878 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Privacy-Preserving Data Mining and Analytics in Big Data

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    Privacy concerns have gotten more attention as Big Data has spread. The difficulties of striking a balance between the value of data and individual privacy have led to the emergence of privacy-preserving data mining and analytics approaches as a crucial area of research. An overview of the major ideas, methods, and developments in privacy-preserving data mining and analytics in the context of Big Data is given in this abstract. Data mining that protects privacy tries to glean useful insights from huge databases while shielding the private data of individuals. Commonly used in traditional data mining methods, sharing or pooling data might have serious privacy implications. On the other hand, privacy-preserving data mining strategies concentrate on creating procedures and algorithms that enable analysis without jeopardizing personal information. Finally, privacy-preserving data mining and analytics in the Big Data age bring important difficulties and opportunities. An overview of the main ideas, methods, and developments in privacy-preserving data mining and analytics are given in this abstract. It underscores the value of privacy in the era of data-driven decision-making and the requirement for effective privacy-preserving solutions to safeguard sensitive personal data while facilitating insightful analysis of huge datasets

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    Statistical Anomaly Discovery Through Visualization

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    Developing a deep understanding of data is a crucial part of decision-making processes. It often takes substantial time and effort to develop a solid understanding to make well-informed decisions. Data analysts often perform statistical analyses through visualization to develop such understanding. However, applicable insight can be difficult due to biases and anomalies in data. An often overlooked phenomenon is mix effects, in which subgroups of data exhibit patterns opposite to the data as a whole. This phenomenon is widespread and often leads inexperienced analysts to draw contradictory conclusions. Discovering such anomalies in data becomes challenging as data continue to grow in volume, dimensionality, and cardinality. Effectively designed data visualizations empower data analysts to reveal and understand patterns in data for studying such paradoxical anomalies. This research explores several approaches for combining statistical analysis and visualization to discover and examine anomalies in multidimensional data. It starts with an automatic anomaly detection method based on correlation comparison and experiments to determine the running time and complexity of the algorithm. Subsequently, the research investigates the design, development, and implementation of a series of visualization techniques to fulfill the needs of analysis through a variety of statistical methods. We create an interactive visual analysis system, Wiggum, for revealing various forms of mix effects. A user study to evaluate Wiggum strengthens understanding of the factors that contribute to the comprehension of statistical concepts. Furthermore, a conceptual model, visual correspondence, is presented to study how users can determine the identity of items between visual representations by interpreting the relationships between their respective visual encodings. It is practical to build visualizations with highly linked views informed by visual correspondence theory. We present a hybrid tree visualization technique, PatternTree, which applies the visual correspondence theory. PatternTree supports users to more readily discover statistical anomalies and explore their relationships. Overall, this dissertation contributes a merging of new visualization theory and designs for analysis of statistical anomalies, thereby leading the way to the creation of effective visualizations for statistical analysis

    Insights on Learning Tractable Probabilistic Graphical Models

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    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    ‘Conclusion: Youth aspirations, trajectories, and farming futures

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    This book commenced with a question of global importance: in a world in which farming populations are ageing, who is going to provide the planet’s peoples with the “sufficient, safe and nutritious food” that is needed to meet the “dietary needs and food preferences for an active and healthy life” (FAO 2006)? In other words, where are the people who are needed to generationally renew farming? As explained in the introduction, addressing this question meant going against the grain of much research on youth and agriculture. Rather than seeking to understand youth’s apparent disinterest in farming and their exodus from the countryside, the research teams focused on those youth and young adults who stayed in, returned, or relocated to rural areas and were involved in farming (often alongside various other economic activities). Thereby, the case studies presented in this book have put in the spotlight the next generation of farmers. In this concluding chapter, we draw out some important issues emerging from across the chapters and reflect on key differences. This way, we reiterate the various pathways of becoming a farmer, the main challenges experienced by these young farming women and men, and the roles that policies and organizations could play in facilitating the process of becoming a farmer

    Analysis of Social Network Data Mining for Security Intelligence Privacy Machine Learning

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    The Modern communication on the Internet platform is most responsive through social media. Social media has changed and is still reshaping how we share our thoughts and emotions in communication. It has introduced a constant real-time communication pattern that was before unheard of. Young and old, organizations, governmental agencies, professional associations, etc., all have social media accounts that they use exclusively for communication with other users. Social media also acts as a powerful network engine that connects users regardless of where they are in the world. The development of global communication will greatly benefit from the availability of this new communication platform in the future. Consequently, there is a pressing need to research usage trends. Therefore, it is vital to investigate social media platform usage trends in order to develop automated systems that intelligence services can use to help avert national security incidents. Through the use of social media data mining, this research study suggests an automated machine learning model that can improve speedy response to crises involving national and International security

    Novel methods for multi-view learning with applications in cyber security

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    Modern data is complex. It exists in many different forms, shapes and kinds. Vectors, graphs, histograms, sets, intervals, etc.: they each have distinct and varied structural properties. Tailoring models to the characteristics of various feature representations has been the subject of considerable research. In this thesis, we address the challenge of learning from data that is described by multiple heterogeneous feature representations. This situation arises often in cyber security contexts. Data from a computer network can be represented by a graph of user authentications, a time series of network traffic, a tree of process events, etc. Each representation provides a complementary view of the holistic state of the network, and so data of this type is referred to as multi-view data. Our motivating problem in cyber security is anomaly detection: identifying unusual observations in a joint feature space, which may not appear anomalous marginally. Our contributions include the development of novel supervised and unsupervised methods, which are applicable not only to cyber security but to multi-view data in general. We extend the generalised linear model to operate in a vector-valued reproducing kernel Hilbert space implied by an operator-valued kernel function, which can be tailored to the structural characteristics of multiple views of data. This is a highly flexible algorithm, able to predict a wide variety of response types. A distinguishing feature is the ability to simultaneously identify outlier observations with respect to the fitted model. Our proposed unsupervised learning model extends multidimensional scaling to directly map multi-view data into a shared latent space. This vector embedding captures both commonalities and disparities that exist between multiple views of the data. Throughout the thesis, we demonstrate our models using real-world cyber security datasets.Open Acces
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