Anchored Bayesian Gaussian Mixture Model
Finite Gaussian mixtures are a flexible modeling tool for irregularly shaped densities and samples from heterogeneous populations. When modeling with mixtures using an exchangeable prior on the component features, the component labels are arbitrary and are indistinguishable in posterior analysis. This makes it impossible to attribute any meaningful interpretation to the marginal posterior distributions of the component features. We present an alternative to the exchangeable prior: by assuming that a small number of latent class labels are known a priori, we can make inference on the component features without post-processing. Our method assigns meaning to the component labels at the modeling stage and can be justified as a data-dependent informative prior on the labelings. We show that our method produces interpretable results, often (but not always) similar to those resulting from relabeling algorithms, with the added benefit that the marginal inferences originate directly from a well specified probability model rather than a post hoc manipulation. We provide practical guidelines for model selection that are motivated by maximizing prior information about the class labels and we demonstrate our method on real and simulated data. …

NtMalDetect
As computing systems become increasingly advanced and as users increasingly engage themselves in technology, security has never been a greater concern. In malware detection, static analysis has been the prominent approach. This approach, however, quickly falls short as malicious programs become more advanced and adopt the capabilities of obfuscating its binaries to execute the same malicious functions, making static analysis virtually inapplicable to newer variants. The approach assessed in this paper uses dynamic analysis of malware which may generalize better than static analysis to variants. Widely used document classification techniques were assessed in detecting malware by doing such analysis on system call traces, a form of dynamic analysis. Features considered are extracted from system call traces of benign and malicious programs, and the task to classify these traces is treated as a binary document classification task using sparse features. The system call traces were processed to remove the parameters to only leave the system call function names. The features were grouped into various n-grams and weighted with Term Frequency-Inverse Document Frequency. Support Vector Machines were used and optimized using a Stochastic Gradient Descent algorithm that implemented L1, L2, and Elastic-Net regularization terms, the best of which achieved a highest of 98% accuracy with 98% recall score. Additional contributions include the identification of significant system call sequences that could be avenues for further research. …

Resilient Computing
The term resiliency has been used in many fields like child psychology, ecology, business, and several others, with the common meaning of expressing the ability to successfully accommodate unforeseen environmental perturbations or disturbances. The adjective resilient has been in use for decades in the field of dependable computing systems however essentially as a synonym of fault-tolerant, thus generally ignoring the unexpected aspect of the phenomena the systems may have to face. These phenomena become of primary relevance when moving to systems like the future large, networked, evolving systems constituting complex information infrastructures – perhaps involving everything from super-computers and huge server ‘farms’ to myriads of small mobile computers and tiny embedded devices, with humans being central part of the operation of such systems. Such systems are in fact the dawning of the ubiquitous systems that will support Ambient Intelligence. With such ubiquitous systems, what is at stake is to maintain dependability, i.e., the ability to deliver service that can justifiably be trusted, in spite of continuous changes. Therefore the term resilience and resilient computing can be applied to the design of ubiquitous systems and defined as the search for the following property: the persistence of service delivery that can justifiably be trusted, when facing changes. …

Recurrent Value Function (RVF)
Despite recent successes in Reinforcement Learning, value-based methods often suffer from high variance hindering performance. In this paper, we illustrate this in a continuous control setting where state of the art methods perform poorly whenever sensor noise is introduced. To overcome this issue, we introduce Recurrent Value Functions (RVFs) as an alternative to estimate the value function of a state. We propose to estimate the value function of the current state using the value function of past states visited along the trajectory. Due to the nature of their formulation, RVFs have a natural way of learning an emphasis function that selectively emphasizes important states. First, we establish RVF’s asymptotic convergence properties in tabular settings. We then demonstrate their robustness on a partially observable domain and continuous control tasks. Finally, we provide a qualitative interpretation of the learned emphasis function. …