AUS Repository

The United Arab Emirates passed the Digital UAE vision that requires government organizations and companies to make the UAE a smart country. The initial stage of Digital UAE vision is digitizing all operations, and this stage is called “digital transformation.” Accounting information systems, the systems that gather, organize and communicate an entity’s accounting information to show the entity’s financial health, need transformation to comply with the Digital UAE vision. This study investigated how accounting information systems within the UAE’s aviation sector have transformed to meet the key digital transformation objectives envisaged under the Digital UAE vision. Semi-structured interviews with 17 UAE aviation accounting information systems experts with at least two years’ experience in digital transition were interviewed. Findings showed that the aviation companies used blockchain technology in their supply chain operations to facilitate traceability of partners’ actions. Artificial intelligence was used in the analysis of big data and in the automation of repetitive tasks, whereas cloud computing was used to lower maintenance costs, avoid idle capacity, and create flexibility to scale the business. Artificial intelligence was also used enhance compliance with international financial reporting standards on such matters as fair value determination that require a high degree of management’s judgment. Further, artificial intelligence was used to enhance compliance with general data protection regulation by enabling companies to identify and isolate vulnerabilities for unauthorized disclosure of sensitive information and thus initiate protective measures automatically. Challenges in transforming accounting information systems were noted concerning employee competence and resistance to new systems. Findings thus show the importance of the Digital UAE vision in encouraging transformation of accounting information systems and reveal the need for organizational preparedness to enable benefits of transformation to be realized.

With the rapid growth in the number of IoT devices being added to the network, a major concern that arises is the security of these systems. As these devices are resource constrained, safety measures are difficult to implement on the edge. We propose a novel approach for the detection of IoT device attacks based on the use of formal modelling and mutation testing. Namely, we model the behaviour of small IoT devices such as motion sensors and RFID card reader as state machines with timeouts. We also model basic IoT attacks; namely, battery draining, sleep deprivation, data falsification, replay, and man in the middle attacks, as special mutants of these specifications. We also consider tests for detecting actual physical device manipulation. Mutation testing is then used to derive tests that distinguish these attacks from the original specifications. The behaviour of these mutants is tested in real environment by running the tests on the data collected while the edge device is still running. Our experiments show that derived number of attack mutants and tests is small and thus these tests can be executed many times with limited overhead on the physical device. Consequently, our approach is not deterred by related high costs of traditional mutation testing. Furthermore, we demonstrate that the tests generated by our method, encompassing the considered IoT attacks, do not adequately cover mutants derived through conventional mutation code-based operators. This highlights the necessity of employing our method. A framework that implements our approach is presented along with some other relevant case studies.

Cancer is considered a world health issue, especially for children. Currently, cancer is the second leading cause of death in the United States and leukemia is the second leading cause of cancer death among children and adolescents younger than 20. B-cell acute lymphoblastic leukemia is a type of blood cancer that affects the body’s immune system and creates immature B-cells that no longer fulfill their designated role. Numerous biological studies have tried to understand this disease and suggest possible treatment strategies. So far these methods have limitations and give mixed results. Mathematical models have recently been used to shed some light on the complexities of cancer and develop a mechanistic understanding of the cancer processes, in particular logical network models have recently been developed to study the development of cancer cells and their interactions with their environment. In this thesis, a discrete logical network model is constructed to study the gene regulatory network in pediatric B-cells with a focus on the genes that are known to play a key role in pediatric acute lymphoblastic leukemia. The dynamics of the model is analyzed and used to suggest hypotheses and predictions.