The influence of organizational and information systems factors on the effectiveness of post-merger technology integration

This dissertation explores how ten specific organizational and information systems factors influence post-merger IS integration success, and the role that degree of IS integration plays in moderating the influence these factors may have on IS integration success. Data were gathered, using a self-administered survey instrument, from senior IS executives at firms that experienced a U.S. public merger greater than $25 million between 2004 and 2007. Support is found for the study\u27s Conceptual Model, indicating that all ten factors in unison influence post-merger IS integration success. The data support the hypotheses that quality of merger planning, quality of communication of merger activities to IS, quality of IS integration planning, degree of end-user involvement in IS integration activities, and quality of technical support to users during the IS integration each have a significant influence on post-merger IS integration success. The data also support the moderating effect of degree of IS integration on the relationship between post-merger IS integration success and executive (non-IS) management support. In a supplemental path model analysis, a complex relationship is hypothesized to exist between the factors and IS Capability and IS Performance, the two IS integration success measures, As a result, four of the five remaining hypotheses are indirectly supported. This research expands the body of knowledge that identifies sources of IS integration performance, thus helping to explain sources of overall merger performance

RATE TO MEASURE MATHEMATICS TEACHING: USING THE MANY-FACET RASCH MODELING TO REEVALUATE THE MATHEMATICS CLASSROOM OBSERVATION PROTOCOL FOR PRACTICES (MCOP2)

Rater-mediated classroom observation protocols are increasingly being used for teaching performance assessments, which makes identifying and controlling for various rater effects a central issue to ensure the rating quality. A series of validation studies under the classical test theory framework, including content validity, interrater reliability, and structure analysis, have been completed for the 16-item Mathematics Classroom Observation Protocol for Practices (MCOP2). However, the MCOP2 data have never been investigated under the Rasch framework. Due to the methodological limitations of the CTT approach for rater-mediated assessments, it is imperative to examine the MCOP2 validity and reliability using the MFRM modeling technique to implement dimensionality analysis, item-level analysis, rater effects control, and ratee and rater ability level calibration. To that end, two existing samples of the MCOP2 data were obtained and analyzed, where twelve raters were asked to rate 237 math classroom observations, using the MCOP2 classroom observation protocol. The data were analyzed under the MFRM framework, using Facets 3.83.3. Results of the Facets analysis showed that both the MCOP2 subscales (i.e., Student Engagement & Teacher Facilitation) were valid, unidimensional, and highly reliable rater-mediated performance measures across raters, ratees, and study samples. However, rater-item bias analyses revealed a type of intra-rater inconsistency, where some raters tended to rate more severely than other raters on certain items while more leniently on some other items. The overall findings are promising in that they provide systematic preliminary psychometric evidence for the viability of the MCOP2 protocol to be used for math teachers’ self-assessment and/or peer-assessment along with other designated raters in the future studies

Anomaly Detection in BACnet/IP managed Building Automation Systems

Building Automation Systems (BAS) are a collection of devices and software which manage the operation of building services. The BAS market is expected to be a $19.25 billion USD industry by 2023, as a core feature of both the Internet of Things and Smart City technologies. However, securing these systems from cyber security threats is an emerging research area. Since initial deployment, BAS have evolved from isolated standalone networks to heterogeneous, interconnected networks allowing external connectivity through the Internet. The most prominent BAS protocol is BACnet/IP, which is estimated to hold 54.6% of world market share. BACnet/IP security features are often not implemented in BAS deployments, leaving systems unprotected against known network threats. This research investigated methods of detecting anomalous network traffic in BACnet/IP managed BAS in an effort to combat threats posed to these systems. This research explored the threats facing BACnet/IP devices, through analysis of Internet accessible BACnet devices, vendor-defined device specifications, investigation of the BACnet specification, and known network attacks identified in the surrounding literature. The collected data were used to construct a threat matrix, which was applied to models of BACnet devices to evaluate potential exposure. Further, two potential unknown vulnerabilities were identified and explored using state modelling and device simulation. A simulation environment and attack framework were constructed to generate both normal and malicious network traffic to explore the application of machine learning algorithms to identify both known and unknown network anomalies. To identify network patterns between the generated normal and malicious network traffic, unsupervised clustering, graph analysis with an unsupervised community detection algorithm, and time series analysis were used. The explored methods identified distinguishable network patterns for frequency-based known network attacks when compared to normal network traffic. However, as stand-alone methods for anomaly detection, these methods were found insufficient. Subsequently, Artificial Neural Networks and Hidden Markov Models were explored and found capable of detecting known network attacks. Further, Hidden Markov Models were also capable of detecting unknown network attacks in the generated datasets. The classification accuracy of the Hidden Markov Models was evaluated using the Matthews Correlation Coefficient which accounts for imbalanced class sizes and assess both positive and negative classification ability for deriving its metric. The Hidden Markov Models were found capable of repeatedly detecting both known and unknown BACnet/IP attacks with True Positive Rates greater than 0.99 and Matthews Correlation Coefficients greater than 0.8 for five of six evaluated hosts. This research identified and evaluated a range of methods capable of identifying anomalies in simulated BACnet/IP network traffic. Further, this research found that Hidden Markov Models were accurate at classifying both known and unknown attacks in the evaluated BACnet/IP managed BAS network

Understanding Semantic Implicit Learning through distributional linguistic patterns: A computational perspective

The research presented in this PhD dissertation provides a computational perspective on Semantic Implicit Learning (SIL). It puts forward the idea that SIL does not depend on semantic knowledge as classically conceived but upon semantic-like knowledge gained through distributional analysis of massive linguistic input. Using methods borrowed from the machine learning and artificial intelligence literature, we construct computational models, which can simulate the performance observed during behavioural tasks of semantic implicit learning in a human-like way. We link this methodology to the current literature on implicit learning, arguing that this behaviour is a necessary by-product of efficient language processing. Chapter 1 introduces the computational problem posed by implicit learning in general, and semantic implicit learning, in particular, as well as the computational framework, used to tackle them. Chapter 2 introduces distributional semantics models as a way to learn semantic-like representations from exposure to linguistic input. Chapter 3 reports two studies on large datasets of semantic priming which seek to identify the computational model of semantic knowledge that best fits the data under conditions that resemble SIL tasks. We find that a model which acquires semantic-like knowledge gained through distributional analysis of massive linguistic input provides the best fit to the data. Chapter 4 generalises the results of the previous two studies by looking at the performance of the same models in languages other than English. Chapter 5 applies the results of the two previous Chapters on eight datasets of semantic implicit learning. Crucially, these datasets use various semantic manipulations and speakers of different L1s enabling us to test the predictions of different models of semantics. Chapter 6 examines more closely two assumptions which we have taken for granted throughout this thesis. Firstly, we test whether a simpler model based on phonological information can explain the generalisation patterns observed in the tasks. Secondly, we examine whether our definition of the computational problem in Chapter 5 is reasonable. Chapter 7 summarises and discusses the implications for implicit language learning and computational models of cognition. Furthermore, we offer one more study that seeks to bridge the literature on distributional models of semantics to `deeper' models of semantics by learning semantic relations. There are two main contributions of this dissertation to the general field of implicit learning research. Firstly, we highlight the superiority of distributional models of semantics in modelling unconscious semantic knowledge. Secondly, we question whether `deep' semantic knowledge is needed to achieve above chance performance in SIIL tasks. We show how a simple model that learns through distributional analysis of the patterns found in the linguistic input can match the behavioural results in different languages. Furthermore, we link these models to more general problems faced in psycholinguistics such as language processing and learning of semantic relations.Alexandros Onassis Foundatio

A Survey of Zero-shot Generalisation in Deep Reinforcement Learning

The study of zero-shot generalisation (ZSG) in deep Reinforcement Learning (RL) aims to produce RL algorithms whose policies generalise well to novel unseen situations at deployment time, avoiding overfitting to their training environments. Tackling this is vital if we are to deploy reinforcement learning algorithms in real world scenarios, where the environment will be diverse, dynamic and unpredictable. This survey is an overview of this nascent field. We rely on a unifying formalism and terminology for discussing different ZSG problems, building upon previous works. We go on to categorise existing benchmarks for ZSG, as well as current methods for tackling these problems. Finally, we provide a critical discussion of the current state of the field, including recommendations for future work. Among other conclusions, we argue that taking a purely procedural content generation approach to benchmark design is not conducive to progress in ZSG, we suggest fast online adaptation and tackling RL-specific problems as some areas for future work on methods for ZSG, and we recommend building benchmarks in underexplored problem settings such as offline RL ZSG and reward-function variation

On the Modeling of Dynamic-Systems using Sequence-based Deep Neural-Networks

The objective of this thesis is the adaptation and development of sequence-based Neural-Networks (NNs) applied to the modeling of dynamic systems. More specifically, we will focus our study on 2 sub-problems: the modeling of time-series, the modeling and control of multiple-input multiple-output (MIMO) systems. These 2 sub-problems will be explored through the modeling of crops, and the modeling and control of robots. To solve these problems, we build on NNs and training schemes allowing our models to out-perform the state-of-the-art results in their respective fields. In the irrigation field, we show that NNs are powerful tools capable of modeling the water consumption of crops while observing only a portion of what is currently required by reference methods. We further demonstrate the potential of NNs by inferring irrigation recommendations in real-time. In robotics, we show that prioritization techniques can be used to learn better robot dynamic models. We apply the models learned using these methods inside an Model Predictive Control (MPC) controller, further demonstrating their benefits. Additionally, we leverage Dreamer, an Model Based Reinforcement Learning (MBRL) agent, to solve visuomotor tasks. We demonstrate that MBRL controllers can be used for sensor-based control on real robots without being trained on real systems. Adding to this result, we developed a physics-guided variant of DREAMER. This variation of the original algorithm is more flexible and designed for mobile robots. This novel framework enables reusing previously learned dynamics and transferring environment knowledge to other robots. Furthermore, using this new model, we train agents to reach various goals without interacting with the system. This increases the reusability of the learned models and makes for a highly data-efficient learning scheme. Moreover, this allows for efficient dynamics randomization, creating robust agents that transfer well to unseen dynamics.Ph.D