4,009 research outputs found

    An assessment of blockchain consensus protocols for the Internet of Things

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    In a few short years the Internet of Things has become an intrinsic part of everyday life, with connected devices included in products created for homes, cars and even medical equipment. But its rapid growth has created several security problems, with respect to the transmission and storage of vast amounts of customers data, across an insecure heterogeneous collection of networks. The Internet of Things is therefore creating a unique set of risk and problems that will affect most households. From breaches in confidentiality, which could allow users to be snooped on, through to failures in integrity, which could lead to consumer data being compromised; devices are presenting many security challenges to which consumers are ill equipped to protect themselves from. Moreover, when this is coupled with the heterogeneous nature of the industry, and the interoperable and scalability problems it becomes apparent that the Internet of Things has created an increased attack surface from which security vulnerabilities may be easily exploited. However, it has been conjectured that blockchain may provide a solution to the Internet of Things security and scalability problems. Because of blockchain’s immutability, integrity and scalability, it is possible that its architecture could be used for the storage and transfer of Internet of Things data. Within this paper a cross section of blockchain consensus protocols have been assessed against a requirement framework, to establish each consensus protocols strengths and weaknesses with respect to their potential implementation in an Internet of Things blockchain environment

    Mobile Devices for Early Literacy Intervention and Research with Global Reach

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    Extensive work focuses on the uses of technology at scale for post-literate populations (e.g., MOOC, Learning Games, LMS’s). Little attention is afforded to non-literate populations, particularly in the developing world. This paper presents an approach using mobile devices with the ultimate goal to reach 770 million people. We developed a novel platform with a cloud backend to deliver educational content to over a thousand marginalized children in different countries:specifically, in remote villages without schools, urban slums with overcrowded schools, and at-risk, rural schools. Here we describe the theoretical basis of our system and results from case studies in three educational contexts. This model will help researchers and designers understand how mobile devices can help children acquire basic skills and aid each other’s learning when the benefit of teachers is limited or non-existent.Italian Development CouncilMRP FoundationRoanoke County School

    DNNShifter: An Efficient DNN Pruning System for Edge Computing

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    Deep neural networks (DNNs) underpin many machine learning applications. Production quality DNN models achieve high inference accuracy by training millions of DNN parameters which has a significant resource footprint. This presents a challenge for resources operating at the extreme edge of the network, such as mobile and embedded devices that have limited computational and memory resources. To address this, models are pruned to create lightweight, more suitable variants for these devices. Existing pruning methods are unable to provide similar quality models compared to their unpruned counterparts without significant time costs and overheads or are limited to offline use cases. Our work rapidly derives suitable model variants while maintaining the accuracy of the original model. The model variants can be swapped quickly when system and network conditions change to match workload demand. This paper presents DNNShifter, an end-to-end DNN training, spatial pruning, and model switching system that addresses the challenges mentioned above. At the heart of DNNShifter is a novel methodology that prunes sparse models using structured pruning. The pruned model variants generated by DNNShifter are smaller in size and thus faster than dense and sparse model predecessors, making them suitable for inference at the edge while retaining near similar accuracy as of the original dense model. DNNShifter generates a portfolio of model variants that can be swiftly interchanged depending on operational conditions. DNNShifter produces pruned model variants up to 93x faster than conventional training methods. Compared to sparse models, the pruned model variants are up to 5.14x smaller and have a 1.67x inference latency speedup, with no compromise to sparse model accuracy. In addition, DNNShifter has up to 11.9x lower overhead for switching models and up to 3.8x lower memory utilisation than existing approaches.Comment: 14 pages, 7 figures, 5 table

    Statically Aggregate Verifiable Random Functions and Application to E-Lottery

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    Cohen, Goldwasser, and Vaikuntanathan (TCC\u2715) introduced the concept of aggregate pseudo-random functions (PRFs), which allow efficiently computing the aggregate of PRF values over exponential-sized sets. In this paper, we explore the aggregation augmentation on verifiable random function (VRFs), introduced by Micali, Rabin and Vadhan (FOCS\u2799), as well as its application to e-lottery schemes. We introduce the notion of static aggregate verifiable random functions (Agg-VRFs), which perform aggregation for VRFs in a static setting. Our contributions can be summarized as follows: (1) we define static aggregate VRFs, which allow the efficient aggregation of VRF values and the corresponding proofs over super-polynomially large sets; (2) we present a static Agg-VRF construction over bit-fixing sets with respect to product aggregation based on the q-decisional Diffie-Hellman exponent assumption; (3) we test the performance of our static Agg-VRFs instantiation in comparison to a standard (non-aggregate) VRF in terms of costing time for the aggregation and verification processes, which shows that Agg-VRFs lower considerably the timing of verification of big sets; and (4) by employing Agg-VRFs, we propose an improved e-lottery scheme based on the framework of Chow et al.\u27s VRF-based e-lottery proposal (ICCSA\u2705). We evaluate the performance of Chow et al.\u27s e-lottery scheme and our improved scheme, and the latter shows a significant improvement in the efficiency of generating the winning number and the player verification

    More than Words: Communication in Intergroup Conflicts

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    Numerous studies suggest that communication may be a universal means to mitigate collective action problems. In this study, we challenge this view and show that the communication structure crucially determines whether communication mitigates or intensifies the problem of collective action. We observe the effect of different communication structures on collective action in the context of finitely repeated intergroup conflict and demonstrate that conflict expenditures are significantly higher if communication is restricted to one's own group as compared to a situation with no communication. However, expenditures are significantly lower if open communication within one's own group and between rivaling groups is allowed. We show that under open communication intergroup conflicts are avoided by groups taking turns in winning the contest. Our results do not only qualify the role of communication for collective action but may also provide insights on how to mitigate the destructive nature of intergroup conflicts.Communication, Conflict, Experiment, Rent-seeking

    Next generation analytics for open pervasive display networks

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    Public displays and digital signs are becoming increasingly widely deployed as many spaces move towards becoming highly interactive and augmented environments. Market trends suggest further significant increases in the number of digital signs and both researchers and commercial entities are working on designing and developing novel uses for this technology. Given the level of investment, it is increasingly important to be able to understand the effectiveness of public displays. Current state-of-the-art analytics technology is limited in the extent to which it addresses the challenges that arise from display deployments becoming open (increasing numbers of stakeholders), networked (viewer engagement across devices and locations) and pervasive (high density of displays and sensing technology leading to potential privacy threats for viewers). In this thesis, we provide the first exploration into achieving next generation display analytics in the context of open pervasive display networks. In particular, we investigated three areas of challenge: analytics data capture, reporting and automated use of analytics data. Drawing on the increasing number of stakeholders, we conducted an extensive review of related work to identify data that can be captured by individual stakeholders of a display network, and highlighted the opportunities for gaining insights by combining datasets owned by different stakeholders. Additionally, we identified the importance of viewer-centric analytics that use traditional display-oriented analytics data combined with viewer mobility patterns to produce entirely new sets of analytics reports. We explored a range of approaches to generating viewer-centric analytics including the use of mobility models as a way to create 'synthetic analytics' - an approach that provides highly detailed analytics whilst preserving viewer privacy. We created a collection of novel viewer-centric analytics reports providing insights into how viewers experience a large network of pervasive displays including reports regarding the effectiveness of displays, the visibility of content across the display network, and the visibility of content to viewers. We further identified additional reports specific to those display networks that support the delivery of personalised content to viewers. Additionally, we highlighted the similarities between digital signage and Web analytics and introduced novel forms of digital signage analytics reports created by leveraging existing Web analytics engines. Whilst the majority of analytics systems focus solely on the capture and reporting of analytics insights, we additionally explored the automated use of analytics data. One of the challenges in open pervasive display networks is accommodating potentially competing content scheduling constraints and requirements that originate from the large number of stakeholders - in addition to contextual changes that may originate from analytics insights. To address these challenges, we designed and developed the first lottery scheduling approach for digital signage providing a means to accommodate potentially conflicting scheduling constraints, and supporting context- and event-based scheduling based on analytics data fed back into the digital sign. In order to evaluate the set of systems and approaches presented in this thesis, we conducted large-scale, long-term trials allowing us to show both the technical feasibility of the systems developed and provide insights into the accuracy and performance of different analytics capture technologies. Our work provides a set of tools and techniques for next generation digital signage analytics and lays the foundation for more general people-centric analytics that go beyond the domain of digital signs and enable unique analytical insights and understanding into how users interact across the physical and digital world
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