An ensemble-based computational approach for incremental learning in non-stationary environments related to schema- and scaffolding-based human learning

The principal dilemma in a learning process, whether human or computer, is adapting to new information, especially in cases where this new information conflicts with what was previously learned. The design of computer models for incremental learning is an emerging topic for classification and prediction of large-scale data streams undergoing change in underlying class distributions (definitions) over time; yet currently, they often ignore significant foundational learning theory that has been developed in the domain of human learning. This shortfall leads to many deficiencies in the ability to organize existing knowledge and to retain relevant knowledge for long periods of time. In this work, we introduce a unique computer-learning algorithm for incremental knowledge acquisition using an ensemble of classifiers, Learn++.NSE (Non-Stationary Environments), specifically for the case where the nature of knowledge to be learned is evolving. Learn++.NSE is a novel approach to evaluating and organizing existing knowledge (classifiers) according to the most recent data environment. Under this architecture, we address the learning problem at both the learner and supervisor end, discussing and implementing three main approaches: knowledge weighting/organization, forgetting prior knowledge, and change/drift detection. The framework is evaluated on a variety of canonical and real-world data streams (weather prediction, electricity price prediction, and spam detection). This study reveals the catastrophic effect of forgetting prior knowledge, supporting the organization technique proposed by Learn++.NSE as the most consistent performer during various drift scenarios, while also addressing the sheer difficulty in designing a system that strikes a balance between maintaining all knowledge and making decisions based only on relevant knowledge, especially in severe, unpredictable environments which are often encountered in the real-world

Expressions 1992

https://openspace.dmacc.edu/expressions/1014/thumbnail.jp

Cinema-going trajectories in the digital age

The activity of cinema-going constantly evolves and gradually integrates the use of digital data and platforms to become more engaging for the audiences. Combining methods from the fields of Human Computer Interaction and Film Studies, we conducted two workshops seeking to understand cinema audiences’ digital practices and explore how the contemporary cinema-going experience is shaped in the digital age. Our findings suggest that going to the movies constitutes a trajectory during which cinemagoers interact with multiple digital platforms. At the same time, depending on their choices, they construct unique digital identities that represent a set of online behaviours and rituals that cinemagoers adopt before, while and after cinema-going. To inform the design of new, engaging cinemagoing experiences, this research establishes a preliminary map of contemporary cinema-going including digital data and platforms. We then discuss how audiences perceive the potential improvement of the experience and how that would lead to the construction of digital identities

Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same regio

A non-inclusive memory permissions architecture for protection against cross-layer attacks

Protecting modern computer systems and complex software stacks against the growing range of possible attacks is becoming increasingly difficult. The architecture of modern commodity systems allows attackers to subvert privileged system software often using a single exploit. Once the system is compromised, inclusive permissions used by current architectures and operating systems easily allow a compromised high-privileged software layer to perform arbitrary malicious activities, even on behalf of other software layers. This paper presents a hardware-supported page permission scheme for the physical pages that is based on the concept of non-inclusive sets of memory permissions for different layers of system software such as hypervisors, operating systems, and user-level applications. Instead of viewing privilege levels as an ordered hierarchy with each successive level being more privileged, we view them as distinct levels each with its own set of permissions. Such a permission mechanism, implemented as part of a processor architecture, provides a common framework for defending against a range of recent attacks. We demonstrate that such a protection can be achieved with negligible performance overhead, low hardware complexity and minimal changes to the commodity OS and hypervisor code.NPRP grant 4-1593-1-260 from the Qatar National Research Fund.Scopu

On the detection of Kernel-level rootkits using hardware performance counters

Recent work has investigated the use of hardware perfor- mance counters (HPCs) for the detection of malware run- ning on a system. These works gather traces of HPCs for a variety of applications (both malicious and non-malicious) and then apply machine learning to train a detector to dis- tinguish between benign applications and malware. In this work, we provide a more comprehensive analysis of the ap- plicability of using machine learning and HPCs for a specific subset of malware: kernel rootkits. We design five synthetic rootkits, each providing a single piece of rootkit functionality, and execute each while collect- ing HPC traces of its impact on a specific benchmark ap- plication. We then apply machine learning feature selection techniques in order to determine the most relevant HPCs for the detection of these rootkits. We identify 16 HPCs that are useful for the detection of hooking based roots, and also find that rootkits employing direct kernel object manipula- tion (DKOM) do not significantly impact HPCs. We then use these synthetic rootkit traces to train a detection system capable of detecting new rootkits it has not seen previously with an accuracy of over 99%. Our results indicate that HPCs have the potential to be an effective tool for rootkit detection, even against new rootkits not previously seen by the detector.This paper was made possible by NPRP grants 4-1593-1-260 and 8-1474-2-626 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. The authors would also like to thank Aisha Hasan as well as the reviewers for their helpful comments on this work.Scopu