This paper presents a novel layered framework that integrates visual foundation models to improve robot manipulation tasks and motion planning. The framework consists of five layers: Perception, Cognition, Planning, Execution, and Learning. Using visual foundation models, we enhance the robot's perception of its environment, enabling more efficient task understanding and accurate motion planning. This approach allows for real-time adjustments and continual learning, leading to significant improvements in task execution. Experimental results demonstrate the effectiveness of the proposed framework in various robot manipulation tasks and motion planning scenarios, highlighting its potential for practical deployment in dynamic environments.

Whether we realize it or not, our digital lives and what we see on the internet are controlled and determined by algorithms and analytics. Through them, businesses learn what our preferences are and what we're drawn to in order to target us with information. The idea is to present us with information that is most relevant to us. In the same way, cybersecurity professionals are constantly faced with an enormous amount of threat data to sift through and prioritize on a daily basis. In fact, "too much data to analyze" is the number one obstacle inhibiting companies from defending against cyber threats according to the 2019 Cyberthreat Defense Report by CyberEdge.

Machine learning has the power to transform your security operations, but as with any powerful technology, it needs to be approached strategically. Through our first-hand experience with helping organizations across the world implement and operationalize machine learning in their SOCs, we have identified four best practices that are critical for achieving success. Terms like artificial intelligence (AI) and machine learning are popular in our industry, but there's a lot of snake oil with vendors claiming to use these technologies. Do your homework to understand what type of machine learning a vendor uses and whether or not that type of machine learning meets your security team's needs. Knowing just a little bit about how machine learning works can help you ask better questions when evaluating a vendor, like "What threats are not covered with existing tools and techniques?" or "Which data feeds contain valuable information but are currently underutilized?"

The global threat of fraud shows no signs of slowing down. Losses related to fraud are valued at $14.7 billion, according to the most recent DataVisor Fraud Index Report. As fraudsters become increasingly aggressive, new global regulations and solutions are being deployed to keep consumers, merchants and banks safe. In the latest Digital Fraud Tracker, PYMNTS highlights the fraud trends and patterns that regulators are closely monitoring, as well as the solutions -- including artificial intelligence (AI) and machine learning (ML) -- that are being deployed to shift the anti-fraud effort from defense to offense. Fraud has become particularly problematic in the United Kingdom, where last year card-based losses increased by 19 percent compared to the previous year.

The global threat of fraud shows no signs of slowing down. Losses related to fraud are valued at $14.7 billion, according to the most recent DataVisor Fraud Index Report. As fraudsters become increasingly aggressive, new global regulations and solutions are being deployed to keep consumers, merchants and banks safe. In the latest Digital Fraud Tracker, PYMNTS highlights the fraud trends and patterns that regulators are closely monitoring, as well as the solutions -- including artificial intelligence (AI) and machine learning (ML) -- that are being deployed to shift the anti-fraud effort from defense to offense. Fraud has become particularly problematic in the United Kingdom, where last year card-based losses increased by 19 percent compared to the previous year.

People tracking is a key technology for autonomous systems, intelligent cars and social robots operating in populated environments. What makes the task difficult is that the appearance of humans in range data can change drastically as a function of body pose, distance to the sensor, self-occlusion and occlusion by other objects. In this paper we propose a novel approach to pedestrian detection in 3D range data based on supervised learning techniques to create a bank of classifiers for different height levels of the human body. In particular, our approach applies AdaBoost to train a strong classifier from geometrical and statistical features of groups of neighboring points at the same height. In a second step, the AdaBoost classifiers mutually enforce their evidence across different heights by voting into a continuous space. Pedestrians are finally found efficiently by mean-shift search for local maxima in the voting space. Experimental results carried out with 3D laser range data illustrate the robustness and efficiency of our approach even in cluttered urban environments. The learned people detector reaches a classification rate up to 96% from a single 3D scan.