What is (Missing or Wrong) in the Scene? A Hybrid Deep Boltzmann Machine for Contextualized Scene Modeling

Scene models allow robots to reason about what is in the scene, what else should be in it, and what should not be in it. In this paper, we propose a hybrid Boltzmann Machine (BM) for scene modeling where relations between objects are integrated. To be able to do that, we extend BM to include tri-way edges between visible (object) nodes and make the network to share the relations across different objects. We evaluate our method against several baseline models (Deep Boltzmann Machines, and Restricted Boltzmann Machines) on a scene classification dataset, and show that it performs better in several scene reasoning tasks

COSMO: Contextualized Scene Modeling with Boltzmann Machines

Scene modeling is very crucial for robots that need to perceive, reason about and manipulate the objects in their environments. In this paper, we adapt and extend Boltzmann Machines (BMs) for contextualized scene modeling. Although there are many models on the subject, ours is the first to bring together objects, relations, and affordances in a highly-capable generative model. For this end, we introduce a hybrid version of BMs where relations and affordances are introduced with shared, tri-way connections into the model. Moreover, we contribute a dataset for relation estimation and modeling studies. We evaluate our method in comparison with several baselines on object estimation, out-of-context object detection, relation estimation, and affordance estimation tasks. Moreover, to illustrate the generative capability of the model, we show several example scenes that the model is able to generate.Comment: 40 pages, 15 figures, 9 tables, accepted to the Robotics and Autonomous Systems (RAS) special issue on Semantic Policy and Action Representations for Autonomous Robots (SPAR

Survey on intrusion detection systems based on deep learning

Intrusion Detection Systems (IDSs) have a significant role in all networks and information systems in the world to earn the required security guarantee. IDS is one of the solutions used to reduce malicious attacks. As attackers always changing their techniques of attack and find alternative attack methods, IDS must also evolve in response by adopting more sophisticated methods of detection. The huge growth in the data and the significant advances in computer hardware technologies resulted in the new studies existence in the deep learning field, including intrusion detection. Deep learning is sub-field of Machine Learning (ML) methods that are based on learning data representations. In this paper, a detailed survey of various deep learning methods applied in IDSs is given first. Then, a deep learning classification scheme is presented and the main works that have been reported in the deep learning works is summarized. Utilizing this approach, we have provided a taxonomy survey on the available deep architectures and algorithms in these works and classify those algorithms to three classes, which are: discriminative, hybrid and generative. After that, chosen deep learning applications are reviewed in a wide range of fields of intrusion detection. Finally, popular types of datasets and frameworks are discussed