34 research outputs found

    The Challenges of Big Data - Contributions in the Field of Data Quality and Artificial Intelligence Applications

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    The term "big data" has been characterized by challenges regarding data volume, velocity, variety and veracity. Solving these challenges requires research effort that fits the needs of big data. Therefore, this cumulative dissertation contains five paper aiming at developing and applying AI approaches within the field of big data as well as managing data quality in big data

    An Approach to Guide Users Towards Less Revealing Internet Browsers

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    When browsing the Internet, HTTP headers enable both clients and servers send extra data in their requests or responses such as the User-Agent string. This string contains information related to the senderā€™s device, browser, and operating system. Previous research has shown that there are numerous privacy and security risks result from exposing sensitive information in the User-Agent string. For example, it enables device and browser fingerprinting and user tracking and identification. Our large analysis of thousands of User-Agent strings shows that browsers differ tremendously in the amount of information they include in their User-Agent strings. As such, our work aims at guiding users towards using less exposing browsers. In doing so, we propose to assign an exposure score to browsers based on the information they expose and vulnerability records. Thus, our contribution in this work is as follows: first, provide a full implementation that is ready to be deployed and used by users. Second, conduct a user study to identify the effectiveness and limitations of our proposed approach. Our implementation is based on using more than 52 thousand unique browsers. Our performance and validation analysis show that our solution is accurate and efficient. The source code and data set are publicly available and the solution has been deployed

    Miniaturized Transistors, Volume II

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    In this book, we aim to address the ever-advancing progress in microelectronic device scaling. Complementary Metal-Oxide-Semiconductor (CMOS) devices continue to endure miniaturization, irrespective of the seeming physical limitations, helped by advancing fabrication techniques. We observe that miniaturization does not always refer to the latest technology node for digital transistors. Rather, by applying novel materials and device geometries, a significant reduction in the size of microelectronic devices for a broad set of applications can be achieved. The achievements made in the scaling of devices for applications beyond digital logic (e.g., high power, optoelectronics, and sensors) are taking the forefront in microelectronic miniaturization. Furthermore, all these achievements are assisted by improvements in the simulation and modeling of the involved materials and device structures. In particular, process and device technology computer-aided design (TCAD) has become indispensable in the design cycle of novel devices and technologies. It is our sincere hope that the results provided in this Special Issue prove useful to scientists and engineers who find themselves at the forefront of this rapidly evolving and broadening field. Now, more than ever, it is essential to look for solutions to find the next disrupting technologies which will allow for transistor miniaturization well beyond siliconā€™s physical limits and the current state-of-the-art. This requires a broad attack, including studies of novel and innovative designs as well as emerging materials which are becoming more application-specific than ever before

    Point Cloud-based Deep Learning and UAV Path Planning for Surface Defect Detection of Concrete Bridges

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    Over the past decades, several bridges have collapsed, causing many losses due to the lack of proper monitoring and inspection. Although several new techniques have been developed to detect bridge defects, annual visual inspection remains the main approach. Visual inspection, using naked eyes, is time-consuming and subjective because of human errors. Light Detection and Ranging (LiDAR) scanning is a new technology to collect 3D point clouds. The main strength of point clouds over 2D images is collecting the third dimension of the scanned objects. Deep Learning (DL)-based methods have attracted the researchersā€™ attention for concrete surface defect detection. However, no point cloud-based DL method is currently available for semantic segmentation of bridge surface defects without converting the raw point cloud dataset into other representations, which results in increasing the size of the dataset and leads to some challenges regarding storage capacity, cost, and training time. Some promising point cloud-based semantic segmentation methods (i.e., PointNet and PointNet++) have been applied in segmenting bridge components (i.e., slabs, piers), but not for segmenting surface defects (i.e., cracks, spalls). Moreover, most of the current point cloud-based concrete surface defect detection methods focus on only one type of defects. On the other hand, in DL, a dataset plays a key role in terms of variety, diversity, accuracy, and size. The lack of publicly available point cloud datasets for bridge surface defects is one of the reasons of the lack of studies in the area of point cloud-based methods. Furthermore, compared with terrestrial LiDAR scanning, LiDAR-equipped Unmanned Aerial Vehicle (UAV) is capable of scanning the inaccessible surfaces of the bridges at a closer distance with higher safety. Although the UAV flying path can be controlled using remote controllers, automating and optimizing UAV path planning is preferable for being able to trace a collision-free path with minimum flight time. To increase the efficiency and accuracy of this approach, it is crucial to scan all parts of the bridge with a near perpendicular view. However, in the case of obstacle existence (e.g., bridge piers), achieving full coverage with near perpendicular view may not be possible. To provide more accurate results, using overlapping views is recommended. However, this method could result in increasing the inspection cost and time. Therefore, overlapping views should be considered only for surface areas where defects are expected. Addressing the above issues, this research aims to: (1) create a publicly available point cloud dataset for concrete bridge surface defect semantic segmentation, (2) develop a point cloud-based semantic segmentation DL method to detect different types of concrete surface defects, and (3) propose a novel near-optimal path planning method for LiDAR-equipped UAV with respect to the minimum path length and maximum coverage considering the potential locations of defects. On this premise, a point cloud-based DL method for semantic segmentation of concrete bridge surface defects (i.e., cracks and spalls), called SNEPointNet++, is developed. To have a network with high-performance, SNEPointNet++ focuses on two main characteristics related to surface defects (i.e., normal vector and depth) and takes into account the issues related to the point cloud dataset (i.e., small size and imbalanced dataset). Sensitivity analysis is applied to capture the best combination of hyperparameters and investigate their effects on network performance. The dataset, which was collected from four concrete bridges, was annotated, augmented, and classified into three classes: cracks, spalls, and no defect. This dataset is made available for other researchers. The model was trained and evaluated using 60% and 20% of the dataset, respectively. Testing on the remaining part of the dataset resulted in 93% recall (69% IoU) and 92% recall (82.5% IoU) for cracks and spalls, respectively. Moreover, the results show that the spalls of the segments deeper than 7 cm (severe spalls) can be detected with 99% recall. On the other hand, this research proposes a 3D path planning method for using a UAV equipped with a LiDAR for bridge inspection to have efficient data collection. The method integrates a Genetic Algorithm (GA) and A* algorithm to solve the Traveling Salesman Problem (TSP), considering the potential locations of bridge surface defects such as cracks. The objective is to minimize the time of flight while achieving maximum visibility. The method provides the potential locations of surface defects to efficiently achieve perpendicular and overlapping views for sampling the viewpoints. Calculating the visibility with respect to the level of criticality leads to giving the priority to covering the areas with higher risk levels. Applying the proposed method on a 3-span bridge in Alberta, the results reveal that considering overlapping views based on the level of criticality of the zones and perpendicular views for all viewpoints leads to accurate and time-efficient data collection

    Recent Development of Hybrid Renewable Energy Systems

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    Abstract: The use of renewable energies continues to increase. However, the energy obtained from renewable resources is variable over time. The amount of energy produced from the renewable energy sources (RES) over time depends on the meteorological conditions of the region chosen, the season, the relief, etc. So, variable power and nonguaranteed energy produced by renewable sources implies intermittence of the grid. The key lies in supply sources integrated to a hybrid system (HS)

    A conceptual model of business intelligence system adoption for the textile and apparel industry in Pakistan

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    Textile and Apparel (T&A) industry is the backbone of Pakistani economy, including one-fourth of the industrial sector, and comprises 40% of industrial employees and approximately 60% share of Pakistani exports. Although, industry is striving hard to compete in international market; a persistent stream of innovation is required to maintain its due share in recent quota free global trade of textiles. Business Intelligence (BI) system is one of the most-used buzzwords in the modern business landscape for well informed decision making. In spite of the great synergies and benefits, BI system grant to the businesses and organizations. The adoption level is low with high failure ratio, especially in developing countries. Further, researchers did not propose any theory or model for the T & A industry. This study aims to fill this gap by conducting a Systematic Literature Review (SLR) for identifying the most appropriate factors, theory and model for the current study. Total of 75 studies were selected which were published during the period of 2011- 2020. A conceptual model is developed with most potential factors by using Technology-OrganizationEnvironment (TOE) framework. This conceptual model will guide the policy makers and industry practitioners to integrate and adopt the BI system successfully that would helpful to achieve competitive edge in the international business markets

    A critical review of the current state of forensic science knowledge and its integration in legal systems

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    Forensic science has a significant historical and contemporary relationship with the criminal justice system. It is a relationship between two disciplines whose origins stem from different backgrounds. It is trite that effective communication assist in resolving underlying problems in any given context. However, a lack of communication continues to characterise the intersection between law and science. As recently as 2019, a six-part symposium on the use of forensic science in the criminal justice system again posed the question on how the justice system could ensure the reliability of forensic science evidence presented during trials. As the law demands finality, science is always evolving and can never be considered finite or final. Legal systems do not always adapt to the nature of scientific knowledge, and are not willing to abandon finality when that scientific knowledge shifts. Advocacy plays an important role in the promotion of forensic science, particularly advocacy to the broader scientific community for financial support, much needed research and more testing. However, despite its important function, advocacy should not be conflated with science. The foundation of advocacy is a cause; whereas the foundation of science is fact. The objective of this research was to conduct a qualitative literature review of the field of forensic science; to identify gaps in the knowledge of forensic science and its integration in the criminal justice system. The literature review will provide researchers within the field of forensic science with suggested research topics requiring further examination and research. To achieve its objective, the study critically analysed the historical development of, and evaluated the use of forensic science evidence in legal systems generally, including its role regarding the admissibility or inadmissibility of the evidence in the courtroom. In conclusion, it was determined that the breadth of forensic scientific knowledge is comprehensive but scattered. The foundational underpinning of the four disciplines, discussed in this dissertation, has been put to the legal test on countless occasions. Some gaps still remain that require further research in order to strengthen the foundation of the disciplines. Human influence will always be present in examinations and interpretations and will lean towards subjective decision making.JurisprudenceD. Phil

    An exploration of cloud-based technology for enhancing emergency management: Cases from New Zealand

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    Despite widespread acknowledgement of the disruptive effect cloud-based technologies have in business, little is known about the role they might play in emergency management, in particular in natural emergencies. This research addresses this gap by exploring emergency management professionalsā€™ perceptions of cloud usage in key emergency management lifecycle stages of preparedness and response. The Diffusion of Innovation theory is used as a theoretical lens to understand the factors that might influence emergency management professionals to utilise cloud-based technologies. It was employed as it focuses on understanding the adoption rate of an innovative technology. A multiple case study approach was employed involving six key New Zealand emergency services organisations. Twenty-nine semi-structured interviews were conducted with emergency professionals at both managerial and operational levels combined with three focus groups and five exercise observations. Data were analysed through grounded theory analysis. A comprehensive framework of multi-dimensional elements that influence the emergency professionalsā€™ perceptions of cloud-based technology deployment was derived from the data. Six key elements were found to have significant influences on the emergency professionalsā€™ perceptions against actual cloud-based technology usage in natural emergencies: organisational readiness, coordination, cloud-based technology characteristics, individual perceptions, individual readiness and non- cloud-based technology redundancy. Organisational readiness is shaped by four critical aspects: usage frequency, usage preparedness, organisational capacity, and training, which is a prerequisite for realising the success of cloud-based technology deployment when managing emergencies. Better inter-agency coordination enhances the emergency professionalsā€™ confidence in using cloud-based technologies during the response stage. The cloud-based technology characteristics also significantly influence cloud-based technology deployment, including perceived advantages, perceived disadvantages, usefulness, and the deployment model type. The individual perception of enhancement expectation reflects the emergency professionalsā€™ real needs in using cloud-based technologies when dealing with emergencies. The individual readiness includes human factors and knowledge, showing that personal attitudes towards cloud-based technology usage and cloud-based technology knowledge sufficiency influence cloud-based technology deployment for managing emergencies. Non- cloud-based technology redundancy eases the emergency professionalsā€™ concerns of using cloud-based technologies when unexpected situations occur during natural emergencies. The framework highlights the need for examining diverse elements in an integrated manner to understand the emergency professionalsā€™ utilisation patterns of cloud-based technologies for managing natural emergencies. The thesis concludes that awareness of the emergency professionalsā€™ cloud-based technology utilisation patterns can help inform the use of cloud-based technologies to improve and integrate emergency preparations and responses. The research contributes to the body of knowledge in the field of both cloud computing and emergency management by providing a comprehensive framework to reveal an in-depth understanding of multi-dimensional elements that influence the emergency professionalsā€™ perceptions of cloud-based technology deployment in natural emergencies. The framework can be used as practical guidelines for similar emergency services organisations to enhance organisational and individual readiness in cloud-based technology utilisation to carry out more effective emergency management performance
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