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Return predictability has always been an interesting topic and discussed on the academic front. In this thesis, we first study the correlation between return predictability and firm level liquidity. We find that the illiquid firm commands higher excess return. Next, we study the return forecasting ability of the short interest ratio. We find that Rapach et al. (2016) conclusions have certain limitations, and the forecasting ability cannot last more than one year. In addition, we confirm that highly shorted stocks perform slightly worse than the stocks with smaller short positions. Finally, we find that information flow impacts a company's liquidity volatility and trading volume after the SEC introduces the EDGAR system. Chapter 2 contributes to the related literature by studying the cross-sectional relation between Amihud (2002) illiquidity measure and expected stock returns using the latest stock data and studying the effect of Regulation Fair Disclosure (Reg FD) implementation on stock-level illiquidity. This chapter focuses on the most commonly used measure, the Amihud (2002). We find that illiquidity has a solid positive cross-sectional relation with future stock returns, which is that illiquid securities command higher expected returns than more liquid securities. Regardless of whether illiquidity is measured using one, three, six, or twelve months of historical data. Reg FD's implementation positively impacts the firms' liquidity, especially for small firms. After the implementation, the policy mandates the small firms to establish a sound system and reduce selective disclosure, resulting in a liquidity improvement. In chapter 3, we study the predictability of the aggregate short interest from the econo metrics and economics views by employing new detrending methods and the tests for dynamic predictive regressions to avoid the unit root. From the econometrics angle, the short interest index (SII) that Rapach et al. (2016) build is not stationary and has a unit root. There is an upward trend in this series. Thus the analysis results obtained by incorporating this series are spurious and not reliable. From the economic angle, the highly shorted stocks consistently underperform the lightly shorted stocks. The lightly shorted portfolio, in which the SIR is less than 2.5% comprises approximate 80% of the listed firms over the market. We doubt the predictive power of SIR could be driven by a small number of firms reporting high short interest. The extremely high SIR value will magnify the predictive power of the aggregated short interest because the number of firms with high SIR constitutes a small percent over the entire market. In chapter 4, we exploit the quasi-natural experiment created by the roll-out of the EDGAR system to study the causal impact of the additional flow of stock-specific information on firms. We find that this information flow to investors results in statistically significant and economically essential changes in illiquidity and trading volume but not in idiosyncratic volatility. Across firms, illiquidity falls for the smallest firms more than it does for the largest firms. Across industry groups, the mining and manufacturing sectors have the largest decreases in illiquidity and increases in trading volume.

Security APIs are designed to enable the storage and processing of confidential data without that data becoming known to individuals who are not permitted to obtain it, and are central to the operation of Automated Teller Machines (ATM) networks, Electronic Point of Sale (EPOS) terminals, set-top boxes for subscription-based TV, pre-payment utility meters, and electronic ticketing for an increasing number of public transport systems (e.g., Oyster in London). However, since the early 2000s, it has become clear that many of the security APIs in widespread use contain subtle flaws which allow malicious individuals to subvert the security restrictions and obtain confidential data that should be protected. In this thesis, we attempt to address this problem by presenting a type system in which specific security properties are guaranteed to be enforced by security APIs that are well-typed. Since type-checking is a form of static analysis, it does not suffer from the scalability issues associated with approaches that simulate interactions between a security API and one or more malicious individuals. We also show how our type system can be used to model an existing security API and provide the same guarantees of security that the API authors proved it upholds. This result follows directly from producing a well-typed implementation of the API, and demonstrates how our type system provides security guarantees without requiring additional API-specific proofs.

The intention of the programme was to evaluate Product Development (PD) strategies within the automotive industry and to identify areas in which improvements could be made in PD project performance that would also provide a business opportunity for the author's employer RLE International (RLE). The research is principally concerned with the automotive industry but also has broader applications within similar industries. The research was undertaken via three projects. Project 1 involved a study of the structure, drivers and trends within the automotive industry. The aim was to assess the implications for PD in the automotive industry and identify significant issues where opportunities for improvement existed. The outcome was a portrayal of an industry under extreme competitive pressure and waiting for something to change but without a clear future state. What was apparent was that the competitive pressures, and thus the need to deliver more products without significantly increased resources, were not going to abate in the near future. PD has to 'deliver more with less' but a definition of success and its associated measures in terms of the PD process is difficult to frame. Therefore, the aim of project 2 focused on performance measurement of the PD process by assessing four internationally diverse development projects carried out by the author's employer with four discrete customers. The projects were all different in their content and were carried out in different countries, i.e. USA, Germany, India and Sweden. Whilst customer specific and cultural aspects of the projects differed, the significant issue identified via the research was common across all the projects. Traditional Key Performance Indicators (KPIs) of cost, time and scope were used but failed to predict issues in project delivery. The key finding was that if project information did not flow as originally planned then resources were wasted resulting in time and cost over-runs. Project 3 researched alternative solutions to the issue of monitoring information flow and proposes a specific method of indicating the likelihood of success in a project by identifying new PD measurement techniques to be used within the automotive PD process. This new measurement criterion of information flow provides a predictive tool that significantly enhances the project control process. The predictive method of information flow tracking developed is new to the automotive PD profession. It was trialled on an existing project and was shown to identify specific issues with the Work-in-Progress (WIP) not found by traditional project management methods. The resulting indication of issues enabled the organisation's management to have a substantially different insight and understanding of project performance at a given point in time and therefore enabled immediate changes in resource allocation to improve project performance. The implementation of these changes as a result of the adoption of information flow monitoring resulted in significantly improved project KPI performance. The contribution of this new PD management method has the potential to significantly impact the competitiveness of any company involved in the design and development process. Its benefits include improved understanding of project performance indicators, powerful predictive attributes resulting in better utilisation of company resources and reductions in both project costs and lead times.

Within corporate governance research, boards of directors constitute an essential part and are described as "the apex of the internal control system" (Jensen, 1993, p.862). Several stands of research have investigated whether, and to which degree, boards’ composition, structure and processes have impact on board task performance, but board processes and specifically the use of knowledge and skills have not been thoroughly researched, yet. Simultaneously, there is a gap within organisational behaviour research on how knowledge is explored, transformed and exploited, which is conceptualised as absorptive capacity. Further, the concept of absorptive capacity has so to date not been researched in a board context. In this thesis board processes are studied by exploring the impact of absorptive capacity on board task performance. Three dimensions of absorptive capacity, exploratory learning, transformative learning and exploitative learning, are used in the analyses. The research is conducted using mixed methods (based on a survey and a case study). A quantitative analysis is based on the Norwegian Value Creating Board Survey, and a case study is conducted based on records, observations from board meetings and interviews in the Norwegian health company Healthy. The findings show that the three dimensions of absorptive capacity, positively and significantly, mediate the relation between presence of knowledge and skills and board task performance. Complementarities between the three learning processes exist with the result that the three learning processes together are a stronger mediator than a single process. The qualitative findings show that 1) information flows have an impact on absorptive capacity, 2) that the role and power of the CEO and the division of labour between the CEO and the chair, might have an impact on board task performance and 3) that a comprehensive utilisation of consensus has an impact on transformative and exploitative learning, 4) that effort norms are positively correlated to use of knowledge and skills and 5) that activation triggers have impacts on the learning processes. The research contributes to theory with an extended application of the concept of absorptive capacity to boards, responding to calls from researchers to conduct new and more extensive research to analyse and integrate the concept. The thesis further contributes by shedding new light on learning processes in boards, underpinning former conceptual models. In the case study several findings are reported which are presented in an extended and modified model of determinants of board tasks. Finally, this thesis contributes to mixed methods research in boards. The findings have implications for board practice with regard to board selections, board evaluations and learning processes in boards. Corporate governance codes should be aligned with these findings.

Living beings, robotic and software artefacts can all be seen as agents acting and perceiving within an environment. When observed under that perspective, a new concept is accessible: information in the sense of Shannon. It has long been known that information and control are interrelated concepts. However it is only recently that this perspective has been better understood and used in order to study cognition. In this thesis, we build upon such an information-theoretic perspective and add some biologically motivated assumptions. They introduce various constraints on the capture, the processing, or the storage of information by an agent. Using such constraints it is possible to understand some limits on the control abilities of agents, and to derive algorithms that optimize these abilities. More specifically this thesis uses the recently introduced concept of empowerment, i.e. the ability to act upon the environment and perceive back the changes through the sensors. Maximizing this quantity leads to a wide range of cognitively interesting properties. This work studies some of these properties. One of them, the ability to capture information that is relevant for the perception-action loop of the agent, is deeply investigated and algorithms for exploiting this ability are presented. The second part of the thesis deals with the use of the information-theoretic framework when multiple agents are interacting with each other. Empowerment maximization in this context leads to two phenomena: the generation of complex structures, and the emergence of synchronised and potentially cooperative interactions. In this thesis, the first phenomenon is empirically investigated through various spatial scenarios in order to understand the kind of structures that are generated and under which conditions they appear. Connections are made between the second phenomenon and the concept of the multiple-access channel. Using recent developments of this information-theoretic model, it is possible to precisely study the kind of interactions that can occur, and the situations that lead to synchronised or cooperative behaviour. The general aim of this work is to give a comprehensive picture of the information-theoretic framework for studying the perception-action loop, bringing both single and multi-agents aspects together. The concepts presented in this thesis allows one to study some fundamental aspects of cognition, to engineer self-motivated robotic systems, or to drive self-organization in multi-agents systems.

This thesis is focused on a simulation modelling approach to address the inventory inaccuracy problems in a warehouse operation. The main motivation which led to this research was a desire to investigate the inventory inaccuracy issues that have been highlighted by a logistics company. Previous and current research into inventory inaccuracy issues is largely related to the development of RFID technology as a possible solution to inventory problems. Since the inventory inaccuracy related to RFID technology is focused on the overall measurement of inventory management and retail business, there are differences between this existing research and the research presented in this thesis which is focused on issues of inventory inaccuracy in a warehouse operation. In this thesis, warehouse operation is studied as a detailed sequence of processes that are involved in the flow of items physically in parallel with related information being stored in the computer system. In these processes there are many places where errors can occur in counting or recording details of inventory, or in physically moving, storing or picking items incorrectly. These details of a warehouse operation are used to develop a conceptual model of inventory inaccuracy in warehouse operations. The study also found that typically a product needs to be considered differently at different stages of its progress through a warehouse (and therefore within different sections of the conceptual model). This is because initially batches of a product are likely to be delivered from a supplier, therefore if errors occur soon after the product is delivered to the warehouse, the error might involve the whole batch (for example the batch may be misplaced and put in an incorrect storage location), or the error might involve just part of the batch (for example poor transportation by forklift truck may damage the packaging carton and some of the items within the carton). When the product is stored ready for meeting customer orders, it needs to be considered as individual items (and errors can occur in counting of individual items or individual items may be misplaced or stolen). Finally, when a customer order is received, the product will be picked and grouped to meet the requirements of the order (for example, one order may require 10 of the product whilst another order may require 20 of the product). Errors might again occur to the whole group or to just part of the group. (Continued ...)

The main aim of the study is to investigate the flows of information and the impact and added value of Information and Communication Technologies (ICTs) on the integration of information flows. This concept is related to the optimal design of organisational (and other) systems that allow the right decision to be made by the right person at the right time in an appropriate location. Clearly, information that is often distributed needs to come together in an efficient way to allow the best use of resources to maximize the information value. The virtual organisation (VO) is a new style of working that allows completion of tasks across time and location. The use of ICTs is viewed as a facilitator of these tasks and their application provides good examples to extend their use. The most common types of VOs are home offices, teleworking centres, mobile offices and `hotelling'. Teleworkers still face several problems, such as managing their work, lack of appropriate communications to complete their work efficiently, and isolation. This study applied an information-driven approach to investigate the current problems in the teleworking environment, and solutions are proposed to improve the flow of information in the VOs. The data required were collected by questionnaire survey, semi-structured interviews, and document analysis. Members of the Telework Association (TCA) in the UK were invited to take part in this survey, and 153 out of 800 respondents came from this source. From subsequent analysis of the data collected, there is indeed an issue with the use and integration of information flows in VOs. An in depth study of six individual cases revealed a lack of information sharing, poor understanding of information needs, and low rates of access to online information. Soft solutions can be achieved through understanding information needs, developing information policy, and training. Hard solutions can be achieved through using online information, using workflow software, upgrading the speed of Internet connection, using information sharing systems, and arranging more open access to information. Some indicators of future research can be mentioned, particularly in conducting the second generation of information audit in teleworking practice, to study knowledge management (KM) practices in teleworking, to develop information policies to enhance teleworking practices, to focus on the information needs of teleworkers, to evaluate the impact of software applications (such as workflow software), to implement information sharing systems, and to study the impact of training on information retrieval and use.

This case study of the information flows within a British biotechnology company involved a population of 156 and took place over five years. It included information provision and information management as embedded studies. The main investigation into information flows was done in three parts, using questionnaires. The parts were: Use of Information Centre information resources, company-wide information flows and assessment of the perceived effectiveness of existing information flows. Combined, these three parts represent a 'snapshot' of the flows over a timespan of about three months. The methodology used to present the individual information flows is novel. The results showed that inter-personal communication or information flows were good, with e-mail being extensively used; that most inter-Group flows were functional, but that flows through the company were poor. Information flow out of the company was restricted. The main barriers to effective flows were excessive secrecy which prevented open exchange of information, lack of finance and the split sites. Although these were only a few miles from the main building, the staff felt isolated. The results further show that the most used information resources were colleagues, and that the most used non-human information resources were not held in the IC. The main users of the IC were the R&D staff, while more than 50% of the company rarely or never used the facility. The investigation represents an early example of Knowledge Management and further documents a stage in the evolution of biotechnology companies.

Since software systems increasingly govern both vital infrastructure and people's daily lives, ensuring their safety and security is vital. Where standard measures like testing are insufficient, deductive verification can be used to mathematically prove software properties once and for all. In the past decades, there has been huge progress in the design and automation of verification techniques, to the point where there are multiple automated verification tools able to prove trace properties for statically-typed programming languages like Java or C. However, several challenges remain: Crucial security properties like non-interference are hyperproperties, which are not supported by most standard verification tools. Additionally, new verification techniques have to be developed to enable the verification of programs written in other mainstream programming languages that offer fewer or different static guarantees. In this thesis, we expand the state of the art in these two directions, by enabling the automated verification of hyperproperties in standard verification tools, and making existing verification techniques accessible for the dynamic Python language as well as Ethereum smart contracts. First, we present a modular verification for Python programs, targeting complex safety and security properties for realistic code. We address the challenges stemming from dynamic typing and the resulting lack of static knowledge about Python programs using a mix of permissive static checks using an unsound type system and additional sound checks in the verifier. The resulting verification technique is able to support typical Python code patterns, and can be integrated with existing specification and verification techniques for safety and security properties. We implement our solution in Nagini, an automated verifier for a substantial subset of Python 3, and demonstrate its ability to verify real-world Python code and prove complex program properties. Second, we present modular product programs, a program transformation that enables the verification of hyperproperties using standard verification tools. Unlike existing solutions, modular product programs can be constructed automatically, impose no restrictions on program control flow, and, crucially, enable modular proofs of hyperproperties using relational specifications. We apply modular product programs to proving non-interference using special information flow specifications, which can express complex properties like value-dependent sensitivity and termination-sensitive non-interference. We implement the product transformation for the Viper verification infrastructure and show that it can prove both information flow properties and other hyperproperties for challenging examples from the literature. Third, we show how product constructions like the aforementioned modular product programs, which are usually defined for small, sequential languages, can be applied to more complex languages and integrated with existing verification tools that are based on intermediate verification languages (IVLs). The key idea is to perform the product construction on the level of the (simple) IVL instead of the (complex) source language, thus enabling existing tools to verify hyperproperties with little engineering effort. We show that this approach is not always sound, but provide a simple criterion that guarantees soundness and can be easily checked in practice. Furthermore, we show how one can verify non-interference properties of concurrent source programs using sequential product constructions. We demonstrate the feasibility of this approach by implementing it for Nagini. Our evaluation shows that this extended Nagini implementation, developed with minimal effort, can compete with existing specialized verification tools for non-interference in terms of its expressiveness, while offering acceptable performance. Fourth, we present a modular specification and verification technique for Ethereum smart contracts, i.e., small programs that execute in the Ethereum blockchain, which typically implement custom resources and resource transfers. Smart contract verification is challenging because contracts execute in an adversarial environment, interacting with malicious outside contracts that can perform re-entrant calls. We present a specification and verification technique that, unlike existing techniques, is sound and precise in this context and does not assume or impose limits on re-entrancy. Furthermore, we expand this technique to enable the contract-modular verification of sets of collaborating contracts for the first time. Finally, we propose a domain-specific specification language that lets users express properties directly in terms of resources and resource transfers, reducing the likelihood of erroneous specifications. We implement our technique in 2vyper, an automated verification tool for the Vyper language (a smart contract language for Ethereum which is distinct from the aforementioned Viper verification language), and demonstrate its ability to verify complex properties of real smart contracts.

Amdahl's law implies that even small sequential bottlenecks can seriously limit the scalability of multi-threaded programs. To achieve scalability, developers must painstakingly identify sequential bottlenecks in their program and attempt to eliminate these bottlenecks by either changing synchronization strategies or rearchitecting and rewriting code with sequential bottlenecks. This can require significant effort by the developer to understand the best approach to address sequential bottlenecks.We bring a new tool, information flow, to the problem of understanding sequential bottlenecks. Information flow is powerful in that it helps developers understand whether a bottleneck is fundamental to the computation, or merely an artifact of the implementation. We combine information flow with field conflict analysis to help developers understand the limits and potential benefits of eliminating sequential bottlenecks through redesigning the locking strategy. Finally, we develop schedule impact analysis to help understand the potential impacts of optimizations before implementing them. We develop an implementation of this in FlowProf. We have evaluated FlowProf on a set of multi-threaded Java applications and manually examined the code to validate FlowProf's findings.

This thesis consists of two parts, representing two separate strands of research.The first part is concerned with the formal analysis of program security. We argue that security properties of computer systems can be thought of causally, and use a formal model of causality, the Halpern-Pearl (HP) model, to represent programs and capture a variety of security properties such as noninterference, robust declassification and endorsement. This provides new insights into both causality, where security-inspired scenarios put the existing theory to the test and motivate us to consider various extensions, and security, where causality lets us express security properties in intuitive terms and see what they denote in natural settings. In the second part, we introduce a theoretical model of information acquisition under resource limitations in a noisy environment. An agent must guess the truth value of a given Boolean formula φ after performing a bounded number of noisy tests of the truth values of variables in the formula. We observe that, in general, the problem of finding an optimal testingstrategy for $\phi$ is hard, but we suggest a useful heuristic. The techniques we use also give insight into two apparently unrelated, but well-studied problems: (1) rational inattention, that is, when it is rational to ignore pertinent information (the optimal strategy may involve hardly ever testing variables that are clearly relevant to φ), and (2) what makes a formula hard to learn/remember.

The brain endogenously generates electrical activity that arises from the complex, nonlinear interactions of its components. During sleep, large amplitude, slow oscillations as well as 10-16 Hz rhythms known as sleep spindles are generated in the cortex and thalamus respectively, and their coupling has been shown to bolster our memory capacities by facilitating cortical plasticity. Identifying where particular sleep rhythms are generated, how they co-occur with other regions, and whether rhythms differ in frequency or other characteristics can inform mechanisms for how they coordinate information exchange during sleep. In this dissertation, I characterized the cortical and thalamic activity of sleep spindles, theta bursts (~6 Hz), a novel sleep rhythm identified here, and the coupling of spindles and theta bursts with slow waves using intracranial recordings from epileptic patients. I also report regional differences in spindle properties, largely inaccessible to non-invasive recordings, that propose a modified view of spindle dynamics across the cortex. The most common characterizations of brain dynamics, including the sleep rhythms reported here, are largely based on linear time-frequency analyses. However, because the brain is a high-dimensional, nonlinear system, applying linear techniques alone may not sufficiently capture the relevant dynamical features. To address this, I helped develop nonlinear tools, based on Delay Differential Analysis (DDA), for analyzing neural time series. I evaluated these tools in simulated, chaotic systems, which are suitable models for recurrent, continuous, and nonlinear dynamics. Specifically, I investigated whether given a set of recorded time series, can we (1) assess whether two signals are causally interacting and (2) rank signals by their amount of dynamical information about the original system. These applications of DDA, in tandem with traditional linear techniques, can improve our understanding of underlying brain activity during seizures and sleep. In Chapter 1, I characterize a novel sleep rhythm, the theta burst, that is distinct from sleep spindles, recorded in both the cortex and thalamus, and which in both structures, precedes downstates. In Chapter 2, I report distinct sources of spindle variability, including variability across channels within a region, across spindles within a single recording site, and across cycles within a spindle, and how these sources are of a size comparable to the frontal-parietal difference typically used to summarize spindle dynamics. Chapter 3 introduces a novel nonlinear signal processing technique, Cross-Dynamical Delay Differential Analysis (CD-DDA), for inferring causal interactions between time series and applies this approach to track seizure spread in a patient with epilepsy. Chapter 4 applies DDA in simulated chaotic dynamical systems to assess the observability of a time series, i.e. how much dynamical information a variable has about the original system it is a part of.

Computing systems that make use of other people's data are an important and pervasive aspect of modern life. However, users have little ability to specify how their information should be used, and system providers have insufficient tools to provide assurance that they correctly handle that information. Decentralized information flow control (DIFC) provides a framework for specifying policies on the use of information and ensuring that systems abide by those policies. However, existing DIFC systems do not address the complexity of the modern software ecosystem, where multiple entities interact to provide the software, data storage, and computational resources for a given application. This dissertation aims to bridge the divide between the strong security guarantees provided by DIFC systems and the complex requirements of today's distributed applications. We present Fabric, a platform for secure distributed storage and computation. Fabric provides a high-level programming model that enables heterogeneously trusted parties to share code, information, and computational resources while enforcing strong information flow constraints. We have evaluated Fabric by building applications that model the data storage, communication, and software evolution patterns present in existing applications, while adding appropriate information flow constraints. Results from these applications suggest that Fabric has a clean, concise programming model, offers good performance, and enforces strong security policies. In a decentralized setting like Fabric, it should be the case that a principal's security policy can only be violated if someone they trust misbehaves. Formalizing these notions in a decentralized system is challenging because statements of trust and definitions of policies are also decentralized. To address this challenge, we show how to identify the data that influences information flows in a broad class of DIFC systems. We use influenced flows to formalize the notion that you can only be harmed by those you trust, and show that definitions of harm based on influenced flows generalize existing semantic definitions of information flow security. Since trust statements in federated systems are distributed, determining whether a computation is safe may require a separate distributed computation, which may in turn introduce potentially unsafe information flows. We present a programming language for distributed authorization queries that tracks information flow. We show that programs in our language do not leak information, and that the language is sufficiently expressive to model nontrivial authorization queries.

The increasing number of hardware-based security attacks along with prevalence of embedded systems in critical applications necessitate robust methods for designing digital circuits with regards to security expectations. However, state of the art secure hardware design practices are mainly based on manual code review and security audit which do not provide formal guarantees of security despite being time-consuming and cumbersome. Thus, a systematic approach for identifying and fixing security vulnerabilities in hardware designs is required.Information flow analysis presents a coherent and systematic method for evaluation of broad types of security policies. Information flow models capture how security-critical data propagates through a system by augmenting the design with security labels. Therefore, evaluating the security labels of the critical components could substitute manual inspection of the design for information leakage. Such analysis accelerates, automates, and formalizes assessment of information flow security properties. This dissertation shows how various security policies such as confidentiality, integrity and timing side-channel can be formalized utilizing information flow tracking (IFT) in hardware designs.Following this formalization, this thesis presents four practical tools -- RTLIFT and Clepsydra for verification of timing-sensitive information flow properties, VeriSketch for synthesis and enforcement of timing-sensitive information flow properties, and lastly an error localization framework for automated inspection of verification failures. Leveraging advances in formal methods and commercial EDA software, the presented tools augment the traditional hardware design cycle with sound and automated security analysis.

Increasing the expressiveness of information flow labels can improve the permissiveness of an enforcement mechanism. This thesis studies two formulations of expressive information flow labels: RIF labels and label chains. Restrictions that a reactive information flow (RIF) label imposes on a value depend on the sequence of operations used to derive that value. This allows declassification, endorsement, and other forms of reclassification to be supported in a uniform way. Piecewise noninterference (PWNI) is introduced as the appropriate security policy. A type system is given for static enforcement of PWNI in programs that associate checkable classes of RIF labels with variables. Two checkable classes of RIF labels are described: general-purpose RIF automata and κ-labels for programs that use cryptographic operations. But labels themselves can encode information, and thus, certain restrictions should be imposed on their use, too. A new family of dynamic enforcement mechanisms is derived to leverage arbitrarily long label chains, where each label in the chain defines restrictions for its predecessor. These enforcers satisfy Block-safe Noninterference (BNI), which proscribes leaks from observing variables, label chains, and blocked executions. Theorems characterize where longer label chains improve permissiveness of dynamic enforcement mechanisms that satisfy BNI. These theorems depend on semantic attributes of such mechanisms as well as on initialization, threat model, and size of lattice of labels.

The creators of technical infrastructure are under social and legal pressure to comply with expectations that can be difficult to translate into computational and business logics. This dissertation bridges this gap through three projects that focus on privacy engineering, information security, and data economics, respectively. These projects culminate in a new formal method for evaluating the strategic and tactical value of data: data games. This method relies on a core theoretical contribution building on the work of Shannon, Dretske, Pearl, Koller, and Nissenbaum: a definition of situated information flow as causal flow in the context of other causal relations and strategic choices. The first project studies privacy engineering's use of Contextual Integrity theory (CI), which defines privacy as appropriate information flow according to norms specific to social contexts or spheres. Computer scientists using CI have innovated as they have implemented the theory and blended it with other traditions, such as context-aware computing. This survey examines computer science literature using Contextual Integrity and discovers, among other results, that technical and social platforms that span social contexts challenge CI's current commitment to normative social spheres. Sociotechnical situations can and do defy social expectations with cross-context clashes, and privacy engineering needs its normative theories to acknowledge and address this fact. This concern inspires the second project, which addresses the problem of building computational systems that comply with data flow and security restrictions such as those required by law. Many privacy and data protection policies stipulate restrictions on the flow of information based on that information's original source. We formalize this concept of privacy as Origin Privacy. This formalization shows how information flow security can be represented using causal modeling. Causal modeling of information security leads to general theorems about the limits of privacy by design as well as a shared language for representing specific privacy concepts such as noninterference, differential privacy, and authorized disclosure. The third project uses the causal modeling of information flow to address gaps in current theory of data economics. Like CI, privacy economics has focused on individual economic contexts and so has been unable to comprehend an information economy that relies on the flow of information across contexts. Data games, an adaptation of Multi-Agent Influence Diagrams for mechanism design, are used to model the well known economic contexts of principal-agent contracts and price differentiation as well as new contexts such as personalized expert services and data reuse. This work reveals that information flows are not goods but rather strategic resources, and that trade in information therefore involves market externalities.

The ability to model the disruptions of adverse events on various systems, such as infrastructural and social, is an important tool to assessing these systems’ resilience. While previous research on system resilience concentrated on physical infrastructure such as transportation systems, two recent research topics include social resilience and dependencies across many infrastructure systems. For example, transportation is dependent on such systems as power, communications, and the workforces that are key to restoring these infrastructure systems. This dissertation contains three disruption modeling studies that have followed the evolution of resilience research over the past decade from physical systems to interrelated topics. The first study uses mesoscopic traffic simulation to evaluate seismic risk of potential travel time increases from earthquake damage to bridges in a roadway network. This analysis successfully obtained system risk curves of network-wide travel time increases. The second study shifts focus towards workforces that participate in restoring infrastructure systems. It identifies transportation and communications workers and calculates these workers’ exposure to the Peak Ground Accelerations (PGAs) of a 7.8 magnitude Southern California scenario earthquake. Indeed, for this scenario, transportation workers are exposed to statistically significant higher PGAs than non-transportation workers, and communication workers to significantly lower PGAs. The third study proposes a model for the travel time of information along communication-equipped vehicles physically traveling in a network. Vehicles are sampled as equipped vehicles, then their trajectories are analyzed to (1) estimate equipped vehicle link flow and turning movement counts and (2) estimate the frequency of equipped vehicles encountering each other on links and at nodes. This study compares two scenarios: the baseline scenario and a work zone scenario that corresponds to a bridge being damaged in the network. It is hypothesized that there would arise a difference in expected path travel times when (1) the representation of a specified subpath within the sample is increased and (2) when vehicles are routed along currently unused subpaths. This dissertation concludes with a discussion of the contributions of all three studies, as well as suggestions for future work.

The application of rational drug design principles in the era of network-pharmacology requires the investigation of drug-target and target-target interactions in order to design new drugs. The presented research was aimed at developing novel computational methods that enable the efficient analysis of complex biomedical data and to promote the hypothesis generation in the context of translational research. The three chapters of the Dissertation relate to various segments of drug discovery and development process. The first chapter introduces the integrated predictive drug discovery platform „SmartGraph”. The novel collaborative-filtering based algorithm „Target Based Recommender (TBR)” was developed in the framework of this project and was validated on a set of 28,270 experimentally determined bioactivity data points involving 1,882 compounds and 869 targets. The TBR is integrated into the SmartGraph platform. The graphical interface of SmartGraph enables data analysis and hypothesis generation even for investigators without substantial bioinformatics knowledge. The platform can be utilized in the context of target identification, drug-target prediction and drug repurposing. The second chapter of the Dissertation introduces an information theory inspired dynamic network model and the novel “Luminosity Diffusion (LD)” algorithm. The model can be utilized to prioritize protein targets for drug discovery purposes on the basis of available information and the importance of the targets. The importance of targets is accounted for in the information flow simulation process and is derived merely from network topology. The LD algorithm was validated on 8,010 relations of 794 proteins extracted from the Target Central Resource Database developed in the framework of the “Illuminating the Druggable Genome” project. The last chapter discusses a fundamental problem pertaining to the generation of similarity network of molecules and their clustering. The network generation process relies on the selection of a similarity threshold. The presented work introduces a network topology based systematic solution for selecting this threshold so that the likelihood of a reasonable clustering can be increased. Furthermore, the work proposes a solution for generating so-called “pseudo-reference clustering” for large molecular data sets for performance evaluation purposes. The results of this chapter are applicable in the lead identification and development processes.

With the recognition of the criticality of information flow in the supply chain, much research has examined various pertinent areas such as information sharing and the use of IT applications, and generated valuable insights about how an organization could reap the benefits through an effective supply chain information flow by building on the implicit assumption that organizations in the supply chain cope with issues around supply chain information in a similar way, which would lead to the conjecture that the quality of supply chain information is equivalent across supply chain members. However, many organizations still struggle for poor information flow in the supply chain. This suggests that the implicit assumption made in the literature may not hold in practice. By challenging this unstated and flawed assumption, this dissertation adopts a governance perspective on supply chain information flow, i.e., information governance, and positions information governance in the realm of supply chain management. Specifically, this dissertation unpacks information governance by identifying its key elements and delves into the nature of the relationships between the key elements of information governance and supply chain performance. This dissertation further investigates the arrangement of the information governance and supply chain strategies and its performance implications in a hospital context. The findings of this dissertation contribute to facilitating an understanding of information governance in the supply chain context by providing theoretical and empirical support. Managerial implications and future research directions are also presented.

Today's web applications remain vulnerable to cross-site scripting attacks that enable data theft. Information flow tracking in the JavaScript VM of a web engine can track data flows through the web application and prevent the communication of sensitive data to unintended recipients, thereby stopping data theft. Existing solutions have focused on the incorporating information flow into the JS interpreter, rather than the just-in-time compiler, rendering the resulting performance non-competitive.This thesis presents an implementation of information flow tracking that works with the just-in-time compiler, outperforming all previous interpreter-based information flow tracking engines by more than a factor of two. The JIT-based engine (i) has the same coverage as previous implementations, (ii) requires comparatively light implementation effort, and (iii) introduces new optimizations to remain performant. When evaluated against three industry standard benchmark suites, the tracking engine retains an average slowdown of 73% over engines that do not support information flow, remaining will within the range that many users will find an acceptable price for obtaining substantially increased security against data theft.

Social, collaboration and information networks are rich in interactions through the exchange of different types of content, such as video, audio, text, short and long hyperlinks. These networks are content-rich due to the heterogeneity and size of the content generated by the nodes over long periods. Some of these networks may not necessarily have content, but they may have meta-data, such as synthetic neural networks and climate networks. Despite their differences, all these networks are extremely complex to comprehend and modeling them is even more difficult when they are dynamic and continuously evolving in time. Moreover, the underlying interaction phenomena in these networks, such as information diffusion, can be best understood only at scale and traditional algorithms are either sequential or do not take advantage of content and its temporal dynamics. In social and collaboration networks, such as Twitter and DBLP, content propagates from one node to another through the influence of the author, nature of the content and the time of posting. Sometimes a message reposted may not be the same, however, sufficiently influenced by the original message. The goal of this task is to understand the causal behavior of topical influence in networks by developing an information flow mining tool. This tool can be used in variety of applications from understanding the most influential authors in social media to finding outlier communication sequences in cyber-crime. Traditionally, mining of content sequences is approached as a sequence mining problem, with no underlying network structure. One can later associate the content sequences with the network structure as a post-processing step. However, the treatment of content and network structure independently is extremely inefficient as the number content sequences extracted in the first step without the knowledge of network structure can be exponentially large and may never finish processing. We propose an integrated approach "InFlowMine", for mining information flow patterns by tightly integrating content and network structure during the mining process. The network structure is used to guide the candidate generation process of content sequence extraction. Our approach to mine these patterns is an order of magnitude faster than state-of-the-art sequence mining techniques. We evaluated the information flow patterns discovered in the context of influence analysis application and found the patterns to be extremely useful. We show that using patterns to mine influencers is equivalent to maximizing a sub-modular function and comes with a $(1-\frac{1}{e})$OPT guarantee. When we deal with extremely large graphs, the InFlowMine approach is still inefficient as it is sequential in nature and runs in a single computer and does not scale. Usually parallelization for such problems is dealt at a sub-graph level or at a flow path level, where each sub-graph or flow path is treated as an independent computational unit. Instead, in our approach we provide the highest level of parallelism by treating each node in the network as an independent computational unit. Our approach integrates network, content and time by exchanging a compact summary of content propagated by each node with time information to its neighbors. Each vertex updates its internal flow representations and creates a new set of summary objects to exchange with its neighbors for the next iteration. In each iteration we discover flow paths of one step longer than the previous iteration and this process terminates when there are no further paths to expand. We use vertex centric computational model for mining the information flow patterns in the context of Gather-Apply-Scatter (GAS) framework. Our approach "pFlower" scales linear with the number of cores and provides three orders of magnitude improvement over the baseline. Today most of the big social networks, such as Twitter or Facebook feeds, are available in a streaming fashion, where each message that propagated along a set of edges in the network arrives in a data stream with time stamp information. The flow volume and velocity of such data streams are huge, especially in tens of thousands of objects per second. Most of this data has to be processed as they arrive, in order to provide near real-time information on flow patterns. In such scenarios, one needs to maintain the approximate information flow patterns that are more recent and highly frequent for different topics of interest. Current techniques for topic modeling completely ignore the availability of network structure, while the network analysis ignores the content. Our approach integrates both these ends by developing online topic models maintained on evolving approximate flow cascades. Our approach for recommendation in social networks improves the precision and recall measures up to 18\% compared to baselines. In summary, this thesis presents a set of information flow mining algorithms and applications, for understanding large scale social and collaboration networks, using content, network and temporal information in an unified and scalable fashion.

Research has shown that the lack of effective communication is one main reason why the construction industry worldwide is falling behind others in advancing productivity, safety and quality. The focus of this thesis is to experimentally test the effectiveness of innovative information technologies in bridging existing information chasms creating large barriers to advancing construction. First, an extensive field study of the state-of-practice on an Australian construction site was conducted to identify the present information and material flows, information sinks, and sources related to rebar and concrete operations. The result of this initial field work was used to develop the Holonic Construction Management Model (HCMM) augmenting existing information models with the goal to create a unified umbrella able to support clearly identified data needs. The second phase of the work focused on testing four hypotheses. Building on the HCMM and the challenges of an active construction site, an Agile Site Communication Network (ASCNet) was created establishing the data conduits needed to connect the identified islands-of-communication. Furthermore, the discovered need to link mobile process resources with each other as well as to the internet involved the development and testing of a novel Embedded Wireless Communication Platform (EWCP). Another essential component of the ASCNet were multi-purpose and mobile data/information hubs to be placed at the work-front. This triggered the design, fabrication and testing of an electronic Construction Kiosk (eCKiosk) equipped with a wide touch screen, digital video/audio, wireless data-acquisition from electronic sensors and a secure data link to the world-wide-web. A third capability involved RFID tags to be embedded into the concrete of a ready-mix truck identifying its spatial location after placement. Finally, the experimental IT capabilities were used to address the four hypotheses established in Chapter 3. The extensive survey of electromagnetic signal attenuation on the test site proved that the existing decay functions were too inaccurate to be applicable for a construction site. Reformulated power decay indices drastically increased the signal strength prediction by more than a factor of 10 thus validating hypothesis 1. RFID embedded rebar bundles shipped on a GPS equipped trailer tuck and staged using feed-forward process information increased the productivity of crane-lifting by over 20% and eliminated 5 hours waiting of the delivery trucks that had been observed. The EWPC attached to a crane hook proved the feasibility and effectiveness of the multi-channel real-time communication link between the moving hook-block, the operator, GPS tracking software and the internet. The test of ZigBee s IEEE 802.15 protocol under field condition provided the background for a successful application to monitor remotely concrete temperature for several days. The electronic hub served as a critical link to the wireless sensors while the RFID tags could be found after 4 days in the curing concrete and their locations automatically traced using a DGPS. Testes at the laboratory showed that doubling the amount of reinforcing steel in a concrete block scarifies 61% of detection range of the embedded tags. The tests on two construction sites not only improved our understanding of how to model wireless networks but proved that: a) A fatal blind-spot for crane operators can be immediately eliminated, b) time and safety risks during the delivery and staging of rebar can be drastically reduced, c) embedded RFID tags in combination with DGPS automate as-built data generation for concrete, and most importantly d) the high performance agile communication technologies of an eCKiosk and ZigBee sensor networks enable the effective coordination of on- and off-site activities. The latter two findings will open the door to cutting large process wastes through feed-forward supply-chain integration.

Abstract: Industrial information systems can improve the management of industrial operations by providing relevant and timely information on the status of physical assets via condition and process monitoring systems, and by enabling the effective use of data by systems and processes that restore proper functionality via control and maintenance systems. A framework and methods have been developed that extend current information system design methods for design and evaluation of effective industrial information systems, as a method for more concretely demonstrating the value of system features prior to system design, and for assessing system performance after implementation for continuous system improvement. An effective system from the framework’s perspective is first, performance-based, i.e. benefits due to the system are balanced with associated costs. Second, it is fit for the system owner’s purpose, i.e. it has been designed with respect to organizational business strategy, objectives, and constraints. And third, it is holistic, i.e. it addresses various performance criteria that are important to the system stakeholders, e.g. operational excellence, financial, safety and environmental. This thesis describes the development and application of the framework and methods, and verifies their effectiveness using data from an industrial operation. The application case is on the development of a machine condition monitoring and maintenance management information system for mobile machine management of mining and production operations. Evaluation methods have been developed to assess several candidate systems and to select the most effective alternative as part of the framework. The first method is a risk assessment method for analytical performance evaluation of the system using a time-based, event-risk tree model. The second method involves simulating the system for evaluating system performance based on a developed probability model linked, stochastic, discrete event simulation method, which includes machine events, operations workflow, information flow, and decision making related to the case operations. After system alternatives are evaluated, the process for selecting the most effective system is then demonstrated using the analytical hierarchy process (AHP) method coupled with the framework as a multi-criteria decision analysis method. Verification of the case description, models, and the assumed parameter values has been achieved by subject matter expert review.

I study the cognitive and behavioral skills that people use to navigate social networks at work, via three separate but related papers. Paper 1 argues that relational schemas, which are the cognitive schemas that people use to guide interactions with other people (Baldwin 1992), are developed over time through exposure to others. However, having a lower variety of schemas (employing generic schemas for multiple relationships) enables a person to interact with more people using fewer cognitive resources (Abelson 1981), but having a greater variety of schemas (having unique schemas for each relation) enables a person to better adapt to the needs of each relationship. Using in-depth interviews, network data, and interaction data from an administrative department of a manufacturing organization, I find a curvilinear relationship between both social experience and network contacts in an environment and the degree to which people develop more specific relational schemas, providing evidence for this tension between the need to adapt to each relationship versus the constraint of cognitive overload. Paper 2 argues that social network position cannot precisely measure access to information flows in a social context. I develop a new method for measuring access to information, using time-ordered communication/interaction data, which accounts for the temporal ordering of communication and the constraints of time, scheduling, and other factors that cause variation in the frequency of communication across and within relationships. Paper 3 focuses on relational acumen, the ability to accurately perceive the strength of one’s relationships. Relational acumen is a form of network perception accuracy, a cognitive skill that provides a basis of power in organizations (Krackhardt 1990). I argue that people who have higher access to social information flows have greater relational acumen. Using data from the same organization, I find that network brokers have greater relational acumen – they are more accurate in perceiving their strong ties. More importantly, I find that having interactions that are in the midst of others’ interactions throughout the day (what I call “interaction efficiency”) provides the greatest benefit for relational acumen. Thus, how one interacts with contacts matters more than who one’s contacts are.

We are entering an era in which federated information systems are widely used to share information and computation. Federated systems support new services and capabilities by integrating computer systems across independent administrative domains. Each domain has policies for security, but does not fully trust other domains to enforce them. This dissertation explores, in two parts, the challenge of designing and building federated information systems that are secure and reliable while supporting mutually distrusting participants. First, this dissertation presents Fabric, a new system and language for building secure federated information systems. Fabric allows heterogeneous network nodes to securely share information and computation despite mutual distrust. It uses optimistic, nested transactions to ensure global consistency, and has a peerto-peer dissemination layer for better availability and load balancing. Fabric's high-level programming language provides a rich, Java-like object model, and keeps distribution and persistence largely transparent to programmers. It supports data shipping and function shipping: both information and computation can move between nodes to meet security requirements or to improve performance. Confidentiality and integrity policies on objects are enforced through a combination of compile-time and run-time mechanisms. Results from building Fabric applications suggest that Fabric has a clean and concise programming model, offers good performance, and enforces security. Next, this dissertation examines the security implications of providing referential integrity in a federated system. Referential integrity ensures that named resources can be accessed when needed. This is an important property for re- liability and security. However, the attempt to provide referential integrity can itself lead to security vulnerabilities that are currently not well understood. This dissertation identifies three such referential security vulnerabilities, and formalizes security conditions corresponding to their absence. A language model captures key aspects of programming distributed systems with named, persistent resources in the presence of an adversary. A new type system is proved to enforce the conditions for referential security.

With the emergence of systems biology, modeling of physiological models startedto adopt more integrative approaches instead of reductionist methods. In order tounderstand the complex system, which is human body, multiscale physiological modelsmust be integrated and analyzed as a whole. However modeling and simulationof multiscale physiological processes deal with the challenges such as high coupling within and among scales. In order to deal with such challenges in a systematic way, there is a significant need for information technology solutions together with related analytical and computational tools that will facilitate integration of models and simulations of complex biological systems.The idea of functional modularity and structural modularity presented in thisthesis helps to cope with the highly complex and coupled nature of the physiological processes in the software level. Functional modularity is proposed through the use of information flow, which aims to separate the information and the flow of information in physiological processes. Ontology based design approaches are suggested for representing the anatomical and structural information of physiological processes, in order to achieve structural modularity.In this thesis, we also present a software framework which is built on the principles of structural and functional modularity. Physiological Model Simulation, Integration and Modeling Framework (Phy-SIM) is an information technology framework to facilitate development, integration and simulation of large-scale highly-integrated models of human physiology.Ultimate aim of Phy-SIM is to enhance the physiological model development processes; but more importantly to accelerate the development, analysis and testing of integration approaches for multiscale and multilevel physiological models.

This dissertation examines the forces that drive organizational structure and boundary decisions. Using theoretical concepts drawn from transaction cost theory and agency theory, this dissertation develops a model that draws attention to behavioral and outcome distinctions in information flow. Specifically it examines how characteristics of these different types of management information alter the organizational design outcomes predicted by the two theory bases above. The foundational arguments regarding firm boundaries and structure are based on the two contracting modes of agency theory, behavioral and outcome. The construct of information localization (IL), defined as theneed to use information within its immediate context (or conversely, the difficulty of transferring and utilizing information outside of its immediate context), is introduced and used to predict organizational design decisions. This construct represents a synthesis of prior conceptions of information characteristics and is used both to capture salient characteristics of management information and to facilitate an examination of the impacts of different information technology (IT) applications. The relationship between information localization and firm boundaries and structure is tested at the task level. The relationship of information technology with boundary and structure decisions is explained using the model and tested concurrently with the test of the information localization hypotheses. A comprehensive model is proposed and propositions to guide future research are developed. The distinction between outcome and behavioral information localization allows a more nuanced assessment of the impacts of information and information technology on the organization of work. The empirical results suggest that the impact of IT on outcome information localization is negligible for tasks with low outcome IL but increases the likelihood of outsourcing for tasks with high outcome IL. Contrary to prior findings utilizing different conceptualizations of information flow, the impact of technology on behavioral IL on autonomy showed that applications of IT may increase autonomy (decentralization of decision-making) rather than decrease it.

Information flow security concerns how to protect sensitive data in computer systems by avoiding undesirable flow of information between the users of the systems. This thesis studies information flow security properties in state-based systems, dealing in particular with modelling and verification methods for asynchronous systems and synchronous systems with schedulers. The aim of this study is to provide a foundational guide to ensure confidentiality in system design and verification. The thesis begins with a study of definitions of security properties in asynchronous models. Two classes of security notions are of particular interest. Trace-based properties disallow deductions of high security level secrets from low level observation traces. Bisimulation-based properties express security as a low-level observational equivalence relation on states. In the literature, several distinct schools have developed frameworks for information flow security properties based on different semantic domains. One of the major contributions of the thesis is a systematic study that compares security notions, using semantic mappings between two state-based models and a particular process algebraic model. An advantage of state-based models is the availability of well-developed verification methods and tools for functional properties in finite state systems. The thesis investigates the application of these methods to the algorithmic verification of the information flow security properties in the asynchronous settings. The complexity bounds for verifying these security properties are given as polynomial time for the bisimulation-based properties and polynomial space complete for the trace-based properties. Two heuristics are presented to benefit the verifications of the properties in practice. Timing channels are one of the major concerns in the computer security community, but are not captured in asynchronous models. In the final part of the thesis, a new system model is defined that deals with timing and scheduling. A group of novel security notions, including both trace-based and bisimulation-based properties, are proposed in this new model. It is further investigated whether these security properties are preserved by refinement of schedulers and scheduler implementations. A case study of a multi- evel secure file server is described, which applies a number of access control rules to enforce a particular bisimulation-based property in the synchronous setting.

Situation (al) awareness (SA) is critical to analyze, predict and perform tasks effectively in a dynamic environment. Many studies on SA have ignored network dynamism and its effect on SA, focusing on simple environments. Many studies involving the network and SA have refrained from attempting to model information space dynamism (i.e. dynamic scenarios which may have more than one probable outcome). Few studies have identified the need for a flexible, robust and overarching framework which could model both the network and information space dynamisms and provide for analysis of different types of networks (heterogeneous/homogeneous) at multiple scales. We utilize the NCOPP (Network Centric Operations Performance & Prediction), a uniform framework with "plug-&-play" capabilities to provide analysis and performance prediction of networked information systems. In this work, we demonstrate the flexibility of the NCOPP framework and its ability to model a hierarchical sensor system satisfactorily. We model the network & information space dynamisms using probability and statistics theory (e.g. Bayesian prediction, probability distribution curves). We model the behavior of entities/nodes involved in the process of sharing information to achieve greatly improved situation awareness about a dynamic environment within hierarchical information network systems. Our behavior model mathematically represents how successful/unsuccessful predictions critically impact the achievement of effective situation awareness. In the behavior model, we tie together the cost of considering predictions which accounts for limited resources and the indirect effect of unsuccessful predictions. We research and show how the NCOPP framework can model real world networked information systems at different levels of granularity. We leverage the framework's capabilities to perform experiments that not only assist in an objective comparison of distributed information filtering and central data processing paradigms but also provide important insights into the effect of network dynamism on the quality and completeness of information in the system. We demonstrate the ability of incorporating key network information, in the process of achieving SA to improve the performance of the system. We exhibit the improvement in performance achieved with inclusion of the network characteristics during dynamic allocation of resources. We were able to show that simple hierarchical filtering (via distributed processing) results in significant reduction in the information in regards to "false alarms" when compared to systems employing central information processing. Experimental results show a direct positive impact in the completeness of SA when information sharing in hierarchical systems is supplemented by network delay information. Overall, we demonstrated the ability of the NCOPP framework to provide meaningful insights into the interactions of key factors involved in operation of networked information systems, with a particular emphasis on SA.

Due to targeting strategies employed by contemporary political campaigns, campaign intensity is not uniform across the whole country. People in different geographical locations would be influenced by campaigns differently depending on where they are. This study argues that political campaigns could shape a person's total communication context in which a person is conditioned, and accordingly both individual and contextual factors within this context should form synergistic influences on this person's cognitive responses to the election. Therefore, this study attempts to investigate how mass media and interpersonal communication factors at the individual level and the campaign level within an individual's communication context that is defined by some geospatial characteristics created by campaigns would influence this individual's political knowledge. The results from a series of multilevel modeling analyses reveal that there is some evidence that political campaign practices, including political ads, candidate appearances, and campaign contacts, do promote some learning about politics. There is also evidence to support the information flow approach to contextual effects. It is found that macro-level political ads, candidate appearances, and campaign contacts influence people's newspaper use in predicting their political knowledge; and that macro-level political ads and candidate appearances influence people's political discussion in predicting their political knowledge. Consistent with literature, people who read newspapers, watch network and cable television news, and engage in political discussion more frequently have more political knowledge. But, people who watch more local TV news have less political knowledge. These findings suggest that communication, including both mass media and interpersonal communication, does play a significant role in informing people in campaigns. However, political learning is conditioned on many factors -- media, people, stimuli, and place, all of which lead to different results. This study not only demonstrates that conditional communication effects also hinge on geospatial, contextual factors but also helps to develop contextual theories in communication science that specifically take into account contextual factors and addresses cross-level inference.

Engineering design processes are recognized by industry as critical for successful new product development. With many existing design processes, it is unexpected that there is not a method for translating between processes. FACETS aims to become a translator between various design processes. To accomplish this, information must be entered, sorted, and displayed in several ways to communicate design intent to the user. A database schema is proposed to store the qualitative information and relationships associated with the early phases of the engineering design process. The proposed database is implemented using a MySQL database. Tools for conducting the needs assessment and concept development are implemented through a series of scripts that provide a user interface to the database. Two case studies test the appropriateness of the database schema and determine areas for further development. The scripts developed render tools for students in the Kate Gleason College of Engineering Multidisciplinary Senior Design class to facilitate navigating the early design process. These scripts will collect the customer driven needs of the project and provide tools to help translate those needs into proposed design(s) to develop further. Information is intended to flow between tools, phases and design processes. To demonstrate the information flow between customer needs and design concepts, a mapping is graphically rendered. Conclusions confirm that the database supports the information contained in the case study. Additional scripts are necessary to provide the graphical wrappers to display the information similar to the case studies. Secondary conclusions highlight a lack of literature regarding the connections between engineering specifications and product functions and the apparent inconsistencies within the quality function deployment House of Quality when implicit relationships are displayed. Recommendations for further work are presented.

The need for hardware-based software protection stems primarily from the increasing value of software coupled with the inability to trust software that utilizes or manages shared resources. By correctly utilizing security functions in hardware, trust can be removed from software. Existing hardware-based software protection solutions generally suffer from utilization of trusted software, lack of implementation, and/or extreme measures such as processor redesign. In contrast, the research outlined in this document proposes that substantial, hardware-based software protection can be achieved, without trusting software or redesigning the processor, by augmenting existing processors with security management hardware placed outside of the processor boundary. Benefits of this approach include the ability to add security features to nearly any processor, update security features without redesigning the processor, and provide maximum transparency to the software development and distribution processes. The major contributions of this research include the the augmentation methodology, design principles, and a graph-based method for analyzing hardware-based security systems.

This dissertation presents the social virtual interface (SVI) model, which was born out of a need to develop a viable model of the complex interactions, information flow and information seeking behaviors among undercover officers. The SVI model was created from a combination of various philosophies and models in the literature of information seeking, communication and philosophy. The questions this research paper answers are as follows: 1. Can we make use of models and concepts familiar to or drawn from Information Science to construct a model of undercover police work that effectively represents the large number of entities and relationships? and 2. Will undercover police officers recognize this model as realistic? This study used a descriptive qualitative research method to examine the research questions. An online survey and hard copy survey were distributed to police officers who had worked in an undercover capacity. In addition groups of officers were interviewed about their opinion of the SVI model. The data gathered was analyzed and the model was validated by the results of the survey and interviews.

Dynamic information flow analysis is concerned with the runtime monitoring and regulation of the flow of information among objects throughout a system and ultimately between the system and the outside world. A new approach to dynamic information flow analysis is presented that can be used to detect and debug insecure flows in programs. It can be applied offline to validate and debug a program against an information flow policy, or, when fast response is not critical, it can be applied online to prevent illegal flows in deployed programs. Dynamic mechanisms are inherently unable to detect implicit information flows; our approach incorporates an optional static preprocessing phase that identifies implicit flows and transforms them into explicit ones. The resulting hybrid mechanism is therefore capable of detecting both explicit and implicit information flows. Program slicing is an integral part of debugging against an information flow policy. Forward computing slicing algorithms, which do not require a previously stored execution trace, are especially suited for interactive debugging. This dissertation proposes a dynamic slicing algorithm, which is characterized as forward computing, precise and applicable to unstructured programs. Observation-based testing (OBT) is an approach in which executions of a program are profiled and then analyzed using cluster analysis and sampling methods to identify unusual or suspicious executions for manual auditing. This technique can be used to detect ordinary failures as well as intrusive behaviors. It can also be the basis for test-case filtering. This dissertation contributes to observation-based testing research by devising two profiling techniques that capture a relatively higher level of detail from a program execution namely, information flow profiling and slice profiling. In order to empirically verify their relative efficiency, we used them as well as other types of profiles such as function calls and data flow profiles to conduct test-case filtering experiments. The test-case filtering experiments involved OBT techniques as well as coverage-based techniques. The comparative results are presented and discussed. Finally, this dissertation presents a prototype tool for detecting and debugging insecure information flows in Java byte programs, it is also capable of generating information flow and slice execution profiles.

How much you know about your congressional candidates depends on where you live. U.S. congressional districts vary greatly in the degree to which their boundaries match those of current media markets. If the boundaries of the district and the television market's Area of Dominant Influence match, people know more about candidates. Past measures of district/market congruency are inadequate. I use two existing measures and one new measure of district/market congruency to demonstrate that each measures something different. The two old measures are congruency and market dominance. Congruency measures how well the local media cover congressional candidates and how useful the candidate's advertising is in communicating to voters. Market dominance measures the degree to which one or two television markets dominate the district. My new measure, district dominance, measures the degree to which the district dominates the television markets that serve it. Each of these measures tells us something different about what voters know about their candidates. But we need to develop new measures, because I show in a qualitative analysis of six congressional districts, that the television market is not the same as the radio market or newspaper market. Each has different boundaries. All three district/market congruency measures look only at the television market boundaries. I use Federal Election Commission data to see how the campaign strategy differs in different districts. I use American National Election Studies to see if citizens recall the names of congressional candidates, like or dislike those candidates or see, hear or read about those candidates. Television market/district mismatch do not the same effect in presidential election years and off-year elections. Media market fit also has a different effect on incumbents and challengers. Both quantitative and qualitative analyses indicate that the impact of media market fit cannot be adequately assessed unless the mismatch between the market reach of each medium is factored into the measures used.

This thesis explores the information flow at a construction site and examines how wireless communication technology can meet these information needs. The construction industry is dynamic. Therefore, traditional methods of communication have limited applicability to a construction jobsite. However, the advances in the wireless communication field are making that form of communication a viable option for the construction jobsite. The information flow models in some other industries are examined as point of comparison with the construction industry. People in the construction industry can learn what information transmission methods have worked in other industries. Also, much can be learned from other industries that have already embraced wireless technology into their workplace. An articulation of information needs at a construction jobsite is made. A large refinery that is under construction in Houston, Texas was the primary a source for articulating the information needs at a construction site. A detailed summary of a data reconnaissance visit to this project is included. A methodology for identifying determining the applicability of different formats of wireless technology for a set of information needs is included. A second methodology for identifying and classifying information needs by a given company is also included. Finally, two examples of how to use these methodologies are presented.

Although international human rights declarations exist, violations of human rights are still sad but also common facts around the world. But for repressive regimes, it becomes more and more difficult to hide committed human rights violations, since society entered the "Information Revolution." This study argues that the volume of international information exchanged influences a country's human rights record. A pooled cross sectional time series regression model with a lagged endogenous variable and a standard robust error technique is used to test several hypotheses. The findings of this study indicate that the flow of information can be related to a country's human rights index. The study also suggests that more empirical work on this topic will be necessary.