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

authors: Ko, Ryan K.L.

abstract: 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.

text: 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.

year: 2014

title: Passive, fractional, battery equivalent-circuit model in time and frequency domains part 1: Linear model

authors: Farrow, Vance

abstract: Existing time-domain fractional model simulations of batteries are either limited to short time sequences, frequently less than 100 s, if truly fractional or use low order RC-ladder fractional approximations to reduce computational burden. Here we present an entirely-passive, truly fractional, equivalent-circuit model of a battery. We rely on a Reimann-Louiville fractional order differintegral to account for long time-scales out to 12 days. An analytical solution is provided for the differintegral, subject to the constraint of piecewise constant current. We validate our model fitting against a multi-day sequence of measured time domain data and EIS measured to 10 μ Hz. The spectral content of the current waveform is identified as a crucial factor. The full evaluation of fractional elements leads to residual error of voltage waveforms that is amongst the best in the literature despite the model having only five parameters. In the time-domain, a root-mean-square error (RMSE) as low as 2.8 mV is achieved while maintaining a frequency-domain RMSE of 14 % from measured impedance values over a span of 6 decades. The use of time-weighted regression is shown to be important to the time-domain fit.

text: Passive, fractional, battery equivalent-circuit model in time and frequency domains part 1: Linear model Existing time-domain fractional model simulations of batteries are either limited to short time sequences, frequently less than 100 s, if truly fractional or use low order RC-ladder fractional approximations to reduce computational burden. Here we present an entirely-passive, truly fractional, equivalent-circuit model of a battery. We rely on a Reimann-Louiville fractional order differintegral to account for long time-scales out to 12 days. An analytical solution is provided for the differintegral, subject to the constraint of piecewise constant current. We validate our model fitting against a multi-day sequence of measured time domain data and EIS measured to 10 μ Hz. The spectral content of the current waveform is identified as a crucial factor. The full evaluation of fractional elements leads to residual error of voltage waveforms that is amongst the best in the literature despite the model having only five parameters. In the time-domain, a root-mean-square error (RMSE) as low as 2.8 mV is achieved while maintaining a frequency-domain RMSE of 14 % from measured impedance values over a span of 6 decades. The use of time-weighted regression is shown to be important to the time-domain fit.

year: 2024

title: Keynote address: Is there a distinctive Māori psychology?

authors: Durie, Mason

subjects: Maori

abstract: Many of the theoretical paradigms that underpin the study of psychology pay marginal attention to culture as a determinant of psychology.
While there are some aspects of human experience that are universal, patterns of thinking, feeling and behaving are by no means divorced from specific cultural influence. A challenge for Māori psychologists is to re-examine psychological theory from a Māori perspective. In attempting to identify the psychological distinctiveness underlying a Māori perspective, this paper has introduced marae encounters as a rich source of information within which distinctive psychological characteristics can be identified.

text: Keynote address: Is there a distinctive Māori psychology? Many of the theoretical paradigms that underpin the study of psychology pay marginal attention to culture as a determinant of psychology.
While there are some aspects of human experience that are universal, patterns of thinking, feeling and behaving are by no means divorced from specific cultural influence. A challenge for Māori psychologists is to re-examine psychological theory from a Māori perspective. In attempting to identify the psychological distinctiveness underlying a Māori perspective, this paper has introduced marae encounters as a rich source of information within which distinctive psychological characteristics can be identified.

year: 2003