Gravitation Theory Based Model for Multi-Label Classification

The past decade has witnessed the growing popularity in multi-label classification algorithms in the fields like text categorization, music information retrieval, and the classification of videos and medical proteins. In the meantime, the methods based on the principle of universal gravitation have been extensively used in the classification of machine learning owing to simplicity and high performance. In light of the above, this paper proposes a novel multi-label classification algorithm called the interaction and data gravitation-based model for multi-label classification (ITDGM). The algorithm replaces the interaction between two objects with the attraction between two particles. The author carries out a series of experiments on five multi-label datasets. The experimental results show that the ITDGM performs better than some well-known multi-label classification algorithms. The effect of the proposed model is assessed by the example-based F1-Measure and Label-based micro F1-measure

Uncertainty Estimation, Explanation and Reduction with Insufficient Data

Human beings have been juggling making smart decisions under uncertainties, where we manage to trade off between swift actions and collecting sufficient evidence. It is naturally expected that a generalized artificial intelligence (GAI) to navigate through uncertainties meanwhile predicting precisely. In this thesis, we aim to propose strategies that underpin machine learning with uncertainties from three perspectives: uncertainty estimation, explanation and reduction. Estimation quantifies the variability in the model inputs and outputs. It can endow us to evaluate the model predictive confidence. Explanation provides a tool to interpret the mechanism of uncertainties and to pinpoint the potentials for uncertainty reduction, which focuses on stabilizing model training, especially when the data is insufficient. We hope that this thesis can motivate related studies on quantifying predictive uncertainties in deep learning. It also aims to raise awareness for other stakeholders in the fields of smart transportation and automated medical diagnosis where data insufficiency induces high uncertainty. The thesis is dissected into the following sections: Introduction. we justify the necessity to investigate AI uncertainties and clarify the challenges existed in the latest studies, followed by our research objective. Literature review. We break down the the review of the state-of-the-art methods into uncertainty estimation, explanation and reduction. We make comparisons with the related fields encompassing meta learning, anomaly detection, continual learning as well. Uncertainty estimation. We introduce a variational framework, neural process that approximates Gaussian processes to handle uncertainty estimation. Two variants from the neural process families are proposed to enhance neural processes with scalability and continual learning. Uncertainty explanation. We inspect the functional distribution of neural processes to discover the global and local factors that affect the degree of predictive uncertainties. Uncertainty reduction. We validate the proposed uncertainty framework on two scenarios: urban irregular behaviour detection and neurological disorder diagnosis, where the intrinsic data insufficiency undermines the performance of existing deep learning models. Conclusion. We provide promising directions for future works and conclude the thesis

Neurobiology of schizotypal phenotypes - Schizotypy as a framework for dimensional psychiatry

Complex, dimensional phenotypes represent a valuable framework for the analysis of fundamental neurobiological mechanisms of psychiatric disorders. They facilitate the deconstruction of diagnostic entities and the study of protective processes that prevent progression into clinical domains. Within the psychosis spectrum, schizotypy describes a multidimensional personality construct with behavioural, cognitive, and emotional characteristics similar to key symptoms of schizophrenia, that can equally be grouped into the dimensions positive (magical thinking, unusual perceptions and beliefs), negative (introversion, anhedonia), and disorganised (cognitive disorganisation, eccentricity). Within a continuum model of psychosis, schizotypy is discussed as variation of healthy function, and as risk phenotype of schizophrenia and psychosis proneness, assuming a (partially) overlapping genetic architecture along the spectrum. Current aetiological models propose an impact of genetic liability, in interaction with environmental risk and modulated by protective factors like cognitive function, through disruptions in neuronal development. In fact, recent studies show that schizotypy is associated with brain structural variation, partially overlapping with regions that are also impaired in patients with schizophrenia spectrum disorders. This dissertation characterised neurobiological determinants of schizotypy regarding its genetic basis and neural networks, aiming to develop a multimodal model to integrate those into a joint framework. STUDIES I and IV investigated the genetic structure of schizotypy, demonstrating its association with common variants (single nucleotide polymorphisms, SNPs) in genes (CACNA1C and ZNF804A) involved in processes of neuronal development and identified as risk genes for schizophrenia and other psychiatric disorders (STUDY I). In this association, biological sex has a moderating role. However, a direct association of a polygenic schizophrenia risk score, based on cumulative SNP-risk, was not established (STUDY IV). STUDIES II and III analysed brain structural correlates of schizotypy dimensions, finding an association of the positive dimension (and symptom-associated distress) with grey matter volume in associative brain areas precuneus, striatum and inferior temporal gyrus. STUDY II further indicates that this relationship can be buffered by above average general cognitive function. Study V ultimately integrates the previous results into a joint multivariate model that proves to explain a substantial amount of phenotypic variance. The model shows that the interaction effect of polygenic and poly-environmental risk on positive schizotypy is mediated through brain structural variation in the precuneus, and modulated by the level of executive function. In conclusion, this dissertation shows that schizotypy is associated with genetic polymorphisms involved in neuronal development and function. While those are identified as schizophrenia risk variants, the lack of an association with polygenic schizophrenia risk suggests a limited overlap of the genetic architectures of the phenotypes. The confirmation of the multivariate model, however, indicates an indirect effect through variations in brain structure and modulated by intra- and extrapersonal factors. Accordingly, particularly positive schizotypy is associated with structural alterations in brain regions central for the integration, evaluation, and attribution of perceptual information within associative neuronal networks. Thus, schizotypy is a valuable endophenotype of the schizophrenia spectrum, showing that pathophysiological aberrations lie on a continuum with variation of healthy functioning. Schizotypy, however, also describes the manifestation of interindividual variation in behaviour, cognition, and emotion, with its underlying mechanisms representing an exemplary framework for the study of dimensional, phenotypic spectra

Recent Advances in Wireless Communications and Networks

This book focuses on the current hottest issues from the lowest layers to the upper layers of wireless communication networks and provides "real-time" research progress on these issues. The authors have made every effort to systematically organize the information on these topics to make it easily accessible to readers of any level. This book also maintains the balance between current research results and their theoretical support. In this book, a variety of novel techniques in wireless communications and networks are investigated. The authors attempt to present these topics in detail. Insightful and reader-friendly descriptions are presented to nourish readers of any level, from practicing and knowledgeable communication engineers to beginning or professional researchers. All interested readers can easily find noteworthy materials in much greater detail than in previous publications and in the references cited in these chapters

Data quality measures for identity resolution

The explosion in popularity of online social networks has led to increased interest in identity resolution from security practitioners. Being able to connect together the multiple online accounts of a user can be of use in verifying identity attributes and in tracking the activity of malicious users. At the same time, privacy researchers are exploring the same phenomenon with interest in identifying privacy risks caused by re-identification attacks. Existing literature has explored how particular components of an online identity may be used to connect profiles, but few if any studies have attempted to assess the comparative value of information attributes. In addition, few of the methods being reported are easily comparable, due to difficulties with obtaining and sharing ground- truth data. Attempts to gain a comprehensive understanding of the identifiability of profile attributes are hindered by these issues. With a focus on overcoming these hurdles to effective research, this thesis first develops a methodology for sampling ground-truth data from online social networks. Building on this with reference to both existing literature and samples of real profile data, this thesis describes and grounds a comprehensive matching schema of profile attributes. The work then defines data quality measures which are important for identity resolution, and measures the availability, consistency and uniqueness of the schema’s contents. The developed measurements are then applied in a feature selection scheme to reduce the impact of missing data issues common in identity resolution. Finally, this thesis addresses the purposes to which identity resolution may be applied, defining the further application-oriented data quality measurements of novelty, veracity and relevance, and demonstrating their calculation and application for a particular use case: evaluating the social engineering vulnerability of an organisation

Using Data Mining Techniques to Assess the Impact of COVID-19 on the Auto Insurance Industry in China

Since coronavirus disease 2019 (COVID-19) was discovered at the end of 2019, the whole world has been severely affected. The insurance industry, regarded as an important factor in recovery, has also been affected by COVID-19. However, effective data mining techniques have rarely been utilized in the insurance industry in China, especially under the circumstances of COVID-19. Although some traditional statistical analysis methods have been applied to this area, the limitation of the lack of data distribution still cannot be efficiently overcome. With the machine learning technique proposed in this thesis, this limitation can be solved by using a stacking model with great generalization ability. In this research, the ElasticNet, LightGBM, and Random Forest approaches were employed as base learners; ridge and LASSO regression were used as meta-models to increase the prediction accuracy; and the SHAP value was utilized to explain the impact of COVID-19 on the insurance industry in China. The stacking meta-model in this thesis has a mean absolute percentage error (MAPE) of 12.57134, whereas the average value in the past week is 21.50972, and the MAPE of ElasticNet is 22.57935. In conclusion, COVID-19 affects the auto insurance industry in China

Unconventional Offshore Petroleum-extracting oil from active source rocks of the Kimmeridge Clay Formation of the North Sea

The organic-rich Upper Jurassic Kimmeridge Clay Formation is the major source rock for conventional oil and gas in the North Sea with a maximum thickness of 1,100 m. TOC values range from 2 wt.%-10 wt.% and predominantly Type II (bacterially-degraded algal, and a mix of Type I (mainly algal) kerogens. The δ13Corg values for the investigated samples range from -29.73 ‰ to -26. 88 ‰, these values are characteristic of marine organic matter with terrestrial input. Sixteen billion barrels of commercial reserves have been discovered in conventional reservoirs in the UK Viking Graben area with 29 billion barrels discovered in the Norwegian sector of the North Sea. However, this principal UK conventional hydrocarbon province is reaching the maturity phase of field exploration, leading to a growing interest for unconventional hydrocarbons in the UK and some part of Europe. The purpose of this study is to evaluate the unconventional hydrocarbon potential of the Kimmeridge shale to identity sweet-spot areas using multidisciplinary analogues from successful unconventional resource plays in North American. Conventional and unconventional source rock analyses show that the Kimmeridge Clay Formation contains a significant amount of un-expelled residual oil both within the source rock and in the interbedded sandstone in the South Viking Graben area. As a consequence, this source rock and juxtaposed non-source lithofacies (sand interbeds) can form a hybrid shale resource system. Due to its high organic richness and favourable sweet-spot reservoir properties such as lithology, thickness, kerogen type, level of thermal maturity and hydrocarbon generative potential, the Kimmeridge Clay Formation could be the first offshore unconventional resource in the future. TOC, Rock-Eval S1, Tmax, mineralogical content and the formation of organic, interparticle and intraparticle porosities at peak oil maturity are all factors that have influenced the retention and drainage of the observed oil. The examination/analysis of their interrelationships provides a useful framework and signature for future prediction of sweet spot areas for viable unconventional resources

Combining SOA and BPM Technologies for Cross-System Process Automation

This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation