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The impact of employees' working relations in creating and retaining trust: the case of the Bahrain Olympic Committee
Introduction: This thesis investigates the impact of employees’ working relations in creating, maintaining and retaining trust in the Bahrain Olympic Committee (BOC).
Aim: The main aim of this thesis is to determine how the three groups of Organisational Trust variables, namely Social System Elements (SSE), Factors of Trustworthiness (FoT) and Third-Party Gossip (TPG), affect employees’ Organisational Trust (OTR) in the BOC and promote Organisational Citizenship Behaviour (OCB). To answer this main aim, a conceptual framework was created that focused on exploring the following research aims: (1) the interrelationship between SSE and FoT, (2) the effect of SSE on OTR, (3) the impact of TPG on OTR and (4) the effect of OTR on overall OCB.
Methodology: The study uses a mixed-method case study research style that included in-depth semi-structured interviews with 17 managers, an online questionnaire survey with 320 employees of the BOC and an analysis of the BOC’s Annual Reports from 2015 to 2018.
Results: The qualitative and quantitative findings indicate, firstly, that there is a significant interrelationship between SSE and FoT, establishing that SSE’s perception of organisational justice (OJ), including that FoTs benevolence and integrity as the most important factors in yielding employees’ trust in the BOC. Secondly, it has been established that SSEs have significant direct and indirect effects on OTR. Thirdly, negative and positive TPG concurrently occurred in the BOC and the prevalence of negative TPG poses more impact on OTR. Finally, this study’s findings demonstrated OTR’s effect in generating OCB, including that Civic Virtue was rated as the most preferred of the five OCB themes; this indicates the managers’ and the employees’ strong emotional attachment and support of the activities taking place at the BOC.
Contributions: Overall, this thesis substantially contributes to OTR literature, particularly in the context of the Middle East. It also proposes several insightful recommendations for future research and practical implications for practitioners in the field of Organisational Trust
Downstream-agnostic Adversarial Examples
Self-supervised learning usually uses a large amount of unlabeled data to
pre-train an encoder which can be used as a general-purpose feature extractor,
such that downstream users only need to perform fine-tuning operations to enjoy
the benefit of "large model". Despite this promising prospect, the security of
pre-trained encoder has not been thoroughly investigated yet, especially when
the pre-trained encoder is publicly available for commercial use.
In this paper, we propose AdvEncoder, the first framework for generating
downstream-agnostic universal adversarial examples based on the pre-trained
encoder. AdvEncoder aims to construct a universal adversarial perturbation or
patch for a set of natural images that can fool all the downstream tasks
inheriting the victim pre-trained encoder. Unlike traditional adversarial
example works, the pre-trained encoder only outputs feature vectors rather than
classification labels. Therefore, we first exploit the high frequency component
information of the image to guide the generation of adversarial examples. Then
we design a generative attack framework to construct adversarial
perturbations/patches by learning the distribution of the attack surrogate
dataset to improve their attack success rates and transferability. Our results
show that an attacker can successfully attack downstream tasks without knowing
either the pre-training dataset or the downstream dataset. We also tailor four
defenses for pre-trained encoders, the results of which further prove the
attack ability of AdvEncoder.Comment: This paper has been accepted by the International Conference on
Computer Vision (ICCV '23, October 2--6, 2023, Paris, France
Reinforcement learning in large state action spaces
Reinforcement learning (RL) is a promising framework for training intelligent agents which learn to optimize long term utility by directly interacting with the environment. Creating RL methods which scale to large state-action spaces is a critical problem towards ensuring real world deployment of RL systems. However, several challenges limit the applicability of RL to large scale settings. These include difficulties with exploration, low sample efficiency, computational intractability, task constraints like decentralization and lack of guarantees about important properties like performance, generalization and robustness in potentially unseen scenarios.
This thesis is motivated towards bridging the aforementioned gap. We propose several principled algorithms and frameworks for studying and addressing the above challenges RL. The proposed methods cover a wide range of RL settings (single and multi-agent systems (MAS) with all the variations in the latter, prediction and control, model-based and model-free methods, value-based and policy-based methods). In this work we propose the first results on several different problems: e.g. tensorization of the Bellman equation which allows exponential sample efficiency gains (Chapter 4), provable suboptimality arising from structural constraints in MAS(Chapter 3), combinatorial generalization results in cooperative MAS(Chapter 5), generalization results on observation shifts(Chapter 7), learning deterministic policies in a probabilistic RL framework(Chapter 6). Our algorithms exhibit provably enhanced performance and sample efficiency along with better scalability. Additionally, we also shed light on generalization aspects of the agents under different frameworks. These properties have been been driven by the use of several advanced tools (e.g. statistical machine learning, state abstraction, variational inference, tensor theory).
In summary, the contributions in this thesis significantly advance progress towards making RL agents ready for large scale, real world applications
On the Principles of Evaluation for Natural Language Generation
Natural language processing is concerned with the ability of computers to understand natural language texts, which is, arguably, one of the major bottlenecks in the course of chasing the holy grail of general Artificial Intelligence. Given the unprecedented success of deep learning technology, the natural language processing community has been almost entirely in favor of practical applications with state-of-the-art systems emerging and competing for human-parity performance at an ever-increasing pace. For that reason, fair and adequate evaluation and comparison, responsible for ensuring trustworthy, reproducible and unbiased results, have fascinated the scientific community for long, not only in natural language but also in other fields. A popular example is the ISO-9126 evaluation standard for software products, which outlines a wide range of evaluation concerns, such as cost, reliability, scalability, security, and so forth. The European project EAGLES-1996, being the acclaimed extension to ISO-9126, depicted the fundamental principles specifically for evaluating natural language technologies, which underpins succeeding methodologies in the evaluation of natural language.
Natural language processing encompasses an enormous range of applications, each with its own evaluation concerns, criteria and measures. This thesis cannot hope to be comprehensive but particularly addresses the evaluation in natural language generation (NLG), which touches on, arguably, one of the most human-like natural language applications. In this context, research on quantifying day-to-day progress with evaluation metrics lays the foundation of the fast-growing NLG community. However, previous works have failed to address high-quality metrics in multiple scenarios such as evaluating long texts and when human references are not available, and, more prominently, these studies are limited in scope, given the lack of a holistic view sketched for principled NLG evaluation.
In this thesis, we aim for a holistic view of NLG evaluation from three complementary perspectives, driven by the evaluation principles in EAGLES-1996: (i) high-quality evaluation metrics, (ii) rigorous comparison of NLG systems for properly tracking the progress, and (iii) understanding evaluation metrics. To this end, we identify the current state of challenges derived from the inherent characteristics of these perspectives, and then present novel metrics, rigorous comparison approaches, and explainability techniques for metrics to address the identified issues.
We hope that our work on evaluation metrics, system comparison and explainability for metrics inspires more research towards principled NLG evaluation, and contributes to the fair and adequate evaluation and comparison in natural language processing
CITIES: Energetic Efficiency, Sustainability; Infrastructures, Energy and the Environment; Mobility and IoT; Governance and Citizenship
This book collects important contributions on smart cities. This book was created in collaboration with the ICSC-CITIES2020, held in San José (Costa Rica) in 2020. This book collects articles on: energetic efficiency and sustainability; infrastructures, energy and the environment; mobility and IoT; governance and citizenship
Technologies and Applications for Big Data Value
This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems
Trends and Prospects in Geotechnics
The Special Issue book presents some works considered innovative in the field of geotechnics and whose practical application may occur in the near future. This collection of twelve papers, in addition to their scientific merit, addresses some of the current and future challenges in geotechnics. The published papers cover a wide range of emerging topics with a specific focus on the research, design, construction, and performance of geotechnical works. These works are expected to inspire the development of geotechnics, contributing to the future construction of more resilient and sustainable geotechnical structures
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