Machine-assisted Cyber Threat Analysis using Conceptual Knowledge Discovery

Over the last years, computer networks have evolved into highly dynamic and interconnected environments, involving multiple heterogeneous devices and providing a myriad of services on top of them. This complex landscape has made it extremely difficult for security administrators to keep accurate and be effective in protecting their systems against cyber threats. In this paper, we describe our vision and scientific posture on how artificial intelligence techniques and a smart use of security knowledge may assist system administrators in better defending their networks. To that end, we put forward a research roadmap involving three complimentary axes, namely, (I) the use of FCA-based mechanisms for managing configuration vulnerabilities, (II) the exploitation of knowledge representation techniques for automated security reasoning, and (III) the design of a cyber threat intelligence mechanism as a CKDD process. Then, we describe a machine-assisted process for cyber threat analysis which provides a holistic perspective of how these three research axes are integrated together

AI-Generated Fashion Designs: Who or What Owns the Goods?

As artificial intelligence (“AI”) becomes an increasingly prevalent tool in a plethora of industries in today’s society, analyzing the potential legal implications attached to AI-generated works is becoming more popular. One of the industries impacted by AI is fashion. AI tools and devices are currently being used in the fashion industry to create fashion models, fabric designs, and clothing. An AI device’s ability to generate fashion designs raises the question of who will own the copyrights of the fashion designs. Will it be the fashion designer who hires or contracts with the AI device programmer? Will it be the programmer? Or will it be the AI device itself? Designers invest a lot of talent, time, and finances into designing and creating each article of clothing and accessory it releases to the public; yet, under the current copyright standards, designers will not likely be considered the authors of their creations. Ultimately, this Note makes policy proposals for future copyright legislation within the United States, particularly recommending that AI-generated and AI-assisted designs be copyrightable and owned by the designers who purchase the AI device

Sliding to predict: vision-based beating heart motion estimation by modeling temporal interactions

Purpose: Technical advancements have been part of modern medical solutions as they promote better surgical alternatives that serve to the benefit of patients. Particularly with cardiovascular surgeries, robotic surgical systems enable surgeons to perform delicate procedures on a beating heart, avoiding the complications of cardiac arrest. This advantage comes with the price of having to deal with a dynamic target which presents technical challenges for the surgical system. In this work, we propose a solution for cardiac motion estimation. Methods: Our estimation approach uses a variational framework that guarantees preservation of the complex anatomy of the heart. An advantage of our approach is that it takes into account different disturbances, such as specular reflections and occlusion events. This is achieved by performing a preprocessing step that eliminates the specular highlights and a predicting step, based on a conditional restricted Boltzmann machine, that recovers missing information caused by partial occlusions. Results: We carried out exhaustive experimentations on two datasets, one from a phantom and the other from an in vivo procedure. The results show that our visual approach reaches an average minima in the order of magnitude of 10-7 while preserving the heart’s anatomical structure and providing stable values for the Jacobian determinant ranging from 0.917 to 1.015. We also show that our specular elimination approach reaches an accuracy of 99% compared to a ground truth. In terms of prediction, our approach compared favorably against two well-known predictors, NARX and EKF, giving the lowest average RMSE of 0.071. Conclusion: Our approach avoids the risks of using mechanical stabilizers and can also be effective for acquiring the motion of organs other than the heart, such as the lung or other deformable objects.Peer ReviewedPostprint (published version