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Epically Powerful: An open-source software and mechatronics infrastructure for wearable robotic systems

Leestma, Jennifer K., Nathella, Siddharth R., Nuesslein, Christoph P. O., Mathur, Snehil, Sawicki, Gregory S., Young, Aaron J.

arXiv.org Artificial Intelligence

Epically Powerful is an open-source robotics infrastructure that streamlines the underlying framework of wearable robotic systems - managing communication protocols, clocking, actuator commands, visualization, sensor data acquisition, data logging, and more - while also providing comprehensive guides for hardware selection, system assembly, and controller implementation. Epically Powerful contains a code base enabling simplified user implementation via Python that seamlessly interfaces with various commercial state-of-the-art quasi-direct drive (QDD) actuators, single-board computers, and common sensors, provides example controllers, and enables real-time visualization. To further support device development, the package also includes a recommended parts list and compatibility guide and detailed documentation on hardware and software implementation. The goal of Epically Powerful is to lower the barrier to developing and deploying custom wearable robotic systems without a pre-specified form factor, enabling researchers to go from raw hardware to modular, robust devices quickly and effectively. Though originally designed with wearable robotics in mind, Epically Powerful is broadly applicable to other robotic domains that utilize QDD actuators, single-board computers, and sensors for closed-loop control.


Analysis Dictionary Learning: An Efficient and Discriminative Solution

Tang, Wen, Panahi, Ashkan, Krim, Hamid, Dai, Liyi

arXiv.org Machine Learning

Yang et al. [7] used Fisher widely advocated for image classification problems. To further Information criterion in their class-specific reconstruction errors sharpen their discriminative capabilities, most state-ofthe-art to compose their approach. DL methods have additional constraints included in Besides SDL, Analysis Dictionary Learning (ADL) [8, 9] the learning stages. These various constraints, however, lead has recently been of interest on account of its fast encoding to additional computational complexity. We hence propose an and stability attributes. ADL provides a linear transformation efficient Discriminative Convolutional Analysis Dictionary of a signal to a nearly sparse representation. Inspired by Learning (DCADL) method, as a lower cost Discriminative the SDL methodology in image classification, ADL has also DL framework, to both characterize the image structures and been adapted to the supervised learning problems by promoting refine the interclass structure representations. The proposed discriminative sparse representations [10, 11]. In [10], DCADL jointly learns a convolutional analysis dictionary and Guo et al. incorporated both a topological structure and a representation a universal classifier, while greatly reducing the time complexity similarity constraint to encourage a suitable classselective in both training and testing phases, and achieving a representation for a 1-Nearest Neighbor classifier.


Predictive analytics can stop ransomware dead in its tracks

#artificialintelligence

This past February marks the two-year anniversary when Livingston County, Michigan, was hit by ransomware. The wealthiest county in the state had three years' worth of tax information possibly at the mercy of cybercriminals. As a local government, county CIO Rich C. Malewicz said they have been a target of ransomware, but in this instance they had backups at the ready. He said the most memorable ransomware attack was a result of a watering hole campaign using malvertizing to infect users visiting a local news website. "This attack was very clever in that all you had to do to get infected was visit the website, you didn't even have to click on the page. Once the user went to the local news website, they were immediately redirected to a site hosting exploit code and the infamous page appeared demanding a ransom with instructions," he said.