Security of Ubiquitous Computing Systems

The chapters in this open access book arise out of the EU Cost Action project Cryptacus, the objective of which was to improve and adapt existent cryptanalysis methodologies and tools to the ubiquitous computing framework. The cryptanalysis implemented lies along four axes: cryptographic models, cryptanalysis of building blocks, hardware and software security engineering, and security assessment of real-world systems. The authors are top-class researchers in security and cryptography, and the contributions are of value to researchers and practitioners in these domains. This book is open access under a CC BY license

Experimental Evaluation of Formal Software Development Using Dependently Typed Languages

We will evaluate three dependently typed languages, and their supporting tools and libraries, by implementing the same tasks in each language. One task will demonstrate the basic dependent type support of each language, the other task will show how to do basic imperative programming combined with theorem proving, to ensure both resource safety and functional correctness.info:eu-repo/semantics/publishedVersio

Machine Learning Advances for Practical Problems in Computer Vision

Convolutional neural networks (CNN) have become the de facto standard for computer vision tasks, due to their unparalleled performance and versatility. Although deep learning removes the need for extensive hand engineered features for every task, real world applications of CNNs still often require considerable engineering effort to produce usable results. In this thesis, we explore solutions to problems that arise in practical applications of CNNs. We address a rarely acknowledged weakness of CNN object detectors: the tendency to emit many excess detection boxes per object, which must be pruned by non maximum suppression (NMS). This practice relies on the assumption that highly overlapping boxes are excess, which is problematic when objects are occluding overlapping detections are actually required. Therefore we propose a novel loss function that incentivises a CNN to emit exactly one detection per object, making NMS unnecessary. Another common problem when deploying a CNN in the real world is domain shift - CNNs can be surprisingly vulnerable to sometimes quite subtle differences between the images they encounter at deployment and those they are trained on. We investigate the role that texture plays in domain shift, and propose a novel data augmentation technique using style transfer to train CNNs that are more robust against shifts in texture. We demonstrate that this technique results in better domain transfer on several datasets, without requiring any domain specific knowledge. In collaboration with AstraZeneca, we develop an embedding space for cellular images collected in a high throughput imaging screen as part of a drug discovery project. This uses a combination of techniques to embed the images in 2D space such that similar images are nearby, for the purpose of visualization and data exploration. The images are also clustered automatically, splitting the large dataset into a smaller number of clusters that display a common phenotype. This allows biologists to quickly triage the high throughput screen, selecting a small subset of promising phenotypes for further investigation. Finally, we investigate an unusual form of domain bias that manifested in a real-world visual binary classification project for counterfeit detection. We confirm that CNNs are able to ``cheat'' the task by exploiting a strong correlation between class label and the specific camera that acquired the image, and show that this reliably occurs when the correlation is present. We also investigate the question of how exactly the CNN is able to infer camera type from image pixels, given that this is impossible to the human eye. The contributions in this thesis are of practical value to deep learning practitioners working on a variety of problems in the field of computer vision

ProCLAIM: an argument-based model for deliberating over safety critical actions

In this Thesis we present an argument-based model – ProCLAIM – intended to provide a setting for heterogeneous agents to deliberate on whether a proposed action is safe. That is, whether or not a proposed action is expected to cause some undesirable side effect that will justify not to undertake the proposed action. This is particularly relevant in safetycritical environments where the consequences ensuing from an inappropriate action may be catastrophic. For the practical realisation of the deliberations the model features a mediator agent with three main tasks: 1) guide the participating agents in what their valid argumentation moves are at each stage of the deliberation; 2) decide whether submitted arguments should be accepted on the basis of their relevance; and finally, 3) evaluate the accepted arguments in order to provide an assessment on whether the proposed action should or should not be undertaken, where the argument evaluation is based on domain consented knowledge (e.g guidelines and regulations), evidence and the decision makers’ expertise. To motivate ProCLAIM’s practical value and generality the model is applied in two scenarios: human organ transplantation and industrial wastewater. In the former scenario, ProCLAIM is used to facilitate the deliberation between two medical doctors on whether an available organ for transplantation is or is not suitable for a particular potential recipient (i.e. whether it is safe to transplant the organ). In the later scenario, a number of agents deliberate on whether an industrial discharge is environmentally safe.En esta tesis se presenta un modelo basado en la Argumentación –ProCLAIM– cuyo n es proporcionar un entorno para la deliberación sobre acciones críticas para la seguridad entre agentes heterogéneos. En particular, el propósito de la deliberación es decidir si los efectos secundario indeseables de una acción justi can no llevarla a cabo. Esto es particularmente relevante en entornos críticos para la seguridad, donde las consecuencias que se derivan de una acción inadecuada puede ser catastró cas. Para la realización práctica de las deliberaciones propuestas, el modelo cuenta con un agente mediador con tres tareas principales: 1) guiar a los agentes participantes indicando cuales son las líneas argumentación válidas en cada etapa de la deliberación; 2) decidir si los argumentos presentados deben ser aceptadas sobre la base de su relevancia y, por último, 3) evaluar los argumentos aceptados con el n de proporcionar una valoración sobre la seguridad de la acción propuesta. Esta valoración se basa en guías y regulaciones del dominio de aplicación, en evidencia y en la opinión de los expertos responsables de la decisión. Para motivar el valor práctico y la generalidad de ProCLAIM, este modelo se aplica en dos escenarios distintos: el trasplante de órganos y la gestión de aguas residuales. En el primer escenario el modelo se utiliza para facilitar la deliberación entre dos médicos sobre la viabilidad del transplante de un órgano para un receptor potencial (es decir, si el transplante es seguro). En el segundo escenario varios agentes deliberan sobre si los efectos de un vertido industrial con el propósito de minimizar su impacto medioambiental

A Tale of Two Unions: The British Union and the European Union After Brexit

Brexit is a tale of two unions, not one: the British and the European unions. Their origins are different, but both struggle to maintain unity in diversity and both have to face the challenge of populism and claims of democratic deficit. Mark Corner suggests that the "four nations" that make up the UK can only survive as part of a single nation-state, if the country looks more sympathetically at the very European structures from which it has chosen to detach itself. This study addresses both academic and lay audiences interested in the current situation of the UK, particularly the strains raised by devolution and Brexit

Are Jewish Organizations Great Places to Work? Results from the Sixth Annual Employee Experience Survey (2022)

This report shares overall findings from the sixth annual Employee Experience Survey conducted by Leading Edge, which gathered responses from more than 12,000 employees of 257 Jewish nonprofit organizations. The survey helps leaders learn about their employees' experiences at work so they can improve them.Being a great place to work is a job that's never done. Like exercise and healthy eating for a person, maintaining a great place to work is something that organizations need to do every day. This report is a snapshot of how that complex, constant, and important work was going in one set of 257 Jewish nonprofits in May 2022

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

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

Deep neural mobile networking

The next generation of mobile networks is set to become increasingly complex, as these struggle to accommodate tremendous data traffic demands generated by ever-more connected devices that have diverse performance requirements in terms of throughput, latency, and reliability. This makes monitoring and managing the multitude of network elements intractable with existing tools and impractical for traditional machine learning algorithms that rely on hand-crafted feature engineering. In this context, embedding machine intelligence into mobile networks becomes necessary, as this enables systematic mining of valuable information from mobile big data and automatically uncovering correlations that would otherwise have been too difficult to extract by human experts. In particular, deep learning based solutions can automatically extract features from raw data, without human expertise. The performance of artificial intelligence (AI) has achieved in other domains draws unprecedented interest from both academia and industry in employing deep learning approaches to address technical challenges in mobile networks. This thesis attacks important problems in the mobile networking area from various perspectives by harnessing recent advances in deep neural networks. As a preamble, we bridge the gap between deep learning and mobile networking by presenting a survey on the crossovers between the two areas. Secondly, we design dedicated deep learning architectures to forecast mobile traffic consumption at city scale. In particular, we tailor our deep neural network models to different mobile traffic data structures (i.e. data originating from urban grids and geospatial point-cloud antenna deployments) to deliver precise prediction. Next, we propose a mobile traffic super resolution (MTSR) technique to achieve coarse-to-fine grain transformations on mobile traffic measurements using generative adversarial network architectures. This can provide insightful knowledge to mobile operators about mobile traffic distribution, while effectively reducing the data post-processing overhead. Subsequently, the mobile traffic decomposition (MTD) technique is proposed to break the aggregated mobile traffic measurements into service-level time series, by using a deep learning based framework. With MTD, mobile operators can perform more efficient resource allocation for network slicing (i.e, the logical partitioning of physical infrastructure) and alleviate the privacy concerns that come with the extensive use of deep packet inspection. Finally, we study the robustness of network specific deep anomaly detectors with a realistic black-box threat model and propose reliable solutions for defending against attacks that seek to subvert existing network deep learning based intrusion detection systems (NIDS). Lastly, based on the results obtained, we identify important research directions that are worth pursuing in the future, including (i) serving deep learning with massive high-quality data (ii) deep learning for spatio-temporal mobile data mining (iii) deep learning for geometric mobile data mining (iv) deep unsupervised learning in mobile networks, and (v) deep reinforcement learning for mobile network control. Overall, this thesis demonstrates that deep learning can underpin powerful tools that address data-driven problems in the mobile networking domain. With such intelligence, future mobile networks can be monitored and managed more effectively and thus higher user quality of experience can be guaranteed