by Kenneth Wing Chung Tang. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 64-65). / LIST OF TABLES --- p.vi / LIST OF FIGURES --- p.vii / Chapter 1. --- INTRODUCTION --- p.1 / Chapter 1.1 --- Staff Scheduling Overview --- p.2 / Chapter 1.1.1 --- Days-off scheduling --- p.7 / Chapter 1.1.2 --- Shift Scheduling --- p.8 / Chapter 1.1.3 --- Tour Scheduling --- p.9 / Chapter 1.2 --- Outline of The Work of The Thesis --- p.11 / Chapter 2. --- NETWORK MODEL FOR STAFF SCHEDULING --- p.13 / Chapter 2.1 --- The Basic Network Model --- p.13 / Chapter 2.1.1 --- General Idea --- p.13 / Chapter 2.1.2 --- Modeling Precedent Relationship Constraints by Arcs --- p.15 / Chapter 2.1.3 --- Modeling Shift Stretch Constraints by Nodes --- p.16 / Chapter 2.1.4 --- Modeling to Handle Side Constraints --- p.17 / Chapter 2.1.5 --- Mathematical Model --- p.21 / Chapter 2.2 --- Solving The Network Model With Side Constraints --- p.23 / Chapter 2.2.1 --- Basis Partitioning Network Simplex method --- p.23 / Chapter 2.2.2 --- A Two-Phase Heuristic for Schedules Construction --- p.29 / Chapter 3. --- APPLICA TION IN AN AIR CARGO TERMINAL --- p.55 / Chapter 3.1 --- Background And Problem Statement --- p.35 / Chapter 3.2 --- Generation of Staff Requirement Patterns --- p.38 / Chapter 3.3 --- A Typical Setting of Parameters --- p.41 / Chapter 3.4 --- Case One: Staff Requirement for Each Shift Is Fixed --- p.43 / Chapter 3.4.1 --- Conversion of hourly requirements to shift requirements --- p.43 / Chapter 3.4.2 --- Network Modeling --- p.44 / Chapter 3.4.3 --- An Example --- p.47 / Chapter 3.4.4 --- Computational result on different staff requirements --- p.49 / Chapter 3.5 --- Case Two: Staff Requirement for Each Shift Is Changing --- p.50 / Chapter 3.5.1 --- Network modeling --- p.51 / Chapter 3.5.2 --- An Example --- p.52 / Chapter 3.5.2.1 --- Overlapping shifts with one kind of break times --- p.54 / Chapter 3.5.2.2 --- Overlapping shifts with two kinds of break times --- p.56 / Chapter 3.5.2.3 --- Overtime work --- p.57 / Chapter 3.5.3 --- Computational results on different staff requirement patterns --- p.60 / Chapter 4. --- CONCLUSION --- p.62 / Chapter 5. --- BIBLIOGRAPHY --- p.64 / Chapter 6. --- APPENDIX --- p.66 / Chapter 6.1 --- Applying the heuristic to complete the incomplete schedules --- p.66 / Chapter 6.2 --- List of Schedules --- p.68 / Chapter 6.2.1 --- Terminologies --- p.68 / Chapter 6.2.2 --- The Optimal Schedules for Case One --- p.69 / Chapter 6.2.3 --- The Optimal Schedules for Case Two --- p.70 / Chapter 6.2.4 --- The Optimal Schedules with One-hour Break in One Shift --- p.71 / Chapter 6.2.5 --- The Optimal Schedules with Breaks after 4 and 3 Hours of Work --- p.72 / Chapter 6.2.6 --- The Optimal Schedules with Overtime Shifts --- p.73

Chen, Liang. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 164-175). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts also in Chinese.

Users of social networking services such as Facebook often want to manage the sharing of information and content with different groups of people based on their differing relationships. The growing popularity of such services has meant that users are increasingly faced with the copresence of different groups associated with different aspects of their lives, within their network of contacts. However, few users are utilising the group-based privacy controls provided to them by the SNS provider. In this thesis we examine the reasons behind the lack of use of group-based privacy controls, finding that it can be largely attributed to the significant burden associated with group configuration. We aim to overcome this burden by developing automated mechanisms to assist users with many aspects of group-based privacy control, including initial group configuration, labeling, adjustment and selection of groups for sharing privacy sensitive content. We use a mixed methods approach in order to understand: how automated mechanisms should be designed in order to support users with their privacy control, how well these mechanisms can be expected to work, what the limitations are, and how such mechanisms affect users’ experiences with social networking services and content sharing. Our results reveal the criteria that SNS users employ in order to configure their groups for privacy control and illustrate that off-the-shelf algorithms and techniques which are analogous to these criteria can be used to support users. We show that structural network clustering algorithms provide benefits for initial group configuration and that clustering threshold adjustments and detection of hubs and outliers with the network are necessary for group adjustment. We demonstrate that public profile data can be extracted from the network in order to help users to comprehend their groups, and that contextual information relating to context, contacts, and content can be used to make recommendations about which groups might be useful for disclosure in a given situation. We also show that all of these mechanisms can be used to significantly reduce the burden of privacy control and that users react positively to such features.

During early development of the nervous system, gene expression patterns are known to vary widely depending on the specific developmental trajectories of different structures. Observable changes in gene expression profiles throughout development are determined by an underlying network of precise regulatory interactions between individual genes. Elucidating the organizing principles that shape this gene regulatory network is one of the central goals of developmental biology. Whether the developmental programme is the result of a dynamic driven by a fixed architecture of regulatory interactions, or alternatively, the result of waves of regulatory reorganization is not known. Here we contrast these two alternative models by examining existing expression data derived from the developing human brain in prenatal and postnatal stages. We reveal a sharp change in gene expression profiles at birth across brain areas. This sharp division between foetal and postnatal profiles is not the result of sudden changes in level of expression of existing gene networks. Instead we demonstrate that the perinatal transition is marked by the widespread regulatory rearrangement within and across existing gene clusters, leading to the emergence of new functional groups. This rearrangement is itself organized into discrete blocks of genes, each associated with a particular set of biological functions. Our results provide evidence of an acute modular reorganization of the regulatory architecture of the brain transcriptome occurring at birth, reflecting the reassembly of new functional associations required for the normal transition from prenatal to postnatal brain development.

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As more data are observed over time, investigating the variation across time has become a vital part of analyzing such data. In this dissertation, we discuss varying-coefficient models and functional data analysis methods for temporally heterogenous data. More specifically, we examine two different types of temporal heterogeneity. The first type of temporal heterogeneity stems from temporal evolution of relational pattern over time. Dynamic networks are commonly used when relational data are observed over time. Unlike static network analysis, dynamic network analysis emphasizes the importance of recognizing temporal evolution of relationship among observations. We propose and investigate a family of dynamic network models, known as varying-coefficient exponential random graph model (VCERGM), that characterize the evolution of network topology through smoothly varying parameters. The VCERGM directly provides an interpretable dynamic network model that enables the inference of temporal heterogeneity in dynamic networks. Furthermore, we introduce a method that analyzes multilevel dynamic networks. If there exist multiple relational data observed at one time point, it is reasonable to additionally consider the variability among the repeated observations at each time point. The proposed method is an extension of stochastic blockmodels with a priori block membership and temporal random effects. It incorporates a variability among multiple relational structures at one time point and provides a richer representation of dependent engagement patterns at each time point. The method is also flexible in analyzing networks with time-varying networks. Its smooth parameters can be interpreted as evolving strength of engagement within and across blocks. The second type of temporal heterogeneity is motivated by temporal shifts in continuously observed data. When multiple curves are obtained and there exists a common curvature shared by all the observed curves, understanding the common curvature may involve a preprocessing step of managing temporal shifts among curves. We explore the properties of continuous in-shoe sensor recordings to understand the source of variability in gait data. Our case study is based on measurements of three healthy subjects. The in-shoe sensor data we explore show both phase and amplitude variabilities; we separate these sources via curve registration. We examine the correlation of temporal shifts across sensors to evaluate the pattern of phase variability shared across sensors. We apply a series of functional data analysis approaches to the registered in-shoe sensor curves to examine their association with current gold-standard gait measurement, so called ground reaction force.

The second problem that is addressed in the thesis is about traffic analysis and classification. Accurate identification of network applications is important to many network activities. Traditional port-based technique has become much less effective since many new applications no longer use well-known fixed port numbers. In this thesis, we propose a novel profile-based approach to identify traffic flows belonging to the target application. In contrast to classifying traffic based on statistics of individual flows in previous studies, we build behavioral profiles of the target application, which describe dominant communication patterns of the application. Based on the behavior profiles, a two-level matching is used in identifying new traffic. We demonstrate the effectiveness of our method on campus traffic traces. Our results show that one can identify the popular P2P applications with very high accuracy. / This thesis represents new intelligent methods for monitoring and classifying network traffic. Internet traffic flow measurement is vitally important for network management, accounting and performance studies. Cisco's NetFlow is a widely deployed flow measurement solution that uses a configurable static sampling rate to control processor and memory usage on the router and the amount of reporting flow records generated. But during flooding attacks the memory and network bandwidth consumed by flow records can increase beyond what is available. Currently available countermeasures have their own problems In this thesis, we propose an entropy based adaptive flow aggregation algorithm. Relying on information-theoretic techniques, the algorithm efficiently identifies the clusters of attack flows in real time and aggregates those large number of short attack flows into a few metaflows. Compared to currently available solutions, our solution not only alleviates the problem in memory and export bandwidth, but also significantly improves the accuracy of legitimate flows. We evaluate our system using both synthetic trace file and real trace files from the Internet. / Hu, Yan. / Adviser: Dah-Mino Chen. / Source: Dissertation Abstracts International, Volume: 70-06, Section: B, page: 3600. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 128-135). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

The maintenance of both the psychological and physiological health of captive animals is a key priority of modern zoos. Recognising that characteristics of the captive environment have the potential to decrease animal welfare, methods for quantifying and assessing welfare have been developed as part of the process for improving animal welfare. Traditionally, observations of animal behaviour and quantifying time budgets in relation to those of the animals’ wild counterparts have been utilised to assess animal welfare. Hormonal assays have also been implemented to quantify the physiological stress response of animals in captivity and identify the extent of stress being experienced. Each of these methods focuses on a different indicator of animal welfare, is quantified in different ways and provides a different perspective on the welfare of the animals. Given the limited time and financial budgets available to zoos and animal carers, identifying the most appropriate method of welfare assessment would be advantageous in helping to secure the best possible health of captive animals and to maximise their value in captivity. This thesis implemented both behavioural observations and hormonal assays to identify the strengths and weaknesses of each methodology, and make recommendations for future research. The study involved a group of four Bornean orangutans (Pongo pygmaeus) housed at Twycross Zoo. Behavioural observations involved continuous group sampling and the development of an ethogram to record a comprehensive account of orangutan activity over the course of a 12 week enrichment programme. Simultaneous to these observations, faecal samples were collected from each orangutan and processed via Enzyme Immunoassay (EIA) to quantify levels of faecal glucocorticoid metabolites (fGCM) in each sample. While recognising the recent developments in ecological analytical methods, the capacity for extending network analysis beyond the application to social networks, and its use as a welfare assessment tool were explored. Behavioural and space-use networks were developed using data from a second study of the orangutans housed at Twycross Zoo. The flexibility of network analysis in visually representing different data types allowed for the intuitive representation of complex behavioural data. Further research investigated the use of network metrics in providing deeper insights into animal behaviour and space use patterns. In addition, bipartite networks were assessed for their potential to detect and show patterns in the relationships between two sets of behavioural data. Each of the methods used had a number of strengths and weaknesses, but importantly each contributed a different perspective in the assessment of behaviour patterns and welfare, suggesting that an integrated approach to behaviour studies utilising several methods would be ideal. Cost and logistic constraints make this unlikely in most cases. However, the thesis ends with a look to the future and the recognition that the current rapid development of technology for use in animal behaviour studies, coupled with equally rapid development of analytical techniques, may help to dramatically increase the amount of information gained from the average animal behaviour study in the future. Such improvements have never been more urgent, with the requirement for understanding animal behaviour in light of current extinction rates within the context of habitat destruction and climate change. It is hoped that this thesis will make a contribution to improving future animal behaviour and welfare studies by providing an assessment of both traditional methods of study as well as demonstrating the use and potential of new ways of applying network analysis within such studies.

Protein-protein interactions are essential for the survival of all living cells, allowing for processes such as cell signalling, metabolism and cell division to occur. Yet in humans there are only >38k annotated interactions of an interactome estimated to range between 150k to 600k interactions and out of a potential 300M protein pairs.Experimental methods to define the human interactome generate high quality results, but are expensive and slow. Computational methods play an important role to fill the gap.To further this goal, the prediction of human protein-protein interactions was investigated by the development of new predictive modules and the analysis of diverse datasets within the framework of the previously established PIPs protein-protein interaction predictor Scott and Barton 2007. New features considered include the semantic similarity of Gene Ontology annotating terms, clustering of interaction networks, primary sequences and gene co-expression. Integrating the new features in a naive Bayesian manner as part of the PIPs 2 predictor resulted in two sets of predictions. With a conservative threshold, the union of both sets is >300k predicted human interactions with an intersect of >94k interactions, of which a subset have been experimentally validated. The PIPs 2 predictor is also capable of making predictions in organisms that have no annotated interactions. This is achieved by training the PIPs 2 predictor based on a set of evidence and annotated interactions in another organism resulting in a ranking of protein pairs in the original organism of interest. Such an approach allows for predictions to be made across the whole proteome of poorly characterised organism, rather than being limited only to proteins with known orthologues. The work described here has increased the coverage of the human interactome and introduced a method to predict interactions in organisms that have previously had limited or no annotated interactions. The thesis aims to provide a stepping stone towards the completion of the human interactome and a way of predicting interactions in organisms that have been less well studied, but are often clinically relevant.

Lam, Chi Ho. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 113-117). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.vi / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.2 --- Organization --- p.4 / Chapter 2 --- Related Works and Background --- p.7 / Chapter 2.1 --- Related Works --- p.7 / Chapter 2.2 --- Background --- p.9 / Chapter 2.2.1 --- Datalite --- p.9 / Chapter 2.2.2 --- Proportional Union Method --- p.14 / Chapter 2.2.3 --- Quasi-Likelihood Approach --- p.18 / Chapter 3 --- Estimation Error of Existing TD-based TMA schemes --- p.24 / Chapter 3.1 --- Error Accumulation and Amplification of Existing Schemes --- p.25 / Chapter 3.1.1 --- Pu --- p.25 / Chapter 3.1.2 --- Qmle --- p.26 / Chapter 3.1.3 --- Datalite --- p.26 / Chapter 3.2 --- Estimation Error of 3-sets intersection cases --- p.28 / Chapter 3.2.1 --- Pu --- p.28 / Chapter 3.2.2 --- Datalite --- p.30 / Chapter 4 --- Error Reduction Via Traffic Digest Splitting --- p.36 / Chapter 4.1 --- Motivation --- p.36 / Chapter 4.2 --- Objective Functions for Optimal TD-splitting --- p.39 / Chapter 4.3 --- Problem Formulation of Threshold-based Splitting --- p.41 / Chapter 4.3.1 --- Minimizing Maximum Estimation Error --- p.42 / Chapter 4.3.2 --- Minimizing R.M.S. Estimation Error --- p.46 / Chapter 4.4 --- Analysis of Estimation Error Reduction Via Single-Level TD-splitting --- p.48 / Chapter 4.4.1 --- Noise-to-signal Ratio Reduction --- p.49 / Chapter 4.4.2 --- Estimation Error Reduction --- p.52 / Chapter 4.5 --- Recursive Splitting --- p.56 / Chapter 4.5.1 --- Minimizing Maximum Estimation Error --- p.57 / Chapter 4.5.2 --- Minimizing R.M.S. Estimation Error --- p.59 / Chapter 5 --- Realization of TD-splitting for Network Traffic Measurement --- p.61 / Chapter 5.1 --- Tracking Sub-TD Membership --- p.64 / Chapter 5.1.1 --- Controlling the Noise due to Non-Existent Flows on a Target Link --- p.64 / Chapter 5.1.2 --- Sub-TD Membership Tracking for Single-level TD-splitting --- p.65 / Chapter 5.1.3 --- Sub-TD Membership Tracking under Recursive Splitting --- p.66 / Chapter 5.2 --- Overall Operations to support TD-splitting for Network-wide Traffic Measurements --- p.67 / Chapter 5.2.1 --- Computation Time for TD-splitting --- p.69 / Chapter 6 --- Performance Evaluation --- p.72 / Chapter 6.1 --- Applying TD-splitting on Generic Network Topology --- p.72 / Chapter 6.1.1 --- Simulation Settings --- p.73 / Chapter 6.1.2 --- Validity of the Proposed Surrogate Objective Functions --- p.75 / Chapter 6.1.3 --- Performance of Single-level TD-splitting --- p.77 / Chapter 6.1.4 --- Performance of Recursive TD-splitting --- p.88 / Chapter 6.1.5 --- Heterogeneous NSR Loading --- p.95 / Chapter 6.2 --- Internet Trace Evaluation --- p.99 / Chapter 6.2.1 --- Simulation Results --- p.100 / Chapter 7 --- Conclusion --- p.105 / Chapter A --- Extension of QMLE for Cardinality Estimation of 3-sets Intersection --- p.107 / Bibliography --- p.113