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Records management and the accountability of governance

Mat Isa, Azman (2009) Records management and the accountability of governance. PhD thesis, University of Glasgow.

Governance is the process by which power and authority are exercised in a society by which government, the private sector, and citizens' groups articulate their interests, mediate their differences, and exercise their legal rights and obligations. Governance in public organisations is different from that in private organisations as they both possess different types of institutional stakeholders. Governments are directly answerable to the public. Therefore, it is essential for governments to be transparent in order to avoid any triggers in the accountability process that might adversely affect people's trust. The proper creation, capture, distribution and preservation of judicial evidence in the form of records can help avoid these problems. A trusted government is one that can demonstrate its accountability and transparency and is continually striving to improve value delivery and increase cost-effectiveness. The freedom of information demands governments to be more transparent and accountable for their actions and decisions. Whilst governments promote corporate governance to provide transparency and objectivity it can only give stakeholders better tools to do their job, it does not and cannot do it for them. The need for managing risk and audit culture is imperative to balance and satisfy the expectation of citizen and stakeholders. The accountability of a government can arguably only be achieved when it demonstrates considerable transparency, which in turn can only happen when trust is supported by authentic and reliable records. The records management community claims that records have to be preserved for accountability, but they rarely explore what 'accountability' is and what role records play in the accountability processes. In addition, the contribution of records management to good governance and accountability are often not recognised by other professions and management. In an age where corporate governance and transparency is a global agenda, it is imperative for the records management community to scrutinise their present role and approach in order to change the perception by other professions about their contribution towards achieving organisational goals in a highly regulated and compliant bound environment in the public and private sectors. The contention of this thesis is that record keeping is just a tool that ensures the availability of evidence for the accountability of governance, which in turn relies on the ethical standard of those involved.

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Item Type:	Thesis (PhD)
Qualification Level:	Doctoral
Subjects:	> > > >
Colleges/Schools:	> >
Supervisor's Name:	Moss, Prof. Michael, Ross, Prof. Seamus and Richmond, Ms. Lesley
Date of Award:	2009
Depositing User:
Unique ID:	glathesis:2009-1421
Copyright:	Copyright of this thesis is held by the author.
Date Deposited:	05 Jan 2010
Last Modified:	10 Dec 2012 13:39
URI:

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The University of Glasgow is a registered Scottish charity: Registration Number SC004401

Corpus ID: 106646476

Records Management and Electronic Records Management Opportunities and Limitations : A case study in Greek companies

Published 2015

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6 Citations

The primarily study of electronic records management system (erms) for yemen oil and gas corporation (yogc) subsidiaries, a study on record management implementation in private sector: analysis on competency, storage and retention, exploring the efficiency of e-tendering services in oman: administrative perspectives from government and business users, current development on in private sector: keeping or discarding records, student registration and records management services of the three private universities in the philippines: basis for academic records digitization, trends in contemporary record management, 20 references, the benefits of electronic records management systems: a general review of published and some unpublished cases.

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RECORDS MANAGEMENT PRACTICES, PROVINCE OF RIZAL

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Introduction

Biometric technologies are among fast-developing fields of information security, gradually entering into all spheres of human activity. Today only three biometric methods have proved their efficiency, namely, identification based on fingerprints, iris or retina, and face. Hand geometry, voice, writing and typing dynamics, etc. are also useful, depending on the purpose and range of application.

ECG as a biometric characteristic

To determine the potential use of ECG as a biometric, it is necessary to evaluate how ECG satisfies the requirements for biometric characteristics.

universal, i.e., each individual possesses this characteristic,
easily measured, i.e., it is quite easy technically and convenient for an individual to obtain the characteristic,
unique, i.e., there are no two individuals with identical characteristics, and
permanent, i.e., the characteristic does not change over time.

The ECG is a universal characteristic, as the heart beat is a necessary sign of life, and it can be recorded with minimum inconvenience to the individual. To evaluate its uniqueness, and especially persistence, is much more difficult. The following are just some empirical arguments in support of ECG as a biometric.


P	0 - 2.5	0.08 - 0.10
Q	0 - 3	0.06 - 0.10
R	6 - 21
S	0 - 6
T	0 - 5	0.10 - 0.25

[ECG variations with different heart rates]

Of course, there are many other natural and artificial, intentional and unintentional causes of ECG variability. For example, taking certain medications may temporarily change the configuration of the cardiac cycle. Some pathologies over time gradually change the form of the cardiac cycle. Moreover, human actions during the ECG recording can significantly distort or change the signal. Thus, an ECG identification system will face a variety of challenges that are similar to those posed by various attacks on other types of biometric systems.

Identification system synthesis

The generic system structure (figure 5, left) shows the sequence of essential data processing stages. Feedforward links show processed data transfer between stages. The output of one stage is the input to the subsequent stage. Each stage can be implemented using different processing methods. The detailed system structure (figure 5, right) shows methods considered in this study for each system stage. For most stages, these methods are alternatives, but the data preprocessing stage is usually comprised of several complementary methods.

Identification system synthesis is a process of selection between alternative methods, determination of composition and sequence of complementary methods, and adjustment of parameters for all methods in such a way as to obtain the best results. It is almost impossible to perform an exhaustive search. In this study, a strategy of result-directed backtracking was applied. Multiple feedback links in the detailed structure scheme show that results obtained on one stage can influence decisions made in previous stages. In other words, usefulness of some changes in data processing on one stage can be evaluated by comparing the results of one of the following stages obtained before and after these changes. This empirical technique doesn't guarantee optimal results, but it yields more or less reasonable method selections without requiring excessive computation.

Data acquisition

Experimental studies involved 90 volunteers. ECG records were made in the sitting position. Heart rate, physical and emotional state were not limited.

For usability, it is necessary to be able to collect the ECG easily and quickly. The procedure for ECG acquisition should be convenient for individuals and should require interaction with a minimal set of equipment; therefore it was decided to use single-lead ECG. Since single-lead ECGs vary significantly within an individual depending on the lead (the locations of the electrodes used to observe the ECG), the choice of lead is important. Lead I is the potential difference between the left and right hands (LA - RA). It was chosen because it is easily measured and it is not sensitive to minor variations in electrode locations. Limb clamp electrodes were used. This type of skin-electrode contact closely imitates the likely scenarios of user interaction with a practical identification system.

The data collected for this study comprise the ECG-ID Database , consisting of 310 I-lead ECG recordings from 90 individuals, each 20 seconds long, sampled at 500 Hz with 12-bit precision.

To train and test the identification system, the collected data were divided into two sets. 195 records were assigned to the training set and 115 records to the test set. Differentiation between the training and test sets aimed to provide for maximum performance complexity, i.e., maximum difference between records in different sets both in monitoring time and human physical state.

Data preprocessing

	Wavelet decomposition: wname = 'db8', N = 9.
	Ws = 50 Hz, dA = 1.5.
	Butterworth filter, Wp = 40 Hz, Ws = 60 Hz, Rp = 0.1 dB, Rs = 30 dB.
	N = 5.

Initial feature space formation

Initial feature space formation is a key stage of the identification system. Although decisions made in other stages significantly influence the final identification results, feature selection determines the potential performance of the identification system most of all.

1.	Correcting PQRST-fragment mutual "vertical" shift due to residual baseline drift. Mean values of all PQRST-fragments were subtracted, so that the corrected segments had mean values of zero.

2.	Culling PQRST-fragments that are distorted due to breathing or motion artifacts, as well as pathological PQRST-fragments. From the set of 10 extracted PQRST-fragments, the "mean" PQRST-fragment was estimated (red line); using Euclidean distance, only the 6 closest PQRST-fragments were selected (blue lines) for further analysis.

3.	Correcting PQRST-fragments depending on heart rate. The ST-fragment (samples from the end of the S wave to the end of the T wave) of PQRST-fragment was scaled using QT interval correction formulas. The Framingham and Bazett's formulas were considered; use of the Framingham formula gave better and more robust results.

Thus in the initial feature space (dimension N =250) the ECG appears as a set of 6 PQRST-fragments with each seen as a separate pattern at subsequent system stages, to be interpreted and classified independently.

Feature space reduction

Initially, the feature space dimension (or number of features for each pattern) is quite large ( N =250). This fact may make subsequent processing difficult or computationally impossible. The initial features, however, may include redundant and useless information due to correlation and interdependencies between features. In other words, the informational content of each initial feature and its contribution to the distinction of classes vary significantly. The feature space reduction procedure aims to make the transition from the initial feature space, with a correlated basis, into a new feature space with an uncorrelated basis. This procedure allows significant dimensionality reduction with minimal information loss.

To compare the initial and reduced feature spaces quantitatively, intra- and interclass metrics were considered. Usually, the class centroid is defined as the mean of all class points, so that the intraclass distance can be defined as the mean distance between the class points and the class centroid, and interclass distance as the distance between class centroids. Conceptually, intraclass distance characterizes the volume of the class or ECG variation within an individual, and interclass distance characterizes the separation of classes from each other or ECG variation between individuals. Obviously, good classification results can be achieved when interclass distances are significantly greater than intraclass distances.

(A)
(B)

In the figure, it is clear that feature space reduction slightly increases intraclass distances. More important, however, it noticeably improves class separability, increasing both minimal and mean interclass distances. In other words, classes in reduced feature space gain some extra volume, but are more clearly separated from each other. Overlap between the plots of intraclass distances and minimal interclass distances indicates the presence of some collisions (ambiguities) in the classification. Significant separation of plots of intraclass distances and mean interclass distances indicates that, potentially, classification can achieve high performance. Of course, it is difficult to predict how the feature space will be filled with new classes (i.e., as the system is required to identify a larger number of individuals), but judging by the ratio of distances there is the potential to increase this number without losing the classification quality.

Classification and Identification

As a result of data processing on previous stages, the original ECG record is represented as a set of six PQRST-fragment patterns in the reduced feature space. At this stage, each PQRST-fragment pattern is classified independently of the others and assigned to some class, and each PQRST-fragment classification result is a vote for the candidate class of the final ECG record identification, which is elected by a majority of votes.

The figure shows that the results obtained are consistent with expectations. Indeed, the Nearest Mean Classifier provides quite good results. Figure 14 shows that classes typically have elongated shapes, i.e., that the variances of the different features are not equal, so that the Weighted Nearest Mean Classifier provides better results. Linear Discriminant Analysis not only provides the best recognition result in the test set, but also minimizes the recognition error in the training set. ECG record identification rate is higher than the PQRTS-fragments recognition rate, since a pair of misclassified PQRTS-fragments do not affect the correctness of the ECG record identification.

As a result of this research, a recognition system was developed to solve the problem of biometric human identification based on ECG on a sufficiently large set of input data. The findings support the use of ECG as a new biometric characteristic in various biometric access control problems. Of course, it is doubtful that ECG is unique enough to be feasible for identification of large numbers of individuals in a general population. More likely, it can be useful for identification within relatively small predetermined groups, or as an additional feature in multi-variable biometric identification systems. Thus, it opens up a brand new perspective for the study of biometric technologies with potential applications in security and modern life amenity systems.


20 (20-55 years)	9 (22-28 years)	20	90 (13-75 years)
12 leads	Wireless, 30 minutes long	Lead I	Lead I, 20 seconds long
135 records, different days	18 records, different days	20 records	210 records, different days during 6 months
4-10 records	2 records	1 record	2-20 records
85 records	9 records of one day, 30 fragments for each record	20 heartbeats for each record	195 records, 6 of 10 heartbeats for each record
50 records	9 records of another day, all fragments for each record	1 heartbeat for each ECG (different part)	115 records, 6 of 10 heartbeats for each record
Heartbeat waves amplitudes and intervals duration ( =30)	Coefficients of the wavelet decomposition of successive ECG fragments 10 seconds long	QRS complex and T wave amplitudes and intervals duration ( =7)	Samples of cardiac cycle fragment containing the QRS complex, P and T waves ( =250)
-	Principal Component Analysis	-	Principal Component Analysis or Wavelet Transform
Soft Independent Modeling of Class Analogy (Multivariate Analysis)	Probabilistic neural network	Template matching (TM), Decision-based neural network (DBNN)	Linear Discriminant Analysis and Majority Vote Classifier
98 %	95 %	TM: 95 %, DBNN: 80 %, Both: 100 %	96 %

[1] Biel L., Pettersson O., Philipson L., Wide P. ECG analysis: a new approach in human identification. IEEE Transactions on Instrumentation and Measurement 2001 June; 50(3):808-812.

[2] Yi WJ, Park KS, Jeong DU. Personal identification from ECG measured without body surface electrodes using probabilistic neural networks. Proc 2003 World Congress on Medical Physics and Biomedical Engineering, Sydney, Australia, 2003 August.

[3] T.W. Shen, W.J. Tompkins, Y.H. Hu. One-lead ECG for identity verification. Proc. of the 2nd Joint Conference of the IEEE Engineering in Medicine and Biology Society and the Biomedical Engineering Society, vol. 1, pp. 62-63, 2002.

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