Online privacy protection methods are critically important but also shield those who conduct criminal activities such as cyber-attacks. In this work, we consider scenarios where Privacy Preserving Technologies (PPTs) are used to obfuscate network source locations of users conducting malfeasance. We present a novel methodology that synthesizes measurements from key locations, and links Pattern of Life (PoL) with obfuscated network traffic, which we call Deobfuscating Using Patterns of Life (DUPOL). We illustrate DUPOL’s utility in an increasingly common scenario: malicious actors transacting on a message board, hiding their network source locations using Privacy Preserving Technologies (PPTs). Using multiple simulated monitoring points and communications from an actual year-long social network message board, we show that DNS over HTTPS (DoH), DNS over TLS (DoT), and Virtual Private Networks (VPNs) can be deobfuscated with up to $100 \%$ accuracy. In this work, we provide recommendations for ideal monitoring vantage points on the Internet to achieve the best deobfuscation accuracy.

Physicians perform minimally invasive percuta-neous procedures under Computed Tomography (CT) image guidance both for the diagnosis and treatment of numerous diseases. For these procedures performed within Computed Tomography Scanners, robots can enable physicians to more accurately target sub-dermal lesions while increasing safety. However, existing robots for this application have limited dexterity, workspace, or accuracy. This paper describes the design, manufacture, and performance of a highly dexterous, low-profile, 8+2 Degree-of-Freedom (DoF) robotic arm for CT guided percutaneous needle biopsy. In this article, we propose CRANE: CT Robot and Needle Emplacer. The design focuses on system dexterity with high accuracy: extending physicians' ability to manipulate and insert needles within the scanner bore while providing the high accuracy possible with a robot. We also propose and validate a system architecture and control scheme for low profile and highly accurate image-guided robotics, that meets the clinical requirements for target accuracy during an in-situ evaluation. The accuracy is additionally evaluated through a trajectory tracking evaluation resulting in$< \boldsymbol{0.2}\mathbf{mm}$and$<\boldsymbol{ 0.71}^{\circ}$tracking error. Finally, we present a novel needle driving and grasping mechanism with controlling electronics that provides simple manufacturing, sterilization, and adaptability to accom-modate different sizes and types of needles.

Even though artificial muscles have gained popularity due to their compliant, flexible and compact properties, there currently does not exist an easy way of making informed decisions on the appropriate actuation strategy when designing a muscle-powered robot; thus limiting the transition of such technologies into broader applications. What’s more, when a new muscle actuation technology is developed, it is difficult to compare it against existing robot muscles. To accelerate the development of artificial muscle applications, we propose a data-driven approach for robot muscle actuator selection using Support Vector Machines (SVM). This first-of-its-kind method gives users insight into which actuators fit their specific needs and actuation performance criteria, making it possible for researchers and engineers with little to no prior knowledge of artificial muscles to focus on application design. It also provides a platform to benchmark existing, new or yet-to-be discovered artificial muscle technologies. We test our method on unseen existing robot muscle designs to prove its usability on real-world applications. We provide an open-access, web-searchable interface for easy access to our models that will additionally allow for the continuous contribution of new actuator data from groups around the world to enhance and expand these models.