Group-Level Frameworks for Data Ethics, Privacy, Safety and Security in Digital Environments

In today\u27s digital age, the widespread collection, utilization, and sharing of personal data are challenging our conventional beliefs about privacy and information security. This thesis will explore the boundaries of conventional privacy and security frameworks and investigate new methods to handle online privacy by integrating groups. Additionally, we will examine approaches to monitoring the types of information gathered on individuals to tackle transparency concerns in the data broker and data processor sector. We aim to challenge traditional notions of privacy and security to encourage innovative strategies for safeguarding them in our interconnected, dispersed digital environment. This thesis uses a multi-disciplinary approach to complex systems, drawing from various fields such as data ethics, legal theory, and philosophy. Our methods include complex systems modeling, network analysis, data science, and statistics. As a first step, we investigate the limits of individual consent frameworks in online social media platforms. We develop new security settings, called distributed consent, that can be used in an online social network or coordinated across online platforms. We then model the levels of observability of individuals on the platform(s) to measure the effectiveness of the new security settings against surveillance from third parties. Distributed consent can help to protect individuals online from surveillance, but it requires a high coordination cost on the part of the individual. Users must also decide whether to protect their privacy from third parties and network neighbors by disclosing security settings or taking on the burden of coordinating security on single and multiple platforms. However, the coordination burden may be more appropriate for systems-level regulation. We then explore how groups of individuals can work together to protect themselves from the harms of misinformation on online social networks. Social media users are not equally susceptible to all types of misinformation. Further, diverse groups of social media communities can help protect one another from misinformation by correcting each other\u27s blind spots. We highlight the importance of group diversity in network dynamics and explore how natural diversity within groups can provide protection rather than relying on new technologies such as distributed consent settings. Finally, we investigate methods to interrogate what types of personal data are collected by third parties and measure the risks and harms associated with aggregating personal data. We introduce methods that provide transparency into how modern data collection practices pose risks to data subjects online. We hope that the collection of these results provides a humble step toward revealing gaps in privacy and security frameworks and promoting new solutions for the digital age

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

30th International Conference on Information Modelling and Knowledge Bases

Information modelling is becoming more and more important topic for researchers, designers, and users of information systems. The amount and complexity of information itself, the number of abstraction levels of information, and the size of databases and knowledge bases are continuously growing. Conceptual modelling is one of the sub-areas of information modelling. The aim of this conference is to bring together experts from different areas of computer science and other disciplines, who have a common interest in understanding and solving problems on information modelling and knowledge bases, as well as applying the results of research to practice. We also aim to recognize and study new areas on modelling and knowledge bases to which more attention should be paid. Therefore philosophy and logic, cognitive science, knowledge management, linguistics and management science are relevant areas, too. In the conference, there will be three categories of presentations, i.e. full papers, short papers and position papers

Deep learning based semantic textual similarity for applications in translation technology

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.Semantic Textual Similarity (STS) measures the equivalence of meanings between two textual segments. It is a fundamental task for many natural language processing applications. In this study, we focus on employing STS in the context of translation technology. We start by developing models to estimate STS. We propose a new unsupervised vector aggregation-based STS method which relies on contextual word embeddings. We also propose a novel Siamese neural network based on efficient recurrent neural network units. We empirically evaluate various unsupervised and supervised STS methods, including these newly proposed methods in three different English STS datasets, two non- English datasets and a bio-medical STS dataset to list the best supervised and unsupervised STS methods. We then embed these STS methods in translation technology applications. Firstly we experiment with Translation Memory (TM) systems. We propose a novel TM matching and retrieval method based on STS methods that outperform current TM systems. We then utilise the developed STS architectures in translation Quality Estimation (QE). We show that the proposed methods are simple but outperform complex QE architectures and improve the state-of-theart results. The implementations of these methods have been released as open source

Collective intelligence: creating a prosperous world at peace

XXXII, 612 p. ; 24 cmLibro ElectrónicoEn este documento se plantea un tema de interes general mas como lo es especificamente el tema de la evolucion de la sociedad en materia de industria y crecimiento de las actividades humanas en el aspecto de desarrollo de la creatividad enfocada a los mercadosedited by Mark Tovey ; foreword by Yochai Benkler (re-mixed by Hassan Masum) ; prefaces by Thomas Malone, Tom Atlee & Pierre Levy ; afterword by Paul Martin & Thomas Homer-Dixon.The era of collective intelligence has begun in earnest. While others have written about the wisdom of crowds, an army of Davids, and smart mobs, this collection of essays for the first time brings together fifty-five pioneers in the emerging discipline of collective intelligence. They provide a base of tools for connecting people, producing high-functioning teams, collaborating at multiple scales, and encouraging effective peer-production. Emerging models are explored for digital deliberative democracy, self-governance, legislative transparency, true-cost accounting, and the ethical use of open sources and methods. Collective Intelligence is the first of a series of six books, which will also include volumes on Peace Intelligence, Commercial Intelligence, Gift Intelligence, Cultural Intelligence, and Global Intelligence.Table of Contents Dedication i Publisher’s Preface iii Foreword by Yochai Benkler Remix Hassan Masum xi The Wealth of Networks: Highlights remixed Editor’s Preface xxi Table of Contents xxv A What is collective intelligence and what will we do 1 about it? (Thomas W. Malone, MIT Center for Collective Intelligence) B Co-Intelligence, collective intelligence, and conscious 5 evolution (Tom Atlee, Co-Intelligence Institute) C A metalanguage for computer augmented collective 15 intelligence (Prof. Pierre Lévy, Canada Research Chair in Collective Intelligence, FRSC) I INDIVIDUALS & GROUPS I-01 Foresight I-01-01 Safety Glass (Karl Schroeder, science fiction author 23 and foresight consultant) I-01-02 2007 State of the Future (Jerome C. Glenn & 29 Theodore J. Gordon, United Nations Millennium Project) I-02 Dialogue & Deliberation I-02-01 Thinking together without ego: Collective intelligence 39 as an evolutionary catalyst (Craig Hamilton and Claire Zammit, Collective-Intelligence.US) I-02-02 The World Café: Awakening collective intelligence 47 and committed action (Juanita Brown, David Isaacs and the World Café Community) I-02-03 Collective intelligence and the emergence of 55 wholeness (Peggy Holman, Nexus for Change, The Change Handbook) I-02-04 Knowledge creation in collective intelligence (Bruce 65 LaDuke, Fortune 500, HyperAdvance.com) I-02-05 The Circle Organization: Structuring for collective 75 wisdom (Jim Rough, Dynamic Facilitation & The Center for Wise Democracy) I-03 Civic Intelligence I-03-01 Civic intelligence and the public sphere (Douglas 83 Schuler, Evergreen State College, Public Sphere Project) I-03-02 Civic intelligence and the security of the homeland 95 (John Kesler with Carole and David Schwinn, IngeniusOnline) I-03-03 Creating a Smart Nation (Robert Steele, OSS.Net) 107 I-03-04 University 2.0: Informing our collective intelligence 131 (Nancy Glock-Grueneich, HIGHEREdge.org) I-03-05 Producing communities of communications and 145 foreknowledge (Jason “JZ” Liszkiewicz, Reconfigure.org) I-03-06 Global Vitality Report 2025: Learning to transform I-04 Electronic Communities & Distributed Cognition I-04-01 Attentional capital and the ecology of online social 163 conflict and think together effectively (Peter+Trudy networks (Derek Lomas, Social Movement Lab, Johnson-Lenz, Johnson-Lenz.com ) UCSD) I-04-02 A slice of life in my virtual community (Howard 173 Rheingold, Whole Earth Review, Author & Educator) I-04-03 Shared imagination (Dr. Douglas C. Engelbart, 197 Bootstrap) I-05 Privacy & Openness I-05-01 We’re all swimming in media: End-users must be able 201 to keep secrets (Mitch Ratcliffe, BuzzLogic & Tetriad) I-05-02 Working openly (Lion Kimbro, Programmer and 205 Activist) I-06 Integral Approaches & Global Contexts I-06-01 Meta-intelligence for analyses, decisions, policy, and 213 action: The Integral Process for working on complex issues (Sara Nora Ross, Ph.D. ARINA & Integral Review) I-06-02 Collective intelligence: From pyramidal to global 225 (Jean-Francois Noubel, The Transitioner) I-06-03 Cultivating collective intelligence: A core leadership 235 competence in a complex world (George Pór, Fellow at Universiteit van Amsterdam) II LARGE-SCALE COLLABORATION II-01 Altruism, Group IQ, and Adaptation II-01-01 Empowering individuals towards collective online 245 production (Keith Hopper, KeithHopper.com) II-01-02 Who’s smarter: chimps, baboons or bacteria? The 251 power of Group IQ (Howard Bloom, author) II-01-03 A collectively generated model of the world (Marko 261 A. Rodriguez, Los Alamos National Laboratory) II-02 Crowd Wisdom and Cognitive Bias II-02-01 Science of CI: Resources for change (Norman L 265 Johnson, Chief Scientist at Referentia Systems, former LANL) II-02-02 Collectively intelligent systems (Jennifer H. Watkins, 275 Los Alamos National Laboratory) II-02-03 A contrarian view (Jaron Lanier, scholar-in-residence, 279 CET, UC Berkeley & Discover Magazine) II-03 Semantic Structures & The Semantic Web II-03-01 Information Economy Meta Language (Interview with 283 Professor Pierre Lévy, by George Pór) II-03-02 Harnessing the collective intelligence of the World- 293 Wide Web (Nova Spivack, RadarNetworks, Web 3.0) II-03-03 The emergence of a global brain (Francis Heylighen, 305 Free University of Brussels) II-04 Information Networks II-04-01 Networking and mobilizing collective intelligence (G. Parker Rossman, Future of Learning Pioneer) II-04-02 Toward high-performance organizations: A strategic 333 role for Groupware (Douglas C. Engelbart, Bootstrap) II-04-03 Search panacea or ploy: Can collective intelligence 375 improve findability? (Stephen E. Arnold, Arnold IT, Inc.) II-05 Global Games, Local Economies, & WISER II-05-01 World Brain as EarthGame (Robert Steele and many 389 others, Earth Intelligence Network) II-05-02 The Interra Project (Jon Ramer and many others) 399 II-05-03 From corporate responsibility to Backstory 409 Management (Alex Steffen, Executive Editor, Worldchanging.com) II-05-04 World Index of Environmental & Social 413 Responsibility (WISER) By the Natural Capital Institute II-06 Peer-Production & Open Source Hardware II-06-01 The Makers’ Bill of Rights (Jalopy, Torrone, and Hill) 421 II-06-02 3D Printing and open source design (James Duncan, 423 VP of Technology at Marketingisland) II-06-03 REBEARTHTM: 425 II-07 Free Wireless, Open Spectrum, and Peer-to-Peer II-07-01 Montréal Community Wi-Fi (Île Sans Fil) (Interview 433 with Michael Lenczner by Mark Tovey) II-07-02 The power of the peer-to-peer future (Jock Gill, 441 Founder, Penfield Gill Inc.) Growing a world 6.6 billion people would want to live in (Marc Stamos, B-Comm, LL.B) II-07-03 Open spectrum (David Weinberger) II-08 Mass Collaboration & Large-Scale Argumentation II-08-01 Mass collaboration, open source, and social 455 entrepreneurship (Mark Tovey, Advanced Cognitive Engineering Lab, Institute of Cognitive Science, Carleton University) II-08-02 Interview with Thomas Homer-Dixon (Hassan 467 Masum, McLaughlin-Rotman Center for Global Health) II-08-03 Achieving collective intelligence via large-scale argumentation (Mark Klein, MIT Center for Collective Intelligence) II-08-04 Scaling up open problem solving (Hassan Masum & 485 Mark Tovey) D Afterword: The Internet and the revitalization of 495 democracy (The Rt. Honourable Paul Martin & Thomas Homer-Dixon) E Epilogue by Tom Atlee 513 F Three Lists 515 1. Strategic Reading Categories 2. Synopsis of the New Progressives 3. Fifty-Two Questions that Matter G Glossary 519 H Index 52

WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words