said on Friday. Two of the drones were shot down by the coalition, but the third hit the compound, wounding two allied Syrian opposition fighters who received treatment, the statement added. "Attacks of this kind are unacceptable," CENTCOM spokesperson Joe Buccino said, without specifying who carried it out. "They place our troops and our partners at risk and jeopardise the fight against ISIL." There was no immediate claim of responsibility for the attack.

The Bosch Research and Technology Center North America with offices in Sunnyvale, California, Pittsburgh, Pennsylvania and Cambridge, Massachusetts is part of the global Bosch Group (www.bosch.com), The Research and Technology Center North America (RTC-NA) is committed to providing technologies and system solutions for various Bosch business fields primarily in the areas of Human Machine Interaction (HMI), Robotics, Energy Technologies, Internet Technologies, Circuit Design, Semiconductors and Wireless, and MEMS Advanced Design. The two research groups at RTC-NA: Wireless connectivity & sensing (WCS), and Intelligent Internet of Things (IIoT) are currently working together with external partners to develop smart docking software for future lunar rovers as part of NASA funded project. The project team is looking for enthusiastic graduate student interns to work on product oriented research. We seek an ideal candidate with good theoretical background and strong desire for practical implementation in the area of multi-sensor fusion (camera, IMUs, wireless) in the context of autonomy/robotics applications.

Automatically generated comments aren't a new problem. For some time, we have struggled with bots, machines that automatically post content. Five years ago, at least a million automatically drafted comments were believed to have been submitted to the Federal Communications Commission regarding proposed regulations on net neutrality. In 2019, a Harvard undergraduate, as a test, used a text-generation program to submit 1,001 comments in response to a government request for public input on a Medicaid issue. Back then, submitting comments was just a game of overwhelming numbers.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Three one-way drones, sometimes called "kamikaze" drones, targeted a U.S. garrison at an outpost in Syria's Al-Tanf region U.S. Central Command said Friday, noting that no Americans were injured in the attack. Two members of the Syrian Free Army received medical attention after they were injured in the strike when one of the drones hit the compound. The other two drones were shot down by Coalition Forces, the U.S. military confirmed.

SmartCardia 7L Patch is a breakthrough 7/14 day patch that offers real-time 7-Lead ECG and vitals with AI SaaS* *SmartCardia solution approved as SCaAI patch and cloud platform in Europe (CE Class IIa) - ECG, respiration, SpO2, activity and cloud based arrhythmia detection.