Unveiling the Cloak: Kernel Theory Use in Design Science Research

Theory is an essential part of design research and helps us to explain what we see or guide what we design. In the paper, we shed light on how kernel theories are used in developing design principles in Design Science Research (DSR). We do this by reporting on a systematic literature review, from which we have extracted a set of six mechanisms to operationalize kernel theory. Each mechanism consists of an activity (e.g., “transform to” or “derive from”) and an application point (e.g., meta-requirements or design principles) representing wherein the chain of concepts the kernel theory was used. The paper reflects on what we have learned about the use of kernel theories and translates this into recommendations and issues for further research. We provide researchers with guidance to use kernel theories more efficiently and give a big picture of the possibilities of kernel theory operationalization

Progger: an efficient, tamper-evident kernel-space logger for cloud data provenance tracking

Cloud data provenance, or "what has happened to my data in the cloud", is a critical data security component which addresses pressing data accountability and data governance issues in cloud computing systems. In this paper, we present Progger (Provenance Logger), a kernel-space logger which potentially empowers all cloud stakeholders to trace their data. Logging from the kernel space empowers security analysts to collect provenance from the lowest possible atomic data actions, and enables several higher-level tools to be built for effective end-to-end tracking of data provenance. Within the last few years, there has been an increasing number of proposed kernel space provenance tools but they faced several critical data security and integrity problems. Some of these prior tools' limitations include (1) the inability to provide log tamper-evidence and prevention of fake/manual entries, (2) accurate and granular timestamp synchronisation across several machines, (3) log space requirements and growth, and (4) the efficient logging of root usage of the system. Progger has resolved all these critical issues, and as such, provides high assurance of data security and data activity audit. With this in mind, the paper will discuss these elements of high-assurance cloud data provenance, describe the design of Progger and its efficiency, and present compelling results which paves the way for Progger being a foundation tool used for data activity tracking across all cloud systems

Pointwise consistency of the kriging predictor with known mean and covariance functions

This paper deals with several issues related to the pointwise consistency of the kriging predictor when the mean and the covariance functions are known. These questions are of general importance in the context of computer experiments. The analysis is based on the properties of approximations in reproducing kernel Hilbert spaces. We fix an erroneous claim of Yakowitz and Szidarovszky (J. Multivariate Analysis, 1985) that the kriging predictor is pointwise consistent for all continuous sample paths under some assumptions.Comment: Submitted to mODa9 (the Model-Oriented Data Analysis and Optimum Design Conference), 14th-19th June 2010, Bertinoro, Ital

User-level Threads

Report on the various characteristics and functionality of user-level and kernel threads, the various approaches of user-level thread design, the various implementation issues, and the adaptability and use of threads in real-time operating systems

A surrogate modeling and adaptive sampling toolbox for computer based design

An exceedingly large number of scientific and engineering fields are confronted with the need for computer simulations to study complex, real world phenomena or solve challenging design problems. However, due to the computational cost of these high fidelity simulations, the use of neural networks, kernel methods, and other surrogate modeling techniques have become indispensable. Surrogate models are compact and cheap to evaluate, and have proven very useful for tasks such as optimization, design space exploration, prototyping, and sensitivity analysis. Consequently, in many fields there is great interest in tools and techniques that facilitate the construction of such regression models, while minimizing the computational cost and maximizing model accuracy. This paper presents a mature, flexible, and adaptive machine learning toolkit for regression modeling and active learning to tackle these issues. The toolkit brings together algorithms for data fitting, model selection, sample selection (active learning), hyperparameter optimization, and distributed computing in order to empower a domain expert to efficiently generate an accurate model for the problem or data at hand

Viability Kernel for Ecosystem Management Models

We consider sustainable management issues formulated within the framework of control theory. The problem is one of controlling a discrete--time dynamical system (e.g. population model) in the presence of state and control constraints, representing conflicting economic and ecological issues for instance. The viability kernel is known to play a basic role for the analysis of such problems and the design of viable control feedbacks, but its computation is not an easy task in general. We study the viability of nonlinear generic ecosystem models under preservation and production constraints. Under simple conditions on the growth rates at the boundary constraints, we provide an explicit description of the viability kernel. A numerical illustration is given for the hake--anchovy couple in the Peruvian upwelling ecosystem

A Quantum Kernel Learning Approach to Acoustic Modeling for Spoken Command Recognition

We propose a quantum kernel learning (QKL) framework to address the inherent data sparsity issues often encountered in training large-scare acoustic models in low-resource scenarios. We project acoustic features based on classical-to-quantum feature encoding. Different from existing quantum convolution techniques, we utilize QKL with features in the quantum space to design kernel-based classifiers. Experimental results on challenging spoken command recognition tasks for a few low-resource languages, such as Arabic, Georgian, Chuvash, and Lithuanian, show that the proposed QKL-based hybrid approach attains good improvements over existing classical and quantum solutions.Comment: Submitted to ICASSP 202