This paper presents a personalized lecture concept using educational blocks and its demonstrative application in a new university lecture. Higher education faces daily challenges: deep and specialized knowledge is available from everywhere and accessible to almost everyone. University lecturers of specialized master courses confront the problem that their lectures are either too boring or too complex for the attending students. Additionally, curricula are changing more rapidly than they have in the past 10-30 years. The German education system comprises different educational forms, with universities providing less practical content. Consequently, many university students do not obtain the practical skills they should ideally gain through university lectures. Therefore, in this work, a new lecture concept is proposed based on the extension of the just-in-time teaching paradigm: Personalized and Demand-Based Education. This concept includes: 1) an initial assessment of students' backgrounds, 2) selecting the appropriate educational blocks, and 3) collecting ongoing feedback during the semester. The feedback was gathered via Pingo, ensuring anonymity for the students. Our concept was exemplarily tested in the new lecture "Practical Tools for Control Engineers" at the Karlsruhe Institute of Technology. The initial results indicate that our proposed concept could be beneficial in addressing the current challenges in higher education.

This paper presents the development of a real-time simulator for the validation of controlling a large vehicle manipulator. The need for this development can be justified by the lack of such a simulator: There are neither open source projects nor commercial products, which would be suitable for testing cooperative control concepts. First, we present the nonlinear simulation model of the vehicle and the manipulator. For the modeling MATLAB/Simulink is used, which also enables a code generation into standalone C++ ROS-Nodes (Robot Operating System Nodes). The emerging challenges of the code generation are also discussed. Then, the obtained standalone C++ ROS-Nodes integrated in the simulator framework which includes a graphical user interface, a steering wheel and a joystick. This simulator can provide the real-time calculation of the overall system's motion enabling the interaction of human and automation. Furthermore, a qualitative validation of the model is given. Finally, the functionalities of the simulator is demonstrated in tests with a human operators.

The potential number of applications for machine learning has grown tremendously in the last several years, as AI models become increasingly more powerful. Machine learning is already being used in many areas of daily life, whether that's in recommendation algorithms, self-driving cars, or being used in novel ways in fields like research or finance. Even more promising is how machine learning models might someday revolutionize healthcare, and may even help us grapple with impossibly complex issues like mitigating climate change. But despite the great potential of machine learning models, they are not foolproof and can make mistakes -- sometimes with disastrous consequences. These unintended impacts are all the more concerning when image recognition algorithms are being increasingly used in evaluating people's biometric data.

Today's artificial intelligence systems used for image recognition are incredibly powerful with massive potential for commercial applications. Nonetheless, current artificial neural networks--the deep learning algorithms that power image recognition--suffer one massive shortcoming: they are easily broken by images that are even slightly modified. This lack of "robustness" is a significant hurdle for researchers hoping to build better AIs. However, exactly why this phenomenon occurs, and the underlying mechanisms behind it, remain largely unknown. Aiming to one day overcome these flaws, researchers at Kyushu University's Faculty of Information Science and Electrical Engineering have published in PLOS ONE a method called "Raw Zero-Shot" that assesses how neural networks handle elements unknown to them.

The tracking of citizens' reactions in social media during crises has attracted an increasing level of interest in the research community. In particular, sentiment analysis over social media posts can be regarded as a particularly useful tool, enabling civil protection and law enforcement agencies to more effectively respond during this type of situation. Prior work on sentiment analysis in social media during crises has applied well-known techniques for overall sentiment detection in posts. However, we argue that sentiment analysis of the overall post might not always be suitable, as it may miss the presence of more targeted sentiments, e.g. about the people and organizations involved (which we refer to as sentiment targets). Through a crowdsourcing study, we show that there are marked differences between the overall tweet sentiment and the sentiment expressed towards the subjects mentioned in tweets related to three crises events.

RALEIGH – Less than a year after graduating from Techstars Kansas City Accelerator, MuukLabs has landed $750,000 in convertible notes. The Raleigh-based startup, which offers a no-code test automation platform powered by artificial intelligence, is now getting ready to scale. NY-based Contour Venture Partners led the round. "As an immigrant from Mexico, I'm experiencing firsthand what makes the United States the land of opportunity," said its co-founder Ivan Barajas Vargas. He added that Techstars was "key" to securing this investment.

Gaussian blurring is a well-established method for image data augmentation: it may generate a large set of images from a small set of pictures for training and testing purposes for Artificial Intelligence (AI) applications. When we apply AI for non-imagelike biological data, hardly any related method exists. Here we introduce the "Newtonian blurring" in human braingraph (or connectome) augmentation: Started from a dataset of 1053 subjects, we first repeat a probabilistic weighted braingraph construction algorithm 10 times for describing the connections of distinct cerebral areas, then take 7 repetitions in every possible way, delete the lower and upper extremes, and average the remaining 7-2=5 edge-weights for the data of each subject. This way we augment the 1053 graph-set to 120 x 1053 = 126,360 graphs. In augmentation techniques, it is an important requirement that no artificial additions should be introduced into the dataset. Gaussian blurring and also this Newtonian blurring satisfy this goal. The resulting dataset of 126,360 graphs, each in 5 resolutions (i.e., 631,800 graphs in total), is freely available at the site https://braingraph.org/cms/download-pit-group-connectomes/. Augmenting with Newtonian blurring may also be applicable in other non-image related fields, where probabilistic processing and data averaging are implemented.

You are free to share this article under the Attribution 4.0 International license. Machine learning and human brain scans have revealed the regions of the brain behind how we form abstract concepts, like justice, ethics, and consciousness, researchers report. "Humans have the unique ability to construct abstract concepts that have no anchor in the physical world, but we often take this ability for granted," says senior author Marcel Just, professor of psychology at Carnegie Mellon University. "In this study, we have shown that newly identified components of meaning used by the human brain [act] like an indexing system, similar to a library's card catalog, to compose the meaning of abstract concepts." The ability of humans to think abstractly plays a central role in scientific and intellectual progress.

Tackle football comes with risks like broken arms and strained muscles, but head-on collisions put football players in even more serious danger. Since concussions can be difficult to diagnose, players are sometimes allowed to carry on even though they should be off the field. And untreated head injuries have lasting consequences. To that end, some schools keep doctors on the sidelines to diagnose head injuries. But many schools don't have full-time athletic trainers and the nearest doctor is sometimes far from the football-loving schools in rural America.

The study provides preliminary data to support a nascent movement to utilize teleconcussion equipment at all school sporting events where neurologists or other concussion experts aren't immediately accessible. "I see teleconcussion being applicable anywhere in the world," said Dr. Bert Vargas, the study's lead author, who directs the sports neuroscience and concussion program at the O'Donnell Brain Institute at UT Southwestern Medical Center. Concussion awareness has moved to the mainstream of national dialogue in recent years, fueled by revelations that former NFL players suffered permanent damage to their brains due to repeated head impacts. Having personnel on hand to quickly identify and remove concussed players from games is an important part of protecting against such long-term injuries, Dr. Vargas said. But across the country -- and most notably in rural regions -- more than half of public high schools don't have athletic trainers available to spot such incidents, increasing the chances that a concussion could go unnoticed and perhaps be exacerbated by additional injuries.