Obfuscation and anonymization methods for locational privacy protection : a systematic literature review

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesThe mobile technology development combined with the business model of a majority of application companies is posing a potential risk to individuals’ privacy. Because the industry default practice is unrestricted data collection. Although, the data collection has virtuous usage in improve services and procedures; it also undermines user’s privacy. For that reason is crucial to learn what is the privacy protection mechanism state-of-art. Privacy protection can be pursued by passing new regulation and developing preserving mechanism. Understanding in what extent the current technology is capable to protect devices or systems is important to drive the advancements in the privacy preserving field, addressing the limits and challenges to deploy mechanism with a reasonable quality of Service-QoS level. This research aims to display and discuss the current privacy preserving schemes, its capabilities, limitations and challenges

Data Science in Healthcare

Data science is an interdisciplinary field that applies numerous techniques, such as machine learning, neural networks, and deep learning, to create value based on extracting knowledge and insights from available data. Advances in data science have a significant impact on healthcare. While advances in the sharing of medical information result in better and earlier diagnoses as well as more patient-tailored treatments, information management is also affected by trends such as increased patient centricity (with shared decision making), self-care (e.g., using wearables), and integrated care delivery. The delivery of health services is being revolutionized through the sharing and integration of health data across organizational boundaries. Via data science, researchers can deliver new approaches to merge, analyze, and process complex data and gain more actionable insights, understanding, and knowledge at the individual and population levels. This Special Issue focuses on how data science is used in healthcare (e.g., through predictive modeling) and on related topics, such as data sharing and data management

Impact of New Method for Laying Separate Sewer System on Pavement Layers

The method of installing underground infrastructure has a significant influence on road resistance and performance under live loads such as traffic. This research presents a new method for laying separate sewer systems by using one trench to sit both sanitary pipe and storm pipe and considers the effects of this approach on the pavement strength. Experimental tests have been conducted in the laboratory using a trench 2.5x0.45x1 metre to install two pipes one over the other (sanitary pipe in the bottom and storm pipe on top). Two cases have tested, the first case using 5 cm surface layer of cold mix asphalt while the second is using soil. A series of loads were applied to test the behaviour of this new system and its effects on the pavement surface layer and the buried pipe. The comparison between the rut print of the live load on the soil layer and the pavement layer was conducted. Results demonstrated that using the cold mix asphalt is still insufficient to provide enough safety to protect buried pipe as a reason of needing to relatively long time to acquire high stiffness. Therefore, minimum cover depth to protect pipelines still required

Digital Transformation in Healthcare

This book presents a collection of papers revealing the impact of advanced computation and instrumentation on healthcare. It highlights the increasing global trend driving innovation for a new era of multifunctional technologies for personalized digital healthcare. Moreover, it highlights that contemporary research on healthcare is performed on a multidisciplinary basis comprising computational engineering, biomedicine, biomedical engineering, electronic engineering, and automation engineering, among other areas

Computational Sleep Behaviour Analysis and Application

Sleep affects a person’s health and is, therefore, assessed if health problems arise. Sleep behaviour is monitored for abnormalities in order to determine if any treatments, such as medication or behavioural changes (modifications to sleep habits), are necessary. Assessments are typically done using two methods: polysomnography over short periods and four-week retrospective questionnaires. These standard methods, however, cannot measure current sleep status continuously and unsupervised over long periods of time in the same way home-based sleep behaviour assessment can. In this work, we investigate the ability of sleep behaviour assessment using IoT devices in a natural home environment, which potential has not been investigated fully, to enable early abnormality detection and facilitate self-management. We developed a framework that incorporates different facets and perspectives to introduce focus and support in sleep behaviour assessment. The framework considers users’ needs, various available technologies, and factors that influence sleep behaviours. Sleep analysis approaches are incorporated to increase the reliability of the system. This assessment is strengthened by utilising sleep stage detection and sleep position recognition. This includes, first, the extraction and integration of influence factors of sleep stage recognition methods to create a fine-grained personalised approach and, second, the detection of common but more complex sleep positions, including leg positions. The relations between medical conditions and sleep are assessed through interviews with doctors and users on various topics, including treatment satisfaction and technology acceptance. The findings from these interviews led to the investigation of sleep behaviour as a diagnostic indicator. Changes in sleep behaviour are assessed alongside medical knowledge using data mining techniques to extract information about disease development; the following diseases were of interest: sleep apnoea, hypertension, diabetes, and chronic kidney disease. The proposed framework is designed in a way that allows it to be integrated into existing smart home environments. We believe that our framework provides promising building blocks for reliable sleep behaviour assessment by incorporating newly developed sleep analysis approaches. These approaches include a modular layered sleep behaviour assessment framework, a sleep regularity algorithm, a user-dependent visualisation concept, a higher-granularity sleep position analysis approach, a fine-grained sleep stage detection approach, a personalised sleep parameter extraction process, in-depth understanding on sleep and chronic disease relations, and a sleep-wake behaviour-based chronic disease detection method.This work has been supported by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 676157

An evaluation of the challenges of Multilingualism in Data Warehouse development

In this paper we discuss Business Intelligence and define what is meant by support for Multilingualism in a Business Intelligence reporting context. We identify support for Multilingualism as a challenging issue which has implications for data warehouse design and reporting performance. Data warehouses are a core component of most Business Intelligence systems and the star schema is the approach most widely used to develop data warehouses and dimensional Data Marts. We discuss the way in which Multilingualism can be supported in the Star Schema and identify that current approaches have serious limitations which include data redundancy and data manipulation, performance and maintenance issues. We propose a new approach to enable the optimal application of multilingualism in Business Intelligence. The proposed approach was found to produce satisfactory results when used in a proof-of-concept environment. Future work will include testing the approach in an enterprise environmen

Text Similarity Between Concepts Extracted from Source Code and Documentation

Context: Constant evolution in software systems often results in its documentation losing sync with the content of the source code. The traceability research field has often helped in the past with the aim to recover links between code and documentation, when the two fell out of sync. Objective: The aim of this paper is to compare the concepts contained within the source code of a system with those extracted from its documentation, in order to detect how similar these two sets are. If vastly different, the difference between the two sets might indicate a considerable ageing of the documentation, and a need to update it. Methods: In this paper we reduce the source code of 50 software systems to a set of key terms, each containing the concepts of one of the systems sampled. At the same time, we reduce the documentation of each system to another set of key terms. We then use four different approaches for set comparison to detect how the sets are similar. Results: Using the well known Jaccard index as the benchmark for the comparisons, we have discovered that the cosine distance has excellent comparative powers, and depending on the pre-training of the machine learning model. In particular, the SpaCy and the FastText embeddings offer up to 80% and 90% similarity scores. Conclusion: For most of the sampled systems, the source code and the documentation tend to contain very similar concepts. Given the accuracy for one pre-trained model (e.g., FastText), it becomes also evident that a few systems show a measurable drift between the concepts contained in the documentation and in the source code.</p

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe