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We introduce Proof-of-Steak (PoS) as a fundamental net-zero block generation technique, often accompanied by Non-Frangipane Tokens. Genesis cut is gradually heated and minted (using the appropriate sauce), enabling the miners to redirect the extracted gold and the dissipated heat into the furnace, hence enabling the first fully-circular economy ever built using blockchain technology, utilising tamper-evident steak hach\'e. In this paper we present the basic ingredients for building Proof-of-Steak, assessing its global impact, and opportunities to save the world and beyond!, Comment: This is a silly article

Social media have become a rich source of data, particularly in health research. Yet, the use of such data raises significant ethical questions about the need for the informed consent of those being studied. Consent mechanisms, if even obtained, are typically broad and inflexible, or place a significant burden on the participant. Machine learning algorithms show much promise for facilitating a 'middle ground' approach: using trained models to predict and automate granular consent decisions. Such techniques, however, raise a myriad of follow-on ethical and technical considerations. In this paper, we present an exploratory user study (n = 67) in which we find that we can predict the appropriate flow of health-related social media data with reasonable accuracy, while minimising undesired data leaks. We then attempt to deconstruct the findings of this study, identifying and discussing a number of real-world implications if such a technique were put into practice.

Social media are a rich source of insight for data mining and user-centred research, but the question of consent arises when studying such data without the express knowledge of the creator. Case studies that mine social data from users of online services such as Facebook and Twitter are becoming increasingly common. This has led to calls for an open discussion into how researchers can best use these vast resources to make innovative findings while still respecting fundamental ethical principles. In this position paper we highlight some key considerations for this topic and argue that the conditions of informed consent are often not being met, and that using social media data that some deem free to access and analyse may result in undesirable consequences, particularly within the domain of health research and other sensitive topics. We posit that successful exploitation of online personal data, particularly for health and other sensitive research, requires new and usable methods of obtaining consent from the user.

Companies and academic researchers may collect, process, and distribute large quantities of personal data without the explicit knowledge or consent of the individuals to whom the data pertains. Existing forms of consent often fail to be appropriately readable and ethical oversight of data mining may not be sufficient. This raises the question of whether existing consent instruments are sufficient, logistically feasible, or even necessary, for data mining. In this chapter, we review the data collection and mining landscape, including commercial and academic activities, and the relevant data protection concerns, to determine the types of consent instruments used. Using three case studies, we use the new paradigm of human-data interaction to examine whether these existing approaches are appropriate. We then introduce an approach to consent that has been empirically demonstrated to improve on the state of the art and deliver meaningful consent. Finally, we propose some best practices for data collectors to ensure their data mining activities do not violate the expectations of the people to whom the data relate., Comment: preprint version; book chapter to appear in "Transparent Data Mining for Big and Small Data", Studies in Big Data Series, Springer

The increasing generation and collection of personal data has created a complex ecosystem, often collaborative but sometimes combative, around companies and individuals engaging in the use of these data. We propose that the interactions between these agents warrants a new topic of study: Human-Data Interaction (HDI). In this paper we discuss how HDI sits at the intersection of various disciplines, including computer science, statistics, sociology, psychology and behavioural economics. We expose the challenges that HDI raises, organised into three core themes of legibility, agency and negotiability, and we present the HDI agenda to open up a dialogue amongst interested parties in the personal and big data ecosystems.

This paper investigates the reproducibility of computational science research and identifies key challenges facing the community today. It is the result of the First Summer School on Experimental Methodology in Computational Science Research (https://blogs.cs.st-andrews.ac.uk/emcsr2014/). First, we consider how to reproduce experiments that involve human subjects, and in particular how to deal with different ethics requirements at different institutions. Second, we look at whether parallel and distributed computational experiments are more or less reproducible than serial ones. Third, we consider reproducible computational experiments from fields outside computer science. Our final case study looks at whether reproducibility for one researcher is the same as for another, by having an author attempt to have others reproduce their own, reproducible, paper. This paper is open, executable and reproducible: the whole process of writing this paper is captured in the source control repository hosting both the source of the paper, supplementary codes and data; we are providing setup for several experiments on which we were working; finally, we try to describe what we have achieved during the week of the school in a way that others may reproduce (and hopefully improve) our experiments., Comment: This paper was written at the First Summer School on Experimental Methodology in Computational Science Research, St Andrews, August 4-8, 2014, http://blogs.cs.st-andrews.ac.uk/emcsr2014

Online advertising is currently a rich source of revenue for many Internet giants. With the ever-increasing number of smart phones, there is a fertile market for personalised and localised advertising. A key benefit of using mobile phones is to take advantage of the significant amount of information on phones – such as locations of interest to the user – in order to provide personalised advertisements. Preservation of user privacy, however, is essential for successful deployment of such a system. In this chapter we provide an overview of existing advertising systems and privacy concerns on mobile phones, in addition to a system, MobiAd, which includes protocols for scalable local advertisement download and privacy-aware click report dissemination. In the final section of this chapter we describe some of the security mechanisms used in detecting click-through fraud, and techniques that can be used to ensure that the extra privacy protections of MobiAd are not abused to defraud advertisers.

Research into, and design and construction of mobile systems and algorithms requires access to large-scale mobility data. Unfortunately, the research community lacks such data. For instance, the largest available human contact traces contain only 100 nodes with very sparse connectivity, limited by experimental logistics. In this paper we pose a challenge to the community: how can we collect mobility data from billions of human participants? We re-assert the importance of large-scale datasets in communication network design, and claim that this could impact fundamental studies in other academic disciplines. In effect, we argue that planet-scale mobility measurements can help to save the world. For example, through understanding large-scale human mobility, we can track and model and contain the spread of epidemics of various kinds. © 2010 ACM.

Research into, and design and construction of mobile systems and algorithms requires access to large-scale mobility data. Unfortunately, the research community lacks such data. For instance, the largest available human contact traces contain only 100 nodes with very sparse connectivity, limited by experimental logistics. In this paper we pose a challenge to the community: how can we collect mobility data from billions of human participants? We re-assert the importance of large-scale datasets in communication network design, and claim that this could impact fundamental studies in other academic disciplines. In effect, we argue that planet-scale mobility measurements can help to save the world. For example, through understanding large-scale human mobility, we can track and model and contain the spread of epidemics of various kinds. © 2010 ACM.

Research into, and design and construction of mobile systems and algorithms requires access to large-scale mobility data. Unfortunately, the research community lacks such data. For instance, the largest available human contact traces contain only 100 nodes with very sparse connectivity, limited by experimental logistics. In this paper we pose a challenge to the community: how can we collect mobility data from billions of human participants? We re-assert the importance of large-scale datasets in communication network design, and claim that this could impact fundamental studies in other academic disciplines. In effect, we argue that planet-scale mobility measurements can help to save the world. For example, through understanding large-scale human mobility, we can track and model and contain the spread of epidemics of various kinds.

Research into, and design and construction of mobile systems and algorithms requires access to large-scale mobility data. Unfortunately, the wireless and mobile research community lacks such data. For instance, the largest available human contact traces contain only 100 nodes with very sparse connectivity, limited by experimental logistics. In this paper we pose a challenge to the community: how can we collect mobility data from billions of human participants? We re-assert the importance of large-scale datasets in communication network design, and claim that this could impact fundamental studies in other academic disciplines. In effect, we argue that planet-scale mobility measurements can help to save the world. For example, through understanding large-scale human mobility, we can track and model and contain the spread of epidemics of various kinds., Comment: 6 pages, 2 figures

So what is all this DTN research about anyway? Sceptics ask: "Why are there no DTN applications?", or "Why is DTN performance so miserable?" This article attempts to address some of these complaints. We present suggestions of expectations for applications, and metrics for performance, which suggest a more tolerant view of research in the area.

So what is all this DTN research about anyway? Sceptics ask: "Why are there no DTN applications?", or "Why is DTN performance so miserable?" This article attempts to address some of these complaints. We present suggestions of expectations for applications, and metrics for performance, which suggest a more tolerant view of research in the area.

There is a growing interest in the use of variants of the Transmission Control Protocol (TCP) in high-speed networks. ns-2 has implementations of many of these high-speed TCP variants, as does Linux. ns-2, through an extension, permits the incorporation of Linux TCP code within ns-2 simulations. As these TCP variants become more widely used, users are concerned about how these different variants of TCP might interact in a real network environment — how fair are these protocol variants to each other (in their use of the available capacity) when sharing the same network. Typically, the answer to this question might be sought through simulation and/or by use of an experimental testbed. So, we compare with TCP NewReno the fairness of the congestion control algorithms for 5 high-speed TCP variants — BIC, Cubic, Scalable, High-Speed and Hamilton — on both ns-2 and on an experimental testbed running Linux. In both cases, we use the same TCP code from Linux. We observe some differences between the behaviour of these TCP variants when comparing the testbed results to the results from ns-2, but also note that there is generally good agreement.

There is a growing interest in the use of variants of the Transmission Control Protocol (TCP) in high-speed networks. ns-2 has implementations of many of these high-speed TCP variants, as does Linux. ns-2, through an extension, permits the incorporation of Linux TCP code within ns-2 simulations. As these TCP variants become more widely used, users are concerned about how these different variants of TCP might interact in a real network environment — how fair are these protocol variants to each other (in their use of the available capacity) when sharing the same network. Typically, the answer to this question might be sought through simulation and/or by use of an experimental testbed. So, we compare with TCP NewReno the fairness of the congestion control algorithms for 5 high-speed TCP variants — BIC, Cubic, Scalable, High-Speed and Hamilton — on both ns-2 and on an experimental testbed running Linux. In both cases, we use the same TCP code from Linux. We observe some differences between the behaviour of these TCP variants when comparing the testbed results to the results from ns-2, but also note that there is generally good agreement.

There is a growing interest in the use of variants of the Transmission Control Protocol (TCP) in high-speed networks. ns-2 has implementations of many of these high-speed TCP variants, as does Linux. ns-2, through an extension, permits the incorporation of Linux TCP code within ns-2 simulations. As these TCP variants become more widely used, users are concerned about how these different variants of TCP might interact in a real network environment — how fair are these protocol variants to each other (in their use of the available capacity) when sharing the same network. Typically, the answer to this question might be sought through simulation and/or by use of an experimental testbed. So, we compare with TCP NewReno the fairness of the congestion control algorithms for 5 high-speed TCP variants — BIC, Cubic, Scalable, High-Speed and Hamilton — on both ns-2 and on an experimental testbed running Linux. In both cases, we use the same TCP code from Linux. We observe some differences between the behaviour of these TCP variants when comparing the testbed results to the results from ns-2, but also note that there is generally good agreement.

There is a growing interest in the use of variants of the Transmission Control Protocol (TCP) in high-speed networks. ns-2 has implementations of many of these high-speed TCP variants, as does Linux. ns-2, through an extension, permits the incorporation of Linux TCP code within ns-2 simulations. As these TCP variants become more widely used, users are concerned about how these different variants of TCP might interact in a real network environment — how fair are these protocol variants to each other (in their use of the available capacity) when sharing the same network. Typically, the answer to this question might be sought through simulation and/or by use of an experimental testbed. So, we compare with TCP NewReno the fairness of the congestion control algorithms for 5 high-speed TCP variants — BIC, Cubic, Scalable, High-Speed and Hamilton — on both ns-2 and on an experimental testbed running Linux. In both cases, we use the same TCP code from Linux. We observe some differences between the behaviour of these TCP variants when comparing the testbed results to the results from ns-2, but also note that there is generally good agreement.

So what is all this DTN research about anyway? Sceptics ask: "Why are there no DTN applications?", or "Why is DTN performance so miserable?" This article attempts to address some of these complaints. We present suggestions of expectations for applications, and metrics for performance, which suggest a more tolerant view of research in the area.

We have moved from a world where computing is siloed and specialised, to a world where computing is ubiquitous and everyday. In many, if not most, parts of the world, networked computing is now mundane as both foreground (e.g., smartphones, tablets) and background (e.g., road tra c management, financial systems) technologies. This has permitted, and continues to permit, new gloss on existing interactions (e.g., online banking) as well as distinctively new interactions (e.g., massively scalable distributed real-time mobile gaming). An e ect of this increasing pervasiveness of networked computation in our environments and our lives is that data are also now ubiquitous: in many places, much of society is rapidly becoming “data driven”.

Companies and academic researchers may collect, process, and distribute large quantities of personal data without the explicit knowledge or consent of the individuals to whom the data pertains. Existing forms of consent often fail to be appropriately readable and ethical oversight of data mining may not be sufficient. This raises the question of whether existing consent instruments are sufficient, logistically feasible, or even necessary, for data mining. In this chapter, we review the data collection and mining landscape, including commercial and academic activities, and the relevant data protection concerns, to determine the types of consent instruments used. Using three case studies, we use the new paradigm of human-data interaction to examine whether these existing approaches are appropriate. We then introduce an approach to consent that has been empirically demonstrated to improve on the state of the art and deliver meaningful consent. Finally, we propose some best practices for data collectors to ensure their data mining activities do not violate the expectations of the people to whom the data relate.

Companies and academic researchers may collect, process, and distribute large quantities of personal data without the explicit knowledge or consent of the individuals to whom the data pertains. Existing forms of consent often fail to be appropriately readable and ethical oversight of data mining may not be sufficient. This raises the question of whether existing consent instruments are sufficient, logistically feasible, or even necessary, for data mining. In this chapter, we review the data collection and mining landscape, including commercial and academic activities, and the relevant data protection concerns, to determine the types of consent instruments used. Using three case studies, we use the new paradigm of human-data interaction to examine whether these existing approaches are appropriate. We then introduce an approach to consent that has been empirically demonstrated to improve on the state of the art and deliver meaningful consent. Finally, we propose some best practices for data collectors to ensure their data mining activities do not violate the expectations of the people to whom the data relate.

We have moved from a world where computing is siloed and specialised, to a world where computing is ubiquitous and everyday. In many, if not most, parts of the world, networked computing is now mundane as both foreground (e.g., smartphones, tablets) and background (e.g., road tra c management, financial systems) technologies. This has permitted, and continues to permit, new gloss on existing interactions (e.g., online banking) as well as distinctively new interactions (e.g., massively scalable distributed real-time mobile gaming). An e ect of this increasing pervasiveness of networked computation in our environments and our lives is that data are also now ubiquitous: in many places, much of society is rapidly becoming “data driven”.

We have moved from a world where computing is siloed and specialised, to a world where computing is ubiquitous and everyday. In many, if not most, parts of the world, networked computing is now mundane as both foreground (e.g., smartphones, tablets) and background (e.g., road tra c management, financial systems) technologies. This has permitted, and continues to permit, new gloss on existing interactions (e.g., online banking) as well as distinctively new interactions (e.g., massively scalable distributed real-time mobile gaming). An e ect of this increasing pervasiveness of networked computation in our environments and our lives is that data are also now ubiquitous: in many places, much of society is rapidly becoming “data driven”.

We have moved from a world where computing is siloed and specialised, to a world where computing is ubiquitous and everyday. In many, if not most, parts of the world, networked computing is now mundane as both foreground (e.g., smartphones, tablets) and background (e.g., road tra c management, financial systems) technologies. This has permitted, and continues to permit, new gloss on existing interactions (e.g., online banking) as well as distinctively new interactions (e.g., massively scalable distributed real-time mobile gaming). An e ect of this increasing pervasiveness of networked computation in our environments and our lives is that data are also now ubiquitous: in many places, much of society is rapidly becoming “data driven”.