If you've ever gone hiking, you know trails can be challenging and unpredictable. A path that was clear last week might be blocked today by a fallen tree. Poor maintenance, exposed roots, loose rocks, and uneven ground further complicate the terrain, making trails difficult for a robot to navigate autonomously. After a storm, puddles can form, mud can shift, and erosion can reshape the landscape. This was the fundamental challenge in our work: how can a robot perceive, plan, and adapt in real time to safely navigate hiking trails?

In April and May, surgeons at West Virginia University placed thin strips of a cellophane-like material on the brains of three patients. Made by New York-based startup Precision Neuroscience, the thumbnail-sized strips are designed to conform to the surface of the brain without damaging its delicate tissue. During the 15 minutes the devices were in place, the implants were able to read, record, and map electrical activity in part of the patients' temporal lobes, which helps process sensory input. The patients were already in the hospital to have brain tumors removed, and doctors used Precision's devices alongside standard electrodes to determine the location of their tumors. Although just a small pilot study, it puts Precision one step closer to building a brain-computer interface, or BCI--a system that provides a direct communication link between the brain and an external device.

In manufacturing settings, data collection and analysis are often a time-consuming, challenging, and costly process. It also hinders the use of advanced machine learning and data-driven methods which require a substantial amount of offline training data to generate good results. It is particularly challenging for small manufacturers who do not share the resources of a large enterprise. Recently, with the introduction of the Internet of Things (IoT), data can be collected in an integrated manner across the factory in real-time, sent to the cloud for advanced analysis, and used to update the machine learning model sequentially. Nevertheless, small manufacturers face two obstacles in reaping the benefits of IoT: they may be unable to afford or generate enough data to operate a private cloud, and they may be hesitant to share their raw data with a public cloud. Federated learning (FL) is an emerging concept of collaborative learning that can help small-scale industries address these issues and learn from each other without sacrificing their privacy. It can bring together diverse and geographically dispersed manufacturers under the same analytics umbrella to create a win-win situation. However, the widespread adoption of FL across multiple manufacturing organizations remains a significant challenge. This study aims to review the challenges and future directions of applying federated learning in the manufacturing industry, with a specific emphasis on the perspectives of Industry 4.0 and 5.0.

There are many benefits for exploring and exploiting underground mines, but there are also significant risks and challenges. One such risk is the potential for accidents caused by the collapse of the pillars, and roofs which can be mitigated through inspections. However, these inspections can be costly and may put the safety of the inspectors at risk. To address this issue, this work presents Rhino, an autonomous robot that can navigate underground mine environments and generate 3D maps. These generated maps will allow mine workers to proactively respond to potential hazards and prevent accidents. The system being developed is a skid-steer, four-wheeled unmanned ground vehicle (UGV) that uses a LiDAR and IMU to perform long-duration autonomous navigation and generation of maps through a LIO-SAM framework. The system has been tested in different environments and terrains to ensure its robustness and ability to operate for extended periods of time while also generating 3D maps.

Saiph Savage, director of the human-computer interaction lab at West Virginia University, advocates for the workers who put in the time to develop training data for artificial intelligence. Many of the most successful and widely used machine-learning models are trained with the help of thousands of low-paid gig workers. Millions of people around the world earn money on platforms like Amazon Mechanical Turk, which allow companies and researchers to outsource small tasks to online crowdworkers. According to one estimate, more than a million people in the US alone earn money each month by doing work on these platforms. Around 250,000 of them earn at least three-quarters of their income this way.

NASA has announced a new round of funding for 18 futuristic projects that could help propel humans further into our solar system and beyond. Many of the ideas'sound like the stuff of science fiction,' the agency acknowledged, but they're not too crazy to one day become a reality. Among those that received funding are micro-probes that take after spiders to safely fly through the air, as well as a futuristic'smart suit' with self-healing skin to protect astronauts. Among those that were funded are micro-probes that take after spiders to safely fly through the air, as well as a futuristic'smart suit' with self-healing skin to protect astronauts (pictured) The cutting edge technologies are part of NASA's Innovative Advanced Concepts (NIAC) Program, which awards applicants up to $500,000 to develop their ideas. There are 12 Phase I ideas, like the smart suit, which are awarded $125,000 over nine months.

Students at West Virginia University at Parkersburg will be able to learn automated manufacturing using high-tech robotic arm equipment, thanks to a grant from the West Virginia Community and Technical College System.

The Third Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises was held from 17-19 April 1994 in Morgantown, West Virginia, hosted by the Concurrent Engineering Research Center at West Virginia University. This report summarizes this year's workshop and outlines the philosophy behind this annual event. This report summarizes this year's workshop and outlines the philosophy behind this annual event. The WET ICE workshop, now in its third year, has become a fixture of the collaborative computing scene. A more specialized event than the Computer-Supported Cooperative Work gathering, which takes in everyone from anthropologists to futurists, this workshop focuses on hardware and software that enables agents of all kinds to interact in a variety of ways to accomplish some task--quickly, correctly, and easily.

With its open architecture Android has always been seen by threat actors as boon for creating and distributing malware. One security vendor estimates as much 20 per cent of Android apps on the market have malware. Security vendors are already using a number of techniques to detect Android malware, including artificial neural networks but at the annual Conference on Knowledge Discovery and Data Mining (KDD 2017), being held this year in Halifax. The researchers say the technique could help vendor and enterprise analysts cut down the amount of time needed to recognize Android malware. The idea is to use algorithms to analyze the relationships between API calls in Android apps, because they can represent behaviours.